diff --git a/09_ON_YOUR_OWN_A_Full_Workflow.ipynb b/09_ON_YOUR_OWN_A_Full_Workflow.ipynb deleted file mode 100644 index a9313ea..0000000 --- a/09_ON_YOUR_OWN_A_Full_Workflow.ipynb +++ /dev/null @@ -1,793 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lesson 9. On Your Own: A Full Workflow\n", - "Now is your chance to pull everything we've learned together and answer the questions: \n", - "- How many polling stations are in each census tract in Alameda County?\n", - "- Which polling stations are within walking distance (100m) from a bus route in Berkeley?\n", - "- How far are these polling stations from the bus routes in Berkeley?\n", - "\n", - "**All on your own!!**\n", - "\n", - "- 9.1 Polling Station Locations\n", - "- 9.2 Tracts data \n", - "- 9.3 Spatial Join \n", - "- 9.4 Aggregate number of stations by census tracts\n", - "- 9.5 Attribute join back to tracts data\n", - "- 9.6 Berkeley outline\n", - "- 9.7 Bus routes\n", - "- 9.8 Polling station distance from bus routes\n", - "\n", - "*We've written out some of the code for you, and you can check your answers by clicking on the toggle solution button*\n", - " \n", - "### Install Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import geopandas as gpd\n", - "\n", - "import matplotlib # base python plotting library\n", - "import matplotlib.pyplot as plt # submodule of matplotlib\n", - "\n", - "# get the solution hider\n", - "from solution_hider import hide_solution\n", - "\n", - "# To display plots, maps, charts etc in the notebook\n", - "%matplotlib inline " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9.1 Polling Station Locations\n", - "\n", - "We'll be using the 2020 General Election voting locations for Alameda County for this analysis. Since the data is *aspatial* we'll need to coerce it to be a geodataframe and define a CRS.\n", - "\n", - "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/ac_voting_locations.csv`)\n", - "- coerce it to a GeoDataFrame\n", - "- define its CRS (EPSG:4326)\n", - "- plot it" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Pull in polling location\n", - "\n", - "# polling_ac_df = pd.read_csv(...)\n", - "# polling_ac_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Make into geo data frame\n", - "\n", - "# polling_ac_gdf = gpd.GeoDataFrame(..., \n", - "# geometry=gpd.points_from_xy(...,...))\n", - "# polling_ac_gdf.crs = ...\n", - "\n", - "# plot it \n", - "\n", - "# polling_ac_gdf.plot(...)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Double-click here to see solution!\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9.2 Tracts data\n", - "\n", - "Since we want to answer the question **How many polling stations are in each census tract?**, we'll pull in our tracts data.\n", - "\n", - "- Bring in the census tracts data which lives at `notebook_data/census/Tracts/cb_2013_06_tract_500k.zip`\n", - "- Narrow it down to Alameda County\n", - "- Check CRS\n", - "- Transform CRS to 26910 if needed\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Bring in census tracts\n", - "# tracts_gdf = gpd.read_file(...)\n", - "\n", - "# Narrow it down to Alameda County\n", - "# tracts_gdf_ac = tracts_gdf[...]\n", - "# tracts_gdf_ac.plot()\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "polling_ac_gdf: epsg:4326\n", - "tracts_gdf_ac CRS: epsg:4269\n" - ] - } - ], - "source": [ - "# Check CRS\n", - "print('polling_ac_gdf:', ...)\n", - "print('tracts_gdf_ac CRS:', ...)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Transform CRS\n", - "polling_ac_gdf_utm10 = ...\n", - "tracts_gdf_ac_utm10 = ..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Double-click here to see solution!\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 9.3 Spatial Join\n", - "\n", - "Alright, now our data is all ready to go! We're going to do a *spatial join* to answer our question about polling stations in each tract.\n", - "\n", - "- Spatial join tracts/acs with the polling data (keep the tracts geometry!)\n", - "- Plot it to make sure you have the right geometry\n", - "- Check out your data and its dimensions" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Spatial join tracts/acs with the polling data (keep the tracts geometry!)\n", - "\n", - "# polls_jointracts = gpd.sjoin(..., ... , how=...)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot it to make sure you have the right geometry\n", - "\n", - "# polls_jointracts.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Check out your data and its dimensions\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Double-click here to see solution!\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9.4 Aggregate number of stations by census tracts\n", - "\n", - "Now that we have a GeoDataFrame with all our polling and tract data, we'll need to *aggregate* to actually count the number of stations we have\n", - "\n", - "- Use `dissolve` to count the number of polls we have\n", - "- Create a choropleth map base don the number of stations there are" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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TRACTCEgeometryNAME_right
0400100POLYGON ((566221.610 4193371.510, 566659.969 4...1
1400200POLYGON ((565335.837 4188666.188, 565441.159 4...0
2400300POLYGON ((564744.993 4188317.651, 564946.532 4...0
3400400POLYGON ((564950.988 4188518.225, 564992.933 4...0
4400500POLYGON ((564276.448 4189213.844, 564317.359 4...0
\n", - "
" - ], - "text/plain": [ - " TRACTCE geometry NAME_right\n", - "0 400100 POLYGON ((566221.610 4193371.510, 566659.969 4... 1\n", - "1 400200 POLYGON ((565335.837 4188666.188, 565441.159 4... 0\n", - "2 400300 POLYGON ((564744.993 4188317.651, 564946.532 4... 0\n", - "3 400400 POLYGON ((564950.988 4188518.225, 564992.933 4... 0\n", - "4 400500 POLYGON ((564276.448 4189213.844, 564317.359 4... 0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Use `dissolve` to count the number of polls we have\n", - "\n", - "# polls_countsbytract = polls_jointracts[['TRACTCE', 'NAME_right', \n", - "# 'geometry']].dissolve(by=..., \n", - "# aggfunc=...).reset_index()\n", - "# polls_countsbytract.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# rename the column to be for the number of polling stations (you dont have to change anything here)\n", - "\n", - "# polls_countsbytract.rename(columns={'NAME_right': 'Num_Polling'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Create a choropleth map base don the number of stations there are\n", - "fig, ax = plt.subplots(figsize = (14,8)) \n", - "\n", - "# polls_countsbytract.plot(ax=ax,\n", - "# column=..., \n", - "# cmap=...,\n", - "# edgecolor=\"grey\",\n", - "# legend=True)\n", - "\n", - "# polling_ac_gdf_utm10.plot(ax=ax, color=..., edgecolor=..., markersize= ...)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Double-click here to see solution!\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9.5 Attribute join back to tracts data\n", - "\n", - "Amazing! Now that we have this information let's do an *attribute join* to add this data into our tracts data" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# merge onto census tract data\n", - "\n", - "# tracts_gdf_ac = tracts_gdf_ac.merge(polls_countsbytract[['TRACTCE', 'Num_Polling']], left_on= ...,right_on= ... , how= ... ) \n", - "# tracts_gdf_ac.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Double-click here to see solution!\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9.6 Berkeley outline\n", - "\n", - "To answer our question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* we'll need to know where Berkeley is! This is the perfect time to bring our Berkeley places data in.\n", - "\n", - "- Read in `outdata/berkeley_places.shp`\n", - "- Check the CRS\n", - "- Transform CRS if necessary to EPSG:26910" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Read in outdata/berkeley_places.shp\n", - "# berkeley_places = gpd.read_file(...)\n", - "\n", - "# Check the CRS\n", - "\n", - "\n", - "# Transform CRS if necessary to EPSG:26910\n", - "berkeley_places_utm10 = ..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Double-click here to see solution!\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8.7 Bus routes\n", - "\n", - "- Bring in bus routes ('notebook_data/transportation/Fall20Routeshape.zip'), transform CRS to 26910\n", - "- Intersect bus routes with Berkeley\n", - "- Plot results of intersection\n", - "- Clip bus routes to everything that is inside the berkley outline\n", - "- Plot bus routes on top of Berkeley outline" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Bring in bus routes, transform CRS to 26910\n", - "bus_routes = ...\n", - "# bus_routes_utm10 = bus_routes.to_crs(...)\n", - "# bus_routes_utm10.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# Look at intersection between bus routes and Berkeley\n", - "# bus_routes_berkeley = .intersects(... .geometry.squeeze())\n", - "\n", - "# Create new geodataframe from these results\n", - "# bus_berk = bus_routes_utm10.loc[bus_routes_berkeley].reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot results of intersection\n", - "\n", - "# fig, ax = plt.subplots(figsize=(10,10))\n", - "# berkeley_places_utm10.plot(ax=ax)\n", - "# bus_berk.plot(ax=ax, column ='PUB_RTE')" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# BONUS: Look at route length\n", - "# bus_berk.length" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# Clip bus routes to everything that is inside the berkley outline\n", - "# bus_berk_clip = gpd.clip(...,...)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# Plot bus routes on top of Berkeley outline\n", - "# fig, ax = plt.subplots(figsize=(10,10))\n", - "# berkeley_places_utm10.plot(ax=ax)\n", - "# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Double-click here to see solution!\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8.6 Polling stations within walking distance of bus routes\n", - "\n", - "Now we can really answer the question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* \n", - "\n", - "- Create buffer around bus route for 100m\n", - "- Intersect polling locations in Alameda County with Berkeley outline \n", - "- Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations\n", - "- Calculate the distance from polling stations to the closest bus route" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# Create buffer around bus route for 100m\n", - "# bus_berk_buf =bus_berk_clip.buffer(distance= ...)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# Intersect polling locations in Alameda County with Berkeley outline\n", - "# polling_berk = ... .intersects(berkeley_places_utm10.geometry.squeeze())\n", - "\n", - "# polling_berk_gdf = polling_ac_gdf_utm10[polling_berk].reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations\n", - "\n", - "# fig, ax = plt.subplots(figsize=(10,10))\n", - "# berkeley_places_utm10.plot(ax=ax)\n", - "# bus_berk_buf.plot(color='pink', ax=ax, alpha=0.5)\n", - "# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')\n", - "# polling_berk_gdf.plot(ax=ax, color= 'yellow')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the distance from polling stations to the closest bus route\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Double-click here to see solution!\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# You're done!!!! \n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "
\n", - "\n", - "\n", - "
\n", - "\n", - "
\n", - "
 D-Lab @ University of California - Berkeley
\n", - "
 Team Geo
\n", - "
\n", - " \n", - "\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/11_OPTIONAL_Basemap_with_Contextily.ipynb b/11_OPTIONAL_Basemap_with_Contextily.ipynb deleted file mode 100644 index 6c2d84c..0000000 --- a/11_OPTIONAL_Basemap_with_Contextily.ipynb +++ /dev/null @@ -1,391 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 11. Adding Basemaps with Contextily\n", - "\n", - "If you work with geospatial data in Python, you most likely are familiar with the fantastic [GeoPandas](https://geopandas.org/) library. GeoPandas leverages the power of [Matplotlib](https://matplotlib.org/) to enable users to make maps of their data. However, until recently, it has not been easy to add basemaps to these maps. Basemaps are the contextual map data, like Google Maps, on top of which geospatial data are often displayed.\n", - "\n", - "\n", - "The new Python library [contextily](https://github.com/geopandas/contextily), which stands for *context map tiles*, now makes it possible and relatively straight forward to add basemaps to Geopandas maps. Below we walk through a few common workflows for doing this.\n", - "\n", - "First, let's load are libraries. This assumes you have the following Python libraries installed in your environment:\n", - "\n", - "- pandas\n", - "- matplotlib\n", - "- geopandas (and all dependancies)\n", - "- contextily\n", - "- descartes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import pandas as pd\n", - "import geopandas as gpd\n", - "import contextily as cx\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read data into a Geopandas GeoDataFrame\n", - "\n", - "Fetch the census places data to map. Census places includes cities and other populated places. Here we fetch the 2019 cartographic boundary (`cb_`) file of California (`06`) places." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ca_places = \"https://www2.census.gov/geo/tiger/GENZ2019/shp/cb_2019_06_place_500k.zip\"\n", - "places = gpd.read_file(ca_places)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the geodatarame `plot` method to make a quick map." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "places.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we can see those cities, let's take a look at the data in the geodataframe." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "places.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can subset the data by selecting a row or rows by place name. Let's select the city of Berkeley, CA." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "berkeley = places[places['NAME']=='Berkeley']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "berkeley.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use Contextily to add a basemap\n", - "\n", - "Above we can see the map of the boundary of the city of Berkeley, CA. The axis labels display the longitude and latitude coordinates for the bounding extent of the city.\n", - "\n", - "Let's use `contextily` in it's most simple form to add a basemap to provide the geographic context for Berkeley. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = berkeley.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", - "cx.add_basemap(ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are a few important things to note about the above code.\n", - "\n", - "- We use `matplotlib` to define the plot canvas as `ax`.\n", - "- We then add the contextily basemap to the map with the code `cx.add_basemap(ax)`\n", - "\n", - "Additionally, we **dynamically transform the coordinate reference system**, or CRS, of the Berkeley geodataframe from geographic lat/lon coordinates to `web mercator` using the method **to_crs('EPSG:3857')**. [Web mercator](https://en.wikipedia.org/wiki/Web_Mercator_projection) is the default CRS used by all web map tilesets. It is referenced by a the code `EPSG:3857` where [EPSG](https://en.wikipedia.org/wiki/EPSG_Geodetic_Parameter_Dataset) stands for the the initials of the organization that created these codes (the European Petroleum Survey Group).\n", - "\n", - "Let's clean up the map by adding some code to change the symbology of the Berkeley city boundary. This will highlight the value of adding a basemap.\n", - "\n", - "First, let's map the boundary with out a fill color." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "berkeley.plot(edgecolor=\"red\", facecolor=\"none\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's build on those symbology options and add the contextily basemap." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = berkeley.to_crs('EPSG:3857').plot(edgecolor=\"red\", \n", - " facecolor=\"none\", # or a color \n", - " alpha=0.95, # opacity value for colors, 0-1\n", - " linewidth=2, # line, or stroke, thickness\n", - " figsize=(9, 9)\n", - " )\n", - "cx.add_basemap(ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mapping Point Data\n", - "\n", - "Let's expand on this example by mapping a point dataset of BART station locations.\n", - "\n", - "First we fetch these data from a D-Lab web mapping tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bart_url = 'https://raw.githubusercontent.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/master/notebook_data/transportation/bart.csv'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bart = pd.read_csv(bart_url)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bart.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting Point Data in a dataframe to Geospatial Data in a geodataframe\n", - "\n", - "Because these data are in a CSV file we read them into a Pandas DataFrame.\n", - "\n", - "In order to map these data we need to convert these data to a GeoPandas GeoDataFame. To do this, we need to specify:\n", - "\n", - "- the data, here the geodataframe `bart`\n", - "- the coordinate data, here `bart['X']` and `bart['Y']`\n", - "- the CRS of the bart coordinate data, here `EPSG:4326`\n", - "\n", - "The CRS code 'EPSG:4326' stands for the World Geodectic System of 1984, or WGS84. This is the most commonly used CRS for geographic (lat/lon) coordinate data.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Convert the DataFrame to a GeoDataFrame. \n", - "bart_gdf = gpd.GeoDataFrame(bart, geometry=gpd.points_from_xy(bart['lon'], \n", - " bart['lat']), \n", - " crs='EPSG:4326') \n", - "\n", - "# and take a look\n", - "bart_gdf.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have the BART data in a geodataframe we can use the same commands as we did above to map it with a contextily basemap." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", - "cx.add_basemap(ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have the full range of `matplotlib` style options to enhance the map, a few of which are shown in the example below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ax = bart_gdf.to_crs('EPSG:3857').plot(\n", - " color=\"red\",\n", - " edgecolor=\"black\",\n", - " markersize=50, \n", - " figsize=(9, 9))\n", - "\n", - "ax.set_title('Bay Area Bart Stations')\n", - "cx.add_basemap(ax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Changing the Basemap\n", - "\n", - "By default `contextiley` returns maptiles from the OpenStreetmap Mapnik basemap. However, ther are other available tilesets from different providers. These tilesets are stored in the contextily `cx.providers` dictionary.\n", - "\n", - "That's a large dictionary and you can view it. Alternatively, and more simply, you can access the list of the providers in this dictionary using the command `cs.providers.keys`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# change basemap - can be one of these\n", - "# first see available provider names\n", - "cx.providers.keys()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Once you have the list of providers, you can find the names of their specific tilesets. \n", - "\n", - "Below, we retrieve the list of the tilesets available from the provider `CartoDB`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Then find the names of the tile sets for a specific provider\n", - "cx.providers.CartoDB.keys()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can specify a different tileset using the **source** argument to the `add_basemap` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cx.providers.Esri.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Change the basemap provider and tileset\n", - "ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", - "cx.add_basemap(ax, source=cx.providers.NASAGIBS.ModisTerraTrueColorCR)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Learning More\n", - "\n", - "Above, we prove a very short introduction to the excellent `contextily` library. You can find more detailed information on the `contextily` homepage, available at: [https://github.com/geopandas/contextily](https://github.com/geopandas/contextily). We especially encourage you to check out the notebook examples provided in that github repo.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geo_env2", - "language": "python", - "name": "geo_env2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_build/.doctrees/README.doctree b/_build/.doctrees/README.doctree new file mode 100644 index 0000000..c0f7ba6 Binary files /dev/null and b/_build/.doctrees/README.doctree differ diff --git a/_build/.doctrees/environment.pickle b/_build/.doctrees/environment.pickle new file mode 100644 index 0000000..1ae9796 Binary files /dev/null and b/_build/.doctrees/environment.pickle differ diff --git a/_build/.doctrees/glue_cache.json b/_build/.doctrees/glue_cache.json 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Welcome to Geospatial Fundamentals in Python

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Overview

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Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The Python programming language is a great platform for exploring these data and integrating them into your research.

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Geospatial Data in Python, part I: Getting started with spatial dataframes

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Part one of this two-part workshop series will introduce basic methods for working with geospatial data in Python using the GeoPandas library. Participants will learn how to import and export spatial data and store them as GeoPandas GeoDataFrames (or spatial dataframes). We will explore and compare several methods for mapping the data including the GeoPandas plot function and the matplotlib library. We will review coordinate reference systems and methods for reading, defining and transforming these. Note, this workshop focuses on vector spatial data.

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Geospatial Data in Python, part 2: Geoprocessing and analysis

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Part two of this two-part workshop series will dive deeper into data driven mapping in Python, using color palettes and data classification to communicate information with maps. We will also introduce basic methods for processing spatial data, which are the building blocks of common spatial analysis workflows. Note, this workshop focuses on vector spatial data.

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Pre-requisites

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Knowledge Requirements

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You’ll probably get the most out of this workshop if you have a basic foundation in Python and Pandas, similar to what you would have from taking the D-Lab Python Fundamentals workshop series. Here are a couple of suggestions for materials to check-out prior to the workshop.

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D-Lab Workshops:

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Other:

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Technology Requirements:

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Bring a laptop with Python and the following packages installed: pandas, geopandas, matplotlib, descartes and dependencies. More details are provided on the workshop github page https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python).

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1.0 Python and Jupyter Notebook installation

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There are many ways to install python and python libraries, distributed as packages, on your computer. Here is one way that we recommend.

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  • Anaconda installs IDEs and several important packages like NumPy, Pandas, and so on, and this is a really convenient package which can be downloaded and installed.

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Anaconda is a free and open-source distribution of Python. Anaconda installs IDEs (integrated development environments, aka where you can write and run code) and several important packages like NumPy and Pandas, making it a really convenient package to use.

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1.1 Download Anaconda:

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Follow this link to download Anaconda: https://www.anaconda.com/distribution. The same link can be used for Mac, Windows, and Linux.

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We recommend downloading the latest version, which will be Python 3. +

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Open the .exe file that was downloaded and follow the instructions in the installation wizard prompt.

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1.2 Launch Anaconda and open a Jupyter Notebook

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Once installation is complete open Anaconda Navigator and launch Jupyter Notebook.

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Jupyter Notebook will open in your web browser (it does not require internet to work). In Jupyter, navigate to the folder where you saved the code file you plan to use and open the .ipynb file (the extension for Jupyter Notebook files written in Python) to view it in the Notebook.

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2.0 Installing Geopandas

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  • From within Anaconda Navigator click on the Environments selection in the left sidebar menu

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  • Click on the arrow to the right of your base (root) environment and select Open Terminal

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  • This will give you access to the command line interface (CLI) on your computer in a window that looks like this:

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+
    +

  • Install some needed software by entering the following commands, one at a time:

    • +
+
conda install python=3 geopandas
+conda install juypter
+conda install matplotlib
+conda install descartes
+conda install mapclassify
+conda install contextily
+
+
+

Once you have those libraries all installed you will be able to go to Anaconda Navigator, launch a Jupyter Notebook, navigate to the workshop files and run all of the notebooks.

+

Optionally you can create a virtual environment In the terminal window, type the conda commands shown on the GeoPandas website for installing Geopandas in a virtual environment. These are:

+
conda create -n geo_env
+conda activate geo_env
+conda config --env --add channels conda-forge
+conda config --env --set channel_priority strict
+conda install python=3 geopandas
+
+
+

After creating your virtual environment, you can process and install the rest of your packages listed above. You will be able to select your geo_env in Anaconda Navigator.

+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
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.tabbed-set>label:hover{color:var(--tabs-color-label-active)} diff --git a/_build/html/_panels_static/panels-variables.06eb56fa6e07937060861dad626602ad.css b/_build/html/_panels_static/panels-variables.06eb56fa6e07937060861dad626602ad.css new file mode 100644 index 0000000..adc6166 --- /dev/null +++ b/_build/html/_panels_static/panels-variables.06eb56fa6e07937060861dad626602ad.css @@ -0,0 +1,7 @@ +:root { +--tabs-color-label-active: hsla(231, 99%, 66%, 1); +--tabs-color-label-inactive: rgba(178, 206, 245, 0.62); +--tabs-color-overline: rgb(207, 236, 238); +--tabs-color-underline: rgb(207, 236, 238); +--tabs-size-label: 1rem; +} \ No newline at end of file diff --git a/_build/html/_sources/README.md b/_build/html/_sources/README.md new file mode 100644 index 0000000..7c61db5 --- /dev/null +++ b/_build/html/_sources/README.md @@ -0,0 +1,116 @@ +# Welcome to Geospatial Fundamentals in Python + +## Overview + +Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The Python programming language is a great platform for exploring these data and integrating them into your research. + +### Geospatial Data in Python, part I: Getting started with spatial dataframes + +Part one of this two-part workshop series will introduce basic methods for working with geospatial data in Python using the [GeoPandas library](https://geopandas.org). Participants will learn how to import and export spatial data and store them as GeoPandas GeoDataFrames (or spatial dataframes). We will explore and compare several methods for mapping the data including the GeoPandas plot function and the matplotlib library. We will review coordinate reference systems and methods for reading, defining and transforming these. Note, this workshop focuses on vector spatial data. + +### Geospatial Data in Python, part 2: Geoprocessing and analysis + +Part two of this two-part workshop series will dive deeper into data driven mapping in Python, using color palettes and data classification to communicate information with maps. We will also introduce basic methods for processing spatial data, which are the building blocks of common spatial analysis workflows. Note, this workshop focuses on vector spatial data. + + +### Pre-requisites + +#### Knowledge Requirements +You'll probably get the most out of this workshop if you have a basic foundation in Python and Pandas, similar to what you would have from taking the D-Lab Python Fundamentals workshop series. Here are a couple of suggestions for materials to check-out prior to the workshop. + +`D-Lab Workshops`: + - [Python Fundamentals](https://github.com/dlab-berkeley/python-fundamentals) + - [Pandas](https://github.com/dlab-berkeley/introduction-to-pandas) + +`Other`: + - [Learn Python on Kaggle](https://www.kaggle.com/learn/python) + - [Programming in Python - Software Carpentry](http://swcarpentry.github.io/python-novice-inflammation/) + - [Learn Pandas on Kaggle](https://www.kaggle.com/learn/pandas) + - [Plotting in Python - Software Carpentry](http://swcarpentry.github.io/python-novice-gapminder/) +: Basic knowledge of geospatial data is expected. R experience equivalent to the D-Lab R Fundamentals workshop series is required to follow along with the tutorial. Knowledge of ggplot helpful. + +#### Technology Requirements: + +Bring a laptop with Python and the following packages installed: pandas, geopandas, matplotlib, descartes and dependencies. More details are provided on the workshop github page https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python). + + +## 1.0 Python and Jupyter Notebook installation + +There are many ways to install python and python libraries, distributed as packages, on your computer. Here is one way that we recommend. + + +* Anaconda installs IDEs and several important packages like NumPy, Pandas, and so on, and this is a really convenient package which can be downloaded and installed. + +Anaconda is a free and open-source distribution of Python. Anaconda installs IDEs (integrated development environments, aka where you can write and run code) and several important packages like NumPy and Pandas, making it a really convenient package to use. + +### 1.1 Download Anaconda: + +Follow this link to download Anaconda: https://www.anaconda.com/distribution. The same link can be used for Mac, Windows, and Linux. + + +We recommend downloading the latest version, which will be Python 3. + + +Open the .exe file that was downloaded and follow the instructions in the installation wizard prompt. + +### 1.2 Launch Anaconda and open a Jupyter Notebook + +Once installation is complete open Anaconda Navigator and launch Jupyter Notebook. + +> + +Jupyter Notebook will open in your web browser (it does not require internet to work). In Jupyter, navigate to the folder where you saved the code file you plan to use and open the .ipynb file (the extension for Jupyter Notebook files written in Python) to view it in the Notebook. + +## 2.0 Installing Geopandas + +- From within Anaconda Navigator click on the `Environments` selection in the left sidebar menu + +> + +- Click on the arrow to the right of your `base (root)` environment and select **Open Terminal** + +> + +- This will give you access to the command line interface (CLI) on your computer in a window that looks like this: + +> + +- Install some needed software by entering the following commands, one at a time: + +``` +conda install python=3 geopandas +conda install juypter +conda install matplotlib +conda install descartes +conda install mapclassify +conda install contextily +``` +Once you have those libraries all installed you will be able to go to Anaconda Navigator, launch a `Jupyter Notebook`, navigate to the workshop files and run all of the notebooks. + + +*Optionally you can create a virtual environment In the terminal window, type the **conda** commands shown on the [GeoPandas website](https://geopandas.org/install.html#creating-a-new-environment) for installing Geopandas in a virtual environment. These are:* + +```` +conda create -n geo_env +conda activate geo_env +conda config --env --add channels conda-forge +conda config --env --set channel_priority strict +conda install python=3 geopandas +```` + +*After creating your virtual environment, you can process and install the rest of your packages listed above. You will be able to select your `geo_env` in Anaconda Navigator.* + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + diff --git a/_build/html/_sources/lessons/01_Overview_Geospatial_Data.ipynb b/_build/html/_sources/lessons/01_Overview_Geospatial_Data.ipynb new file mode 100644 index 0000000..fe8f8cc --- /dev/null +++ b/_build/html/_sources/lessons/01_Overview_Geospatial_Data.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 1. Overview of Geospatial Data\n", + "\n", + "Before diving into any coding, let's first go over some core concepts.\n", + "\n", + "- 1.1 Geospatial Data\n", + "- 1.2 Coordinate Reference Systems\n", + "- 1.3 Types of Spatial Data\n", + "- 1.4 Other Resources\n", + "\n", + "Note that this Jupyterbook covers *a lot*! There's so much to learn and understand about the world of doing geospatial work. But we want you to keep in mind that this really only the start of your journey. All the authors who contributed to this are still learning too :)\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.1 Geospatial Data\n", + "\n", + "So there are a couple of terms that get confused when we're trying to talk about work in this area:\n", + "- *Geographic Information Systems (GIS)*\n", + "- *Geographic Data*\n", + "- *Geospatial Data*\n", + "We'll walk through each of these term-by-term.\n", + "\n", + "**Geographic Information Systems (GIS)** is probably a term that you've heard of before and it integrates many types of data, which includes spatial location. You can think of it as a framework to analyze spatial and geographic data.\n", + "> **Note**: GIS can also be an acronym for Geographic Information Science, which is the study of the study of geographic systems.\n", + "\n", + "**Geographic data** can answer the questions \"where\" and \"what\". To make this a little bit more concrete, let's use this sign in Anatone, WA, USA as an example.\n", + "\n", + "\n", + "\n", + "
Image Credit: Dsdugan at English Wikipedia
\n", + "\n", + "\n", + "Dsdugan at English Wikipedia\n", + "\n", + "Here, our answer to the question to \"where\" is Anatone, WA. The \"what\" question is answered by all the details on the sign, for example we know that the number of dogs in Anatone is 22. These types of details are also called *attributes*.\n", + "\n", + "Another component of geographic data is *metadata*. This component includes things such as when the data was taken, by whom, how, the quality, as wel as other information about the geographic data itself. \n", + "\n", + "**Geospatial Data** is a location that is given by a set of coordinates. For example, the location for Anatone could be specified with a specific latitude and longitude ($46.135570$, $-117.132659$). \n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 Coordinate Reference Systems\n", + "\n", + "A **Coordinate Reference System** or **CRS** is a system for associating specific numerical coordinates with a position on earth. So depending on the CRS that is used the numbers for the latitude and longitude could differ.\n", + "\n", + "\n", + "\n", + "
Image Credit: Wikimedia Commons
\n", + "\n", + "\n", + "There are many CRSs because our understanding and ability to measure the surface of the earth has evolved over time. We can think of these different reasonings as an orange peel or a lamp.\n", + "\n", + "Think if we take a regular orange as our earth:\n", + "\n", + "\n", + "\n", + "
Image Credit: ESRI project package by j_nelson
\n", + "\n", + "\n", + "And the first assumption we make is that it is spherical: \n", + "\n", + "\n", + "\n", + "
Image Credit: ESRI project package by j_nelson
\n", + "\n", + "\n", + "Assuming that it's spherical will introduce some distortion, as well as how I choose to draw all of my continents on it. Plus when I decide to peel it, depending on how I do that, It'll look like different maps on a flat surface:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Image Credit: ESRI project package by j_nelson
\n", + "\n", + "\n", + "Another way to think about this is by thinking about our planet earth as a lamp in a dark room.\n", + "\n", + "\n", + "\n", + "
Image Credit: Brando
\n", + "\n", + "\n", + "\n", + "Depending on factors such as how we tilt the lamp and if our walls our flat the image that we project onto the wall will be different.\n", + "\n", + "*In short, since our earth isn't flat, our earth is distorted to make it feasible to show it on a flat surface*.\n", + "\n", + "\n", + "There are two types of coordinate reference systems.\n", + "- *Geographic CRS*\n", + "- *Projected CRS*\n", + "\n", + "*Geographic CRS* are great for storing data and has units of degrees and are widely used. WGS84 is the most commonly used CRS and is basd on satellites and used by cellphones and GPS. It has the best overall fir for most places on earth. Another common one is NAD83 which is based on both satellite and survey data. It's a great fit for USA based work and is utilized in a lot of federal data products such as the census data. Both of these CRS have *EPSG codes*, which a 4+ digit number used to reference a CRs. For WGS84 the code is 4326, while for NAD83 its 4269. You'll be using these codes when you're using CRS in Python.\n", + "\n", + "*Projected CRS* are good for mapping and spatial analysis. They transform the geographic coordinates (latitude, longitude) to be 2D (X, Y) with units such as meters. All map projections include some type of distortion, whether that be in area, shape, distance or direction. Depending on the CRS it'll probably be minimizing distortion for one of these characteristics. For example, the Mercator projection places importance on shape and direction, but in turn has distorted area as you move away from the equator.\n", + "\n", + "\n", + "\n", + "
Image Credit: QGIS Documentation
\n", + "\n", + "\n", + "\n", + "Of course some projections are worse than others. This joke projection has somehow made all continents look like South America! This story of distortion tells us that some projections are better than others.\n", + "\n", + "\n", + "\n", + "
Image Credit: xkcd comics
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "> **Note**: Here are some videos related to the concept of CRS. \n", + "> - Drawing projections on fruits: [Link](https://www.youtube.com/watch?v=wkK_HsY7S_4&t=399s)\n", + "> - West Wing discussion on using specific projections: [Link](https://www.youtube.com/watch?v=vVX-PrBRtTY&t=55s)\n", + "> - Vox on why world maps are wrong: [Link](https://www.youtube.com/watch?v=kIID5FDi2JQ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 Types of Spatial Data\n", + "\n", + "As you start to gather geospatial data, you'll encounter two types: **vector** and **raster** data.\n", + "\n", + "**Vector data** can be thought of as that that you can connect the dots with. This type of data includes points, lines, and polygons.\n", + "\n", + "\n", + "\n", + "As an example, we can look at these different types of vector data by looking at different data in San Francisco.\n", + "\n", + "\n", + "\n", + "Each of these geometry types can be used for different types of information. Point geometries are great for showing where crimes have occurred historically. Lines can show us the location and length of the freeways in the city. Polygons could help us show information such as population per square mile in different neighborhoods.\n", + "\n", + "Now let's think about what this vector data could look like when you open it up.\n", + "\n", + "\n", + "\n", + "You might get something like this. Each row represents one geospatial feature. So for our second attribute we have the ID number 2, the plot size 20, vegetation type, and a vegetation class of deciduous. Those additional information like the plot size, are **attributes**. These help describe our features. \n", + "\n", + "Furthermore, each of these features have an associated geometry or geometry collection. So in our first table our geometry is a point,\n", + "\n", + "One last thing about vector data-- each group of features is called a layer. So you could have all three of these data, and each dataset would be its own layer. \n", + "\n", + "\n", + "**Raster data** on the other hand is continous. Each location is represented by a grid cell, which are usually all the same size. There a fixed number of rows and columns, and each cell has a value that represents the attribute of interest. \n", + "\n", + "\n", + "\n", + "
Image Credit: Humboldt GSP
\n", + "\n", + "\n", + "\n", + "Raster data should feel familiar to you since images are basically raster data! \n", + "\n", + "Now that we know we have these two types of datasets, we can talk about when to use each. Vector data are better for when you have discreetly bounded data. This could be for counties, rivers, etc. On the other hand, raster data is better for continuous data (like the image we just looked at), or maybe something like temperature, elevation or rainfall.\n", + "\n", + "Now these two datasets come in different file formats, so you’ll know what it is before you pull it in for whatever GIS software you’re using. Some common ones I use are shapefile and geojsons for vector data, and geotiffs for raster data. \n", + "\n", + "| Vector | Raster |\n", + "| ----------- | ----------- |\n", + "| Shapefile (.shp…) | GeoTIFF |\n", + "| GeoJSON, JSON | netCDF |\n", + "| KML | DEM |\n", + "| GeoPackage | |\n", + "\n", + "Although these two types of data look different, and come in different formats, you can still use a combination of raster and vector data to answers questions that you’re probably aiming to answer through your own work.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.4 Other Resources\n", + "\n", + "This is really only a brief introduction to geospatial concepts! If you want to dive a little deeper, here are a couple of resources you can check out:\n", + "\n", + "- [Kaggle Learn: Geospatial Analysis in Python](https://www.kaggle.com/learn/geospatial-analysis), an online interactive tutorial\n", + "\n", + "- [Campbell & Shin, Geographic Information System Basics, v1.0](https://2012books.lardbucket.org/books/geographic-information-system-basics/index.html)\n", + "\n", + "- [ESRI Introduction to Map Design](https://www.esri.com/industries/k-12/education/~/media/Files/Pdfs/industries/k-12/pdfs/intrcart.pdf)\n", + "\n", + "- [AxisMaps Cartography Guide](https://www.axismaps.com/guide/)\n", + "\n", + "- [Gentle Introduction to GIS (QGIS)](https://docs.qgis.org/3.16/en/docs/gentle_gis_introduction/index.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/02_Introduction_to_GeoPandas.ipynb b/_build/html/_sources/lessons/02_Introduction_to_GeoPandas.ipynb similarity index 99% rename from 02_Introduction_to_GeoPandas.ipynb rename to _build/html/_sources/lessons/02_Introduction_to_GeoPandas.ipynb index 9665383..e758c62 100644 --- a/02_Introduction_to_GeoPandas.ipynb +++ b/_build/html/_sources/lessons/02_Introduction_to_GeoPandas.ipynb @@ -199,7 +199,7 @@ "### Geopandas Geometries\n", "There are three main types of geometries that can be associated with your geodataframe: points, lines and polygons:\n", "\n", - "\n", + "\n", "\n", "In the geodataframe these geometries are encoded in a format known as [Well-Known Text (WKT)](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry). For example:\n", "\n", diff --git a/03_CRS_Map_Projections.ipynb b/_build/html/_sources/lessons/03_CRS_Map_Projections.ipynb similarity index 99% rename from 03_CRS_Map_Projections.ipynb rename to _build/html/_sources/lessons/03_CRS_Map_Projections.ipynb index cbcf2fd..32c99ab 100644 --- a/03_CRS_Map_Projections.ipynb +++ b/_build/html/_sources/lessons/03_CRS_Map_Projections.ipynb @@ -318,7 +318,7 @@ " - a map projection is a mathematical model used to transform coordinate data\n", "\n", "### A Geographic vs Projected CRS\n", - "" + "" ] }, { diff --git a/04_More_Data_More_Maps.ipynb b/_build/html/_sources/lessons/04_More_Data_More_Maps.ipynb similarity index 100% rename from 04_More_Data_More_Maps.ipynb rename to _build/html/_sources/lessons/04_More_Data_More_Maps.ipynb diff --git a/05_Data-Driven_Mapping.ipynb b/_build/html/_sources/lessons/05_Data-Driven_Mapping.ipynb similarity index 99% rename from 05_Data-Driven_Mapping.ipynb rename to _build/html/_sources/lessons/05_Data-Driven_Mapping.ipynb index 37cfca7..ec25627 100644 --- a/05_Data-Driven_Mapping.ipynb +++ b/_build/html/_sources/lessons/05_Data-Driven_Mapping.ipynb @@ -216,6 +216,8 @@ "\n", "\n", "\n", + "
Image Credit: Dsdugan at English Wikipedia
\n", + "\n", "> **Pro-tip**: You can actually see all your color map options if you misspell what you put in `cmap` and try to run-in. Try it out!\n", "\n", "> **Pro-tip**: Sites like [ColorBrewer](https://colorbrewer2.org/#type=sequential&scheme=Blues&n=3) let's you play around with different types of color maps. If you want to create your own, [The Python Graph Gallery](https://python-graph-gallery.com/python-colors/) is a way to see what your Python color options are.\n" diff --git a/06_Spatial_Queries.ipynb b/_build/html/_sources/lessons/06_Spatial_Queries.ipynb similarity index 98% rename from 06_Spatial_Queries.ipynb rename to _build/html/_sources/lessons/06_Spatial_Queries.ipynb index 210a7ca..18cf6f3 100644 --- a/06_Spatial_Queries.ipynb +++ b/_build/html/_sources/lessons/06_Spatial_Queries.ipynb @@ -94,7 +94,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 6.0 Load and prep some data" + "## 6.0 Load and prep some data" ] }, { @@ -146,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 6.1 Measurement Queries" + "## 6.1 Measurement Queries" ] }, { @@ -422,7 +422,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 6.2 Relationship Queries" + "## 6.2 Relationship Queries" ] }, { @@ -652,7 +652,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Exercise: Spatial Relationship Query\n", + "## Exercise: Spatial Relationship Query\n", "\n", "Let's use a spatial relationship query to create a new dataset containing Berkeley schools!\n", "\n", @@ -717,7 +717,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Double-click to see solution!\n", + "### Double-click to see solution!\n", "\n", "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.2 Tracts data\n", + "\n", + "Since we want to answer the question **How many polling stations are in each census tract?**, we'll pull in our tracts data.\n", + "\n", + "- Bring in the census tracts data which lives at `notebook_data/census/Tracts/cb_2013_06_tract_500k.zip`\n", + "- Narrow it down to Alameda County\n", + "- Check CRS\n", + "- Transform CRS to 26910 if needed\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bring in census tracts\n", + "# tracts_gdf = gpd.read_file(...)\n", + "\n", + "# Narrow it down to Alameda County\n", + "# tracts_gdf_ac = tracts_gdf[...]\n", + "# tracts_gdf_ac.plot()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check CRS\n", + "print('polling_ac_gdf:', ...)\n", + "print('tracts_gdf_ac CRS:', ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform CRS\n", + "polling_ac_gdf_utm10 = ...\n", + "tracts_gdf_ac_utm10 = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.3 Spatial Join\n", + "\n", + "Alright, now our data is all ready to go! We're going to do a *spatial join* to answer our question about polling stations in each tract.\n", + "\n", + "- Spatial join tracts/acs with the polling data (keep the tracts geometry!)\n", + "- Plot it to make sure you have the right geometry\n", + "- Check out your data and its dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Spatial join tracts/acs with the polling data (keep the tracts geometry!)\n", + "\n", + "# polls_jointracts = gpd.sjoin(..., ... , how=...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot it to make sure you have the right geometry\n", + "\n", + "# polls_jointracts.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check out your data and its dimensions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.4 Aggregate number of stations by census tracts\n", + "\n", + "Now that we have a GeoDataFrame with all our polling and tract data, we'll need to *aggregate* to actually count the number of stations we have\n", + "\n", + "- Use `dissolve` to count the number of polls we have\n", + "- Create a choropleth map base don the number of stations there are" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use `dissolve` to count the number of polls we have\n", + "\n", + "# polls_countsbytract = polls_jointracts[['TRACTCE', 'NAME_right', \n", + "# 'geometry']].dissolve(by=..., \n", + "# aggfunc=...).reset_index()\n", + "# polls_countsbytract.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# rename the column to be for the number of polling stations (you dont have to change anything here)\n", + "\n", + "# polls_countsbytract.rename(columns={'NAME_right': 'Num_Polling'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a choropleth map base don the number of stations there are\n", + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "\n", + "# polls_countsbytract.plot(ax=ax,\n", + "# column=..., \n", + "# cmap=...,\n", + "# edgecolor=\"grey\",\n", + "# legend=True)\n", + "\n", + "# polling_ac_gdf_utm10.plot(ax=ax, color=..., edgecolor=..., markersize= ...)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.5 Attribute join back to tracts data\n", + "\n", + "Amazing! Now that we have this information let's do an *attribute join* to add this data into our tracts data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# merge onto census tract data\n", + "\n", + "# tracts_gdf_ac = tracts_gdf_ac.merge(polls_countsbytract[['TRACTCE', 'Num_Polling']], left_on= ...,right_on= ... , how= ... ) \n", + "# tracts_gdf_ac.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.6 Berkeley outline\n", + "\n", + "To answer our question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* we'll need to know where Berkeley is! This is the perfect time to bring our Berkeley places data in.\n", + "\n", + "- Read in `outdata/berkeley_places.shp`\n", + "- Check the CRS\n", + "- Transform CRS if necessary to EPSG:26910" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in outdata/berkeley_places.shp\n", + "# berkeley_places = gpd.read_file(...)\n", + "\n", + "# Check the CRS\n", + "\n", + "\n", + "# Transform CRS if necessary to EPSG:26910\n", + "berkeley_places_utm10 = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.7 Bus routes\n", + "\n", + "- Bring in bus routes ('notebook_data/transportation/Fall20Routeshape.zip'), transform CRS to 26910\n", + "- Intersect bus routes with Berkeley\n", + "- Plot results of intersection\n", + "- Clip bus routes to everything that is inside the berkley outline\n", + "- Plot bus routes on top of Berkeley outline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Bring in bus routes, transform CRS to 26910\n", + "bus_routes = ...\n", + "# bus_routes_utm10 = bus_routes.to_crs(...)\n", + "# bus_routes_utm10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at intersection between bus routes and Berkeley\n", + "# bus_routes_berkeley = .intersects(... .geometry.squeeze())\n", + "\n", + "# Create new geodataframe from these results\n", + "# bus_berk = bus_routes_utm10.loc[bus_routes_berkeley].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot results of intersection\n", + "\n", + "# fig, ax = plt.subplots(figsize=(10,10))\n", + "# berkeley_places_utm10.plot(ax=ax)\n", + "# bus_berk.plot(ax=ax, column ='PUB_RTE')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# BONUS: Look at route length\n", + "# bus_berk.length" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Clip bus routes to everything that is inside the berkley outline\n", + "# bus_berk_clip = gpd.clip(...,...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Plot bus routes on top of Berkeley outline\n", + "# fig, ax = plt.subplots(figsize=(10,10))\n", + "# berkeley_places_utm10.plot(ax=ax)\n", + "# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.6 Polling stations within walking distance of bus routes\n", + "\n", + "Now we can really answer the question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* \n", + "\n", + "- Create buffer around bus route for 100m\n", + "- Intersect polling locations in Alameda County with Berkeley outline \n", + "- Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations\n", + "- Calculate the distance from polling stations to the closest bus route" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create buffer around bus route for 100m\n", + "# bus_berk_buf =bus_berk_clip.buffer(distance= ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Intersect polling locations in Alameda County with Berkeley outline\n", + "# polling_berk = ... .intersects(berkeley_places_utm10.geometry.squeeze())\n", + "\n", + "# polling_berk_gdf = polling_ac_gdf_utm10[polling_berk].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations\n", + "\n", + "# fig, ax = plt.subplots(figsize=(10,10))\n", + "# berkeley_places_utm10.plot(ax=ax)\n", + "# bus_berk_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')\n", + "# polling_berk_gdf.plot(ax=ax, color= 'yellow')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the distance from polling stations to the closest bus route\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## You're done!!!! \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/10_OPTIONAL_Fetching_Data.ipynb b/_build/html/_sources/lessons/10_OPTIONAL_Fetching_Data.ipynb similarity index 100% rename from 10_OPTIONAL_Fetching_Data.ipynb rename to _build/html/_sources/lessons/10_OPTIONAL_Fetching_Data.ipynb diff --git a/_build/html/_sources/lessons/11_OPTIONAL_Basemap_with_Contextily.ipynb b/_build/html/_sources/lessons/11_OPTIONAL_Basemap_with_Contextily.ipynb new file mode 100644 index 0000000..48d7b31 --- /dev/null +++ b/_build/html/_sources/lessons/11_OPTIONAL_Basemap_with_Contextily.ipynb @@ -0,0 +1,839 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 11. Adding Basemaps with Contextily\n", + "\n", + "If you work with geospatial data in Python, you most likely are familiar with the fantastic [GeoPandas](https://geopandas.org/) library. GeoPandas leverages the power of [Matplotlib](https://matplotlib.org/) to enable users to make maps of their data. However, until recently, it has not been easy to add basemaps to these maps. Basemaps are the contextual map data, like Google Maps, on top of which geospatial data are often displayed.\n", + "\n", + "\n", + "The new Python library [contextily](https://github.com/geopandas/contextily), which stands for *context map tiles*, now makes it possible and relatively straight forward to add basemaps to Geopandas maps. Below we walk through a few common workflows for doing this.\n", + "\n", + "First, let's load are libraries. This assumes you have the following Python libraries installed in your environment:\n", + "\n", + "- pandas\n", + "- matplotlib\n", + "- geopandas (and all dependancies)\n", + "- contextily\n", + "- descartes" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import contextily as cx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read data into a Geopandas GeoDataFrame\n", + "\n", + "Fetch the census places data to map. Census places includes cities and other populated places. Here we fetch the 2019 cartographic boundary (`cb_`) file of California (`06`) places." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ca_places = \"https://www2.census.gov/geo/tiger/GENZ2019/shp/cb_2019_06_place_500k.zip\"\n", + "places = gpd.read_file(ca_places)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the geodatarame `plot` method to make a quick map." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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STATEFPPLACEFPPLACENSAFFGEOIDGEOIDNAMELSADALANDAWATERgeometry
00636490024101021600000US06364900636490Industry2530529397723181POLYGON ((-118.05750 34.01640, -118.05603 34.0...
10640130024116201600000US06401300640130Lancaster25244187339681671POLYGON ((-118.32517 34.75176, -118.32073 34.7...
20675000024119871600000US06750000675000Stockton251610256317985703POLYGON ((-121.41881 38.04418, -121.41801 38.0...
30643000024108661600000US06430000643000Long Beach2513130222275937543MULTIPOLYGON (((-118.12890 33.75801, -118.1273...
40678106024120421600000US06781060678106Tehama2520572100POLYGON ((-122.13364 40.02417, -122.13295 40.0...
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" + ], + "text/plain": [ + " STATEFP PLACEFP PLACENS AFFGEOID GEOID NAME LSAD \\\n", + "0 06 36490 02410102 1600000US0636490 0636490 Industry 25 \n", + "1 06 40130 02411620 1600000US0640130 0640130 Lancaster 25 \n", + "2 06 75000 02411987 1600000US0675000 0675000 Stockton 25 \n", + "3 06 43000 02410866 1600000US0643000 0643000 Long Beach 25 \n", + "4 06 78106 02412042 1600000US0678106 0678106 Tehama 25 \n", + "\n", + " ALAND AWATER geometry \n", + "0 30529397 723181 POLYGON ((-118.05750 34.01640, -118.05603 34.0... \n", + "1 244187339 681671 POLYGON ((-118.32517 34.75176, -118.32073 34.7... \n", + "2 161025631 7985703 POLYGON ((-121.41881 38.04418, -121.41801 38.0... \n", + "3 131302222 75937543 MULTIPOLYGON (((-118.12890 33.75801, -118.1273... \n", + "4 2057210 0 POLYGON ((-122.13364 40.02417, -122.13295 40.0... " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "places.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can subset the data by selecting a row or rows by place name. Let's select the city of Berkeley, CA." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley = places[places['NAME']=='Berkeley']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "berkeley.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Contextily to add a basemap\n", + "\n", + "Above we can see the map of the boundary of the city of Berkeley, CA. The axis labels display the longitude and latitude coordinates for the bounding extent of the city.\n", + "\n", + "Let's use `contextily` in it's most simple form to add a basemap to provide the geographic context for Berkeley. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = berkeley.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a few important things to note about the above code.\n", + "\n", + "- We use `matplotlib` to define the plot canvas as `ax`.\n", + "- We then add the contextily basemap to the map with the code `cx.add_basemap(ax)`\n", + "\n", + "Additionally, we **dynamically transform the coordinate reference system**, or CRS, of the Berkeley geodataframe from geographic lat/lon coordinates to `web mercator` using the method **to_crs('EPSG:3857')**. [Web mercator](https://en.wikipedia.org/wiki/Web_Mercator_projection) is the default CRS used by all web map tilesets. It is referenced by a the code `EPSG:3857` where [EPSG](https://en.wikipedia.org/wiki/EPSG_Geodetic_Parameter_Dataset) stands for the the initials of the organization that created these codes (the European Petroleum Survey Group).\n", + "\n", + "Let's clean up the map by adding some code to change the symbology of the Berkeley city boundary. This will highlight the value of adding a basemap.\n", + "\n", + "First, let's map the boundary with out a fill color." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "berkeley.plot(edgecolor=\"red\", facecolor=\"none\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's build on those symbology options and add the contextily basemap." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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4-122.26712037.828705MACARTHURBARTALA
\n", + "
" + ], + "text/plain": [ + " lon lat STATION OPERATOR COUNTY\n", + "0 -122.283348 37.874061 NORTH BERKELEY BART ALA\n", + "1 -122.268249 37.869689 DOWNTOWN BERKELEY BART ALA\n", + "2 -122.270119 37.853207 ASHBY BART ALA\n", + "3 -122.251777 37.844510 ROCKRIDGE BART ALA\n", + "4 -122.267120 37.828705 MACARTHUR BART ALA" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bart.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting Point Data in a dataframe to Geospatial Data in a geodataframe\n", + "\n", + "Because these data are in a CSV file we read them into a Pandas DataFrame.\n", + "\n", + "In order to map these data we need to convert these data to a GeoPandas GeoDataFame. To do this, we need to specify:\n", + "\n", + "- the data, here the geodataframe `bart`\n", + "- the coordinate data, here `bart['X']` and `bart['Y']`\n", + "- the CRS of the bart coordinate data, here `EPSG:4326`\n", + "\n", + "The CRS code 'EPSG:4326' stands for the World Geodectic System of 1984, or WGS84. This is the most commonly used CRS for geographic (lat/lon) coordinate data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Convert the DataFrame to a GeoDataFrame. \n", + "bart_gdf = gpd.GeoDataFrame(bart, geometry=gpd.points_from_xy(bart['lon'], \n", + " bart['lat']), \n", + " crs='EPSG:4326') \n", + "\n", + "# and take a look\n", + "bart_gdf.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have the BART data in a geodataframe we can use the same commands as we did above to map it with a contextily basemap." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have the full range of `matplotlib` style options to enhance the map, a few of which are shown in the example below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = bart_gdf.to_crs('EPSG:3857').plot(\n", + " color=\"red\",\n", + " edgecolor=\"black\",\n", + " markersize=50, \n", + " figsize=(9, 9))\n", + "\n", + "ax.set_title('Bay Area Bart Stations')\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Changing the Basemap\n", + "\n", + "By default `contextiley` returns maptiles from the OpenStreetmap Mapnik basemap. However, ther are other available tilesets from different providers. These tilesets are stored in the contextily `cx.providers` dictionary.\n", + "\n", + "That's a large dictionary and you can view it. Alternatively, and more simply, you can access the list of the providers in this dictionary using the command `cs.providers.keys`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['OpenStreetMap', 'OpenSeaMap', 'OpenPtMap', 'OpenTopoMap', 'OpenRailwayMap', 'OpenFireMap', 'SafeCast', 'Thunderforest', 'OpenMapSurfer', 'Hydda', 'MapBox', 'Stamen', 'Esri', 'OpenWeatherMap', 'HERE', 'FreeMapSK', 'MtbMap', 'CartoDB', 'HikeBike', 'BasemapAT', 'nlmaps', 'NASAGIBS', 'NLS', 'JusticeMap', 'Wikimedia', 'GeoportailFrance', 'OneMapSG'])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# change basemap - can be one of these\n", + "# first see available provider names\n", + "cx.providers.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Once you have the list of providers, you can find the names of their specific tilesets. \n", + "\n", + "Below, we retrieve the list of the tilesets available from the provider `CartoDB`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['Positron', 'PositronNoLabels', 'PositronOnlyLabels', 'DarkMatter', 'DarkMatterNoLabels', 'DarkMatterOnlyLabels', 'Voyager', 'VoyagerNoLabels', 'VoyagerOnlyLabels', 'VoyagerLabelsUnder'])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Then find the names of the tile sets for a specific provider\n", + "cx.providers.CartoDB.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can specify a different tileset using the **source** argument to the `add_basemap` method." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['WorldStreetMap', 'DeLorme', 'WorldTopoMap', 'WorldImagery', 'WorldTerrain', 'WorldShadedRelief', 'WorldPhysical', 'OceanBasemap', 'NatGeoWorldMap', 'WorldGrayCanvas'])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cx.providers.Esri.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/contextily/tile.py:632: UserWarning: The inferred zoom level of 11 is not valid for the current tile provider (valid zooms: 1 - 9).\n", + " warnings.warn(msg)\n" + ] + }, + { + "ename": "ConnectionError", + "evalue": "('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mConnectionResetError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m 700\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 446\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 441\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1346\u001b[0m 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Connection reset by peer", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mProtocolError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m resp = conn.urlopen(\n\u001b[0m\u001b[1;32m 440\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 754\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 755\u001b[0;31m retries = retries.increment(\n\u001b[0m\u001b[1;32m 756\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_method_retryable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 532\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m 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"\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m 700\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;31m# Otherwise it looks like a bug in the 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url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 540\u001b[0m }\n\u001b[1;32m 541\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 654\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 657\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mProtocolError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 500\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mMaxRetryError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mConnectionError\u001b[0m: ('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Change the basemap provider and tileset\n", + "ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", + "cx.add_basemap(ax, source=cx.providers.NASAGIBS.ModisTerraTrueColorCR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning More\n", + "\n", + "Above, we prove a very short introduction to the excellent `contextily` library. You can find more detailed information on the `contextily` homepage, available at: [https://github.com/geopandas/contextily](https://github.com/geopandas/contextily). We especially encourage you to check out the notebook examples provided in that github repo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb b/_build/html/_sources/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb similarity index 83% rename from 12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb rename to _build/html/_sources/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb index e7d17cc..f3cf55d 100644 --- a/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb +++ b/_build/html/_sources/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb @@ -23,9 +23,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "import pandas as pd\n", "import geopandas as gpd\n", @@ -57,20 +66,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unknown\n" + ] + } + ], "source": [ "print(folium.__version__) # Make sure you have version 0.10.1 or later of folium!" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9.0\n" + ] + } + ], "source": [ "print(gpd.__version__) # Make sure you have version 0.7.0 or later of GeoPandas!" ] @@ -97,11 +122,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%%html\n", "" @@ -121,11 +159,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a new folium map and save it to the variable name map1\n", "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", @@ -147,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -198,9 +250,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "map1 # display map in notebook" ] diff --git a/13_OPTIONAL_geocoding.ipynb b/_build/html/_sources/lessons/13_OPTIONAL_geocoding.ipynb old mode 100755 new mode 100644 similarity index 100% rename from 13_OPTIONAL_geocoding.ipynb rename to _build/html/_sources/lessons/13_OPTIONAL_geocoding.ipynb diff --git a/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb b/_build/html/_sources/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb similarity index 100% rename from 14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb rename to _build/html/_sources/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb diff --git a/15_OPTIONAL_Voronoi_Tessellation.ipynb b/_build/html/_sources/lessons/15_OPTIONAL_Voronoi_Tessellation.ipynb similarity index 100% rename from 15_OPTIONAL_Voronoi_Tessellation.ipynb rename to _build/html/_sources/lessons/15_OPTIONAL_Voronoi_Tessellation.ipynb diff --git a/16_OPTIONAL_Introduction_to_Raster_Data.ipynb b/_build/html/_sources/lessons/16_OPTIONAL_Introduction_to_Raster_Data.ipynb similarity index 100% rename from 16_OPTIONAL_Introduction_to_Raster_Data.ipynb rename to _build/html/_sources/lessons/16_OPTIONAL_Introduction_to_Raster_Data.ipynb diff --git a/99_Questions_Answers.md b/_build/html/_sources/lessons/99_Questions_Answers.md similarity index 100% rename from 99_Questions_Answers.md rename to _build/html/_sources/lessons/99_Questions_Answers.md diff --git a/_build/html/_sources/lessons/intro.md b/_build/html/_sources/lessons/intro.md new file mode 100644 index 0000000..4d81790 --- /dev/null +++ b/_build/html/_sources/lessons/intro.md @@ -0,0 +1,131 @@ +# Welcome to Geospatial Fundamentals in Python: From A to Z to Fancy + +## Overview + +Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The Python programming language is a great platform for exploring these data and integrating them into your research. This JupyterBook explores everything from *A to Z* to get started to work with Geospatial data in Python. We then take you all the way to *fancy* to work with online data sources, basemaps, interactive maps, geocoding, tessellation, and raster data. + +### 1. Getting Started with Spatial Dataframes + +Part one will introduce basic methods for working with geospatial data in Python using the [GeoPandas library](https://geopandas.org). You will learn how to import and export spatial data and store them as GeoPandas GeoDataFrames (or spatial dataframes). We will explore and compare several methods for mapping the data including the GeoPandas plot function and the matplotlib library. We will review coordinate reference systems and methods for reading, defining and transforming these. + + +### 2. Geoprocessing and Analysis + +Part two dives deeper into data driven mapping in Python, using color palettes and data classification to communicate information with maps. We will also introduce basic methods for processing spatial data, which are the building blocks of common spatial analysis workflows. + + +### 3. Exercises + +Part 3 provides two full workflows for you to try to work through on your own. These exercises uses techniques and concepts from both the first and second parts. + +### 4. Get Fancy + +Part 4 dives builds off of the foundational work from the earlier sections. The topics included involve: +- Reading in online sources data +- Adding basemaps +- Creating interactive maps +- Geocoding addresses +- Using Altair for plotting +- Creating voronoi tessellations +- Starting out with raster data + + +### Pre-requisites + +#### Knowledge Requirements +You'll probably get the most out of this workshop if you have a basic foundation in Python and Pandas, similar to what you would have from taking the D-Lab Python Fundamentals workshop series. Here are a couple of suggestions for materials to check-out prior to the workshop. + +`D-Lab Workshops`: + - [Python Fundamentals](https://github.com/dlab-berkeley/python-fundamentals) + - [Pandas](https://github.com/dlab-berkeley/introduction-to-pandas) + +`Other`: + - [Learn Python on Kaggle](https://www.kaggle.com/learn/python) + - [Programming in Python - Software Carpentry](http://swcarpentry.github.io/python-novice-inflammation/) + - [Learn Pandas on Kaggle](https://www.kaggle.com/learn/pandas) + - [Plotting in Python - Software Carpentry](http://swcarpentry.github.io/python-novice-gapminder/) +: Basic knowledge of geospatial data is expected. R experience equivalent to the D-Lab R Fundamentals workshop series is required to follow along with the tutorial. Knowledge of ggplot helpful. + +#### Technology Requirements: + +Bring a laptop with Python and the following packages installed: pandas, geopandas, matplotlib, descartes and dependencies. More details are provided on the workshop github page https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python). + + +## 1.0 Python and Jupyter Notebook installation + +There are many ways to install python and python libraries, distributed as packages, on your computer. Here is one way that we recommend. + + +* Anaconda installs IDEs and several important packages like NumPy, Pandas, and so on, and this is a really convenient package which can be downloaded and installed. + +Anaconda is a free and open-source distribution of Python. Anaconda installs IDEs (integrated development environments, aka where you can write and run code) and several important packages like NumPy and Pandas, making it a really convenient package to use. + +### 1.1 Download Anaconda: + +Follow this link to download Anaconda: https://www.anaconda.com/distribution. The same link can be used for Mac, Windows, and Linux. + + +We recommend downloading the latest version, which will be Python 3. +![downloadinstruc](assets/images/anaconda_download_instructions.png) + +Open the .exe file that was downloaded and follow the instructions in the installation wizard prompt. + +### 1.2 Launch Anaconda and open a Jupyter Notebook + +Once installation is complete open Anaconda Navigator and launch Jupyter Notebook. +![launchnav](assets/images/anaconda_navigator_launch.png) + +Jupyter Notebook will open in your web browser (it does not require internet to work). In Jupyter, navigate to the folder where you saved the code file you plan to use and open the .ipynb file (the extension for Jupyter Notebook files written in Python) to view it in the Notebook. + +## 2.0 Installing Geopandas + +- From within Anaconda Navigator click on the `Environments` selection in the left sidebar menu +> ![anacondanav](assets/images/anaconda1_navigator_home.png) + +- Click on the arrow to the right of your `base (root)` environment and select **Open Terminal** + +> ![anacondanav](assets/images/anaconda2_base_open_teriminal.png) + +- This will give you access to the command line interface (CLI) on your computer in a window that looks like this: + +> ![openterminal](assets/images/anaconda2_base_open_teriminal.png) + +- Install some needed software by entering the following commands, one at a time: + +``` +conda install python=3 geopandas +conda install juypter +conda install matplotlib +conda install descartes +conda install mapclassify +conda install contextily +``` +Once you have those libraries all installed you will be able to go to Anaconda Navigator, launch a `Jupyter Notebook`, navigate to the workshop files and run all of the notebooks. + + +*Optionally you can create a virtual environment In the terminal window, type the **conda** commands shown on the [GeoPandas website](https://geopandas.org/install.html#creating-a-new-environment) for installing Geopandas in a virtual environment. These are:* + +```` +conda create -n geo_env +conda activate geo_env +conda config --env --add channels conda-forge +conda config --env --set channel_priority strict +conda install python=3 geopandas +```` + +*After creating your virtual environment, you can process and install the rest of your packages listed above. You will be able to select your `geo_env` in Anaconda Navigator.* + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + diff --git a/notebook_data/README.md b/_build/html/_sources/lessons/notebook_data/README.md similarity index 100% rename from notebook_data/README.md rename to _build/html/_sources/lessons/notebook_data/README.md diff --git a/_build/html/_sources/ran/02_Introduction_to_GeoPandas-Copy1.ipynb b/_build/html/_sources/ran/02_Introduction_to_GeoPandas-Copy1.ipynb new file mode 100644 index 0000000..8d73671 --- /dev/null +++ b/_build/html/_sources/ran/02_Introduction_to_GeoPandas-Copy1.ipynb @@ -0,0 +1,1175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 2. Introduction to Geopandas\n", + "\n", + "In this lesson we'll learn about a package that is core to using geospatial data in Python. We'll go through the structure of the data (it's not too different from regular DataFrames!), geometries, shapefiles, and how to save your hard work.\n", + "\n", + "- 2.1 What is GeoPandas?\n", + "- 2.2 Read in a shapefile\n", + "- 2.3 Explore the GeoDataFrame\n", + "- 2.4 Plot the GeoDataFrame\n", + "- 2.5 Subset the GeoDataFrame\n", + "- 2.6 Save your data\n", + "- 2.7 Recap\n", + "- **Exercise**: IO, Manipulation, and Mapping\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/california_counties/CaliforniaCounties.shp'\n", + " - 'notebook_data/census/Places/cb_2018_06_place_500k.zip'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 5 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 What is GeoPandas?\n", + "\n", + "### GeoPandas and related Geospatial Packages\n", + "\n", + "[GeoPandas](http://geopandas.org/) is a relatively new package that makes it easier to work with geospatial data in Python. In the last few years it has grown more powerful and stable. This is really great because previously it was quite complex to work with geospatial data in Python. GeoPandas is now the go-to package for working with `vector` geospatial data in Python. \n", + "\n", + "> **Protip**: If you work with `raster` data you will want to checkout the [rasterio](https://rasterio.readthedocs.io/en/latest/) package. We will not cover raster data in this tutorial.\n", + "\n", + "### GeoPandas = pandas + geo\n", + "GeoPandas gives you access to all of the functionality of [pandas](https://pandas.pydata.org/), which is the primary data analysis tool for working with tabular data in Python. GeoPandas extends pandas with attributes and methods for working with geospatial data.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries\n", + "\n", + "Let's start by importing the libraries that we will use." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Read in a shapefile\n", + "\n", + "As we discussed in the initial geospatial overview, a *shapefile* is one type of geospatial data that holds vector data. \n", + "\n", + "> To learn more about ESRI Shapefiles, this is a good place to start: [ESRI Shapefile Wiki Page](https://en.wikipedia.org/wiki/Shapefile) \n", + "\n", + "The tricky thing to remember about shapefiles is that they're actually a collection of 3 to 9+ files together. Here's a list of all the files that can make up a shapefile:\n", + " \n", + ">`shp`: The main file that stores the feature geometry\n", + ">\n", + ">`shx`: The index file that stores the index of the feature geometry \n", + ">\n", + ">`dbf`: The dBASE table that stores the attribute information of features \n", + ">\n", + ">`prj`: The file that stores the coordinate system information. (should be required!)\n", + ">\n", + ">`xml`: Metadata —Stores information about the shapefile.\n", + ">\n", + ">`cpg`: Specifies the code page for identifying the character set to be used.\n", + "\n", + "But it remains the most commonly used file format for vector spatial data, and it's really easy to visualize in one go!\n", + "\n", + "Let's try it out with California counties, and use `geopandas` for the first time. `gpd.read_file` is a flexible function that let's you read in many different types of geospatial data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the counties shapefile\n", + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot out California counties\n", + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bam! Amazing! We're off to a running start." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Explore the GeoDataFrame\n", + "\n", + "Before we get in too deep, let's discuss what a *GeoDataFrame* is and how it's different from `pandas` *DataFrames*.\n", + "\n", + "### The GeoPandas GeoDataFrame\n", + "\n", + "A [GeoPandas GeoDataFrame](https://geopandas.org/data_structures.html#geodataframe), or `gdf` for short, is just like a pandas dataframe (`df`) but with an extra geometry column and methods & attributes that work on that column. I repeat because it's important:\n", + "\n", + "> `A GeoPandas GeoDataFrame is a pandas DataFrame with a geometry column and methods & attributes that work on that column.`\n", + "\n", + "> This means all the methods and attributes of a pandas DataFrame also work on a Geopandas GeoDataFrame!!\n", + "\n", + "With that in mind, let's start exploring out dataframe just like we would do in `pandas`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(58, 59)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find the number of rows and columnds in counties\n", + "counties.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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20LakeCalifornia6466548.66525349.0823345203312322049...72.311329.4606106None0NoneNoneNoneNoneMULTIPOLYGON (((-240632.150 93056.104, -240669...
30LassenCalifornia348957.4350397.4228562553228341234...120.924720.4206086None0NoneNoneNoneNonePOLYGON ((-45364.032 352060.633, -45248.844 35...
40Los AngelesCalifornia98186052402.399043412423.264150493659985687472828...187.944087.1906073San Bernardino,Kern2874841NoneNoneNoneNoneMULTIPOLYGON (((173874.519 -471855.293, 173852...
\n", + "

5 rows × 59 columns

\n", + "
" + ], + "text/plain": [ + " FID_ NAME STATE_NAME POP2010 POP10_SQMI POP2012 POP12_SQMI \\\n", + "0 0 Kern California 839631 102.9 851089 104.282870 \n", + "1 0 Kings California 152982 109.9 155039 111.427421 \n", + "2 0 Lake California 64665 48.6 65253 49.082334 \n", + "3 0 Lassen California 34895 7.4 35039 7.422856 \n", + "4 0 Los Angeles California 9818605 2402.3 9904341 2423.264150 \n", + "\n", + " WHITE BLACK AMERI_ES ... AVG_SALE07 SQMI CountyFIPS \\\n", + "0 499766 48921 12676 ... 1513.53 8161.35 06103 \n", + "1 83027 11014 2562 ... 1203.20 1391.39 06089 \n", + "2 52033 1232 2049 ... 72.31 1329.46 06106 \n", + "3 25532 2834 1234 ... 120.92 4720.42 06086 \n", + "4 4936599 856874 72828 ... 187.94 4087.19 06073 \n", + "\n", + " NEIGHBORS PopNeigh NEIGHBOR_1 PopNeigh_1 NEIGHBOR_2 \\\n", + "0 San Bernardino,Tulare,Inyo 2495935 None None None \n", + "1 Fresno,Kern,Tulare 2212260 None None None \n", + "2 None 0 None None None \n", + "3 None 0 None None None \n", + "4 San Bernardino,Kern 2874841 None None None \n", + "\n", + " PopNeigh_2 geometry \n", + "0 None POLYGON ((193446.035 -244342.585, 194033.795 -... \n", + "1 None POLYGON ((12524.028 -179431.328, 12358.142 -17... \n", + "2 None MULTIPOLYGON (((-240632.150 93056.104, -240669... \n", + "3 None POLYGON ((-45364.032 352060.633, -45248.844 35... \n", + "4 None MULTIPOLYGON (((173874.519 -471855.293, 173852... \n", + "\n", + "[5 rows x 59 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the first couple of rows in our geodataframe\n", + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FID_', 'NAME', 'STATE_NAME', 'POP2010', 'POP10_SQMI', 'POP2012',\n", + " 'POP12_SQMI', 'WHITE', 'BLACK', 'AMERI_ES', 'ASIAN', 'HAWN_PI',\n", + " 'HISPANIC', 'OTHER', 'MULT_RACE', 'MALES', 'FEMALES', 'AGE_UNDER5',\n", + " 'AGE_5_9', 'AGE_10_14', 'AGE_15_19', 'AGE_20_24', 'AGE_25_34',\n", + " 'AGE_35_44', 'AGE_45_54', 'AGE_55_64', 'AGE_65_74', 'AGE_75_84',\n", + " 'AGE_85_UP', 'MED_AGE', 'MED_AGE_M', 'MED_AGE_F', 'HOUSEHOLDS',\n", + " 'AVE_HH_SZ', 'HSEHLD_1_M', 'HSEHLD_1_F', 'MARHH_CHD', 'MARHH_NO_C',\n", + " 'MHH_CHILD', 'FHH_CHILD', 'FAMILIES', 'AVE_FAM_SZ', 'HSE_UNITS',\n", + " 'VACANT', 'OWNER_OCC', 'RENTER_OCC', 'NO_FARMS07', 'AVG_SIZE07',\n", + " 'CROP_ACR07', 'AVG_SALE07', 'SQMI', 'CountyFIPS', 'NEIGHBORS',\n", + " 'PopNeigh', 'NEIGHBOR_1', 'PopNeigh_1', 'NEIGHBOR_2', 'PopNeigh_2',\n", + " 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at all the variables included in our data\n", + "counties.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like we have a good amount of information about the total population for different years and the densities, as well as race, age, and occupancy info." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.4 Plot the GeoDataFrame\n", + "\n", + "We're able to plot our GeoDataFrame because of the extra `geometry` column.\n", + "\n", + "### Geopandas Geometries\n", + "There are three main types of geometries that can be associated with your geodataframe: points, lines and polygons:\n", + "\n", + "\n", + "\n", + "In the geodataframe these geometries are encoded in a format known as [Well-Known Text (WKT)](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry). For example:\n", + "\n", + "> - POINT (30 10)\n", + "> - LINESTRING (30 10, 10 30, 40 40)\n", + "> - POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))\n", + ">\n", + "> *where coordinates are separated by a space and coordinate pairs by a comma*\n", + "\n", + "Your geodataframe may also include the variants **multipoints, multilines, and multipolgyons** if the row-level feature of interest is comprised of multiple parts. For example, a geodataframe of states, where one row represents one state, would have a POLYGON geometry for Utah but MULTIPOLYGON for Hawaii, which includes many islands.\n", + "\n", + "> It's ok to mix and match geometries of the same family, e.g., POLYGON and MULTIPOLYGON, in the same geodatafame.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " **Question** What kind of geometry would a roads geodataframe have? What about one that includes landmarks in the San Francisco Bay Area?\n", + "\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the types of geometries in a geodataframe or a subset of the geodataframe by combining the `type` and `unique` methods." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 POLYGON ((193446.035 -244342.585, 194033.795 -...\n", + "1 POLYGON ((12524.028 -179431.328, 12358.142 -17...\n", + "2 MULTIPOLYGON (((-240632.150 93056.104, -240669...\n", + "3 POLYGON ((-45364.032 352060.633, -45248.844 35...\n", + "4 MULTIPOLYGON (((173874.519 -471855.293, 173852...\n", + "Name: geometry, dtype: geometry" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's check what geometries we have in our counties geodataframe\n", + "counties['geometry'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Polygon', 'MultiPolygon'], dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's check to make sure that we only have polygons and multipolygons \n", + "counties['geometry'].type.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like with other plots you can make in Python, we can start customizing our map with colors, size, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# We can run the following line of code to get more info about the parameters we can specify:\n", + "\n", + "# ?counties.plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Q1brgLYJiXG0vRLGRQJImw1Bsr6/MYyDJ/NFVbjfTOezPy0LJ2dZ6nVS5rTMP/2hMYzIUAxMBLg5OAfFpzJVOC0c6fADUldupdFo43TeO1rCzsYJjnXNT2V9e5JZOTxYQQhQfCSQZaPI6C30J17CaDZq8zplP/qlaXWGnweNAa7g4OElUa3xT8QSUFpPCaTWxrtrFeCCCx2VlYDxI+9AUl4anFj1vOHrlPACXxwJcHgvM/H0ymJ98YrlWhAmJhSg6EkgyUFNmK/QlzKEUbKkru+YT+/X15VhNBiZDMTQRorrMxunLY4z6I3idFlpq3JzqHaN39MoD3lDxAFJmNxOKaEb9EY5MtxwGJnN2zf40E1PK81yI4iWBJAM/e64vb+fOpP6F1WRwsnsUk4I1Hgej/jAbatwcTnQrTbswOInbZqZ1rYf2ocl508nHNMSimuHJ3KTFX0jvqJ+2Zi8aTdfIFL2jixcGk5aBEMVLAkkGNqxy5+3cmSxbMJRi91oPJ3vG6Bz2oxQc6fTNu+9EMMLBIqhHEtOwvz0+IcBsKDbVuhdNO5NKGd4yu7noE1MKsRzJ9N8MfPj2zfzu3qa8nDuTBXD+cBQNrCq3A/FgVErr6Fw2M+Ek6dqTtUj2tngZD0RornKycZWbPc2eHF6hEGIxEkgy9Fev3cabWxtzft5YhqmwDrSP0D40yfb6Is7uu4BRfxiP07LoPou1SNpavBzr8gHQPjTF2f4JDrSPUGaPd+NJr5gQ+SWBJENmk8Hf37Wdj79ma05Xo0cyjSTEWyH5TPqYLyZD8Xzv4rPNjnSOsKO+AutV2ZHbWrzsvzhMIHztfRsPxLvxNta6WV1RXBMkhFhOSu+pU0QMQ3HvLS186LZNOTtnJMsVeKYSHJW+bnUZgcjiATSm4Xj3KJtqy2hr8WIxFCZFSulf4mMvmd2XXGUaEGI5k8H2HHj3i9ZhNhRffPIifWOLzz5KJpsWCcBzST7ZF5P6SgdWs5HW4s7pdTLb68tRSqW8SLF3NECDx0Gvz0861XNLMTALsdSkRZIDZpPBu1+8np99+FZ2NVVmda5Qkk/myWxZXTpjJC6bKePxixPdY2mvdO8a8bOjsfKa7jEhRHYkkOSQ22bm46/ZltU5sg0keSpnknNOq4kKh4Vwli2wdB3p8LGjoRKXNX8ljYVYaaRrK8fWVNizOj7bQJJNrq58a65yUu22oVQ8ZcqB9hHMhpoZMF8qBy+NUOGwsKc53nobD0R4/vL4vPsW8e0UomhIiyTHKpwW/u7O7Tgz/MQbCGeXg6orSQ6sQqp22zh4aYQD7SMziSUjMc1EARYRjvrDHGiPX8tUKMqNWXZJCrGSSYskx2xmE2/d24TTauKzT5zHbCjMhsIwFIZS8fUQ8f/QGmJodCyeMj0Si3Eyy6zCuZyKnEv1HgcXBq5due6wmgpehbBjeIoKx/zrWKRFIkRyEkjy5HU3rOGJswN8+3B3yscoYGdjJbGYJhKLB5ZIVBOOxghFY/E/I5pgJLrgSvBRf5hKp2VO5t1iUOWycnzk2vT219WVXZMTrBA6FmjJpZKaRYiVTgJJniil+PNXXcePTvTOu1huPho4ukCOrPlYTAqbycBiNrCYpr8UtRU29l0ofD4tiGdKtpqNhR/URfKRfywQnnesRkuNRCGSkjGSPKp22/ir12Y3i2sx4ahmIhRlZCpM/3iQbp+f9qEpIpHiePjtaKjANxmie8S/YAupWNZpaB1fg3P1bC5pkQiRnASSPHvzniY21eYvW/DVXFZTUXzKVwrahyYJJ1mp7w8XT7be8UCEeo+DMtuVYGLIvxAhkpJ/JkvgnS9oWbL3avA4iiJNfJPXyZg/eZAotizFZ/om5izqlBQpQiSX9RiJUqoR+CpQB8SAh7TW/6aU8gLfAJqBduBNWuuRxDEfBd4FRIH/rbV+LLF9N/BlwAH8CPiA1lorpWyJ99gNDAFv1lq3J465B/iLxOX8jdb6K9n+TLn21r1N1JTZeN/DhwlF87sAr2yB2Ue5dkNjJSZDYahE94+KRwSFIhKNcWEwtWqKTlvxDdPNLglcrLPghCgmufhXHAE+rLU+rJQqAw4ppR4H3gn8TGv9D0qpjwAfAf5MKbUVuBvYBqwBfqqU2qS1jgIPAPcBzxAPJHcAPyYedEa01huUUncDnwTenAhWHwdaiY9VH1JKfX86YBWT27bWsmFVvLRtPkWzTPqYCrfNTMfQJMM5mBnWPjjJ7iYPhzqK539ZbZl9Zp2LWfq2hEgq638lWuterfXhxPfjwHNAPfA6YLp18BXg9YnvXwc8orUOaq0vAueANqXUaqBca/20jteb/epVx0yf65vAy1R8IOAVwONa6+FE8HicePApSi01rry/R99ogPpKO/WVDuorHdSV2/C6rLhtJsw5eiYqYDyYm7GN/vEgR7t8ea06ma7ZvVlmaZEIkVRO+xWUUs3ALmAfUKu17oV4sFFKrUrsVk+8xTGtK7EtnPj+6u3Tx3QmzhVRSo0CVbO3z3PM1dd2H/HWDk1N+alumMxS9Lf3jQUWzW6rFNjNJqxmA5OhsJoMLGaFSSnMJgOzoRJdVgrDiM+qurKYMt7sU8S3P3VhKCfXHI1pRv1hvE4rw1OhnJwzV6RBIkRyOQskSik38C3gj7TWY4vMHJrvBb3I9kyPmbtR64eAhwBaW1sLMsS7cQlaJIahaPI4uDg4/7oNreOlef1ZpmKpcFhwWAz8Ka6RSWZgPEijx8Gon5TTvFtNilA6OeFTNHvKrwy2C5FcTj5vKaUsxIPIw1rrbyc29yW6q0j82Z/Y3gXMrlHbAPQktjfMs33OMUopM1ABDC9yrqL03pdsYNua/KZ5D0c1Xpc1r+8B8RX0XrcNpyV3H9k7R/xsritPqQtu25pyaivstOWhNnsweiXISiARIrmsnwKJsYovAM9prT8166XvA/ckvr8H+N6s7XcrpWxKqRZgI7A/0Q02rpS6KXHOd1x1zPS5fgf4eWIc5THgdqWURynlAW5PbCtKZpPBbVtr8/4+Z/omWFOZXRbiVHSP+Kkus7M6y4zHs53qHWNTbXnSNO/tg5N0DvvZ3z7C3hZvzt5/e30Fxzuv1DmxyBiJEEnlomvrFuDtwAml1NHEtj8H/gF4VCn1LqADeCOA1vqkUupR4BTxGV/vS8zYAngPV6b//jjxBfFA9TWl1DniLZG7E+caVkr9NXAgsd8ntNZLl488A/WVjry/x3gggseZ/1YJgG8qhCvHU3hP9Y6xp9nDgfb5Z3JZDDWna27fxWGavE4sJoXFpHj+8sQ1wUVf8w1zOkanv7WYFHuavTMbinF6shDFJut/JVrr37BwQeyXLXDM3wJ/O8/2g8D182wPkAhE87z2ReCLqV5voS3VugS9BCv9astt1LhtM+Vvc+lop48bGio52uW75rUbmiqvCTLTubzMhmJXYyX7clTfpGEJAr8QpU7mpCyxbAe5U2Wz5L8CYDSmaR9KbeFhusJRzZn+cZqrnDPbLAbsbqrkyCLZgiMxzZE0El8KIbIn7fYl9tJN1ZgMlfeFg5kW1kqH1WwQzGNgnApFsVtMrK9xMRWK0lLt4qnzuZlyLITIHWmRLLEo+Q8iAJOJBYOKeCLHXA5IT1tT4WA8mN8W1vOXxymzm5kIRvKeXmY+hgy2C5GUtEiW2A+P9+b1/OV2MzVlNgYmgkB8bHkyFM1LVY18P2QVsKupkmNdo0RjWhK6C1GkJJAsId9UiAd/dT7n521r8dLr82M2GURjMc4PzDNukYdI0jU8Retaz8y4j9lQHOsaTXJUcjazIqbjM9ymqye2rl14FpcQorAkkCyhJ84O5qUE7vFOH4HI4t0++VhX1zMaoGf0SqbcbBcntrV40VpzfmCCYDhGcNbP1D8exGU1MRlavCvNbChuaKxkKhTBYjKwmU3sb898Bpf0bAmRnASSJfRcHjL/1pbb6BsL5vy8mXDbLUyFM7sWm1lxrn+C4ckrubZmB42O4SmavA4mh6+t+z7btjXlc+qxmA1ornLSPjR/yphkJI28EMnJYPsS6vEt/hDMxJqK1NY5jPpz3xK62lgW73Hd6oo5QWQ+jkWmNJfbzexeW3lN11okFq8bbzFlFhAkRYoQyUmLZAl1jeQ+kJzrn6DabWVwYvGHcC5miq2tcuJxWonGNNGY5sLgBIFE0sbVFXZ6Z3VzpSucwows66wkXF6XhQaPE5vZIBLTPNs9yqFLvnmPO9A+QpXLisWkUEqh1JUVtPGiXGBSJF6Lp2ycjh8VS1QoTIhSJoFkiWitk37izsR4MMKGVZWLBpId9RVYzQataz3x0raJB6lS8U/cGkCDRl/5XkMsvpGY1sQ0mE1qzmLAnY0V9PgCRGMxmrzOjAPJtjXlnFxkdbzFFE9x73XZaKl24ZsKMTwZZngy9YH9oQzv/VItIBWilEkgWSJKKd7U2sAnf3I6Dydf/OXxYITuPj+hJAPy6TrWmf0MLVh88eSeZg89Pj/940F+dWYgJ++XDpN0bQmRlASSJZSvvE1nLo8vOqPJZjZyHkSulskYjMNisKNh/rxYhoLrVpcXfMqvLEgUIjkJJEtoU607L+lRJkNRymyLDUTnt59fqfgaGY/TQiSqiWqNTnSVQbybrLnKxag/RLXbhsNqonvEz8BEcMHkijsbKrJKBmk3G3Om7l5daC02zzXOV0VNSu0KkZwEkiW0qa6cLXVli44HZOq6NRXsX+ChfKpnlF1Jkh1mI9liQbtZ0TcewDcV5nIKU5XrKuyc658gnGb1Q7MBu5o8DE2EuDg0SS4SIEvPlhDJyfTfJaSUYlNtWX7OvchrE6EoRzt87MlDNcEKh4WT3YuPlVQ4rHMWFy5mb4uXy6OBtHN4OS0GG2vLONA+woXB3AQRIURqJJAssVs2VOf8nC3VLg4mWb2tgZPdo7Q1e9nb4qW+0oHZUBmvr5hmMxtMJanbbjaplH/R+sbSn/m1qsxGbYWd53rH0z42GVlHIkRyEkiW2J276nNat311hR2X1UQqvUBT4Rj724fZd3GY/rEAhoqXls1G/3iQRs/ikwh6fAEqU6jYWOGwpL0CvXWth/FAhIuDma1cF0JkTwLJEjMZijfurs/Z+crs5owGpcMxTSiqOdzhY1dTZcbvX+GwJF1oqYFKZ/IB/3THtZurnBzqGMnrWo/IEqT8F6LUSSApgNfsrM9JMsAym5nz/RNZncNmVviTJEJczHggzPaG5K2aVAptpZrXympStK710D40lfexkKWoHSNEqZNAUgBVbhtrcrCmxGU3p9SltRi3zcLzl7MbW7CZk/8apfI8TmU8wmIoNtfNTcyYTxJIhEhOAkmB3LSuKutz2M0GZfbsZnAPTYaodicfv1hITMOxrlG8SbquJoPhpGsyUln8t6vJw4kks8RySQKJEMlJICkQty37JTztQ1M5mU5sXySrbipCkRjrV7nZXr/wJAKzYSQdb0ilZ6vLt7SD6qkkkxRipZNAUiBHOn05OU/fWABXCuMPi5kMRmjyOrM6x4H2EcYDkQVftyUJVpvryqgrty+6TzzvVuYZhlPR1uKlyeukyetkbZUTuzm7eyvESiAr2wvgqfODHMtRIOka8XPd6jJcNjMHM8xLNTIVZmQqzJa6sqzGS9qHplhX7eLC4LWlfiOxGEpxzeC412VlVZmN5y+P47AY7F7rYXA8SAxNbZkdreFQxwgepyXrlfkmBTsbKwlFYgSjMaJRjaHAaTVjt5oIRWLXZAcYDeS/josQpU4CyRKLxTT/57vP5vScz/WOU+XKfJxjWi7KAFe5rfMGktO949gtJvyhKPUeBw2VDpSCU71jM8HLH45xaNYgemeiGuKaSjtj/si8XWMWQ7GjoQKlFIahFkwTA7C5rnymBnyqfFO5T/0vxHIjXVtLzDAUL9pUk/PzTgQjrCqzZXWO1RW2OcWj0tXkdXJ0gQd1MBLDbCh2N1XSPeJn38Vhnr88zph/4e6waT2+ABPBK/tZZ63Gv6GpkkMdPg5eGmH/xWEaPY45r8+WySr+nz7Xzyd+cIqYDLoLsSBpkRTAR165hTN94zx5bihn5wxG4sWl+sezq99+c4uXyVCUaCzG7HHmeHGreIGrmI5XSIxEY0RiEInGCMc05XYzHQs8cDWwocbNoVmBJtP0Izc0eegd9WNS6pppwJ0jfjaucuO2mznZPUooMT96b4t3wUzDyXzxyYv0+Pz86903ZD0xQYjlSOkVmN2utbVVHzx4sKDXMBWKcOdnnuJ0X+7yQykVTxm/usKe9liHzawwG8aCNU1SYTLiCwUXemBbTWrmwX5jUyXnByYYTaFFAlBbZmNtlYv2ocmUg2Wj14HXacUfjnKmL7uFmwC3ba3lk3dtx+vOruUnRKlSSh3SWrdevV26tgrEaTXzJ6/YlFW9izKbie315Vy3uoyWahd15XZMStE3FqCt2Uu5PfVPz8GI5voUVqgvJhrTi640D81aPRmKxFIOIgBrKh3sbx9Oq8XVOeznWNdoToIIwOOn+njnl/ZzaejaMSAhVjIJJAX08q11fO6ea4J7SrauLsdlt3Cie4znese5ODhJ72iA4akQI1Nh9rcP0+h14Ukhx9W0WExzQ2MFNzRWUp/hyvtne0bZtqacG5Pk70p3yGFoMrsuu1xwWEx0jPi56/6nOJqjWXdCLAcSSArsRRuq+c8/2DvvWpC9LV5WldlYU2lnTYWdRo+DlmonG1e5Ods/zuXRxddUnOwZw2Expbxy/UD7COcHJjna6ct4Id5UKMrJnjEGJxaf7RRLo0t1T7OH3iQ/61JYX+PCNxVmaDLE3Q89zU9P9RX6koQoChJICsxkMrh5fTV//4btrK9x4bKaqKuw0+h18FzvGP3jQXp8AXpGA3SO+Lk4OMXZNKoH9owGWF/jTmnfzXVlM4sK+8eD7Miiq2tgIsiuxoWPTzWOVDotdI74066WmGs7G+eW/g2EY9z3tYP8xzOXCnhVQhQHCSRF4re3r+F1N9QzGYpyeTRA57CfsUVWiqfjaKcv6dTg9TUuOq6qBWI1Zf7r4Q9F6R0L4rQYtDV72HlVUIqmGEmaPM6kLa98q3RamJjn/0VMw19891k++ZPnZXqwWNEkkBQJk6H43y/byBfuaU05nXqqgpEYLdWuBV83VLyr6eq6Hmf7x9MaY7na5dEA9R4n+9tHsM1KNVLttqa80K/Q9UBqy21YTQbnBxYeYH/gl+f50KNHCaVYTliI5UbWkRSZl11Xy1vaGvmPZzpyet6TPWMzuaq6fVcKUVW7rayrcbH/4rXpVUb9EXY1VjIy5cv4fc8m6qVMhiK4rSZQ8aBZbrfQ4HFgMRkopYhGYwQiUcYCEYYnQkyGonidFs4P5GbGVSbWVbsY9cfHRJL57tEeBiaCPPC23ZTbMw++QpQiWUdShPyhKLf+0y/oG8v9TKW2Fi+BcBSb2Yh3oSWpbthQ6aB71J9VAak9zR4OpJkHzG4xWFVmZ3WFPeOFhJna1VSJ22bmYPsw/iT16K/24k01/P1d23NSb0aIYiPrSEqIw2riAy/blJdzB8JRjneNcqB9JGkQAejy+Wlr9mb1nmP+CDZzet11gXCMjuEpLg5OkuEC+IwNTgQJhKNpBxGAiWCYO+9/kpM9S1czRYhCk0BSpO7e08jeluwe4PNxZJDi41inL6sCWqf7xtmwqoxta9KvndI/HmRzDmqupKq5yjmTLDKTYw9d8tE3FuRNDz7Nr88O5PjqhChOEkiKlGEoPp+HgffBifS7ywKRGGuzrFdysmcMt82SUa36ckf+xxxa13rYurps5v6oDJpBrlnFyiZDUe790gG+cSC3Y11CFCMJJEWszG7hP961N6NWxEI8LiuZhKZne8ZwZllA62inL6Pswr2+zFoIqXLbzDzXO8ap3nEmgomZaxmMCQ1PhuZMs47ENH/2rRP8+8/OshLHIsXKIYGkyN28vopvvPsmyrOszT7tYPsIDR4HTkv6/+sXm0KcimTTkBfSOeJfsIKj1WxkdW8qnRaUIqtkldN6RwM0z/PzferxM3zsu89K2V6xbMn03xKwo6GSH//Ri/jED07y2Mns03J0jvgzqobospqwmQ3C0RgxHZ86vL7GTUxrfFNhPC4rgVAUq8UAHZ/y+1zv+FXnyOxXrq7CTsfwlQWTZkOxq6mSY50+/OEoVS4rzdUuLg1NLpqexW4x2LamYqaL7UjHCLlc/nFxcJIym4nx4NzA9PV9HXQOT3H/795ImUwPFsuMTP8tMT860csHHjmSdcoQk6Fw2UwpFZaatjtRRGpPs4dRf5jxQGTRHFhtLd45FQvTTR0/27pqJ9VldkKRGIFwlJHJEH3zZAK2mRU7Gyo53TfBZDDCuhoXFpOBy2rm8pify6OBOVmI52MouGldFU+dz6xezGLTna9bXc6X791DbZL69EIUo4Wm/0qLpMS8avtqPE4rb/ncM1mdJxrTNHqcdDGV+oM9MQCd7pqQaSNTIWrL7Yz6019kOBmMcmEw+XqSYESzf9b1JUshP13HPRCOYTJAoRiYCBLJIlD3LBJcn+sd4677n+KL79zD5rqlm40mRD7JGEkJunl9Fa+8vi7r85zsGWNzXXnK+5/JsgjXxcEpzvRNZJR2JRdrSRoqHVy3uoy2Zg+taz1sqStDKcXhDh+nesc40T3G8e5RekcDaPSCJXuTqa9YfDFit8/PGx98imcu5K5CphCFJIGkRH3otk1Z1VefFoqkPsjscaaWjn7afI/hvS1eRqbCaZ0H4tOhM7XW66DabaVvPMBzvePsbx/h4KURnr88vmAur8MdPlqq3UmTXc4nGE1+T8cCEd7xhf1872h32ucXothIIClRG2vLeFUOWiVHO0fZXp9aq8RqNrJe1xJII3DNZmQwaXl9jYt1NS56x4IMToRSHlda63Wwu8lD58gUU6Eou+Yp0rXW66Ctxcu6atdMun2LSdFS7SSY4or4UDTGBx45ymd+cU6mB4uSJoGkhO1oqMxogd/VlFIppTA51z/B9vrsyvHOzgKcjnS7tho8Ds4PTHJhYDLlrLz1lQ6aq5xcGvazv32YqVCUiWCEIx0+Wtd6sJsNrCbF3hYvAxMh9l8c5sLg5Mw6n0avk4uDU2nPhvvHx07zse8+S0SmB4sSJYGkhFnMBu98QQsWk+L6+nI2rnKzrib9dRrHu0bZ2ehJad90hg2u/ozdutbDuQzHWdINmJkkTbRZDNqvqsky7eClEarLbFQ6rey7GA8y06YXIla70+8Gm/b1fR28/+tH8OdgPYsQS01mbZWwu3bV47KZcdlM/L+fnwPAalLsaKjgeFd6SQMvLJCufUudG6UUJkNhKMWIP/XxjXA0xo1NlcQ0OCwGT1/IPItvqilLNtW6sVtMc6YdpyKVDMVdCyS5PNs/QZXLysB4dgW4fnLyMpc/9wxffOcevK70xqOEKCRZR7IMTAYjvPLffj2zYM9QsKvJw6FLqU/TXVVmwx+KXLOQbtuaMk72ZDdbC8BtM11JP5Km1rUeTIZaMJ18md3MqjIbU6FoRrXdN6xyc64/u7onhoovkky2RiUV62pcfOmde1hblV0mASFyTdLIL2ODE0FuXlc18/eYhkOXRmhLI3tw/3iQrWuuHf9w23KzCnv2IH2l00J95eIL8iocZtqavVQ4LBy8NMK+i8M0eq7trtrb4mUqFOX8wGRGQaTabaV/LPtSviZDsW2e+5eJCwOTvO4zT6bdqhKiUCSQLAO15XZqy23cuat+zvb9F4dpa4k/jFOxv30Yy1WDEaNpdGUtZvZMpo2r3HT7ArQ1e2jyOlhdYWNXYyUbV7nZ0VDB1tXlTASj7G8fnvP+V497tLV42XdxmGgW5XjXVrkYm6cee7rCUU3feCDtuisL8U2Fedvn9/HD4z05OZ8Q+SRjJMuA3WIiEtPsbKjgB8d65qyN2H9xGLfNxJ5mD8OTIXxTYWrKbPPOLNIaKlyWObmqcpHCvd7joDsxvnD9mvKZsYjZK9B7R5Ont+8fC7CrsZLxYAStddaf2Hc2VHC4I7NV+vNZXeGgx5d962ZaKBrj/V8/wuXRAO96YUtGqe2FWArSIlkmXryphi88eZHX7lxzzWsTwSgH2kc4PzDJ0GSI05fH2bjKPWcft83MrsbKaxMeZtHlrxS0tXjwzap5HspiiuvFoSn6xgKc65/g/MBk5hdGfPC/dzSQVQnhq4UiMdZ6c19i92/++zk+8cNTxLJoeQmRTxJIlomz/RN0Dvupq7BTlyQhoAa6RqbYMivX0y3rq1hdaafcbqbabaWuwk6Dx0H7UOYP7N1rPey/ODKTor2+0rHg9NpUNXiyK7A1bXtDBf3zJH3MxonuUQYnQlS74zOu3FnWb5ntS0+284ePHCGY4YJOIfJJuraWiR8/2wvA/b88z723NPP1fR0EF1mI5w/H0Brue9E6bmis5I5tdSgFT58f4k++eZzuHBSTmgpeGXvYXFvG5bFAyosDFzIWyH7MZtuaMvZfzF2X1myToSgxDa3NHhTx+i+5akf89/FeBsaDPPi23TI9WBQVaZEsA90+P6cvT8y0MB7e15HSSvDTfePctrWWV21fjWEolFK8YEM1X/+DvVlXQ6wps9E+OMnqCjt7mj3UlFmZCGY/qH368njKkwfm43Va8KeYwiRT/nCUg+0jHGgfYVOtmz3NHmozyNk1n/0Xh7nr/idpH8yua0+IXFoWgUQpdYdS6rRS6pxS6iOFvp6l9sj+Dvau81KZyKobr9mR/GFpKFg3T0W/tVUu3nPrelqbPay/aqV8c5WTjbVuWtd6aJw1HtDW4mV7fQWba8toqXYRjWk21ZXTNxbgQPsIo/5IVrOrpmlg6+rM069vrC3jQpbjK+m4NDzFgfYRJoIR9qYxHXsx7UNTvOGBpzje5cvJ+YTIVsl3bSmlTMBngNuALuCAUur7WutThb2ypRGKxPjl6QE++sot1HscPHFmgAd+eX7RmhjTNqxyU7VAWo/+sSAHE7Oq2lq8hCIxpkKROfU9LCbFjU2VKKU41TNGNBab+bS/ptKObyrEdOzItoUzWyAcT6R4pMOX1nGba91pLdLMhemAPhmKsu/iMLvXprdQdCFDkyHufugZHnzbbl60qSbr8wmRjeXQImkDzmmtL2itQ8AjwOsKfE1L5tLQJO+4eS1711WxtsrF229u5sd/9CL2NCfPnXVpaIp/++nZmdlARzt9/PB4D//7P4/wtWcuzey3/+IwRzt91xSJCkc1hzt8HLoU/8Td4HHS1uxl6+pyBsaDcwbW24cmceUomCileK5nbN7W1ILHAJGYXjBt/FIZyOEA/1Qoyu99+QD/dbAzZ+cUIhMl3yIB6oHZ/5K6gL1X76SUug+4D6CpqWlprmwJbKwtY2Pt3K6eCoeFL93bxu2f+tWiLZNgJMZkKMyoP4zDauKxk5d54JfnM76Ws4ukGWnwOHPWGlAoApEYw1MhmrzOObXc57O6wsaaSmdRdAXZclBDZrZITPMn3zzOpaEpPnz7JllrIgpiObRI5vuXc83HTq31Q1rrVq11a03N8u8KcNvM/PErNi/64HLbzLz31g0AfOw7z/LwrFZIrk2Fsh9on6Hi/3t9U2H8oejMdNt5dwW8LhuHLo1kXec+FyozqA6Zik//4hwffvRY1rPihMjEcggkXUDjrL83AJJXArjrxgb+876bsJgUb9t7bSvs+vpywlGNx2XlBeur8vqgvTAwmbOHqJr12WFgIojbZl50DMZuyd34TLZ6fAGq8jR199tHunnXVw7kZHacEOlYDoHkALBRKdWilLICdwPfL/A1FY1djZV88Z17+JM7tvCBl22c81ooovnU46cZnQrz6p2reeS+vXzmrbu4qcWb0hhLOhxWE/4ctUqu7r1pH5pacLxkV1Plkg+wL6bb52cqHGVjrTsnRcmu9uuzg7zpwafpy0EiSiFSVfKBRGsdAd4PPAY8BzyqtT5Z2KsqHkopfmtjDRUOC3/08o186d49lNnjQ2PNVU72NHv51Zk+bGYTOxs9vHBDDe1D8Smrbc25ma4KsNbrJBjJX4vn2Z6xa6bX3thUec0EgXzzuqzsbKygde3CgdgfinK2b4ItdeVpFQpL1aneMe66/ynOZlhETIh0lXwgAdBa/0hrvUlrvV5r/beFvp5ipZTiJZtX8ZM/ehHVbivfPtJNg8fJzevjY0Zaa9x2My/cWA3AwUvDbFuTWj33ZIKRGNfn6FwL5cfad3F4piXV1uzhWNfoknfzrK9xcaxzlIOXRtjT7KGtxXtNXrNpMa3JV29it8/PGx54in0XhvLzBkLMshxmbYk01Vc6+PZ7bmFoMojHaWEiEMFqNjjZM4rNbLDvYvzhE9MwEQizrtrFhSxXUj9/eZy2HHWXLfbsPdA+wos2VvHE2aV9gDZ6HRioOVUWZ3+/p9nD873jjM8KbIMTQaym3BTDms9YIMLbv7CfT715J6/ecW0yTyFyZVm0SFaaSCT7T9kNHjsnukZ52aee4CX//Es+9I2jvGB9NSe6RtlcW8YL1scLZV0a9nNhcJItdW52NVbS1uKd6RpLRaPHQVuLl821bs4vUM43bUmeu89cGGZ7feZFptw2M7VlNnav9eC2pfazVjqsXFpkGvKB9hGaqpyYZw2MuG3mvAWRadOp6D/7q/OsxGqoYmlIIClBZnP2DUnDMPjsExdm0pY8d3mMWEzzuzet5Y2tjfhDEV64oXpm/+cvT3Ck08f+i8O4rCasC0wrtpkNdtRXsKfZQ7XbSueIn/0XhzndN0Gu1gLqJJEkFNU81zvKDY3pBZO9LV52r/WwusJO33iQQ5dGsJkNbmyqXPCYthYPW+rcPNs9mvT8J3vG2DCrm6smR/m3UvH3P36ej3//ZE7S1AhxNenaKmFa66wWoFW7rTNZfnt8AX59bpAXb6rhFdvq0Boe3jf/upJoDMpsZoYi8Toj9R4H9RUOJkMRzvSNc3yBh2o0w0/EbqsJh9VMc7WT53rHUqohEonBsc7RlFKSbFtTjstq4tnu0ZmU99OGJkMMTYbY1ViB1WwiEI5iNinMhkHfWCCtLMIOqwnQtK71ENN6TtfXUvjq05e4PBrg3+7elbgWIXJDAkkJU0oxNhmg3LV4/ZGF7Gn2YreY0GhWVzjYPGuF/M3rq3h43yXWVNivWR0/kOjb391USdfIFN0j/pkKiIuJptGNYzMrNq4qIxLT9I0FmAhGONA+QoXDQudIajVNNPHa9a1rPRy8KphUu620VLvQGo53+ZJ2MR3pTN7iWIzDYmKt18nzl8dxWgxuaKrE67LS6HFgMhSGoWZym+XT/5zq462ff4bPv6N1wTxrQqRLrcR+09bWVn3w4MFCX0bB/fP/nOb//fwcAC+/rpbP39M653WtNb85N8i9XzqQkxxVNrNKaQpwtctKU5WTw2kmZVzM1cGkweOgK4Xglws2s8G6GhfP9Y5ftX3u/djVWMmRTt+SXFNzlZMv39tGcxr5yoRQSh3SWrdevV3GSFaw2d0bvaP+awZjp9egvHrH6py8Xyr989vWlDM0FcppEAE4eGmEtpb4rLG9LV40sHuRsY9csZoNNqy6NogA1wTVo50+6ioya12mq31oirseeCqnNevFyiWBZAUb81+Z/RUIRxdMfrhtTeYzoGZLlgZqT7OHkz2pjYFkYv/FeDDRaMKRGJFYfvNSWUyKTbVuTvaktjBQE28pLZXhyRBveegZfpKorilEpiSQrFAD40GeODMw8/eLg5Oc6ZuYtyZ47RJ8St7T7Mn74LOhIBzRmJSifzzIsa6xvL2XxaTYUlfGs93pvceRSyMpTznOhWAkxnsePszXnm5fsvcUy48EkhWqx+fnVO+Vh1xMw3v+4xC3feoJHtnfwbn+K5+i06n7sRDLIrlA2pq9eQ0iNrMRL3dbbudIoq5KjrO5z2ExKa6rK+dEmkEEIKqhtnxpB8G1hv/zvZP83Y+em6lNI0Q6JJCsUPMNnkdimo7hKT7y7RO880sHePr8IFprrq+v4NbN2aXeX2iacluLl/3tw1mdeyFWs0FbsxeH1cSB9hF6E7PPhiZD3NCY26SU00yGYuvq8gWnQCezo76Ckakwu9dWsqUu85LCmXjoiQt84BtHJRW9SJsEkhWqwmG+JovubF0jft7yuX389Ll+AF53Q3YpNsLRGLuaKuesim9r8bL/Yu6DiMNqoq3Fg9tqZn/7ML6p8DX79Pj88xayyZbHaeFYV2ZB5Po15Tx3eYzhyRCHLvl4/vI4u5vyE/AW8oNjPdz75f2MB669Z0IsRALJCrVhVRlP/MlLuGNb3aL7ffAbR3nfw4epLbfzl6/ZmnE3l9ZwpMPHmgoHNpPKSxAps5nZ2+LFrOID68NToQX37RkNZJVGZSGZLhDdtqacM/0T19SEOdQRXweTz664qz15bojXf+ZJ2rPMryZWDlmQuII1ep3c3dbIT05eXnCfiWCE/z7Ry0u31PDOW1qYCkV58vwgU6EoU6EoncNTbK+vYGgiiNlkYCgwVHzlt2HEv1fqSjEqjWbXWg/PXMhdEKl2W1lX4+ZU9yj70ghOsRxPDzMZirpyW9p12bfUlXGuf2LBLqXpTMJKKS6P+ukYzt36F6tJYTEZmBN/Oizx9DeGofjTbx3n//z2VrY35D7giuVFFiSucCe6RnnNp3+z4Ovb68vp9gVQwO61Hlw2M4+f6iOmNZFoDIfVxKg/vSSSbS2etFKLLKS23EaT18nhSyMZp2Pf2VCRcVfUbFazwQ0NlWmP92ysddM14scfuna23HzWVjnxOK2EozG0jgfDmNZoHZ8+rLUmGkt8aU0sBpFYjGhME9MQicaIak0kqlNaZOq0mnjgbbt58ablX55aJLfQgkRpkaxw19eXs2GVm3P982fmDUU0w5PxLqL/OdV37etpBhGYWyo3E/WVDurKbRzt9NE3lt6n/6vlqgyv3WykHUQ21LjoSSOIQLwo1vDkBOOBpamzMhWK8q4vH+Af37iDO3c1LMl7itIjYyQrnFKKu/c0Lvi6P5z6Qy5VnSNTbFzlXnSwfz515XZ2NVXS7fNzqMOXk6JQ862bSVVbs5d11S5aql140qxHv77GRd9Y8Jokkck0VTmXLIhMi8Q0H/zGMR74paSiF/OTQCJ4xQID7l6nZcHV7tno8QVoH5qk3J7ew7fCYeFIjlOnHO0czagK5J5mD/vbh7kwOMnFwUkupTFu0VLton88OKfIVaqi0cJNzf3kT57nz751XKYHi2tIIBELGp4Ks70+N+Vxr1ZutzCW5hTT3lE/Dkvuf2VHpkJp1U7PZhW+y2pi1B/OuFVhMRU2/fujB7v4vS8fWPISxqK4SSAR9PgW/jRtM+fnwTU0GaLJ60zrmOtWl+MP5/7TcI8vwI1rU1uvkW0qly115TNjTpk42jlCW4uXthZvxufI1m/ODXL3Q0+nPTtNLF8SSATtQwuvFzh4aSTnrZJViTK2l4ZS7zbb2+JNa2pvus70TVCbpGJhLlK5ZFGHDIhXf9x/cZj9F4cz6pLLlWe7x7jrgScXnKQhVhYJJIKjSWpgmIx4KvRcKXdYklYtnK0tz0EEYNQfpsptXfD1vTlK5WIYuVtPH8jDRIh0dA77uev+J3nmwlBBr0MUngQSwYZV1+Z0spgULqsJi0kxHggTiuikn9hTddnnZ01lahmF25rzk0ZlPj5/mJZ5Vu4vRSDLRHURVDgcC0R4xxf2850jXYW+FFFAEkgEu2YVeHJZTawqs7GrsZLJUJRwVHN+YJKO4SkmghF25GCV80QoitWU/FevrTl/CR3nMz2b7Pr6clYnUufvzXEql3AOZ12lUihsKYSiMT74jWN85hfnZHrwCiWBRHBjk4fvvu8WADbVldE/HmT/PGMBk6EoY/5wWjOcFtI+NIXJUGyvL8dsKFZd1dqZnl671LSO9/+bDJXzcZlVZbZFJzak6/nLY+Swpyxr//jYaf7y+yeLJsCJpSMr2wUQTxVy5676pA+69qEpNteWMTwZYmAiu1k7u9d62H9xGK/TSiSqaW32cLZvgo217rwXuZpmMSnWVrmodFjQxPNloeHS0GTOgsjW1WUMjIeoLbdlVKNkIcFIDJfVnNF6lHz5ytOX6B8P8i9vviFnWQNE8ZNcW2LG8S4f3zzUxdeeuZS03G2Fw8KWujJO9owyEczdoO8LN1Tzm3ODOTvfQhwWE9vrK/LS6tnRUIHNbKCUQmvN+YF4SpOrM/vmwuoKO3aLCa/LmtYEhnxrXevhc+9oxeNaeAKDKD2Sa0sktaOhkh0NlRhK8eWn2hfdd9QfZt/FYRxWEzsbKpgKRTmbg6mg+ZyJVO9x0FDp4NLQJBOBSN66zsb8YaIxTTiqcdlMDE/mr7bHdLGui4OT1JbZqK2wczwHSSizdfDSCG948Cm+cm8bjWmuFxKlR8ZIxDU+dPsmqlL8JOkPRTnWNcq5gQl2ZFnfw2wo8lFtqsxuZnOtm+4RP/suDnN5LMhEmjmu0uGymbk8FuDyWIDzA0tX06NvPMjxrlH2NHtSnhWXTxcGJrnz/qc4UQSBTeSXBBJxjXK7hX958w1pHaM1HO8eZWdj5sEkEtOYsl2xdxW3LT4L7XTf0iyc21JXhs8fzks3VqoOtI9g5KX+Y/oGJ4K88bNP8dgiNW9E6ZNAIub1ok01/PMbd8YHn9NwrHM04/Qd5XYzx7t8GR07nzK7mZ0NlUvSKlhf4+LGpkrOD0zQPZK7mVmZ6vL52bzENd8XEgjH+F//cYivPt1e6EsReSKBRCzoDbsb+Pw9rVQukCLdscCsnKGJIGu9jgXP63XNfz6lVNqBazGNHidPnh9iT3N+6563NccD5+EOX0FbIlerdKSXXTmftIb/+72T/P2PnyMm04OXHZm1JZLq9vl58JfnefLcIPUeBy/cUE0kpvmD31rHQ0+c55/+58w1x2yqdRON6TmtAaVgXbWLHp+f7Q2V9I8FiCWq/EVjGpvZwG0z82xPbqbIbq4t43TfOHaLQYXdQl8ekgxmm8Qxn2rLbAxPhYoquAG8Zuca/umNO/KWEFTkj8zaEhmrr3Tw16+/ft7X3v/SjVweC/Afz3TM2X6mb4Imr5PWZg8HEw/aG5s8M1NUF1otPv3pPlOVTgvra9xorWdmkQXCMXY0uPISSIp58V3feJC2HK/Mz4UfHOthcDzIZ9+xO+2aNKI4SdeWyNoHXrYJe6JOyKoy20ya81VlNqaCUbauLmNHQwVHOpJ/cp9utWTC47RQbo8nhDzc4ZtT8+NIxwheZ27XNGypK+NIkoSXhdY7Wvjxmvk8fWGINz7wdE5X+ovCkRaJyFpNmY17bm7mqfODTASjcz4Bmw2IxcBuMUjlw/u+i8NsWOXGbEA6hfi8Tisuu2nBio52s4nJUO7Wc1hMim6fP+nCzULrHPZTbjcztsTleVNxum+c13/mSb74zj1cn+XUcVFY0iIROXHvLS2MByJcHJw7Q2pHQyUxYCqNglTn+ifY1ZT6ALnXacVlM9G5SLnb8WCElmp3yudciEmB22rihsbKJa+dnqmyIu4+6h8P8qbPPs0vT/cX+lJEFiSQiJyoq7DPO930aKePvRlMB740NEWDZ+GZX9OmWyKdKUy57R8PsKUuvWBiNmDbmnL2NHvYXFtGmd2C3WriRHdpLLLb0+yhfzxQ6MtY1FQoyru+cpBHD3QW+lJEhqRrS+TMnmYvj53sm7MtpuPdVenObuofD2KoeGLHruGpeQfKvS4rTuviLZHZhifDBMKxBa/FULC2ysWqMhuj/jBldjMne8Y4maNZZIWQrxxfuRaNaf70W8fp9vn5o5dvROV4YarILwkkImcWq5R3oH2EXY2VaQ1OxzQcujSCxaTY0+zhfP8Ew1PxcQ6vy4rDaqIrzcV/U6Eow5Mh2lq89Pr8dI74cVkMrltTQSSmOd7lu6Z7rpRNFlFm4FT828/O0jvq52/v3I4lhZo1ojhIIBE5cyxJTqUT3T6aq5wMT4bSGvwNRzUH2kewWwzaWrx0jkxhKJXxCvLzA5OcH5icKeJlNhQHiyhzbqaq3Vaq3DYMBYZSGEqhAKfVtGQpYnLh0YNd9I0Fuf93b8Rlk0dUKZD/SyIntNb4pkKL7hOJxVe9b6wt43CHL+33CIRj7L84zLY15ZzpG8/wSq+YDEWZzGPyxmzUldsZD1yZZaa1ZnYHlU4s5NRALKaJahicCDE4ce3/A6fFwGJSJdHFNe1XZwa4+6Fn+MI7W1lVVvgElGJx0nYUOdE57E/pQVXusGSd5vxkzxjX1ZVjyUWpxiJlMqmZQDcZijIVjuGf9RWIxAhF46nqk93269ZUFP005fmc6B7lrvuf4vxA6bSmVioJJCInfvZ8X/KdgG5fgPpKR8aJHacd7x7lutXxMr3LUS5/rIuDkyVbYKprxM8bHniKAwUouyxSJ4FEZERrzZOJSobBSJQvPdme8rGXhqc4cHGY2jJbRlODpx3vGmXbmtILJnuaPawqs1FbZqPR46DJ62Ct18m6ahdNXgfNVc6ctiCGJ0O0ZJgtoBj4psL87uf38aMTvYW+FLEAGSMRGfmvQ1382beO87ev384zF4YWXFG+EE08F9TwVIh11S4uZDhT6lhXvAbKs91jRZ33arZINEZ/HvJ+LeZ4p4/rVpfxXG/2Y0uFEIrEeN/XD/OxV13H7//WukJfjriKBBKRtmAkyr/99Cxaw59/50RW5wpHNU5rdllgj3WOsrOhgpM9Y0RKIZgUYI1EIBJbMO1/qdAa/ua/n6Pb5+cvfntrTksOiOxI15ZIm81s4hvvvonmqtzU4s7Fp/NjXaNcv6ai6B4ue1u83NBQyQ2NlexqrGRXU2XBCl/FNFQUUY2STH3pyXbe9/BhAuHinHG3EkmLRGTEZCjKc/RQMhQ5mZ56tMvHrsZKjnX5UkoQmW97mj3sK6IU7kc7fWypK2M8EC6K+5ONn5y8zFs/9wxfuGdPyU4kWE6kRSIyEghFs57GO81uMbG+xs3GVdknVTzS6WNnY2VOZz1lolgLXj1/eRzvMnnwHu7w8YYHnuLS0PLJRFCqJJCIjFSV2bIe25jWPjTF85fHOT8wkZOulyMdPm5orMz+wjL0oo3VRbv4z201LavUIxcGJ7nr/qc43uUr9KWsaMvnN0osqXK7hZvWVeX0nDEN5Q5zTsY50s3BlQvxJJOVPHF2kEgsjWIqS2giFC14ay3XhiZD3P3QM/zqzEChL2XFkkAiMnbLhuqcn7Nz2M+O+oqsH3ZL+ax028ysqbSzvb6CQ5d8S/jOmekdDXD9mnKuX1NOmX15DJNOhaK868sH+K+Dkoq+EJbHb5EoiBduyG2LZNqRTh83NlVytDPzQXMjhx+RzIai0eucSYY4PXtXAUopLg1N0uML0OO7UvcjGI6xqdbN5dFA0VUnjGl4tmeMLXXuZTXzKRLT/Mk3j9M5PMUHb9skqeiXkAQSkTGHOX8N2sMd8WBypNOX4Srv3D1EvC5r2qnlz/bH80Pd0FjB0c7iLIJ1rn+C7Q2VHMkggWYx+/efn6PL5+cf7tqBNY+/o+IKucsiY/VeF6sr8peZdTqYFJqRxSfb4hxyj4vE4MzlcbauLi/0peTctw93884v7WfUH06+s8iaBBKRMa113sciDl1Kv1Rvo8dBYwplelMVyyLxVbF3rphNirP9pZk2JZmnzg/xxgefosdXmAWgK0lWgUQp9Y9KqeeVUseVUt9RSlXOeu2jSqlzSqnTSqlXzNq+Wyl1IvHav6tER6ZSyqaU+kZi+z6lVPOsY+5RSp1NfN0za3tLYt+ziWOXxwT5EmE2GTz0jlbW5LFVAlce5HXldhq9DrbUlbGjoWJOppHdaz3UJJJAdo742Z/DNRzZtCqyac0shfU17qKdqpwLZ/omuPP+JzlVwuWSS0G2LZLHgeu11juAM8BHAZRSW4G7gW3AHcD9SqnpRQcPAPcBGxNfdyS2vwsY0VpvAP4F+GTiXF7g48BeoA34uFLKkzjmk8C/aK03AiOJc4gldH19BX9/1/a8vke3z8+NTZUEI1EiUc3zl8c53jXK7rWemX0uDU0Sjem81CjRGbRIvC4ru5oqi7Zw1rRU692Xsr6xIG/67NP8+qxMD86XrAKJ1vp/tNbTU1KeARoS378OeERrHdRaXwTOAW1KqdVAudb6aR3/1/lV4PWzjvlK4vtvAi9LtFZeATyutR7WWo8QD153JF57aWJfEsdOn0ssoRdvXsWn3rSTXXkaz+jxBTjc4WNkKkzv6JWZUQfbR2hr8WKoeHXAlionwUhxrN9YXWHnSIeP05eLu9toYCJIkzc3OdOK2UQwwr1fOsA3D3UV+lKWpVyOkfwe8OPE9/XA7AndXYlt9Ynvr94+55hEcBoFqhY5VxXgmxXIZp9LLLG7bmzgW//rBfz5q7bkbMV7KvZfHJ5pmZgMgwPtI1nVOMmVTFoxhVJbblt2ixTnE4lp/vi/jvHvPztbUv9/SkHSQKKU+qlS6tl5vl43a5+PARHg4elN85xKL7I9k2MWO9d8P8d9SqmDSqmDAwPSxM0Hw1Dc96L1fP6eVqxLmIbjQPsItWU2LgzGp9wWxzOidJ7MB9pH2NNc+OC7VD71+Bk+8q0ThKPF0XpdDpL+a9dav1xrff08X9+D+EA48Grgd/WVMN8FNM46TQPQk9jeMM/2OccopcxABTC8yLkGgcrEvlefa76f4yGtdavWurWmpibZjy2y8IL11XzxnbuXtHJh33iQwYkQ62tcnO7LXXeS2VDUV6Y/AyybmV6FUGKXm7VvHOzkD756kIlgcS0WLVXZztq6A/gz4LVa69kl8r4P3J2YidVCfFB9v9a6FxhXSt2UGON4B/C9WcdMz8j6HeDnicD0GHC7UsqTGGS/HXgs8dovEvuSOHb6XKLAXrhxFR+8bdOSv+9UKII/R6u17RaDzXVlHMsgy7FjCbv3cqJ0GlA588vTA9z90NP0jweS7ywWlW3/w6eBMuBxpdRRpdSDAFrrk8CjwCngJ8D7tNbT/7rfA3ye+AD8ea6Mq3wBqFJKnQM+BHwkca5h4K+BA4mvTyS2QTyIfShxTFXiHKJI/P5vtVDtti3pe/aOBtleX0GZLbukDU6rieYqFycznDZaahl2Iyu0m+fZ7jHuuv8pziUyEYjMqJU46NTa2qoPHjxY6MtYER5+5hIf++6zS/6+NW4bAxOZVV4st5tZVW7jXH/mdS7amr3sby+eolapMBuqNEoV50Gl08JDb2+lrQgmahQzpdQhrXXr1dtL62OTKDlv3dtUkOmlAxNB2po9aWe39bqseJzWrIJIqdpcV1boSygY31SYt31+Hz88vuAwq1iEBBKRV0op/u7O7dyxrY6lXuS9v32E+koH66pdKe1fV27HZja4NDyVfOckdFFn2Zqfw1Ji4zo5ForGeP/Xj/DAL8/L9OA0SSAReffCjdU8+PbdfPGePUv+3s9fHufC4CR7mj2L7tfocRCJxeYseMyGKsHR64OXRooiSWahffInz/OX3z9JdIV282VCAolYMrdurqGuPL95uRYSXyvh4camSuquyg3WXOVkIhhhcCJUkGsrJsVWO6VQvvL0Jd778KFlVa8lnySQiCWjlGLvusINZh5oH+Fwh4/RqdDM2pD1NS6GJkOMTOU23XiR52pcUOfwJO4sZ7wtF4+d7OMtn3uGwQwnbawkEkjEkrqpgIFkmj8co6bMxpa6Mi6PBhiXT+EzghFdskEwH450+Ljz/idlenASEkjEkrp5XVVRDOoe7fTROTzF+lXu/GQMzvkZl4bVpLJeg7OcOCwmrCaDj377OIculdZ07qUkvzFiSTVXu/nSvXv48KPH6C5wwaHJUJTjXaNcv6acZ6VeBQA3NFbmtJZLMbGaDexmA5vFhM1sYDMbWEwGZpPCbBiYDIWhmFXrXRMIxzjeNcr5gUne8tA+/v0tN3DH9asL+nMUIwkkYsndtK6Kv7nzeu790oFCXwoWk0q7HnsqSrV3KN8tKUMx8yA3GwqLyUh8Kcym+DaTUhiJP5UBBmruDdXx6dWxGMR0jGgsntssEtNEojEiMU04Gv8+HI0RCEfxh2OEIvEvMuzKDEVjvOfhw/zfV2/l3ltacnNDlgkJJKIgXrJ5FQ++bTd/9q1j+ENRwtHCrLwIRzXVbguGoXI6VlLoZQjWxIM6GtNEYzrxaTv+idtiNrCaZn8aV5gMhdbTqWGchKOa+koHMR3//6K1JqbjD+zp78vtFnz+UOJn1URjEI7GiGpNLPG+4agmHIsRicQIRmJENfhDUfxFXvBrIVrDX/3gFJdHA/zZHVswVkL+/RRIIBEFc8f1dYz5w/zpt47PbJv+ZGoAdosJs0nNeggqTAaJPxUKhZH4u6HiKzeUgvgH2Ct/V4lPtLP/yU/3Xkyv97BbDALh2PTGK+YNCImHa2LX2UFDqSt/n6ngqK8sUNSamQdz4vk762E9nTVYo7Uihp7zkNZaJ46PP8inv9c6/no0Fv8+FI0xEYwyezLznNQnizzEz84aVE7W9biu2sWFPLTmSsFnn7hAz2iAf/ydHdiLYMyv0CSQiIJ6xfW1fPz7ppmMveGoJhyNf7+UZWqbvE46crCifSUp1QkFufKDYz30jQb47Nt343FZC305BSWztkRBVTisaefDyodSTGlSaDVLnNm5GO1vH+YNDzxFx9DK/hAigUQU3LtftK7QlyAyoNE0Vy3/eu/JXBic5K4HnuTZ7vTr1iwXEkhEwb11b1PBM8+WYm6sQjvW6cNpLXxrshgMToR482ef5pen+wt9KQUhgUQUnMNq5kd/eAuPvvtm7tpVT4Mn/dK2YumVOyyc6pX1N9MmQ1He9ZWD/Of+jkJfypKTjxOiKJhMJtpavLS1eGkfnOQV//oEwcjSVe2LaU2l0xKf6YVieEoSOCYTW5lFFRcVjWk++u0TdI/4+fDtm2YtblzepEUiik5ztYt/ftPOJX3PrhE/vqkwI1NhhqdClDvkM1YyoUhU8nIt4NO/OMeHHj0WXwC5AkggEUXpt7evLmhtjJjUokhqIhSlaoVPe13Md450c++X9zMeyG1m6WIkgUQUJaUUH3nldVjNBfoVlTiSEpOs7F7Uk+eGeOODT9M7Wti8cvkmgUQUrbYWL//0xp2YC/Cw2lRXhs0sD8lkTNK3ldTzl8e58zNPcWoZJwaVQCKK2mt3ruFv77x+yd/3cIePrWsqcFkl/cVizCZ5hKTi8liAN332aX51ZqDQl5IX8lsgit6b9zTx4Ntu5B03r6XMbsa2RN1dRzp8bF1TviTvVaqmQlIULFUTwQjv+vIB/utgZ6EvJeeULnSa0gJobW3VBw8eLPRliAydvjzOq//frwlH8/+763VZaal2zSxXHBgPcklycs2QHGWZ+cDLNvJHL99YctODlVKHtNatV2+XOY6i5GyuK+OV16/m+8d68v5ew5MhhievrCnZXi8tlNkKMX61HPzbz87SO+rnb+/cjmUZdA+W/k8gVqTt9RWFvgQBhZtVtww8erCLP/jqQSaDpd89KL8FoiTdvL6qIKlUHFYTNzZV0lApaVwgXgtGZO6Xpwe4+6Fn6B8LFPpSsiKBRJSkzuEpvAVYDLf/4giHO3yskUACwDLolSm4E92j3Hn/U5ztGy/0pWRMfg1EyQlHY/zVD05xvGvlpu0uFtIiyY1un583PPAU+y4MFfpSMiKBRJScx05e5nKBuwKUgtoyG6vKbFS7rVSs0NxcUrM8d8YCEd7+hf38YAkmkeTayvztFyXNYTHNqY1eCPsuDs/5+5a6Mkb9pds1kSlpkeRWKBrjD//zCD0+P/e9aF3JTA+WFokoOS+7rpY/ecXmQl+GACkHlid//+Pn+b/fO0m0RJKHSiARJem9t27gP//gpplpwMU0DdVtNa2YB2yJfGAuSV975hLv/trBksgeUDz/+oRI083rq/ju+27hn9+4kxN/eTufe0crNWW2glzLhcFJPE4LZkMxEYqumFldpdL1Uqp++lw/b3noGQYngoW+lEVJIBElzWQo3rC7AZvZxG1ba3n6Iy9lc+3S138PRWKMTIWJJLoiSuFTZC5IGMm/Y12j3HX/U1wYmCj0pSxIAolYVswmg5detyqrc7Su9dBc5czqHBOBlRFISqMHv/R1DE/x6V+cK/RlLEhmbYllx+vMbKFijdtGldvKwUsjOK0mWtd66Bn10+NLf6rx1jXlBMIxlNI8f7l4P0mK0uGyFu/junivTIgMBSPRjI7zuCw8fzk+hXcqFOXgpRHcNjNtzV6UgjN944xMpVY29VhiseSuxsqMrqVUSNfW0un2FW+VRQkkYtlJ5x/cmko7HqcFu8XMs93XrpSfCEbY3x5fM7KqzMa2NeWMTIVSbqUs966f5f7zFZNwNFboS1iQBBKx7Fxd0rTGbWMqFGEydKWl0rrWQ7fPz8B4MOWg0D8epH88SKPHwa7GCo50ppKiZXk/aldiPaNCKebFnzLYLpYV31SIwYkgOxsqaGv20Oh1MDARZDIUpdHrYEtdfEbX8S4fDospo+JYnSN+jnSOUu22srfFS1uzlxubKuft5pHnrMiVX50Z4GPfOVGUMwKlRSKWlafOD9HtC9A9Tyujczje5bWn2cOB9hE8LisMTmb8XoMTIQYnrqRK2bjKTaXTwoH2kYzPWWpKZOH1svHwvg48Tit/XGSZHaRFIpaVw5eSP8QPtI+wbU15ztNPnO2f4ED7CHuaPVhN8fbJcn/OStfW0vvh8R4iRTZeIoFELCu/u7eJRm/yVeUne8Y415+fabkH2keoLbdzY1Ml4WiMF22sZsMqN7uaKoHlNdMpVlzPsxWhfWiKf378DLEiag5KIBHLSkuNm/vfuhtrChWXJvJY4rRzxM/hDh/P9Y4zGYxwrn+CIx0+IN5K2V5fweqKwqRzyaWYtEgK4oFfnueDjx5lYLw4UqfIGIlYdq6vL8dqNggVS/N/ntk2J7pHuaGxgt7R3D8ITCr+lhaTgdVsYDWZMJviq/6tJgNDKUwGiT8VJmVgGPG8WYYChZppNilAo9E6PnEgpuPfx9BUOix0jRTv2obl7ntHezh0aYRvvecF1JbbC3otEkjEstM+NJXX1ka6FhpH6B0N0LrWc2XD7Ie3jucRm6570tbipWt4ihgQi2miMU1Ua6JRTTgWI6bBpBT+cJSoBjREYjH84RiQn3uxq7GS8wOZT1YQ2esa8fP1fR188LZNBb0OCSRi2fnN2YFCX0JK+saC9I0t3CKxmg2aq5zENEwGI/SMFrYq5NWi0q1VFH76XB/vfcl6bGZTwa5BxkjEsnOk01foS5gj0+dtKBKjfWiKjuEpTvaMsXV1eW4vLEulUnRpuTvZM8a+C8PJd8wjCSRi2flfL16PxVQ8c6NyNSBtMRfPzwSy2LKYPH6qr6DvL4FELDubasu4aV1VoS8j5050jVLjLp6ZXhGZ+1s0Hj3Yyf+cvFyw95dAIpadWEzPZPFdTmIa1tW4Cn0ZM6Rrq3gEIzH+7/dOMlmgSSYSSMSyc7hjpGjm1+fauf4JiqXXrthWV690l8cCHGgvzFiJBBKx7Hz7SHehLyFvhiZD7CySGicRiSNFx1mg4lcSSMSyEovpgvYVL4XxIinjKy2S4jPqD3OiK5XyBrklgUQsK9FYjNUVDspsxbNEKtcjCWf7J1hfBGMlfeNBvC5LoS9DzPIHXz3Iaz79G/70m8cIhDOrFJqJ4vnXJkQOWMwmvvPeF2AyFI+d7ONff3qm8APveRiTrnRagcKvKreaTEBq5YfF0nn0YBcD40H+5BVb2Lom/+uPpEUilh2zyUApxR3X1/H997+Q6wq8kE/nIZIc7/JR6Sx8a6DY1raIK35xeoA3PPAUPz7Ry1ggv8E+J4FEKfXHSimtlKqete2jSqlzSqnTSqlXzNq+Wyl1IvHavysVz2inlLIppb6R2L5PKdU865h7lFJnE1/3zNrektj3bOJYay5+HrF8WM0GH3nllkJfRs6Fo5rNtWWFvgwshnwWLWb+cJT3PHyYF/9/v6BvLH8pdrLu2lJKNQK3AR2ztm0F7ga2AWuAnyqlNmmto8ADwH3AM8CPgDuAHwPvAka01huUUncDnwTerJTyAh8HWol3EhxSSn1faz2S2OdftNaPKKUeTJzjgWx/JrG8vHhTDZtry5gIRlhX48LjtNJS7WJvi5eYBrfdjM1sYDEpyuwWfv58P53DU5gNxaXhKX54vDerNRP5Wm1xfmACQxW2SqHDWrj8TiJ1I1Nh/uSbx/ncO3bnJSdXLsZI/gX4U+B7s7a9DnhEax0ELiqlzgFtSql2oFxr/TSAUuqrwOuJB5LXAX+ZOP6bwKcTrZVXAI9rrYcTxzwO3KGUegR4KfDWxDFfSRwvgURc43vvvwW7JbV/QG9pa5rz9z986QZ+fOIy/32iN6PxllTfN12DEyF2NVXO1DkpBJtZWiSl4okzA3QMTbExDy3ZrAKJUuq1QLfW+piaW3OhnniLY1pXYls48f3V26eP6QTQWkeUUqNA1eztVx1TBfi01pF5zjXftd5HvCVEU1PTQruJZSqbh/mGVWX84cvKeO9LNvCNA53YLQa7mjyc65/gGwc6eK53nL9+/TbW17ipLbdzrn+Cbx3u4stPtaN1vO5HvvhDSzczZz5m6doqGa/duWa+0jg5kTSQKKV+CtTN89LHgD8Hbp/vsHm26UW2Z3LMYue69gWtHwIeAmhtbZXcDiJtJkPx1r1XPoS0VLu4bWvtNftdX1/B9fUVvLWticdOXubr+zuu2SdXnr88zlqvg0vDBSowJWPtJeG3Nlbzz2/aiSWFyqGZSBpItNYvn2+7Umo70AJMt0YagMNKqTbirYPGWbs3AD2J7Q3zbGfWMV1KKTNQAQwntt961TG/BAaBSqWUOdEqmX0uIQpuY20ZG2vLeOX21XzoG0c5lqeFYjXl9oIFElmUWPzW17j4zO/emLcgAlnM2tJan9Bar9JaN2utm4k/8G/UWl8Gvg/cnZiJ1QJsBPZrrXuBcaXUTYnxj3dwZWzl+8D0jKzfAX6u46XlHgNuV0p5lFIe4i2gxxKv/SKxL4ljZ4/TCFEU1te4+c/7buKuG+tx5GG85GT3GG5bYQa9l3LRm0hffaWDv3ztNsrt+Z0qnpcQpbU+CTwKnAJ+ArwvMWML4D3A54FzwHniA+0AXwCqEgPzHwI+kjjXMPDXwIHE1yemB96BPwM+lDimKnEOIYqO02rmU2+6gS/duwdnjmc6+cNRttQVZq1MfEGiKEb/59VbefIjL+W3Ntbk/b3UQvWkl7PW1lZ98ODBQl+GWKE6h6f47pFu/vnxMzk7Z1uLl/0Xlz7z694W70xdeVEcmrxOPnz7Jl6zYw2GkdtBLKXUIa1169XbJUWKEEus0evk/S/dwA+O93CmbyLr8+1oqGB4MpSDK0ufdG0VjzKbmT//7eu4Y1sdHtfSrs2WQCJEASiluHldVU4CicNi4ngBMr4C9Izmb7W0mJ/DaqLMZsZhNWEzG9jMJiwmxcuuq71mDdRSkUAiRIG84wXNfPWZSyVb+9zjtCzbAmL5YDEpPE4rbpsZq9nAajIwmRQmpTAMhQK01sR0vPpkKBojHI0RDMcIhKNMhSJMBKP4Q9F51w/dtnW+VRpLQwKJEAWyvsbNvS9o4UtPXUw5mDisJq6rK6N9cArQhCIxDneM5PU6F9Jc5WJkyleQ9y4Vbc1enu8bYzIQIRzV9I8H6c9T8B2eLFxQl0AiRAF95JVb2NFQwce+c4LJJKvUW6pdhCMxDhcwJcpsphwP5JYypeLZExwWA7vZFG9xmA00mjF//guRbap18+HbN+f9fRYigUSIArKaDV6/q54dDRV85NsnGJkMcbb/2nGT1rUejnX5CEeLox/MUMx7ncXOZCispvhD3mY2sFtMM91MZpPCYjIwVHwMK97VFC8DEI1pIjFNJBojHNWEIlGCkRj+cLybKRTVs7qclrY+i8Wk+MvXbstbTrdUSCARK85kMIIrgwqKhztGqHbZqPc4cv5pfF2Nm0fffTMAz/WO8d/He/ncry8QjMRoa/awv70w3VcL2VJXxqne/BYM21JXhslQ8a/EOIKhEllZEg/6aTGt0YmxhahOPPijMSKJsYbxQIRxf5hoTOOPRfEvk9lmbpuZR99985IUr1qMBBKxYuw7P8jp/gn2Xxzm/S/dkNYivlAkyo9O9PL5X1+k0evgrW1NDE+G2FJXzks21+B124D4g+zHz/bidVq4eX01KoMsedetLue61eW87yUb+OahTp4+P0SDx0HvaGDedParymxYTAZOqwmPy0r3iJ9uX/5SpjgsBmXzrJRuqXZR4bAQSzzMI1FNJBajym0jEI5iUvEooGDe7jmLSWEzx2ciWc0GFwYmCBVJC6xYVTgsWIsgA7MsSBQrwtDoBN870Y9vKsx/HerC47TyhXe2srrCMbNPLKZ57ORlGr1Ofv58Py/eVMPOxso55/nQN47y7SPdc7bVlNn4f2/ZxebaMp44O8AHHjnKqjIb33j3zbRUu4glPhVbTQaBcBRnhvXkA+Eon3viAl948iJ/fPtmXrplFdGYpqbMNqdbIxrT/ONjp3nwV+czep/FrKm0YzYMOoanrnlt6+pyTvWOpXSeunIb4USgiUQ1gXAUiRmZ+cTrtvGOm5uX5L1kQaJY0aoq3PzeC90AfPC2TTMtBa01f/ej5+gdDXBpaIoT3VfWY2yuK2PnVeeZb8bNwHiQux96BkOBxWSws6GC33thCy3VLgCiWtM5PIXXZcWbxUIxu8XEH75sI2/a00htuX3B/UyG4iOv3ELvqJ9fnRnAN5W7PvvV5Q4OzTNLrK3ZS/tQ6jXkL4/JtOFc+fXZwSULJAuRFolY8SaCEV776d9wYWDug9BQsLOxkg/dtmkmX9Hzl8f4l8fPcKB9hFF/GJfVRJXbxntuXc9du+oxGSqj7qx8CEaivP3z+9nfnlkKk7py20zGWLPJwGZW1JbZ+fW5wWuqMjZ6HXQWKpX9Cve9991yTcs5X6RFIsQC3DYzf3fndu5+6Jk522MaIlHN4MSVT89b6sp58G27icViKGWgtY4vJiuS4DGbzWziHS9Ym1EgcVtN87Yanr88wdbVZXT7Aoz6w3hdVtbXuDKqHCly41z/xJIFkoUUfpRGiCJw07oq3nvreiwmhcWkWFfj4u/u3M73338Ld+5qmLOvUgqTyYRhKEwmoyiDyLQ7ttXR6HUk3/EqDV7ngq+d6h3HbCi21JVRV27nQPsI44H8r5UQ8/vF6f5CX4K0SISY9qd3bOF1N9Sj0QVLy55rZpPBPTc38zf//VzKx+xsrOB8/+LjHUOTIYYnQ+xe68n2EkWWfvLsZaZCEZzWwj3OpUUixCyb68qWTRCZ9vpd9dhSnCK6q7GCY52jTASTtzA08Gz3KC3VC7deRP4ZSqX0/yuv11DQdxdC5F2128bOhsqU9g1G0iudG4jE8IeilNulc6NQQtEYPzzWW9BrkEAixDJ3cXCSs/3JB8N3r/VktFr98liQhjys9hepedcLW7j3luaCXoN8jBBimfvrH55iZNZaEpfVxOa6MpxWM72jfsJRzWQwQjZx4FTvOBtWuahwWDl0qbjSuSx3I5Ohgk/4kEAixDL3tpuamAxGONUzxngwQpPXSZPXyR+8aB3nByZ5zY7VHO8c4lOPn8diUhknhjzXP8n2eqnhvtTS7Y7MBwkkQixzL91Sy0u31NI5PMVXn26n2+fnTa2NXFdXzrY1FQDsbKrmoXs83PfVQ/zqzEDG79U+OMmNTZVYTAYHL43MmxtM5Nam2rJCX4IEEiFWikavk4/99tYFX7eZTdz/uzfyls89k3Hp3vFgdCYh4+ZaN4MTIYYKVE9+pdAUPljLYLsQYobLZubh39/Lb22szvpcp/smqKtYOCeYyI1iyP5b+CsQQhSVMruFr9zbxsdfs5WyLKb13thUycme1LIBi8zsbfHy3ls3FPoyJJAIIa5lGIp7b2nhP//gJu7e00il89r6I4tx20xpZQMW6dtU6+Y/fn9voS8DkEAihFjE9fUV/MMbdvCLD9/K625Yk/JxW1aXMzy5tCVnV5qPvuq6mezMhSaD7UKIpDwuK//65htoXevh//383Lx1Waat9TpkLUmOKeKtPIfVjMNiwmE1cfO6qkJf1gwJJEKIlCilePvNzexq8vCWh55hfIH8ThaTwfoaFyalZmqun+ufwB8u/HqHQjIpcNvNOK0m7BYzNrOB2VCYTfE/jcSK0Fii5nw4GmMyGOXyaAB/OMp4MEpLjZvX7FjD6kr7nKqYhSaBRAiRluvrK/inN+3kPf9x6JoCVxDPOHx1fZLWtR4mgpGSqltiUuC0mnBazdgtBhaTgdVsSjz84w9+g3iA1cSrbcZ0vCRyOBojFIl/+cNRJkNRQpEYo/4Io/4IkFmFyPtetI5X70i9i3GpSCARogg9caafF21aldYxwUiU7x3tIRbT3L6tLquyvsm8YlsdH/vtrRy+NMKPnu1Fa/A6LdQuMN33YKKrq9ptZV2Nm8OXhsl0QbbFpLBbTNjMBlazgdU095P9dCso/qBXKMWcB/10nXgFoMBiKMYCkZmHfjAc/zMS04wH4y2BYvGvPz0rgUQIkZrWtd60j7GZTZiU4k+/fZwHfnWeP759M7+1sZpKZ34Cyrte2MK7XtjCU+cGue9rh9hYW8a+i4tXYxycCDE4MczmujK01oQiMWIazCZFNKYxqfin/WAkRiQW7+KJxuL7BSNRAuEY4agmHI2Qq7aN22ZiooiCxWKKtTS61GwXYpkZmghyonuUz/7qAmf6xrl5fRV37qrnRZtq8jbL51dnBvinx05zojuzFfGFZFKQYXqxJffou2+mrSX9Dxm5slDN9uKYOyaEyJkqt41bN6/i3S9ex9BkiB8e7+VdXznIr89mnkMrmRdvquGeF6yl2m3L23vkS1SDuURS4H/2V+d5/FRfoS/jGhJIhFimrq6j/ufffpYLAxN5e7/f2d3Izz78Yl6yuSZv75Ev2azgX0o/e76fR/Z3FPoyriGBRIhlamOte06J3ctjAd79tUOMB/K3ULDCYeFz72jl9q21eXuPfHDZSiOQAJzP44eBTJXO3RNCpGVLXTk/+sBvcXFgEo/LyokuHw/86jzdPj9b6tJLeZIOs8ngX+++gTs/8xSn+0pjuq+jiNZkJDMWKGx99vlIIBEiTwLhaMEXja2vcbO+xg3ES+m+85aWJXlfp9XMZ373Rl776d8wFSr+GVGF/v+UjlARFLK6mnRtCZFj/WMB7rz/SV78j7/g6/s6GMtjV1Ix27DKzV+9dluhLyMlZlNpDLYDtDZ7Cn0J15BAIkSOrSq3s7elivFAhD//zgle/e+/YXRqZQaT39ndwJtbGwt9GUmZClzzPB1vbWsq9CVcQ7q2hMiDj7xyC++5dT1PnBlg1B8uqU+8uaSU4i9fu43j3aM811u8tUmMEgokw0VYcVJaJELkSYXDwmt2ruFtN60tqVlBueawmvj0W3flNWVLtkoojvBXPzhF++AksfkSnRXIyv3tFkIsmfU1bj542yb+z3efLfSllDx/OMrf/Pdz+MMR3vXCFn5wrJdgJMrde5p40abCrOGRQCKEWBKvv2EN//bTswTCUSYWSEFfCFUuK+f6i29txmJ++lx8dfuT54Zmtj3fO87P//jWglyPdG0JIZZEmd3CrZtriiqIQDwR4lARjjuk68LgJJ96/ExB3lsCiRBiybzy+rpCX8I1SmGdS6qOdBSmMqUEEiGWiVgshm8qVFSDsFd76ZZVbKkrK/RlzBEowgV+mbowMIlvaulbVxJIhFgmDMPgA48c5eEiTOo3TSlVlHm4ZuckK2XdPj/945lVX8zG8rh7QggAPvG6bfz9j54r6gWQewpYT2MhpZQiJZlCpMSXQCLEMtLkdXLLhmr+4KsHi7aL68YmD6Yiq/+xXFoku5oqqfc4lvx9l8fdE0IA8a6j//vqrexvH+ZTj58pytKsLpuZtV5noS9jDpul9B+Ft2yo4uu/fxM289K3rkr/7gkh5mjwOHj5dav49C/O8b6vHyYcLb7B5HWJjMTFwpqnEsRL6Z0vaMFhLUwXXenfPSHEHEop/v6uHawqs/GjE5e590sHGPUXz5jJ5dEAFweLawFgsXW1pavB48BewFaVrGwXYhmqKbPx/pdu4K9/eIrfnBvkTQ8+zefvaaWxCLqUvnmok/MDk4W+jDksJdQiqa908P6XbuBs3wSTwQhtLV5+e8fqgk4YkEAixDL1jpub2bamgg88cgSbxeCbhzp570s2FKQPfbZ33tLCD4/3EghHaR+aKui1TCvETKdUeJwWmqpcNFc5WVvlYq3XyY6GCjbWFtdaHAkkQixju9d6+Nb/uhmv21Y0n7rdNjMfeeUWvnO4q2gCSaG6tqwmgwaPg6YqJ03e+Fej98r3pZI1ujSuUgiRsdqKpZ8OmszT54f44YnLhb6MGfmcKe11WWn0OFhb5ZoJENOBo67cjlGkraF0SCARQiy5wYkQ77t1PavK7XziB6cIFXhmWTYVEs2GSrQqXDR5HVeChddFU5UTd4m0KrKx/H9CIUTR2bDKzVQownePdPMXr76Oh5/p4HTfeMGux0jS61dmN9PocdI4HSiqXLRUuVhb5WRNpaPkZ31lK+tAopT6Q+D9QAT4b631nya2fxR4FxAF/rfW+rHE9t3AlwEH8CPgA1prrZSyAV8FdgNDwJu11u2JY+4B/iLxln+jtf5KYnsL8AjgBQ4Db9dal34+aCGWuVfvWE1dhZ0P3baJo50+/uyVm/m9Lx8s2PUYStHoddDouXacornKRYXTUrBrKwVZBRKl1EuA1wE7tNZBpdSqxPatwN3ANmAN8FOl1CatdRR4ALgPeIZ4ILkD+DHxoDOitd6glLob+CTwZqWUF/g40Apo4JBS6vta65HEPv+itX5EKfVg4hwPZPMzCSHyb/Y05LVVLjTxKcsDeUw46LaZafQ6WZsYo5gOFmu9TtZU2rEWeDZbKcu2RfIe4B+01kEArXV/YvvrgEcS2y8qpc4BbUqpdqBca/00gFLqq8DriQeS1wF/mTj+m8CnlVIKeAXwuNZ6OHHM48AdSqlHgJcCb00c85XE8RJIhCghXpeVi4MTvOHGBh781fmMz6MUrC63z2lNzJ4N5XVZUaVUnL2EZBtINgG/pZT6WyAA/LHW+gBQT7zFMa0rsS2c+P7q7ST+7ATQWkeUUqNA1eztVx1TBfi01pF5ziWEKCHfPdLDz5/vT7qfy2qKtyqqnPFuqFktiwaPo+BrZFaqpIFEKfVTYL6yZh9LHO8BbgL2AI8qpdYB84V9vch2MjhmsXNdQyl1H/EuNZqamhbaTQhRAHUVdrp9/jmtiuluqMZEy6K5yoXHaZFWRRFKGki01i9f6DWl1HuAb+t4itH9SqkYUE28ddA4a9cGoCexvWGe7cw6pkspZQYqgOHE9luvOuaXwCBQqZQyJ1ols88138/xEPAQQGtra/GlRBViBXtLWxOvvL6OemlVlKRsl7p+l/g4BUqpTYCV+AP++8DdSilbYmbVRmC/1roXGFdK3ZQY/3gH8L3Eub4P3JP4/neAnycC1GPA7Uopj1LKA9wOPJZ47ReJfUkcO30uIUQJ8bqsrKtxSxApUdmOkXwR+KJS6lkgBNyTeMCfVEo9CpwiPi34fYkZWxAfoP8y8em/P058AXwB+FpiYH6Y+KwvtNbDSqm/Bg4k9vvE9MA78GfAI0qpvwGOJM4hhBBiCaliLHyTb62trfrgwcLNWRdCiFKklDqktW69entxZHETQghRsiSQCCGEyIoEEiGEEFmRQCKEECIrEkiEEEJkRQKJEEKIrEggEUIIkRUJJEIIIbIigUQIIURWJJAIIYTIigQSIYQQWZFAIoQQIisSSIQQQmRFAokQQoisrMg08kqpAeDSAi9XEy/OtdLJfbhC7kWc3IcrVuq9WKu1rrl644oMJItRSh2cL9/+SiP34Qq5F3FyH66QezGXdG0JIYTIigQSIYQQWZFAcq2HCn0BRULuwxVyL+LkPlwh92IWGSMRQgiRFWmRCCGEyMqyDSRKqT9WSmmlVPWsbR9VSp1TSp1WSr1i1vbdSqkTidf+XSmlEtttSqlvJLbvU0o1zzrmHqXU2cTXPbO2tyT2PZs41rpEP/I1lFL/qJR6Xil1XCn1HaVU5azXVtS9yIRS6o7E/TmnlPpIoa8nU0qpRqXUL5RSzymlTiqlPpDY7lVKPZ74//O4Usoz65i8/34UilLKpJQ6opT6YeLvK/I+5JTWetl9AY3AY8TXilQntm0FjgE2oAU4D5gSr+0HbgYU8GPglYnt7wUeTHx/N/CNxPde4ELiT0/ie0/itUeBuxPfPwi8p4D34XbAnPj+k8AnV+q9yODemRL3ZR1gTdyvrYW+rgx/ltXAjYnvy4Azid+B/w/4SGL7R5b696OA9+NDwNeBHyb+viLvQ07vaaEvIE+/KN8EdgLtXAkkHwU+OmufxxK/CKuB52dtfwvw2dn7JL43E1+ApGbvk3jts4ltKrHP9MP7ZuCxQt+PxLXcCTws9yLl+zXneq++Z6X8BXwPuA04DaxObFsNnF6q348C/uwNwM+Al3IlkKy4+5Drr2XXtaWUei3QrbU+dtVL9UDnrL93JbbVJ76/evucY7TWEWAUqFrkXFWAL7Hv1ecqtN8j/skJ5F6kYqGfq6Qlulp2AfuAWq11L0Diz1WJ3Zbi96NQ/hX4UyA2a9tKvA85ZS70BWRCKfVToG6elz4G/DnxLp1rDptnm15keybHLHauvFjsXmitv5fY52NABHh4+rB59i/5e5FjpX7911BKuYFvAX+ktR5LdOvPu+s823L9+7HklFKvBvq11oeUUremcsg820r+PuRDSQYSrfXL59uulNpOvC/zWOIfSQNwWCnVRvwTQOOs3RuAnsT2hnm2M+uYLqWUGagAhhPbb73qmF8Sb8ZWKqXMiU8js8+VFwvdi2mJQb1XAy/TiTY1y/Re5NhC96gkKaUsxIPIw1rrbyc29ymlVmute5VSq4H+xPal+P0ohFuA1yqlXgXYgXKl1H+w8u5D7hW6by2fX8wdI9nG3IGzC1wZODsA3MSVgbNXJba/j7kDZ48mvvcCF4kPmnkS33sTr/0XcweY31vAn/8O4BRQc9X2FXcvMrh35sR9aeHKYPu2Ql9Xhj+LAr4K/OtV2/+RuYPM/99S/n4U+J7cypUxkhV7H3J2Pwt9AXn+ZWknEUgSf/8Y8ZkXp0nMskhsbwWeTbz2aa4s1LQnHobniM/SWDfrmN9LbD8H3Dtr+7rEvucSx9oK+POfI94vezTx9eBKvRcZ3r9XEZ/hdJ54V2HBrynDn+OFxLtRjs/6XXgV8b77nwFnE396Zx2T99+PAt+TW7kSSFbsfcjVl6xsF0IIkZVlN2tLCCHE0pJAIoQQIisSSIQQQmRFAokQQoisSCARQgiRFQkkQgghsiKBRAghRFYkkAghhMjK/w/OhtIZxSknygAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make the figure size bigger\n", + "counties.plot(figsize=(6,9))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties.plot(figsize=(6,9), \n", + " edgecolor='grey', # grey colored border lines\n", + " facecolor='pink' , # fill in our counties as pink\n", + " linewidth=2) # make the linedwith a width of 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.5 Subset the GeoDataframe\n", + "\n", + "Since we'll be focusing on Berkeley later in the workshop, let's subset our GeoDataFrame to just be for Alameda County." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Kern', 'Kings', 'Lake', 'Lassen', 'Los Angeles', 'Madera',\n", + " 'Marin', 'Mariposa', 'Mendocino', 'Merced', 'Modoc', 'Mono',\n", + " 'Monterey', 'Napa', 'Nevada', 'Orange', 'Placer', 'Plumas',\n", + " 'Riverside', 'Sacramento', 'San Benito', 'San Bernardino',\n", + " 'San Diego', 'San Francisco', 'San Joaquin', 'San Luis Obispo',\n", + " 'San Mateo', 'Santa Barbara', 'Santa Clara', 'Santa Cruz',\n", + " 'Shasta', 'Sierra', 'Siskiyou', 'Solano', 'Alameda', 'Alpine',\n", + " 'Sonoma', 'Amador', 'Stanislaus', 'Sutter', 'Butte', 'Calaveras',\n", + " 'Tehama', 'Colusa', 'Trinity', 'Tulare', 'Contra Costa',\n", + " 'Del Norte', 'Tuolumne', 'Ventura', 'El Dorado', 'Yolo', 'Fresno',\n", + " 'Glenn', 'Yuba', 'Humboldt', 'Imperial', 'Inyo'], dtype=object)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# See all county names included in our dataset\n", + "counties['NAME'].values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like Alameda county is specified as \"Alameda\" in this dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
340AlamedaCalifornia15102712029.815345512062.4022266491221904519799...95.92744.0606068None0NoneNoneNoneNoneMULTIPOLYGON (((-197580.800 -24065.060, -19763...
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" + ], + "text/plain": [ + " FID_ NAME STATE_NAME POP2010 POP10_SQMI POP2012 POP12_SQMI \\\n", + "34 0 Alameda California 1510271 2029.8 1534551 2062.402226 \n", + "\n", + " WHITE BLACK AMERI_ES ... AVG_SALE07 SQMI CountyFIPS NEIGHBORS \\\n", + "34 649122 190451 9799 ... 95.92 744.06 06068 None \n", + "\n", + " PopNeigh NEIGHBOR_1 PopNeigh_1 NEIGHBOR_2 PopNeigh_2 \\\n", + "34 0 None None None None \n", + "\n", + " geometry \n", + "34 MULTIPOLYGON (((-197580.800 -24065.060, -19763... \n", + "\n", + "[1 rows x 59 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.loc[counties['NAME'] == 'Alameda']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create a new geodataframe called `alameda_county` that is a subset of our counties geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county = counties.loc[counties['NAME'] == 'Alameda'].copy().reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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h60e73Ucb+PRTH/LCnbMYl61TeVX31bV4WL+/midXlLCltLZPr+0fHFvDhU2TRTdkJLo43tgW+sRu+NZLW7jrsgK+8rHxADx+03R+vqyY5jYvd15SQGlNM69uKKPNFyW/gT1UVtPCTU99yHN3zGTqKN1BV3WsrsXDhgPVrNlXxZp91ewor+t0gDpcvihZqKvJIoR4l5MvXpTfZ/tBVTW18T9LdjM2K4mrzhnGnPFZTB5ZSFyMg3d2VTA8NY6vXFbAnc9toKSisU9ec6ipamrj1kVreHphIbMLsuwOR0WA441u1u+vZu3+atbtr2bX0XoGqndIxyzUCbfNzmfX0QaW7TyGu4OZT73x6Ju7ePTNXdw0I49PXzCK5LQEctPj+fSvP+SzhXm8+OVZXPqTFX32ekNNg9vL7b9fx52XjuPuy8frgsEoU17bwvoDgeSwdl9Vrwen+0K0TLrQZNENaYkufvW5CyitbmZHeR1Ldxzjtc2Hw7pm+xjFz5cVU9ngZuqoVG65cAw/++x0fvT6dh64dgoup0OTRRfafH6eWrmXl9Yd4t65k7ht9tioWBwVjepaPLy35+Sah9LqyJmG7oqSdUOaLHpgdGYiozMT+dikYVQ2uDlY3URNk4fG02ZL9YTfwMaDNVw0Potv/Hkz3547iaX3XoYxBpdT//B1R02zh/98YwclFY08eO0UXUU+ROyrbOSdXRW8s/sY6w/UROzYQKTG1dd0b6g+UNHQSml1Mw/9Yxdbw5xpMTozgUduOJeR6Qks332MR9/c3TdBRomCnCS+M/csPjUt1+5QVA8db3Sz6WANq/dVsbKossvdAiLJuaPS+Ps3LrE7jG7TvaFslBrvIi7GSU5ybNjXKq1u4fZn1uFyCl++pIA5BVms3hddGxeGY19lE/e+soXxw5I4e0Sq3eGoLhhj2FJay2ubD7OyqHLQTh+Pi4mObihtWfSxkooG/rrpMC+vL8Xt8SEiXHnOMN7YUh66cgcKx6YzZ3w2L60v7fb2AyqwU+4b91yi24ZEGGMMeysbWbargiXbjrC1rM7ukMI2uyCTl+6aY3cY3aYtiwgxYVgKP5h/Nl+9bDx/Xn+Ij8pq+fbcSRypayUp1smKop7dG3zDwVq2lNbx+VljaHT7+NvmsgGbEjiYFR9r5OF/7uTRG8+1O5So5/b6WLe/muW7K3hnV8WgbUF0Rge4VVjSEl2UVjfz3avPYmxWEotum0Flg7vHyQLA6zc8t/ogw1Li+M7cSYP+HuAD5cW1h7hkQjafPFfHLwZaRX1rIDnsruD9Pcdp8QzdLW00WaiwPXjdFN4vOY7b6+ec3FTSE2O5bvpIFm/tXZdURYOb17eUc+nE7BO72aqu/eDVj5g6Mo0xWYl2hzKk+f2G7eV1vLOrguW7K9h2ePB3L3VXtMxa1GTRj1xOB1ecNeyUsodvmEp9q4eVvWhhAJRUNHLttFxNFt3U4PbyzZc385evzNH7aPSx5jYv7+85zvLdgQRREaVjarFD/Haq7TRZDLC0BBfPfmkm6/ZX86sVJawq7nnScIiQ4HJy/ph09lU20ej2hrXWY6jbfKiWXy4v4d65k+wOZdArrW5mRVFg7GF1J/dyiTbaslD9aua4TGaOm8mafVX8dtXeHo1l3DpzDNdOH8nR+lY+/7u1OHXVcpdGpsVz2aQcu8MYlLw+P5sO1bJ8dwUrdldE5V0iVYAmC5vNLshidkEWH5Qc54m3i9kQ4h7fF43PIjsljuyUODKSYikcm8Ha/dUDFO3g8/Gzh/G/N08nIyn8NTDRorqpjVXFFSzfXcm7xZXUtXjsDimiRcuWPJosIsTFE7K5aHwWu4408MKag7y57cgZ/0mvOmcY/7fg/BNfpyW4+OXnzueKx1fS7PHplNogTofwvXlncdelBbr9Rwgen59NB2tYXlTByt2V2nrooZgo+f3SZBFBRITJI1P5n0+fy3984mze2RXYsNDldDBpeArfm3cWztN+MYelxPPQ9VN56B879RNgkN98YQZzJw+3O4yIVVHfysriSlYVV/JecSX1rTrm1Vun/58cqjRZRAhjDEt3HKOsppnE2BhiHEKMU/jiRfl4/YZ4l5MNB6oZbt2XOt7lxOPz43I62FJaq4niNBdP0PtcBGsfe1hZVMHKokp2Hun8/ueqZ6JlzFCTRQQorW7mh69v79bMqB9+6hzOHpHKjLEZbCmtpbS6mW98fAKLt5ZrwrDEOh0kuKJjOmNXjtW3sqqokpXFFby35zgN2nroFzE6G0oNhJVFFdzzp000dXGf72D//c9dAEwYlswrX5nD/a9vIz3RxQ8/dQ7fe/Wj/gx10IhzOfD6o2+L9/axh5XFlazS1sOA0W4oNSA2HartdqIIVlLRyJf+sI4Hr53Cb1bt5XhjdC6I6khDq5dvvbSF/1tw3pBfiHekroVVRZWsKKrgw5IqGnS9zYCLcQzt37F2mixs9vUrJrD5UE2vVmRvLavjw71V3DZ7LHf/aVM/RDd4/XPbEWJjHPz05ulD6pOfx+dn48EaVhRVsKqokt1HdeaS3aKlBavJwmaxMQ4evG4Kn/rFe7R6ej5f+/XNhynISeqHyAa/1zYfJi7GwaM3njuop8+W17awqriSlUUVfFBSpav1I4xTWxZqoIzPSeZnnz2Pr/WidXC0vpWk2BgcErhFqzrVS+tLiY1x8F/XTRk09+du8/rZcLCalUWBBFF8rNHukFQXdJ1FN4jIw8D1gB+oAL5ojCm3jt0H3An4gH83xiy1ymcAzwIJwJvAN40xRkTigOeBGUAVcIsx5oBVZyHwQ+tl/9sY81w4cUeiT56by3ev7t3245sO1TBrnN5RrzPPrz7I+yXHOXdUGlecNYwbzh9ld0hnOFrXysqiClYUBbb07s04lrLHUOrm7Eq4LYvHjTE/AhCRfwf+E/iqiEwGFgBTgJHAMhGZZIzxAU8BdwFrCCSL+cASAomlxhgzQUQWAI8Bt4hIJvAAUAgYYKOILDbGdL0vxiB0zxUTKKlo5PUe3lXvT2sPMnFYSj9FNTTsq2xiX2UTS7YdZcbYDEZn2rtludvrY+OBGlZZC+N07GHw0pZFNxhjgufmJRH4Yw6B1sZLxhg3sF9ESoCZInIASDXGrAYQkeeBGwgki+uBB636rwJPSqDfYB7wtjGm2qrzNoEE8+dwYo9EIsItF47pcbJo9fij6v4B4Wjz+Zn/83f52hUTuPOSccQP4HqM0upma1prBR/uraJZWw9DwlCfcdcu7DELEXkEuB2oA66wikcRaDm0K7PKPNbz08vb65QCGGO8IlIHZAWXd1Dn9FjuItBqYcyYMb3+nuyypbSWW59eE/pEFZamNh+PLy3iN6v2cuXZw5g7eQTDU+MozM/s09dpaPWw4WAN7xUfZ2VRBfuON/Xp9VVk0NlQFhFZBozo4ND9xpg3jDH3A/dbYxRfJ9Bl1NG7Z7oop5d1Ti00ZhGwCKCwsHDQDfeeOyqNa6bl8o+PjtgdSlRoaPXy+pbyEy25e6+axDevmtjr6/n8ho/Kanm3+Djv7qlkS2ktPp11MOTpmIXFGHNVN6/1IvBPAsmiDBgddCwPKLfK8zooJ6hOmYjEAGlAtVV++Wl1VnYzpkHF6RD+b8H5ZCfH8eyHB+wOJ+o8sayYYalx3Dqze61Sn99wtL6VoqP1/G3TYVYVV+qWGlFIxyy6QUQmGmP2WF9eB+y2ni8GXhSRnxEY4J4IrDPG+ESkQURmA2sJdF/9MqjOQmA1cBOw3JoltRR4VEQyrPOuBu4LJ+5I5nQID1w7mfREFz9ftid0BdWn7n9tG9nJcZ3uWNvq8fFRWR3vFlfy1s6jOq1V6TqLbvqxiJxFYOrsQeCrAMaYHSLyCrAT8AL3WDOhAO7m5NTZJdYD4PfAC9ZgeDWB2VQYY6qtKbrrrfMeah/sHqpEhG9dNYnYGAc/+VeR3eFEFb+Br7+4iRf/bRYzxgbGMA5WNfFucSUriyr5YO/xXi2eVENXtLQsxAzRO+YUFhaaDRs22B1G2P645iA/emN7t29sJAKTc1NpbvOxXwdUey0twcU103J5v+Q4B6ua7Q5HRbD/vXk6n5mRF/rECCEiG40xhT2tpyu4I9wXZo8lJT6Gb7+ytdPB0tT4GK6eMoJLJ2ZT2eDmlQ2lmijCVNfi4U9rD9kdhhoEdItyFTGuP28U8S4n33hxM22+M7tA/vjlWUzLSwfge3/ZeqIfPS7GETX3B1bKLtGy62x0fJdDwLwpI/jdwkLiXWf+yMZln9xIMHir8mGpcQMSm1LRLFqmzmqyGEQum5TDC3fOIiX+1AZh8P2TPzdrLO375fn9kJkUO5AhKhV1omWAW5PFIHNhfiZ//rfZZAUlgaagLavnTh7O379+CQ9dP4Xf3jaDued0PAVUKdU3dMxCRaypo9J49e6L+M4rWxiXnczEYclnHJ86Kg2AI/WtdoSoVNSIlm4oTRaD1LjsJP72tYs7Pd7c5uX1zeVEyYcepWzjHCT3SQmXdkMNUR6fITUhho9NymFYig50K9VfomXX2ej4LqNQWoKLSyZkkxLvYsrIVLvDUWrIipZuKE0WQ1h6Yiz52UlcM20ko9IT7A5HqSEpWrYo12QxxM0Ym8FVk4fzhy9dSGyUNJeVGkjaslBDRlqCi0nDU7juvJF2h6LUkKMruNWQ87lZg+/ugUpFOm1ZqCHn/NHpfPmScTo7Sqk+pGMWasgREX54zWTeuvcyTRhK9RFtWaghKz0xlufvnMklE7LtDkWpQU/HLNSQdvaIVH6+4DxinY6oaUYr1R+iZW8oTRZRLDs5jtvmjCUuxml3KEoNWrrdh4oK1583kuF63wules2hYxYqGkzLS+eBa6fYHYZSg1a0dONqslCMP22Lc6VU9+lsKBU1RqUn6N5RSvWSzoZSUeXiCVl2h6DUoKQtCxVVbi4cTVyM/joo1RPRcv9t0GShLBfmZzIuO8nuMJQaVKJljQVoslBBxufoQLdSPeGKkvEK0GShgkzWO+op1SNObVmoaDQ9L93uEJQaVHTMQkWlGWMz7A5BqUElWmZCgSYLFcRvjM6IUqoHomWNBfRRshCR74qIEZHsoLL7RKRERIpEZF5Q+QwR2WYd+4VIYBcuEYkTkZet8rUikh9UZ6GI7LEeC/siZnWmpLgYZo7LtDsMpQYNbVn0gIiMBuYCh4LKJgMLgCnAfODXItK+telTwF3AROsx3yq/E6gxxkwAngAes66VCTwAzAJmAg+IiPaX9BOdPqtU9+nU2Z55Avg+YILKrgdeMsa4jTH7gRJgpojkAqnGmNXGGAM8D9wQVOc56/mrwJVWq2Me8LYxptoYUwO8zckEo/rYmMxExmQm2h2GUoOCTp3tJhG5DjhsjNl62qFRQGnQ12VW2Sjr+enlp9QxxniBOiCri2t1FM9dIrJBRDZUVlb26nuKdtdMG8nXPz6BrKRYu0NRKuJFUzdUTKgTRGQZMKKDQ/cD/w+4uqNqHZSZLsp7W+fUQmMWAYsACgsLOzxHdW3pjqM8sHiH3WEoNShEUzdUyGRhjLmqo3IRORcYB2y1xqjzgE0iMpPAp//RQafnAeVWeV4H5QTVKRORGCANqLbKLz+tzspQcaveOXtECkmxTprafHaHolTEi6aWRa+7oYwx24wxw4wx+caYfAJ/1C8wxhwFFgMLrBlO4wgMZK8zxhwBGkRktjUecTvwhnXJxUD7TKebgOXWuMZS4GoRybAGtq+2ylQ/iHc58fi1UaZUd0TTmEXIlkVvGGN2iMgrwE7AC9xjjGn/qHo38CyQACyxHgC/B14QkRICLYoF1rWqReRhYL113kPGmOr+iFvB9NHpXD4ph7d2HrM7FKUiXhTlir5LFlbrIvjrR4BHOjhvAzC1g/JW4OZOrv0M8EyfBKq61Oj2aqJQqptczujJFtHznapuSY6LISWuXxqcSg05OmaholpqgsvuEJQaFKJpI0H9CKlO2FvZyGubDlPZ4LY7FKUGBW1ZqKjkcjj49ysnctucsXaHotSgoBsJqqg0JiuR2BgHC+fk2x2KUoNCNC3K02ShzjAyPT6q+mKV6i2dDaWiWozTwdRRaXaHoVTEc2nLQkW70brzrFIhWVsdRQVNFqpDYzIT7A5BqYgXTb21mixUh0akxtsdglIRT2dDqai3+2iD3SEoFfFio+ie9dHznaoeyUqOszsEpSKeDnCrqGaMoby2xe4wlIp4OnVWRbX6Vi+vbiwLfaJSUU6ThYpqaQkuzslNtTsMpSJeNC1e1WShOnTOiBS7Q1Aq4ulGgirqVTbqzrNKheLQRXkq2k3LS9O1FkqFoC0LFfXuvWoSd14yzu4wlIpoDk0WKtrFOB3MGZ9ldxhKRTSndkMpBVNHpXHpxGy7w1AqYkXRzFlNFqpr37pqot0hKBWxdNdZpSwXjMlgep7e20Kpjmg3lFIWEeHHn5lmdxhKRSSdDaVUkHNyU7kwP8PuMJSKODobSqnTXDBWk4VSp9NuKKVOc/OMPC4Yk253GEpFFJ0NpdRpJgxL4Sc3Tbc7DKUiis6GUqoD43OSuPH8UXaHoVTE0G4opTogIlw2SRfpKdUuinJFeMlCRB4UkcMissV6fDLo2H0iUiIiRSIyL6h8hohss479Qqx2nIjEicjLVvlaEckPqrNQRPZYj4XhxKzCc+P5eUwYlmx3GEpFBJ0N1TNPGGPOsx5vAojIZGABMAWYD/xaRJzW+U8BdwETrcd8q/xOoMYYMwF4AnjMulYm8AAwC5gJPCAiOjXHRrqqW6kA7YYK3/XAS8YYtzFmP1ACzBSRXCDVGLPaGGOA54Ebguo8Zz1/FbjSanXMA942xlQbY2qAtzmZYJQN5k0ZwSemjrA7DKVsp/ez6Jmvi8hHIvJM0Cf+UUBp0DllVtko6/np5afUMcZ4gTogq4trnUFE7hKRDSKyobKyMrzvSnXK5XTw3XlnkRofY3coStkqinqhQicLEVkmIts7eFxPoEtpPHAecAT43/ZqHVzKdFHe2zqnFhqzyBhTaIwpzMnJ6fybUmEbn5PMvXMn2R2GUraKpjGLkB8NjTFXdedCIvI08A/ryzJgdNDhPKDcKs/roDy4TpmIxABpQLVVfvlpdVZ2JybVv+JdztAnKTWEaTdUN1ljEO1uBLZbzxcDC6wZTuMIDGSvM8YcARpEZLY1HnE78EZQnfaZTjcBy61xjaXA1SKSYXVzXW2VKZvdeP4o8jIS7A5DKdtE0wrucDudfyIi5xHoFjoAfAXAGLNDRF4BdgJe4B5jjM+qczfwLJAALLEeAL8HXhCREgItigXWtapF5GFgvXXeQ8aY6jDjVn0g3uWkICeZspoWu0NRyhbR1LIIK1kYY27r4tgjwCMdlG8ApnZQ3grc3Mm1ngGe6X2kqr98tjCPd4t1MoGKTq4oalpEz3eq+sXHJuVQkJ1kdxhK2WLJ9iN2hzBgNFmosKTEu5hVkGl3GErZ4lcr9nLns+vZWV5vdyj9TpOFCtuMsZosVPR6Z3cFDyzeHvrEQU6ThQrblJGpdoeglK02H6rl71vLQ584iGmyUGEbm5VodwhK2crrN/zhg/2U1TTbHUq/0WShwpbgckbVtgdKdWTToVrm//w9VuyuoLy2hcAysaFDN/dRYRMRUhNc1DZ77A5FKVs1ur186dnAkrAZYzO4aUYeV549jJyUuEF/Vz1NFqpPJLqc1KLJQql2Gw/WsPFgDU6HMD4niYeun8rM/EyONbQiCMNS4nA4hLpmD80eL7lpkb0bgiYL1ScaWr12h6BURPL5DcXHGlmwaA3xLgetHj8AsU4Hw9PiKK9tJdHl5Kefnc68KZG79b+OWaiwNbm9NLg1WSgVSnuiAGjz+SmtbsHnNzS4vXznla20enxd1LaXJgsVtvf2HLc7BKUGvYdvmBLROzlrslBh6+7OszfNyOPp2wv7ORqlBqf0hFi7Q+iSJgsVtsTY0J+GrpmWy+M3TWPu5OGMSI0fgKiUGlzuf20b9a2RO0lEk4UKW0FOMuePSe/ynAUXjkFEeGvHUSoaWgcmMKUGkfK6Vp5+d5/dYXRKk4XqE/9783RczlPnkafExXD5WTk8ftM0LpmYDcCO8npmjssk3uXAqSv5lDrFku1H7Q6hUzp1VvWJgpxknrtjJiUVjYxMS2Di8GTyMhLPSAjt9+3eeLCGFbsrWH+gmpKKRqqa2uwIW6mIUlLRSH2rh9R4l92hnEGTheozF43P5qLx2d06d8bYDGaMzQDAGMPBqmaeW32A5z48gH9o7ZKgVLfFxThIiYvMP8vaDaVsJyLkZyfxwLVT+N3CQuJi9NdSRSe318/T7+2jye2lye3F7Y2cdRcy1Da7aldYWGg2bNhgdxiqF9YfqGb74Tr8JjDTqr7Fw5bSWpbuOKqtDhVV0hJc/OFLF3LBmIw+u6aIbDTG9HgOe2S2d1TUKj7WwPf+spWqpjYa3V7aP8vkpMTxhdljeX71QXsDVGoA1bV4+OIz61j1vSvISLJ3HYa291VEyc9KoqLBTUPryUQBUNngZsn2o8wcp3flU9GlvtXLj5fsps3rD31yP9JkoSKKyymd/qeobHBH9HYISvWXlzeUcsljy6lqdNsWgyYLFVFEhJHpnW8f0hS0YaEu01DRpKLBzR3PbaCi3p5FrTpmoSLOtLw0DlV3fHvK4NkhBTnJlFQ0DlRYA+aLF+Vz/ph0Wj0+2rx+Wj1+2nx+3B4frd7Av26vn1aPjxbrucfnx+MzNLm91DZ7gsr8tHn9eHVmwJCwtbSWhX9Yzz+/cQmOAf60pMlCRZxZBVn846MjHR47XNPCyLR4mtp8HKsbetuGPHjtZL548bg+v67fb2jz+TEGRMAhggi0eQPJxOMLJCSPz+Dx+WlpCyQivzF4rbLAOQZv0HO3x0ebL5DQ3F4f7lP+DSS0Vq+PRrePhhYP9a1e6ls8tPm63/8eF+PgU9NyWbuvmsO1LX3+3gw2KfExA54oQJOFikCJXYxL1DR7iEt1kpbg6rT1MVjNKchi4UX5/XJth0OId5z5vrqcDpLi+uUlu+T2+mho9VoPD81tPrw+Q5vPdyJheXx+nA4HH5uUQ1qCi+JjDawsqsDjCyQwrz+Q4LxWAmvzGWKdws2Fo3l5fSlH61vxWMd9/jMTntdvTiRKr3W8PTFGckvs6snDbXldTRYq4mw8VNPl8aMD0Gc7a1wmu47UUz9AdwC88uxhPH174aC/T3N3xcU4iUt2kp3c/Uw1aXgKk4andOvcqaPSehsaENhVwOs3VgILtL7c3pNdfhC4A96JpOUNJCSHBMbdHBJI0H6/odnjo9nto6nNS0tb4F+P10pe/pPJzuM3+HzmZJn/ZBdiexJr8fg4a0T33oO+pslCRZxpo9J40eYYJg5Lpra5jRFpUHysf8dFvnJZAbdcONqWrgXVMRHB5RRcTkggsmbg2bWQWmdDqYgzuyCr319j/pQRXDYp55Qyp0NIjHVy68zRrD9QTZvXz4tfnsWUkal9/vqxzsB/vbNHpPD9+WdTkJPc56+hhia7Wp/aslARJy8jgZnjMtl/vInU+BimjEyjICeJsVmJjEhNIDMplka3l8qGVrYfrmd/VRMjUuPJTYsnOzmOtEQXGNh5pJ51+6tZvbfqlAHVOy4ex47yOlq9ft761mX8c1s5Da0+zhqezK6jDfxtU9mJ7qe9lU24vX5mF2SyZl91n3x//3nNZG6dOYaj9a2MTI/XrdrVoBD23lAi8g3g64AX+Kcx5vtW+X3AnYAP+HdjzFKrfAbwLJAAvAl80xhjRCQOeB6YAVQBtxhjDlh1FgI/tF7yv40xz4WKS/eGUu1Kq5v58ZLdbDhYzfmjM6hrbWP13moSXE7+7bICXlh9gC9enM8Tb+85o+4HP/g4MU7hhdUHeXJFSa9juDA/g0+dm0taoovrp4/SLidlG1v2hhKRK4DrgWnGGLeIDLPKJwMLgCnASGCZiEwyxviAp4C7gDUEksV8YAmBxFJjjJkgIguAx4BbRCQTeAAoBAywUUQWG2O6HgVVyjI6M5Ffff4CqhrdfP53a9l9tAGArORYdh+pp6bZg8vp4NxRaeSmxdPo9rLhQA1tPj/J8TGkJbhYeFE+8S4HP32ruNuvGxvjYGZ+Jp88N5fPFuYR49ReXzV4hdsNdTfwY2OMG8AYU2GVXw+8ZJXvF5ESYKaIHABSjTGrAUTkeeAGAsnieuBBq/6rwJMS6JybB7xtjKm26rxNIMH8OczYVZTJSo5jyTcvpaSikbX7A2MS00en88NPTWZMViJfu3zCiXPrWjy8W1xJsnVvgZyUOL76sfGICB/uPc4HJVWdvk6s08E3r5rIrTPHkGnz5m9K9ZVwk8Uk4FIReQRoBb5rjFkPjCLQcmhXZpV5rOenl2P9WwpgjPGKSB2QFVzeQZ1TiMhdBFotjBkzJqxvTA1NIsLE4SlMDDEFMy3BxbXTR55SFuN0cM8VE7hsYg5/21zGHz44cEa9GWMzeOwz05gwTAes1dASMlmIyDJgRAeH7rfqZwCzgQuBV0SkAOioQ9Z0UU4v65xaaMwiYBEExiw6OkepcJ2bl8aq4goyk2Kptm4HGxvj4D/mn83tc8Zqd5MakkImC2PMVZ0dE5G7gb+ZwCj5OhHxA9kEPv2PDjo1Dyi3yvM6KCeoTpmIxABpQLVVfvlpdVaGilup/pQcF8Pfv3EJ//HXjxidmcgdF+czYZg9i6WUGgjhdkO9DnwcWCkik4BY4DiwGHhRRH5GYIB7IrDOGOMTkQYRmQ2sBW4HfmldazGwEFgN3AQst2ZJLQUeFZH2W0VdDdwXZtxKhaV9/6YX7pxlcyRKDYxwk8UzwDMish1oAxZarYwdIvIKsJPAlNp7rJlQEBgUf5bA1Nkl1gPg98AL1mB4NYHZVBhjqkXkYWC9dd5D7YPdSimlBobeg1sppaJIb9dZ6EicUkqpkDRZKKWUCkmThVJKqZA0WSillApJk4VSSqmQNFkopZQKSZOFUkqpkIbsOgsRqQQO2h1HB7IJrHKPRBpb72hsvaOx9U64sY01xuSEPu1UQzZZRCoR2dCbBTEDQWPrHY2tdzS23rErNu2GUkopFZImC6WUUiFpshh4i+wOoAsaW+9obL2jsfWOLbHpmIVSSqmQtGWhlFIqJE0WSimlQjPG6KOHD+BxYDfwEfAakB507D6gBCgC5gWVzwC2Wcd+wckuwDjgZat8LZAfVGchsMd6LAwqH2edu8eqGxt07GZgB+AHCoPKY4E/WDFsBS6PoNhcwHNWDLuA+yIots8DW4IefuC8SIjNOjaNwN0ld1ixxEdCbEA+0BL0vv0mUn6mQcfHAI3AdyMlNmBm0Hu2FbhxoGPr9O/eQPxxHWoPArd2jbGePwY8Zj2fbP2A46wfxl7AaR1bB8wBhMDdAT9hlX+t/T8SgbsDvmw9zwT2Wf9mWM8zrGOvAAus578B7g6K7RzgLAL3KQ/+JbwH+IP1fBiwEXBESGyfA16ynicCB9p/4e2O7bSf+7nAvqCv7X7fYgh8YJlufZ1F5Py+5QPbO3kfI+JnCvwV+AunJgu737dETv5tyQUqgr4ekNg6/bs3EH9ch/IDuBH4k/X8Pk79VLzU+uHmAruDym8Ffht8jvU8hsDKTAk+xzr2W6tMrHPaf4HmAEs7iOv0X8JfAV8I+vodAp9iIiG2W4G/W6+RBRRbv+C2x3basUeBR6zntscGfBL4YwfnRUJs+XSQLCIhNqvsBgI9BA9iJYtIiS3o2DjgmPV6Ax7b6Q8dswjfHZy8j/gooDToWJlVNsp6fnr5KXWMMV6gjsAfzM6ulQXUWueefq2ubAWuF5EYERlHoEk7OkJiexVoAo4Ah4CfmsB91iMhtmC3AH8Oen27Y5sEGBFZKiKbROT7ERQbwDgR2Swiq0Tk0kiJTUSSgB8A/3XaIdtjs+KbJSLt3Ypfta5he2wx3Qk+GonIMmBEB4fuN8a8YZ1zP+AF/tRerYPzTRflvakjwEgR2W6VxQD51tcnYuvAMwSavhsI7Jn1oRV7JMQ2E/ABIwk0l9+z3v9IiC3wYiKzgGZjTHv9SIgtBrgEuBBoBt4RkY1AfQTEdgQYY4ypEpEZwOsiMqWL1xnI2P4LeMIY0yhyyqUjITaMMWuBKSJyDvCciCzph9g6u1anNFl0whhzVVfHRWQhcA1wpbHacgQy9Oig0/KAcqs8r4Py4DplIhIDpAHVVvnlp9VZSaD52ExgkNUrInOAB40x80J8P17g3qD4PyQwuFVjd2wExiz+ZYzxABUi8gFQCLwXAbG1W8DJVkX769sdWxmwyhhzHEBE3gQuAP5od2zGGDfgtp5vFJG9BFpCkfC+zQJuEpGfAOmAX0RaCYxh2B3bCcaYXSLSBEyl79+3dBGJsf4uBF+rU9oN1QsiMp9AM/Y6Y0xz0KHFwAIRibO6eiYC64wxR4AGEZktgY8ytwNvBNVZaD2/CVhuJZ+lwNUikiEiGQQG1Zdax1ZY52LV7fJTsRVzotX8RkTmAl5jzM5IiI1A19PHJSAJmE2gfzYSYkNEHARmr7zUXhYhsS0Fplk/2xjgY0BE/ExFJEdEnNbzAgL/F/ZFQmzGmEuNMfnGmHzg58CjxpgnIyE2ERln/SwRkbEEBsEPREJs/TbwO5QfBKaoldLxtMD7CcyCKsKarWCVFwLbrWNPcnLaWzyBGRklBGY7FATVucMqLwG+FFReYJ1bYtWNCzp2I4FPFG4Cg2NLrfJ8K6ZdwDIC2xRHSmzJ1rk7gJ3A9yIlNuvY5cCaDn4PIiG2L1jv23bgJ5ESG/AZK66twCbg2kiJ7bSf4YOcOhvK7vftNut922K9bzcMdGydPXS7D6WUUiFpN5RSSqmQNFkopZQKSZOFUkqpkDRZKKWUCkmThVJKqZA0WSillApJk4VSSqmQ/j9erJyNHLpWnQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot our newly subsetted geodataframe\n", + "alameda_county.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice! Looks like we have what we were looking for.\n", + "\n", + "*FYI*: You can also make dynamic plots of one or more county without saving to a new gdf." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bay_area_counties = ['Alameda', 'Contra Costa', 'Marin', 'Napa', 'San Francisco', \n", + " 'San Mateo', 'Santa Clara', 'Santa Cruz', 'Solano', 'Sonoma']\n", + "counties.loc[counties['NAME'].isin(bay_area_counties)].plot()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.6 Save your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's not forget to save out our Alameda County geodataframe `alameda_county`. This way we won't need to repeat the processing steps and attribute join we did above.\n", + "\n", + "We can save it as a shapefile." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.shp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the problems of saving to a shapefile is that our column names get truncated to 10 characters (a shapefile limitation.) \n", + "\n", + "Instead of renaming all columns with obscure names that are less than 10 characters, we can save our GeoDataFrame to a spatial data file format that does not have this limation - [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) or [GPKG](https://en.wikipedia.org/wiki/GeoPackage) (geopackage) file.\n", + "- These formats have the added benefit of outputting only one file in contrast tothe multi-file shapefile format." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.json\", driver=\"GeoJSON\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.gpkg\", driver=\"GPKG\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can read these in, just as you would a shapefile with `gpd.read_file`" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alameda_county_test = gpd.read_file(\"outdata/alameda_county.gpkg\")\n", + "alameda_county_test.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alameda_county_test2 = gpd.read_file(\"outdata/alameda_county.json\")\n", + "alameda_county_test2.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are also many other formats we could use for data output.\n", + "\n", + "**NOTE**: If you're working with point data (i.e. a single latitude and longitude value per feature),\n", + "then CSV might be a good option!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.7 Recap\n", + "\n", + "In this lesson we learned about...\n", + "- The `geopandas` package \n", + "- Reading in shapefiles \n", + " - `gpd.read_file`\n", + "- GeoDataFrame structures\n", + " - `shape`, `head`, `columns`\n", + "- Plotting GeoDataFrames\n", + " - `plot`\n", + "- Subsetting GeoDatFrames\n", + " - `loc`\n", + "- Saving out GeoDataFrames\n", + " - `to_file`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: IO, Manipulation, and Mapping\n", + "\n", + "Now you'll get a chance to practice the operations we learned above.\n", + "\n", + "In the following cell, compose code to:\n", + "\n", + "1. Read in the California places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`)\n", + "2. Subset the data to Berkeley\n", + "3. Plot, and customize as desired\n", + "4. Save out as a shapefile (`outdata/berkeley_places.shp`)\n", + "\n", + "\n", + "*Note: pulling in a zipped shapefile has the same syntax as just pulling in a shapefile. The only difference is that insead of just putting in the filepath you'll want to write `zip://notebook_data/census/Places/cb_2018_06_place_500k.zip`*\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_build/html/_sources/ran/03_CRS_Map_Projections-Copy1.ipynb b/_build/html/_sources/ran/03_CRS_Map_Projections-Copy1.ipynb new file mode 100644 index 0000000..d7c1fce --- /dev/null +++ b/_build/html/_sources/ran/03_CRS_Map_Projections-Copy1.ipynb @@ -0,0 +1,2324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 3. Coordinate Reference Systems (CRS) & Map Projections\n", + "\n", + "Building off of what we learned in the previous notebook, we'll get to understand an integral aspect of geospatial data: Coordinate Reference Systems.\n", + "\n", + "- 3.1 California County Shapefile\n", + "- 3.2 USA State Shapefile\n", + "- 3.3 Plot the Two Together\n", + "- 3.4 Coordinate Reference System (CRS)\n", + "- 3.5 Getting the CRS\n", + "- 3.6 Setting the CRS\n", + "- 3.7 Transforming or Reprojecting the CRS\n", + "- 3.8 Plotting States and Counties Togther\n", + "- 3.9 Recap\n", + "- **Exercise**: CRS Management\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - ‘notebook_data/california_counties/CaliforniaCounties.shp’\n", + " - ‘notebook_data/us_states/us_states.shp’\n", + " - ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 10 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 California County shapefile\n", + "Let's go ahead and bring back in our California County shapefile. As before, we can read the file in using `gpd.read_file` and plot it straight away." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')\n", + "counties.plot(color='darkgreen')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even if we have an awesome map like this, sometimes we want to have more geographical context, or we just want additional information. We're going to try **overlaying** our counties GeoDataFrame on our USA states shapefile." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 USA State shapefile\n", + "\n", + "We're going to bring in our states geodataframe, and let's do the usual operations to start exploring our data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in states shapefile\n", + "states = gpd.read_file('notebook_data/us_states/us_states.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
STATEGEOIDABBREVgeometry
0Alabama01ALMULTIPOLYGON (((-88.05338 30.50699, -88.05109 ...
1Alaska02AKMULTIPOLYGON (((-134.73726 58.26135, -134.7344...
2Arizona04AZPOLYGON ((-114.81629 32.50804, -114.81432 32.5...
3Arkansas05ARPOLYGON ((-94.61783 36.49941, -94.61765 36.499...
4California06CAMULTIPOLYGON (((-118.60442 33.47855, -118.5987...
\n", + "
" + ], + "text/plain": [ + " STATE GEOID ABBREV geometry\n", + "0 Alabama 01 AL MULTIPOLYGON (((-88.05338 30.50699, -88.05109 ...\n", + "1 Alaska 02 AK MULTIPOLYGON (((-134.73726 58.26135, -134.7344...\n", + "2 Arizona 04 AZ POLYGON ((-114.81629 32.50804, -114.81432 32.5...\n", + "3 Arkansas 05 AR POLYGON ((-94.61783 36.49941, -94.61765 36.499...\n", + "4 California 06 CA MULTIPOLYGON (((-118.60442 33.47855, -118.5987..." + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the first few rows\n", + "states.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(56, 4)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count how many rows and columns we have\n", + "states.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot our states data\n", + "states.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might have noticed that our plot extends beyond the 50 states (which we also saw when we executed the `shape` method). Let's double check what states we have included in our data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California',\n", + " 'Colorado', 'Connecticut', 'Delaware', 'District of Columbia',\n", + " 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa',\n", + " 'Kansas', 'Maryland', 'Minnesota', 'Mississippi', 'Montana',\n", + " 'Nevada', 'New Jersey', 'New Mexico', 'North Dakota', 'Oklahoma',\n", + " 'Pennsylvania', 'South Carolina', 'South Dakota', 'Utah',\n", + " 'Vermont', 'West Virginia', 'Wyoming', 'American Samoa',\n", + " 'Puerto Rico', 'Florida', 'Kentucky', 'Louisiana', 'Maine',\n", + " 'Massachusetts', 'Michigan', 'Missouri', 'Nebraska',\n", + " 'New Hampshire', 'New York', 'North Carolina', 'Ohio', 'Oregon',\n", + " 'Rhode Island', 'Tennessee', 'Texas', 'Virginia', 'Washington',\n", + " 'Wisconsin', 'Guam',\n", + " 'Commonwealth of the Northern Mariana Islands',\n", + " 'United States Virgin Islands'], dtype=object)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states['STATE'].values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beyond the 50 states we seem to have American Samoa, Puerto Rico, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands included in this geodataframe. To make our map cleaner, let's limit the states to the contiguous states (so we'll also exclude Alaska and Hawaii)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Define list of non-contiguous states\n", + "non_contiguous_us = [ 'American Samoa','Puerto Rico','Guam',\n", + " 'Commonwealth of the Northern Mariana Islands',\n", + " 'United States Virgin Islands', 'Alaska','Hawaii']\n", + "# Limit data according to above list\n", + "states_limited = states.loc[~states['STATE'].isin(non_contiguous_us)]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot it\n", + "states_limited.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To prepare for our mapping overlay, let's make our states a nice, light grey color." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "states_limited.plot(color='lightgrey', figsize=(10,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Plot the two together\n", + "\n", + "Now that we have both geodataframes in our environment, we can plot both in the same figure.\n", + "\n", + "**NOTE**: To do this, note that we're getting a Matplotlib Axes object (`ax`), then explicitly adding each our layers to it\n", + "by providing the `ax=ax` argument to the `plot` method." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh no, what happened here?\n", + "\n", + " **Question** Without looking ahead, what do you think happened?\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "If you look at the numbers we have on the x and y axes in our two plots, you'll see that the county data has much larger numbers than our states data. It's represented in some different type of unit other than decimal degrees! \n", + "\n", + "In fcat, that means if we zoom in really close into our plot we'll probably see the states data plotted. We can explore this in two ways:\n", + "\n", + "- Set our matplotlib preferences to `%matplotlib notebook` to zoom in and out of our plot\n", + "- Limit the extent of our plot using `set_xlim` and `set_ylim`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.'\n", + " );\n", + " }\n", + "};\n", + "\n", + "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = document.createElement('div');\n", + " titlebar.classList =\n", + " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", + " var titletext = document.createElement('div');\n", + " titletext.classList = 'ui-dialog-title';\n", + " titletext.setAttribute(\n", + " 'style',\n", + " 'width: 100%; text-align: center; padding: 3px;'\n", + " );\n", + " titlebar.appendChild(titletext);\n", + " this.root.appendChild(titlebar);\n", + " this.header = titletext;\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute(\n", + " 'style',\n", + " 'border: 1px solid #ddd;' +\n", + " 'box-sizing: content-box;' +\n", + " 'clear: both;' +\n", + " 'min-height: 1px;' +\n", + " 'min-width: 1px;' +\n", + " 'outline: 0;' +\n", + " 'overflow: hidden;' +\n", + " 'position: relative;' +\n", + " 'resize: both;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'keydown',\n", + " on_keyboard_event_closure('key_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'keyup',\n", + " on_keyboard_event_closure('key_release')\n", + " );\n", + "\n", + " this._canvas_extra_style(canvas_div);\n", + " this.root.appendChild(canvas_div);\n", + "\n", + " var canvas = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute('style', 'box-sizing: content-box;');\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\n", + " var backingStore =\n", + " this.context.backingStorePixelRatio ||\n", + " this.context.webkitBackingStorePixelRatio ||\n", + " this.context.mozBackingStorePixelRatio ||\n", + " this.context.msBackingStorePixelRatio ||\n", + " this.context.oBackingStorePixelRatio ||\n", + " this.context.backingStorePixelRatio ||\n", + " 1;\n", + "\n", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + " if (this.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n", + " }\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", + " var nentries = entries.length;\n", + " for (var i = 0; i < nentries; i++) {\n", + " var entry = entries[i];\n", + " var width, height;\n", + " if (entry.contentBoxSize) {\n", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\n", + " width = entry.contentRect.width;\n", + " height = entry.contentRect.height;\n", + " }\n", + "\n", + " // Keep the size of the canvas and rubber band canvas in sync with\n", + " // the canvas container.\n", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'width: ' + width + 'px; height: ' + height + 'px;'\n", + " );\n", + "\n", + " rubberband_canvas.setAttribute('width', width);\n", + " rubberband_canvas.setAttribute('height', height);\n", + "\n", + " // And update the size in Python. We ignore the initial 0/0 size\n", + " // that occurs as the element is placed into the DOM, which should\n", + " // otherwise not happen due to the minimum size styling.\n", + " if (width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband_canvas.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\n", + " this._resize_canvas = function (width, height, forward) {\n", + " if (forward) {\n", + " canvas_div.style.width = width + 'px';\n", + " canvas_div.style.height = height + 'px';\n", + " }\n", + " };\n", + "\n", + " // Disable right mouse context menu.\n", + " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " continue;\n", + " }\n", + "\n", + " var button = (fig.buttons[name] = document.createElement('button'));\n", + " button.classList = 'mpl-widget';\n", + " button.setAttribute('role', 'button');\n", + " button.setAttribute('aria-disabled', 'false');\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib notebook\n", + "\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20.0, 50.0)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)\n", + "ax.set_xlim(-140,-50)\n", + "ax.set_ylim(20,50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a key issue that you'll have to resolve time and time again when working with geospatial data!\n", + "\n", + "It all revolves around **coordinate reference systems** and **projections**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----------------------------\n", + "\n", + "## 3.4 Coordinate Reference Systems (CRS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **Question** Do you have experience with Coordinate Reference Systems?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

As a refresher, a CRS describes how the coordinates in a geospatial dataset relate to locations on the surface of the earth. \n", + "\n", + "A `geographic CRS` consists of: \n", + "- a 3D model of the shape of the earth (a **datum**), approximated as a sphere or spheroid (aka ellipsoid)\n", + "- the **units** of the coordinate system (e.g, decimal degrees, meters, feet) and \n", + "- the **origin** (i.e. the 0,0 location), specified as the meeting of the **equator** and the **prime meridian**( \n", + "\n", + "A `projected CRS` consists of\n", + "- a geographic CRS\n", + "- a **map projection** and related parameters used to transform the geographic coordinates to `2D` space.\n", + " - a map projection is a mathematical model used to transform coordinate data\n", + "\n", + "### A Geographic vs Projected CRS\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### There are many, many CRSs\n", + "\n", + "Theoretically the number of CRSs is unlimited!\n", + "\n", + "Why? Primariy, because there are many different definitions of the shape of the earth, multiplied by many different ways to cast its surface into 2 dimensions. Our understanding of the earth's shape and our ability to measure it has changed greatly over time.\n", + "\n", + "#### Why are CRSs Important?\n", + "\n", + "- You need to know the data about your data (or `metadata`) to use it appropriately.\n", + "\n", + "\n", + "- All projected CRSs introduce distortion in shape, area, and/or distance. So understanding what CRS best maintains the characteristics you need for your area of interest and your analysis is important.\n", + "\n", + "\n", + "- Some analysis methods expect geospatial data to be in a projected CRS\n", + " - For example, `geopandas` expects a geodataframe to be in a projected CRS for area or distance based analyses.\n", + "\n", + "\n", + "- Some Python libraries, but not all, implement dynamic reprojection from the input CRS to the required CRS and assume a specific CRS (WGS84) when a CRS is not explicitly defined.\n", + "\n", + "\n", + "- Most Python spatial libraries, including Geopandas, require geospatial data to be in the same CRS if they are being analysed together.\n", + "\n", + "#### What you need to know when working with CRSs\n", + "\n", + "- What CRSs used in your study area and their main characteristics\n", + "- How to identify, or `get`, the CRS of a geodataframe\n", + "- How to `set` the CRS of geodataframe (i.e. define the projection)\n", + "- Hot to `transform` the CRS of a geodataframe (i.e. reproject the data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Codes for CRSs commonly used with CA data\n", + "\n", + "CRSs are typically referenced by an [EPSG code](http://wiki.gis.com/wiki/index.php/European_Petroleum_Survey_Group). \n", + "\n", + "It's important to know the commonly used CRSs and their EPSG codes for your geographic area of interest. \n", + "\n", + "For example, below is a list of commonly used CRSs for California geospatial data along with their EPSG codes.\n", + "\n", + "##### Geographic CRSs\n", + "-`4326: WGS84` (units decimal degrees) - the most commonly used geographic CRS\n", + "\n", + "-`4269: NAD83` (units decimal degrees) - the geographic CRS customized to best fit the USA. This is used by all Census geographic data.\n", + "\n", + "> `NAD83 (epsg:4269)` are approximately the same as `WGS84(epsg:4326)` although locations can differ by up to 1 meter in the continental USA and elsewhere up to 3m. That is not a big issue with census tract data as these data are only accurate within +/-7meters.\n", + "##### Projected CRSs\n", + "\n", + "-`5070: CONUS NAD83` (units meters) projected CRS for mapping the entire contiguous USA (CONUS)\n", + "\n", + "-`3857: Web Mercator` (units meters) conformal (shape preserving) CRS used as the default in web mapping\n", + "\n", + "-`3310: CA Albers Equal Area, NAD83` (units meters) projected CRS for CA statewide mapping and spatial analysis\n", + "\n", + "-`26910: UTM Zone 10N, NAD83` (units meters) projected CRS for northern CA mapping & analysis\n", + "\n", + "-`26911: UTM Zone 11N, NAD83` (units meters) projected CRS for Southern CA mapping & analysis\n", + "\n", + "-`102641 to 102646: CA State Plane zones 1-6, NAD83` (units feet) projected CRS used for local analysis.\n", + "\n", + "You can find the full CRS details on the website https://www.spatialreference.org" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.5 Getting the CRS\n", + "\n", + "### Getting the CRS of a gdf\n", + "\n", + "GeoPandas GeoDataFrames have a `crs` attribute that returns the CRS of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83 / California Albers\n", + "Axis Info [cartesian]:\n", + "- X[east]: Easting (metre)\n", + "- Y[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: USA - California\n", + "- bounds: (-124.45, 32.53, -114.12, 42.01)\n", + "Coordinate Operation:\n", + "- name: California Albers\n", + "- method: Albers Equal Area\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.crs" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: WGS 84\n", + "Axis Info [ellipsoidal]:\n", + "- Lat[north]: Geodetic latitude (degree)\n", + "- Lon[east]: Geodetic longitude (degree)\n", + "Area of Use:\n", + "- name: World\n", + "- bounds: (-180.0, -90.0, 180.0, 90.0)\n", + "Datum: World Geodetic System 1984\n", + "- Ellipsoid: WGS 84\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can clearly see from those two printouts (even if we don't understand all the content!),\n", + "the CRSs of our two datasets are different! **This explains why we couldn't overlay them correctly!**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------------------------\n", + "The above CRS definition specifies \n", + "- the name of the CRS (`WGS84`), \n", + "- the axis units (`degree`)\n", + "- the shape (`datum`),\n", + "- and the origin (`Prime Meridian`, and the equator)\n", + "- and the area for which it is best suited (`World`)\n", + "\n", + "> Notes:\n", + "> - `geocentric` latitude and longitude assume a spherical (round) model of the shape of the earth\n", + "> - `geodetic` latitude and longitude assume a spheriodal (ellipsoidal) model, which is closer to the true shape.\n", + "> - `geodesy` is the study of the shape of the earth." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE**: If you print a `crs` call, Python will just display the EPSG code used to initiate the CRS object. Depending on your versions of Geopandas and its dependencies, this may or may not look different from what we just saw above." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epsg:4326\n" + ] + } + ], + "source": [ + "print(states_limited.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.6 Setting the CRS\n", + "\n", + "You can also set the CRS of a gdf using the `crs` attribute. You would set the CRS if is not defined or if you think it is incorrectly defined.\n", + "\n", + "> In desktop GIS terminology setting the CRS is called **defining the CRS**\n", + "\n", + "As an example, let's set the CRS of our data to `None`" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# first set the CRS to None\n", + "states_limited.crs = None" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Check it again\n", + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "...hummm...\n", + "\n", + "If a variable has a null value (None) then displaying it without printing it won't display anything!" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "# Check it again\n", + "print(states_limited.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll set it back to its correct CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Set it to 4326\n", + "states_limited.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: WGS 84\n", + "Axis Info [ellipsoidal]:\n", + "- Lat[north]: Geodetic latitude (degree)\n", + "- Lon[east]: Geodetic longitude (degree)\n", + "Area of Use:\n", + "- name: World\n", + "- bounds: (-180.0, -90.0, 180.0, 90.0)\n", + "Datum: World Geodetic System 1984\n", + "- Ellipsoid: WGS 84\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Show it\n", + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE**: You can set the CRS to anything you like, but **that doesn't make it correct**! This is because setting the CRS does not change the coordinate data; it just tells the software how to interpret it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.7 Transforming or Reprojecting the CRS\n", + "You can transform the CRS of a geodataframe with the `to_crs` method.\n", + "\n", + "\n", + "> In desktop GIS terminology transforming the CRS is called **projecting the data** (or **reprojecting the data**)\n", + "\n", + "When you do this you want to save the output to a new GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited_utm10 = states_limited.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now take a look at the CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83 / UTM zone 10N\n", + "Axis Info [cartesian]:\n", + "- E[east]: Easting (metre)\n", + "- N[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: North America - 126°W to 120°W and NAD83 by country\n", + "- bounds: (-126.0, 30.54, -119.99, 81.8)\n", + "Coordinate Operation:\n", + "- name: UTM zone 10N\n", + "- method: Transverse Mercator\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_limited_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see the result immediately by plotting the data." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(134312.9521453322, 5295973.096958174, 2936443.847710154, 8098103.992522996)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot geographic gdf\n", + "states_limited.plot();\n", + "plt.axis('square');\n", + "\n", + "# plot utm gdf\n", + "states_limited_utm10.plot();\n", + "plt.axis('square')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Your thoughts here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What two key differences do you see between the two plots above?\n", + "1. Do either of these plotted USA maps look good?\n", + "1. Try looking at the common CRS EPSG codes above and see if any of them look better for the whole country than what we have now. Then try transforming the states data to the CRS that you think would be best and plotting it. (Use the code cell two cells below.)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Double-click to see solution!**\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.8 Plotting states and counties together\n", + "\n", + "Now that we know what a CRS is and how we can set them, let's convert our counties GeoDataFrame to match up with out states' crs." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert counties data to NAD83 \n", + "counties_utm10 = counties.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot it together!\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "states_limited_utm10.plot(color='lightgrey', ax=ax)\n", + "counties_utm10.plot(color='darkgreen',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we know that the best CRS to plot the contiguous US from the above question is 5070, let's also transform and plot everything in that CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "counties_conus = counties.to_crs(\"epsg:5070\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'states_limited_conus' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstates_limited_conus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lightgrey'\u001b[0m\u001b[0;34m,\u001b[0m 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "states_limited_conus.plot(color='lightgrey', ax=ax)\n", + "counties_conus.plot(color='darkgreen',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.9 Recap\n", + "\n", + "In this lesson we learned about...\n", + "- Coordinate Reference Systems \n", + "- Getting the CRS of a geodataframe\n", + " - `crs`\n", + "- Transforming/repojecting CRS\n", + " - `to_crs`\n", + "- Overlaying maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: CRS Management\n", + "\n", + "Now it's time to take a crack and managing the CRS of a new dataset. In the code cell below, write code to:\n", + "\n", + "1. Bring in the CA places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`)\n", + "2. Check if the CRS is EPSG code 26910. If not, transform the CRS\n", + "3. Plot the California counties and places together.\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_build/html/_sources/ran/04_More_Data_More_Maps-Copy1.ipynb b/_build/html/_sources/ran/04_More_Data_More_Maps-Copy1.ipynb new file mode 100644 index 0000000..c4c99d2 --- /dev/null +++ b/_build/html/_sources/ran/04_More_Data_More_Maps-Copy1.ipynb @@ -0,0 +1,1349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 4. More Data, More Maps!\n", + "\n", + "Now that we know how to pull in data, check and transform Coordinate Reference Systems (CRS), and plot GeoDataFrames together - let's practice doing the same thing with other geometry types. In this notebook we'll be bringing in bike boulevards and schools, which will get us primed to think about spatial relationship questions.\n", + "\n", + "- 4.1 Berkeley Bike Boulevards\n", + "- 4.2 Alameda County Schools\n", + "- **Exercise**: Even More Data!\n", + "- 4.3 Map Overlays with Matplotlib\n", + "- 4.4 Recap\n", + "- **Exercise**: Overlay Mapping\n", + "- 4.5 Teaser for Day 2\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/parcels/parcel_pts_rand30pct.geojson'\n", + " - ‘notebook_data/berkeley/BerkeleyCityLimits.shp’\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Berkeley Bike Boulevards\n", + "\n", + "We're going to bring in data bike boulevards in Berkeley. Note two things that are different from our previous data:\n", + "- We're bringing in a [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) this time and not a shapefile\n", + "- We have a **line** geometry GeoDataFrame (our county and states data had **polygon** geometries)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1Heinz/RussellRUS8th9thRUS02E/WEzistingNo100.8140720.101MULTILINESTRING ((562391.553 4189820.949, 5624...
2Heinz/RussellRUS9th10thRUS03E/WExistingNo100.0373960.100MULTILINESTRING ((562489.017 4189846.721, 5625...
3Heinz/RussellRUS10thSan PabloRUS04E/WExistingNo106.5928780.107MULTILINESTRING ((562585.723 4189872.321, 5626...
4San PabloRUSHeinzRussellRUS05N/SExistingNo89.5634780.090MULTILINESTRING ((562688.854 4189899.267, 5627...
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" + ], + "text/plain": [ + " BB_STRNAM BB_STRID BB_FRO BB_TO BB_SECID DIR_ Status \\\n", + "0 Heinz/Russell RUS 7th 8th RUS01 E/W Existing \n", + "1 Heinz/Russell RUS 8th 9th RUS02 E/W Ezisting \n", + "2 Heinz/Russell RUS 9th 10th RUS03 E/W Existing \n", + "3 Heinz/Russell RUS 10th San Pablo RUS04 E/W Existing \n", + "4 San Pablo RUS Heinz Russell RUS05 N/S Existing \n", + "\n", + " ALT_bikeCA Shape_len len_km \\\n", + "0 No 101.128166 0.101 \n", + "1 No 100.814072 0.101 \n", + "2 No 100.037396 0.100 \n", + "3 No 106.592878 0.107 \n", + "4 No 89.563478 0.090 \n", + "\n", + " geometry \n", + "0 MULTILINESTRING ((562293.786 4189795.092, 5623... \n", + "1 MULTILINESTRING ((562391.553 4189820.949, 5624... \n", + "2 MULTILINESTRING ((562489.017 4189846.721, 5625... \n", + "3 MULTILINESTRING ((562585.723 4189872.321, 5626... \n", + "4 MULTILINESTRING ((562688.854 4189899.267, 5627... " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_blvds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(211, 11)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_blvds.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['BB_STRNAM', 'BB_STRID', 'BB_FRO', 'BB_TO', 'BB_SECID', 'DIR_',\n", + " 'Status', 'ALT_bikeCA', 'Shape_len', 'len_km', 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_blvds.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our bike boulevard data includes the following information:\n", + " - `BB_STRNAM` - bike boulevard Streetname\n", + " - `BB_STRID` - bike boulevard Street ID\n", + " - `BB_FRO` - bike boulevard origin street\n", + " - `BB_TO` - bike boulevard end street\n", + " - `BB_SECID`- bike boulevard section id\n", + " - `DIR_` - cardinal directions the bike boulevard runs\n", + " - `Status` - status on whether the bike boulevard exists\n", + " - `ALT_bikeCA` - ? \n", + " - `Shape_len` - length of the boulevard in meters \n", + " - `len_km` - length of the boulevard in kilometers\n", + " - `geometry`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "Why are there 211 features when we only have 8 bike boulevards?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your reponse here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now take a look at our CRS..." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: WGS 84 / UTM zone 10N\n", + "Axis Info [cartesian]:\n", + "- E[east]: Easting (metre)\n", + "- N[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: World - N hemisphere - 126°W to 120°W - by country\n", + "- bounds: (-126.0, 0.0, -120.0, 84.0)\n", + "Coordinate Operation:\n", + "- name: UTM zone 10N\n", + "- method: Transverse Mercator\n", + "Datum: World Geodetic System 1984\n", + "- Ellipsoid: WGS 84\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_blvds.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's tranform our CRS to UTM Zone 10N, NAD83 that we used in the last lesson." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10 = bike_blvds.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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BB_STRNAMBB_STRIDBB_FROBB_TOBB_SECIDDIR_StatusALT_bikeCAShape_lenlen_kmgeometry
0Heinz/RussellRUS7th8thRUS01E/WExistingNo101.1281660.101MULTILINESTRING ((562293.837 4189794.938, 5623...
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2Heinz/RussellRUS9th10thRUS03E/WExistingNo100.0373960.100MULTILINESTRING ((562489.067 4189846.568, 5625...
3Heinz/RussellRUS10thSan PabloRUS04E/WExistingNo106.5928780.107MULTILINESTRING ((562585.773 4189872.168, 5626...
4San PabloRUSHeinzRussellRUS05N/SExistingNo89.5634780.090MULTILINESTRING ((562688.904 4189899.113, 5627...
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" + ], + "text/plain": [ + " BB_STRNAM BB_STRID BB_FRO BB_TO BB_SECID DIR_ Status \\\n", + "0 Heinz/Russell RUS 7th 8th RUS01 E/W Existing \n", + "1 Heinz/Russell RUS 8th 9th RUS02 E/W Ezisting \n", + "2 Heinz/Russell RUS 9th 10th RUS03 E/W Existing \n", + "3 Heinz/Russell RUS 10th San Pablo RUS04 E/W Existing \n", + "4 San Pablo RUS Heinz Russell RUS05 N/S Existing \n", + "\n", + " ALT_bikeCA Shape_len len_km \\\n", + "0 No 101.128166 0.101 \n", + "1 No 100.814072 0.101 \n", + "2 No 100.037396 0.100 \n", + "3 No 106.592878 0.107 \n", + "4 No 89.563478 0.090 \n", + "\n", + " geometry \n", + "0 MULTILINESTRING ((562293.837 4189794.938, 5623... \n", + "1 MULTILINESTRING ((562391.603 4189820.796, 5624... \n", + "2 MULTILINESTRING ((562489.067 4189846.568, 5625... \n", + "3 MULTILINESTRING ((562585.773 4189872.168, 5626... \n", + "4 MULTILINESTRING ((562688.904 4189899.113, 5627... " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_blvds_utm10.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Alameda County Schools\n", + "\n", + "Alright! Now that we have our bike boulevard data squared away, we're going to bring in our Alameda County school data." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYSiteAddressCityStateTypeAPIOrg
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933Public
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932Public
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853Public
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927Public
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894Public
\n", + "
" + ], + "text/plain": [ + " X Y Site Address \\\n", + "0 -122.238761 37.744764 Amelia Earhart Elementary 400 Packet Landing Rd \n", + "1 -122.251856 37.738999 Bay Farm Elementary 200 Aughinbaugh Way \n", + "2 -122.258915 37.762058 Donald D. Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org \n", + "0 Alameda CA ES 933 Public \n", + "1 Alameda CA ES 932 Public \n", + "2 Alameda CA ES 853 Public \n", + "3 Alameda CA ES 927 Public \n", + "4 Alameda CA ES 894 Public " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(550, 9)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **Questions** \n", + "\n", + "Without looking ahead:\n", + "\n", + "1. Is this a geodataframe? \n", + "2. How do you know?\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your reponse here:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "This is not a GeoDataFrame! A couple of clues to figure that out are..\n", + "\n", + "1. We're pulling in a Comma Separated Value (CSV) file, which is not a geospatial data format\n", + "2. There is no geometry column (although we do have latitude and longitude values)\n", + "\n", + "\n", + "-------------------------------\n", + "\n", + "Although our school data is not starting off as a GeoDataFrame, we actually have the tools and information to make it one. Using the `gpd.GeoDataFrame` constructor, we can transform our plain DataFrame into a GeoDataFrame (specifying the geometry information and then the CRS)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYSiteAddressCityStateTypeAPIOrggeometry
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933PublicPOINT (-122.23876 37.74476)
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932PublicPOINT (-122.25186 37.73900)
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853PublicPOINT (-122.25892 37.76206)
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927PublicPOINT (-122.23484 37.76525)
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894PublicPOINT (-122.23808 37.75396)
\n", + "
" + ], + "text/plain": [ + " X Y Site Address \\\n", + "0 -122.238761 37.744764 Amelia Earhart Elementary 400 Packet Landing Rd \n", + "1 -122.251856 37.738999 Bay Farm Elementary 200 Aughinbaugh Way \n", + "2 -122.258915 37.762058 Donald D. Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org geometry \n", + "0 Alameda CA ES 933 Public POINT (-122.23876 37.74476) \n", + "1 Alameda CA ES 932 Public POINT (-122.25186 37.73900) \n", + "2 Alameda CA ES 853 Public POINT (-122.25892 37.76206) \n", + "3 Alameda CA ES 927 Public POINT (-122.23484 37.76525) \n", + "4 Alameda CA ES 894 Public POINT (-122.23808 37.75396) " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"\n", + "schools_gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll notice that the shape is the same from what we had as a dataframe, just with the added `geometry` column." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(550, 10)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_gdf.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And with it being a GeoDataFrame, we can plot it as we did for our other data sets.\n", + "Notice that we have our first **point** geometry GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But of course we'll want to transform the CRS, so that we can later plot it with our bike boulevard data." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf_utm10 = schools_gdf.to_crs( \"epsg:26910\")\n", + "schools_gdf_utm10.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*In Lesson 2 we discussed that you can save out GeoDataFrames in multiple file formats. You could opt for a GeoJSON, a shapefile, etc... for point data sets it is also an option to save it out as a CSV since the geometry isn't complicated*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Even More Data!\n", + "Let's play around with another point GeoDataFrame.\n", + "\n", + "In the code cell provided below, compose code to:\n", + "\n", + "1. Read in the parcel points data (`notebook_data/parcels/parcel_pts_rand30pct.geojson`)\n", + "1. Set the CRS to be 4326\n", + "1. Transform the CRS to 26910\n", + "1. Plot and customize as desired!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.3 Map Overlays with Matplotlib\n", + "\n", + "No matter the geometry type we have for our GeoDataFrame, we can create overlay plots.\n", + "\n", + "Since we've already done the legwork of transforming our CRS, we can go ahead and plot them together." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds_utm10.plot(ax=ax, color='red')\n", + "schools_gdf_utm10 .plot(ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to answer questions like *\"What schools are close to bike boulevards in Berkeley?\"*, the above plot isn't super helpful, since the extent covers all of Alameda county.\n", + "\n", + "Luckily, GeoDataFrames have an easy method to extract the minimium and maximum values for both x and y, so we can use that information to set the bounds for our plot." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "561541.1531499997 4189007.11635 566451.5549499998 4193483.09445\n" + ] + } + ], + "source": [ + "minx, miny, maxx, maxy = bike_blvds.total_bounds\n", + "print(minx, miny, maxx, maxy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `xlim` and `ylim` we can zoom in to see if there are schools proximal to the bike boulevards." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4189007.11635, 4193483.09445)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds_utm10.plot(ax=ax, color='red')\n", + "schools_gdf_utm10 .plot(ax=ax)\n", + "plt.xlim(minx, maxx)\n", + "plt.ylim(miny, maxy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.4 Recap\n", + "\n", + "In this lesson we learned a several new skills:\n", + "- Transformed an a-spatial dataframe into a geospatial one\n", + " - `gpd.GeoDataFrame`\n", + "- Worked with point and line GeoDataFrames\n", + "- Overlayed point and line GeoDataFrames\n", + "- Limited the extent of a map\n", + " - `total_bounds`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Overlay Mapping\n", + "\n", + "Let's take some time to practice reading in and reconciling new datasets, then mapping them together.\n", + "\n", + "In the code cell provided below, write code to:\n", + "\n", + "1. Bring in your Berkeley places shapefile (and don't forget to check/transform the crs!) (`notebook_data/berkeley/BerkeleyCityLimits.shp`)\n", + "1. Overlay the parcel points on top of the bike boulevards\n", + "1. Create the same plot but limit it to the extent of Berkeley city limits\n", + "\n", + "***BONUS***: *Add the Berkeley outline to your last plot!*\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click the see the solution!\n", + "\n", + "\n", + "\n", + "-----------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.5 Teaser for Day 2...\n", + "\n", + "You may be wondering if and how we could make our maps more interesting and informative than this.\n", + "\n", + "To give you a tantalizing taste of Day 2, the answer is: Yes, we can! And here's how!" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Public and Private Schools, Alameda County')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = schools_gdf_utm10.plot(column='Org', cmap='winter', \n", + " markersize=35, edgecolor='black',\n", + " linewidth=0.5, alpha=1, figsize=[9, 9],\n", + " legend=True)\n", + "ax.set_title('Public and Private Schools, Alameda County')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
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 D-Lab @ University of California - Berkeley
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 Team Geo
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\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/california_counties/CaliforniaCounties.shp'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 15 minutes\n", + "\n", + "\n", + "\n", + "### Types of Thematic Maps\n", + "\n", + "There are two primary types of maps used to convey data values:\n", + "\n", + "- `Choropleth maps`: set the color of areas (polygons) by data value\n", + "- `Point symbol maps`: set the color or size of points by data value\n", + "\n", + "We will discuss both of these types of maps in more detail in this lesson. But let's take a quick look at choropleth maps. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.1 Choropleth Maps\n", + "Choropleth maps are the most common type of thematic map.\n", + "\n", + "Let's take a look at how we can use a geodataframe to make a choropleth map.\n", + "\n", + "We'll start by reloading our counties dataset from Day 1." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
00KernCalifornia839631102.9851089104.2828704997664892112676...1513.538161.3506103San Bernardino,Tulare,Inyo2495935NoneNoneNoneNonePOLYGON ((193446.035 -244342.585, 194033.795 -...
10KingsCalifornia152982109.9155039111.42742183027110142562...1203.201391.3906089Fresno,Kern,Tulare2212260NoneNoneNoneNonePOLYGON ((12524.028 -179431.328, 12358.142 -17...
20LakeCalifornia6466548.66525349.0823345203312322049...72.311329.4606106None0NoneNoneNoneNoneMULTIPOLYGON (((-240632.150 93056.104, -240669...
30LassenCalifornia348957.4350397.4228562553228341234...120.924720.4206086None0NoneNoneNoneNonePOLYGON ((-45364.032 352060.633, -45248.844 35...
40Los AngelesCalifornia98186052402.399043412423.264150493659985687472828...187.944087.1906073San Bernardino,Kern2874841NoneNoneNoneNoneMULTIPOLYGON (((173874.519 -471855.293, 173852...
\n", + "

5 rows × 59 columns

\n", + "
" + ], + "text/plain": [ + " FID_ NAME STATE_NAME POP2010 POP10_SQMI POP2012 POP12_SQMI \\\n", + "0 0 Kern California 839631 102.9 851089 104.282870 \n", + "1 0 Kings California 152982 109.9 155039 111.427421 \n", + "2 0 Lake California 64665 48.6 65253 49.082334 \n", + "3 0 Lassen California 34895 7.4 35039 7.422856 \n", + "4 0 Los Angeles California 9818605 2402.3 9904341 2423.264150 \n", + "\n", + " WHITE BLACK AMERI_ES ... AVG_SALE07 SQMI CountyFIPS \\\n", + "0 499766 48921 12676 ... 1513.53 8161.35 06103 \n", + "1 83027 11014 2562 ... 1203.20 1391.39 06089 \n", + "2 52033 1232 2049 ... 72.31 1329.46 06106 \n", + "3 25532 2834 1234 ... 120.92 4720.42 06086 \n", + "4 4936599 856874 72828 ... 187.94 4087.19 06073 \n", + "\n", + " NEIGHBORS PopNeigh NEIGHBOR_1 PopNeigh_1 NEIGHBOR_2 \\\n", + "0 San Bernardino,Tulare,Inyo 2495935 None None None \n", + "1 Fresno,Kern,Tulare 2212260 None None None \n", + "2 None 0 None None None \n", + "3 None 0 None None None \n", + "4 San Bernardino,Kern 2874841 None None None \n", + "\n", + " PopNeigh_2 geometry \n", + "0 None POLYGON ((193446.035 -244342.585, 194033.795 -... \n", + "1 None POLYGON ((12524.028 -179431.328, 12358.142 -17... \n", + "2 None MULTIPOLYGON (((-240632.150 93056.104, -240669... \n", + "3 None POLYGON ((-45364.032 352060.633, -45248.844 35... \n", + "4 None MULTIPOLYGON (((173874.519 -471855.293, 173852... \n", + "\n", + "[5 rows x 59 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FID_', 'NAME', 'STATE_NAME', 'POP2010', 'POP10_SQMI', 'POP2012',\n", + " 'POP12_SQMI', 'WHITE', 'BLACK', 'AMERI_ES', 'ASIAN', 'HAWN_PI',\n", + " 'HISPANIC', 'OTHER', 'MULT_RACE', 'MALES', 'FEMALES', 'AGE_UNDER5',\n", + " 'AGE_5_9', 'AGE_10_14', 'AGE_15_19', 'AGE_20_24', 'AGE_25_34',\n", + " 'AGE_35_44', 'AGE_45_54', 'AGE_55_64', 'AGE_65_74', 'AGE_75_84',\n", + " 'AGE_85_UP', 'MED_AGE', 'MED_AGE_M', 'MED_AGE_F', 'HOUSEHOLDS',\n", + " 'AVE_HH_SZ', 'HSEHLD_1_M', 'HSEHLD_1_F', 'MARHH_CHD', 'MARHH_NO_C',\n", + " 'MHH_CHILD', 'FHH_CHILD', 'FAMILIES', 'AVE_FAM_SZ', 'HSE_UNITS',\n", + " 'VACANT', 'OWNER_OCC', 'RENTER_OCC', 'NO_FARMS07', 'AVG_SIZE07',\n", + " 'CROP_ACR07', 'AVG_SALE07', 'SQMI', 'CountyFIPS', 'NEIGHBORS',\n", + " 'PopNeigh', 'NEIGHBOR_1', 'PopNeigh_1', 'NEIGHBOR_2', 'PopNeigh_2',\n", + " 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a plain map of our polygons." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for comparison, let's create a choropleth map by setting the color of the county based on the values in the population per square mile (`POP12_SQMI`) column." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties.plot(column='POP12_SQMI', figsize=(10,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's really the heart of it. To set the color of the features based on the values in a column, set the `column` argument to the column name in the gdf.\n", + "> **Protip:** \n", + "- You can quickly right-click on the plot and save to a file or open in a new browser window." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default map colors are linearly scaled to data values. This is called a `proportional color map`.\n", + "\n", + "- The great thing about `proportional color maps` is that you can visualize the full range of data values.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also add a legend, and even tweak its display." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True,\n", + " legend_kwds={'label': \"Population Density per m$^2$\",\n", + " 'orientation': \"horizontal\"},)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "Why are we plotting `POP12_SQMI` instead of `POP2012`?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note: Types of Color Maps\n", + "\n", + "There are a few different types of color maps (or color palettes), each of which has a different purpose:\n", + "- *diverging* - a \"diverging\" set of colors are used so emphasize mid-range values as well as extremes.\n", + "- *sequential* - usually with a single color hue to emphasize changes in magnitude, where darker colors typically mean higher values\n", + "- *qualitative* - a diverse set of colors to identify categories and avoid implying quantitative significance.\n", + "\n", + "\n", + "\n", + "> **Pro-tip**: You can actually see all your color map options if you misspell what you put in `cmap` and try to run-in. Try it out!\n", + "\n", + "> **Pro-tip**: Sites like [ColorBrewer](https://colorbrewer2.org/#type=sequential&scheme=Blues&n=3) let's you play around with different types of color maps. If you want to create your own, [The Python Graph Gallery](https://python-graph-gallery.com/python-colors/) is a way to see what your Python color options are.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.2 Issues with Visualization\n", + "\n", + "### Types of choropleth data\n", + "\n", + "There are several types of quantitative data variables that can be used to create a choropleth map. Let's consider these in terms of our ACS data.\n", + "\n", + "- **Count**\n", + " - counts, aggregated by feature\n", + " - *e.g. population within a census tract*\n", + "\n", + "- **Density**\n", + " - count, aggregated by feature, normalized by feature area\n", + " - *e.g. population per square mile within a census tract*\n", + "\n", + "- **Proportions / Percentages**\n", + " - value in a specific category divided by total value across in all categories\n", + " - *e.g. proportion of the tract population that is white compared to the total tract population*\n", + "\n", + "- **Rates / Ratios**\n", + " - value in one category divided by value in another category\n", + " - *e.g. homeowner-to-renter ratio would be calculated as the number of homeowners (c_owners/ c_renters)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretability of plotted data\n", + "The goal of a choropleth map is to use color to visualize the spatial distribution of a quantitative variable.\n", + "\n", + "Brighter or richer colors are typically used to signify higher values.\n", + "\n", + "A big problem with choropleth maps is that our eyes are drawn to the color of larger areas, even if the values being mapped in one or more smaller areas are more important.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see just this sort of problem in our population-density map. \n", + "\n", + "***Why does our map not look that interesting?*** Take a look at the histogram below, then consider the following question." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(counties['POP12_SQMI'],bins=40)\n", + "plt.title('Population Density per m$^2$')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What county does that outlier represent? What problem does that pose?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.3 Classification schemes\n", + "\n", + "Let's try to make our map more interpretable!\n", + "\n", + "The common alternative to a proportionial color map is to use a **classification scheme** to create a **graduated color map**. This is the standard way to create a **choropleth map**.\n", + "\n", + "A **classification scheme** is a method for binning continuous data values into 4-7 classes (the default is 5) and map those classes to a color palette. \n", + "\n", + "### The commonly used classifications schemes:\n", + "\n", + "- **Equal intervals**\n", + " - equal-size data ranges (e.g., values within 0-10, 10-20, 20-30, etc.)\n", + " - pros:\n", + " - best for data spread across entire range of values\n", + " - easily understood by map readers\n", + " - cons:\n", + " - but avoid if you have highly skewed data or a few big outliers\n", + " \n", + " \n", + "- **Quantiles**\n", + " - equal number of observations in each bin\n", + " - pros:\n", + " - looks nice, becuase it best spreads colors across full set of data values\n", + " - thus, it's often the default scheme for mapping software\n", + " - cons:\n", + " - bin ranges based on the number of observations, not on the data values\n", + " - thus, different classes can have very similar or very different values.\n", + " \n", + " \n", + "- **Natural breaks**\n", + " - minimize within-class variance and maximize between-class differences\n", + " - e.g. 'fisher-jenks'\n", + " - pros:\n", + " - great for exploratory data analysis, because it can identify natural groupings\n", + " - cons:\n", + " - class breaks are best fit to one dataset, so the same bins can't always be used for multiple years\n", + " \n", + " \n", + "- **Manual** \n", + " - classifications are user-defined\n", + " - pros: \n", + " - especially useful if you want to slightly change the breaks produced by another scheme\n", + " - can be used as a fixed set of breaks to compare data over time\n", + " - cons:\n", + " - more work involved" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classification schemes and GeoDataFrames\n", + "\n", + "Classification schemes can be implemented using the geodataframe `plot` method by setting a value for the **scheme** argument. This requires the [pysal](https://pysal.org/) and [mapclassify](https://pysal.org/mapclassify) libraries to be installed in your Python environment. \n", + "\n", + "Here is a list of the `classification schemes` names that we will use:\n", + "- `equalinterval`, `quantiles`,`fisherjenks`,`naturalbreaks`, and `userdefined`.\n", + "\n", + "For more information about these classification schemes see the [pysal mapclassifiers web page](https://pysal.org/mapclassify/api.html) or check out the help docs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------\n", + "\n", + "### Classification schemes in action\n", + "\n", + "Let's redo the last map using the `quantile` classification scheme.\n", + "\n", + "- What is different about the code? About the output map?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Population Density per Sq Mile')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot population density - mile^2\n", + "fig, ax = plt.subplots(figsize = (10,5)) \n", + "counties.plot(column='POP12_SQMI', \n", + " scheme=\"quantiles\",\n", + " legend=True,\n", + " ax=ax\n", + " )\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### User Defined Classification Schemes\n", + "\n", + "You may get pretty close to your final map without being completely satisfied. In this case you can manually define a classification scheme.\n", + "\n", + "Let's customize our map with a `user-defined` classification scheme where we manually set the breaks for the bins using the `classification_kwds` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Population Density per Sq Mile')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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fpsx09BYzOrMJncmYkNVVCkW8GTx4cIeiDEVkLF68ON4u+FFBL4q0VdZ0e93Z3NLrptqeyJo4irovvorELT9Cr0dnNiH0OnQGTxDVmYyegGg0IPR6hEEPUiJdbqTbDVJ62hgNCIMBhMCYloIhLQVDeip6bzDVmYwYUqwYs9KP309LRW81e4KuyegPvgarxfMBIC0FQ4pVZcIKhSJqqKAXRfRmk+Z9um12zfqSLheullbN+tMKndmE3mrBkGLxBMUU6/FgaTaRNnww9pp6dGYjepMJt8MBOh16kxG91eIPynqrBWNWOobUFPRmI0KvB53On90KnTfLFQJhMKAzeO77gr9/75F33VvodKATCJ3O86X3/IsQSJfLk2U7XZ4+vXbS7bHNHDMCa/8+MX8tFQpFR1TQiyJtR6s17S+nZBw1GxNvYV1r3DY7bpsdR11Dt/fttQ3Ubt4ZY68iY/qLjzH8uu/E240TFtnahHPramRDNSIjF0PxWQhr4m5VUMQPFfSiSOPu/ZF3IgRISf6Mqdiq6yLvL8nRGQ007T0YbzdCR62dRg37+4txfPhPsB8XE7W/9keMF1yJaebVcfRMkYioxZMoYauupf7LvRH3k3/6JNIKh1K5agMNO/do4FlyI0xGHPWN8XZDkSDY31+M498vdgh4nhttOP79Ivb3wy+gaG1t5Vvf+hYulytom+uuu45hw4b5ZXt8lZ9SSu644w4KCwuZMGECmzdv7tZ+//79TJs2jaKiIubNm4fdbg/JPhgCSR71JA3Uk1+dCdV+6dKlFBYWxrQiVQU9Dehur+ORd1f514LCRWc20XqsmqbSAxH1cyKh0+vj7YIiQZCtTZ4MLwCOD/+JbG0Oq/8XXniB73znO+hD/J174okn2Lp1K1u3bvXr8b377rvs2bOHPXv28Nxzz3H77d1vml+4cCELFixgz549ZGdn8/zzz4dkHww+yaOrrrqqw3WfNNC6detYv349Dz/8MLW1tQH9itR+3rx5HfT8YoEKehrgK3iQUuJ2uwHY9+LrEfVpys7AnJdN0x4V8Noj9OpXVuHBuXV11wyvM/Y2nFtXhdX/4sWL+fa3vx2WbWdWrFjBtddeixCC6dOnU1dX1+E4MPB8eP7444/9e9nmz5/P8uXLg7YPlp4kj3qSBgrkl5b2sUK9g2iEvyJQCI7+dz1HP14bUX/22gZSCwZq4dqJRZJuZ5DeD0MK7ZANwRWKyYbutw4Fwm63s2/fPgoKCkK2ve+++5gwYQILFizAZrMBwcn2VFdXk5WVhcFg6NImFrI9PY0RyC8t7WNFcr6DJChCCIQQ5Jw6nsLbvqdJf4qO6AxJOr15Eh73F21ERm6Q7UI//7GqqoqsrKyQ7R577DG++uorNmzYQE1NDY8//jgQnGxPoDaxkO2JVDIoWSSHVNCLAkarlal/eoiLd7xN/1kz4u3OCYVI0jU93349hXYYis8CUy+K4iYLhuLQ/watVittbb1MnXZD//79EUJgNpu5/vrrWb9+PRCcbE9eXh51dXU4nc4ubWIh29PTGIH80tI+VqigFyWEEGSNLWLiIwvC7sPewz61kxlnUwv6VGu83QgdlelpjrCmYbzgyoBtjBdcibCmhtx3dnY2Lpcr5MDnW2eTUrJ8+XLGjRsHeOR4Fi1ahJSStWvXkpmZSf/+/TvYCiE455xz/BWVL7/8sn9NMZD9eeedp8l0YU/SQIH80tI+VqigF2VSh4b/icbVatPQkxMDR0MTeos53m6EjFrTiw6mmVdjvOT6rhmfyYLxkusj2qd34YUXsmbNmpBsrr76asaPH8/48eOpqqri/vvvB+Diiy9m+PDhFBYWcvPNN/OnP/3Jb3PxxRdz5MgRAB5//HGeeuopCgsLqa6u5sYbbwxo73a7KS0tDUnCZ8OGDQwaNIh//etf3HrrrYwdOxboKA00derUDtJAPfm1ceNGbrrpprDt44GSFooybpeLf6VNwtUWegDLnzGVylUbouBVcmPMyujxtJZE5dS//JLCW+bF242kIVRpIdnajHPrKmRDDSIjB0PxjLAyvPZs2bKFp556ildeeSWifqLJjh07eOGFF3jqqafi7UrYfPLJJzz55JO8/fbbYdmHKi2kMr0oo9PrKfj+7LBsq9dv09ibEwMZwmbhhCFJq06TBWFNxXjaLE/md9qsiAMewKRJkzjnnHNC2pwea8aNG5fUAW/p0qX84Ac/IDs7O2ZjavaXKITQCyG2CCHe9n6fI4T4UAixx/tvdru29wohSoUQu4UQM9tdnyKE2O6997TwlvgIIcxCiKXe6+uEEAXtbOZ7x9gjhJiv1fPRkv4XnhGyTVrhUIypKVHwJvlJxqlCVYibnNxwww0hb05XBM+8efPYtWtXTLNpLT9+/j/gy3bf3wOslFIWASu93yOEGAN8DxgLXAT8SQjh+616FrgFKPJ+XeS9fiNQK6UsBH4LPO7tKwd4EJgGnAo82D64Jgr9Lj0bnckYmpEQ2Kpro+NQkqMqIRUKRbhoEvSEEIOAS4D258l8G/AdvvYycHm760uklDYp5X6gFDhVCNEfyJBSfi49C42LOtn4+loGnOfNAmcCH0opa6SUtcCHHA+UCYPBZApZI07oBLokLNiIBe7W0EvJ445K9RSKhECrTO93wN1A+3mnvlLKCgDvvz4xsYFAe0nicu+1gd7Hna93sJFSOoF6IDdAX10QQtwihNgohNhYWVkZ6vOLiHDW9VytbZpq5ynii3Qm7rrQiYB025CN25F1n3v+davKZ0X3RCwtJIS4FDgmpdwkhDg7GJNurskA18O16XhRyueA58BTvdm7m9qSNnxw743a0XqkkrzTiqndthtXU3gq6yckXqklhcKHrF8L9etAOo5frP0YmTkNkTk9fo4pEhItMr0zgNlCiDJgCXCuEOLvwFHvlCXef49525cD7SPAIOCI9/qgbq53sBFCGIBMoCZAXwmHdUBoqtnS6aTqsy2khGh3ohPqNHHCkKx+Jziyfi3UrekY8MDzfd0az/0wCVZa6L777mPw4MGkpXUUrbXZbMybN4/CwkKmTZtGWVmZ/15PEjxa2gfDhx9+yJQpUxg/fjxTpkzh448/9t/btGkT48ePp7CwkDvuuMN/nFggv9oTqv3evXspLi7u8jpqTcR/iVLKe6WUg6SUBXgKVD6WUn4feBPwVVPOB1Z4H78JfM9bkTkMT8HKeu8UaKMQYrp3ve7aTja+vuZ6x5DA+8CFQohsbwHLhd5rCUdOybiw7Mx5CVeXE1eSVWVB6NSantZIt82T4QWifl3YU53BSgtddtll/uPG2vP888+TnZ1NaWkpCxYsYOHChUBgCR4t7YMhLy+Pt956i+3bt/Pyyy9zzTXX+O/dfvvtPPfcc35Jo/feey+gX50J1X7EiBF+/cFoEs13kF8DFwgh9gAXeL9HSrkTeBXYBbwH/FBK6fsodTueYphSYC/wrvf680CuEKIUuBNvJaiUsgb4FbDB+/VL77WEQ28xY8rJxNI/n9ShA0krHErG6BFkTxlL3umTyD9zCvlnlWDMyuhg52ppQxiUwL2PkKtgEwVVyKI9zV93zfA6Ix3Q8nVY3QcrLTR9+vQuR4qBRw5o/nzPZ/W5c+eycuVKpJQ9SvBobR8MkyZN8p+DOXbsWNra2rDZbFRUVNDQ0MBpp52GEIJrr722g8xRd361J1L7aKLpu6mU8hPgE+/jauC8Hto9AjzSzfWNQJeUSErZBvxPD329ALwQrs+xwtncgr2mvtd25r55Hb5vOXI0OTdjR4lkPXA6Wf1OaFxNQbYLXUQ2EmkhH+2ldgwGA5mZmVRXVwctExSpfai89tprTJo0CbPZzOHDhxk06PhqU08yR+39yss7/t4VqX00USlEjGg+ENxSo7v9cWVCYLBasSVm8hoXknZ6UyV62qMPcu1HH/rpLOFKC7UnmaR6du7cycKFC/nggw8Cjt3bPR/xlkkKRHK+gyQhjUEqoOvNRvRpKQiDgczxp9BScax3o5MJFT0UPlJPAdHLdLcwQsopIXcdrrRQe9pL7TidTurr68nJyQlaJihS+2ApLy9nzpw5LFq0iBEjRvjHLi8v79CmO5mj9n519j0S+2iigl6MqNm4I6h2bcdqcDW1IJ1O6rd/jU6n67AWmDNlXJd1v5MJV0sSbkwHFayjgNCZIXNa4EaZ0zztQiRcaaH2zJ49219ZuWzZMs4991yEED1K8Ghhf++99/LGG28E7WNdXR2XXHIJjz32GGeccfy4xP79+5Oens7atWuRUrJo0aIOMkfd+dWeSO2jiQp6MaLqsy2hG0mJq82GvaaetopKmg8cpmbTjphXAloH9IUEWZMSyaqcrogKInM6ZJ3ZNeMTRsg6M6J9esFKC919990MGjSIlpYWBg0axEMPPQTAjTfeSHV1NYWFhTz11FP8+te/BgJL8PziF7/gzTffDNt++/bt9OvXL+jn+Mwzz1BaWsqvfvUriouLKS4u5tgxz+zSs88+y0033URhYSEjRoxg1qxZAf0CKC4u9j8Oxz4WKGmhGNBacYw3BpylWX8ZY4to2LlHs/56I72oAEu/PJoPHKHlYHy3QSajrBDAaa/8H8O+Hz/hzGQjZGkht81Tpelq9qzhpZwSVobXnmSQFurMzJkzef/9hNy1FTRpaWk0NQVZpETo0kKqkCUGVK3TViLImBYb9YX8s0qQbje1m3fRuKcMc142KUMGxDXwqf1uiu4QOjOkjde0z/bSQsmitJDMAW/v3r1897vfpW/fvlEdRwW9GHBMYyFYZ1OL54SPKErsmPNyqN9Vir26zn/NmJVBU2lwBTnRImmnN9WaXlJyww03xNuFk4YTYXO6wsuxT7qe1hAJ9Tv3YMqMzlE9how08s+YjNvp7BDwACx9c6MyZigk6zFksVyoVygUPaMyvSjTdqyaui++0rTPlMH9cTZrfwi1KTeLrLFFPWemCSDeqjZ5KxSKSEjOj81JxIEl/9Zc6bvlUAWZY4s07dPcNw+33RFwKtbeEPrJFlqTrJmeIrq4pI1623aq2z6n3rYdl1TSQoruUZlelNn38vKo9Fu7ZRe6VCvu5taw7FOGDMDSNwed0YitqhZHYzPOxsBBTZ8I514maSHLyVglHSuq29ZS07YOyfFzOI+1fkyOZRq5FiUtpOiI+tgcRb544HfUbt4Zlb5zJo8la1wRuacVY84P7TSD3FMnYK+tp2bDDqo+20Lj12W0VfQurOtsbqHv+afTZ8ZU8meUkH9Wiedf3+Mzp5B3+iRypxeTO20CWRNHkTm2iIxRw0kvGkraiMGkDB2AdWBfLH3zMOVmYcxIQ59iQWcyBpXF6dT0pqId1W1rqW5b0yHgAUgcVLetobotutJCLS0tXHLJJYwaNYqxY8dyzz33+O8lg7TQ+vXr/fvzJk6c2GFj+4kqLaQyvShSu3lX1Pp22ezUeLdCpI0YTMao4VSuDm7voau1rdesrjsavy6j5fBRrP37RLeKU69HZ9Aj9HqEXofQ69GbjQiDAZfDQcqQ/gihA70gvXAozWXHD9sVOh3odJ6tDcL3r0DodJ5iEt/3wvMvwqtE7Cs08d3zZWbtrneL8P3vuKax7zvPY4nEo7Kh0BaXtFHTFlhaqKZtHVnmSehF6K9/sNJCd911F+eccw52u53zzjuPd999l1mzZnWQ0FmyZAkLFy5k6dKlfmmgjRs3IoRgypQpzJ49m+zsjjJikdoHw7hx49i4cSMGg4GKigomTpzIZZddhsFg8EsDTZ8+nYsvvpj33nsv4PPqTKj2vupNFfSSGH2KJWp922uPKzY07T1Ec9kRsqeMpXZT4MzSkJEW9Dmg3eFqbkVnNoVtH9wgLtydPl33JCCTMqAvjV+XRdcfDSi69XvxduGEo8n+dZcMrzMSB032r8k0h76Hb/HixfzjH/8I2CYlJYVzzjkHAJPJxOTJk/1nTq5YscJ/OsvcuXP50Y9+1EUaCPBLA1155ZUd+o7UPhhSUo7v+W1ra/NXGbeXBgL80kCzZs3q0a/2FcqR2kcTNb0ZRayDu2psaUH+jKld3uily4V0OHu1TR8xBFdbBIv8QmD7pip8e61Ra2UnLU4Z3KkdThkbaaG6ujreeustzjvPo0KReDUAACAASURBVKiWLNJC69atY+zYsYwfP54///nPGAyGsKSBOvseiX00UZleFJn0+F3YvqnkwD//rWm/7jYbqUMH0nyg4y963bbdZE0Yic5swu1wgtuNMBo6ZH+NpQfIO30S9TtLcdQ3Bj1mytABpA4dSEv5NzTvO9S7QYxIlpCnClm0xyCCmwYziOhLCzmdTq688kruuOMOhg8fDiSPtNC0adPYuXMnX375JfPnz2fWrFlKWkgRHnqjkYKrZ2veb/X6baQM7V5KpG7bbmo2bKdu65fUbdtN3Re7yZ02EWH0fL5xNjZT9dkWsiaODGnM1MH9qVy1IaECHqAyvZOYNNMpCAJXFAuMpJmiLy10yy23UFRUxE9+8hP/tWSRFvIxevRoUlNT2bFjh5IWUoRP9bovotKvsym4KRvpdFK97osu+/rajoY2naD1XkOFIlL0wkyOJbC0UI5lWlhFLKFIC91///3U19fzu9/9rsP1ZJAW2r9/P06nZ1nkwIED7N69m4KCghNaWkhNb0aZb1Z+rn2nQmCvDU1pQLpcZIwpxNnQhCEjFXNeDo279wdlq7da0BkS9FdFZXonNb59eJ336QmMEe/T80kLnX/++T22KS8v55FHHmHUqFFMnjwZgB/96EfcdNNN3HjjjVxzzTUUFhaSk5PDkiVLgI7SQEAXaaGSkhJmz54dlv327duZPTv42aU1a9bw61//GqPRiE6n409/+hN5eXmARxrouuuuo7W1lVmzZnWQBurOL/BIC/nOzwzHPhYoaaEoYquu5fU+p2ueJeWfMZnKTzdr2mdPpAzpj7V/PtUaK0VoRe70YqrXRv+Q2kg57e9PMCwKU90nKqFKC7mkjSb71zhlMwaRSprplLAyvPYoaaH4oKSFkpgj762OKOAJvR5L31zSCoeC203lp5sx5WTistk19LIXHwyGhA14AEg17arwTHWGsy0hEEpaKLYoaaETgAP/eDsie+lyYczKoNJ7Hmbu9Im4bQ5qNu7Qwr3eEYK2owm0PaE73EkyU3ESzqicCChpodgRK2khFfSiRNO+Q5pMQZrzjpdNV6+NTlFMT5iy0nE0aa/moCVJE0uSxlGF4sRGVW9GiaP/Xc9lpR8wY/kfMeeFfjyQj+aywxizMjT0LAR0uoSX8tEZE9s/HyrmKRSJgQp6UaDh6/2g02HJyyH/rJKIju1qOVhB1oTQ9tRphaVvLu5ITm+JAYkelP2oqBdVWp0trDmymn/vf5s1R1bT6kzsGQpF/FDTm1Hg0LL3OfbpJg4v/4jKNZuwVdVG1mGAU96jRf5ZJUEfYB1XkkWRXAW9qPFO2du8d+BdbK7jH9Be3bOEi4bO4uKCS+PomSIRUZmexhz9ZB1NZYepeGcV5cs/ijzgQVzW1VoPH435mCcyanN/dHin7G1W7FveIeAB2Fw2Vuxbzjtl4ReTBSMtBHDRRRcxceJExo4dy2233eZvnwzSQj4OHjxIWloaTz75pP/aiSotpIKehki3G53ZhLNZ2yBlq6zRtL8TiiRJ9FSmpz2tzhbeO/BuwDbvHXiXVmd4QsvBSgu9+uqrfPHFF+zYsYPKykr+9a9/AR2lgRYsWMDChQsB/NJA69atY/369Tz88MPU1nb9cBypfSgsWLDAv3nch08aaM+ePezZs4f33nsvoF+dCdU+VtWbKuhpSMuhCpyNzRFvVeiMo6HJf3ZmtBF6PfkzSmhKtDM2e0DFvJOXTcc2dcnwOmNz2dh8bFNY/S9evNh/dFYgMjI8hWZOpxO73e4/UmvFihXMnz8f8EjorFy5sos0UHZ2tl8aqDOR2gfL8uXLGT58OGPHjvVfay8NJITwSwMF8qs9kdpHExX0NKL1m0q++egzj7qBxjibWjBmRDfl95F/xiQqVyXBWp4XmSxhT0U9zam31ffeCKi314Xcd6jSQjNnzqRPnz6kp6czd+5cIDmkhZqbm3n88cd58MEHu4x9okoLqaCnEVsXPsm6m+5n3U33R6V/Z0vwJ75HQtIEES/J4q1a09OeTHNmcO1MwUsE+QhVWuj999+noqICm83Gxx9/DCSHtNCDDz7IggULuqyjKWkhRUDqd5VStvgtANq+qYzKGO7W6Ae9nKnjqd+5J+rjaEmy5E/RmAE42ZnSZwpmfeDzNc16M5P7TAm571ClhQAsFguzZ89mxYoVQHJIC61bt467776bgoICfve73/Hoo4/yzDPPKGkhRWC23P0EMorbCnQxWs/TW8zYq0OfClL0TjR/P05WrIYULho6K2Cbi4bOwmqwhtx3sNJCTU1NVFRUAJ438HfeeYdRo0YBySEttHr1asrKyigrK+MnP/kJP//5z/nRj36kpIUUPVO79UuO/PuTqI6hs5hjkiloXXUaG5Ik11NrelHBtw+v8z49s94c8T69YKSFmpubmT17NjabDZfLxbnnnsttt90G9Cyhk0jSQoFQ0kInEFpKC3127d2UvbJCk756wtI3LyYHP+tMRrInjqJ6w/aoj6UVybKJfvJT9zJqwXXxdiNpCFVaqNXZyuZjm6i315FpymJynylhZXjtUdJC8UFJCyUwrUerOLjknaiPozMboz4GgNvuQG+NTIMs1iTLh7Zk8TNZsRqsnDHgTE37VNJCsUVJCyUBXz7xPG6Ho/eGEaIzhX92ZyikFw1FJotUj49kqYpUQS8pUdJCsUNJCyU4UkpKn1sak7F0puj+mIROR96ZU2jYvZ/GPQeiOpbWJEsGpbYsKBSJgQp6YdJ84DDOxuaYjKUzRnd6M2PMCL9QbdKRJEEvaTJSheIERwW9MCl97tWYjSUM0VlPMPfJwdovn+aDFVHpPyYkSdBLumnjJKO+tYllmz+mor6K/pl5zJ18LpnW2JxipEguVNALAyllTCscdVEIehljRoCEum27Ne87lrgdSbL/LUmCczLyyLsv8tj7i2i2HT9Y+v/967fcO/Na7pt1fRw9UyQianN6GAghOOX2K2M3nsaVY9mTRiN0Ohq+3Ktpv/HAXtcQbxcUceSRd1/k/jf/0iHgATTbWrn/zb/wyLsvht13sNJCPmbPns24ceP83yeStNCqVauYPHkyBoOBZcuW+a//5z//obi42P9lsVj8B0PfeOONTJw4kQkTJjB37lz/NoLa2lrmzJnDhAkTOPXUU9mxY4e/v9/+9reMHTuWcePGceWVV3a7uX/FihVMmDCB4uJiSkpKWLNmjf9eQUEB48eP99/z8bOf/Yx+/fp1kD4KF5Xphcng71zIoMvPp3z5R1EfS+i0+WySMXoE5rzsxNjXptORWjAQpEQIQdO+Q2SMGo6lTy5tVbU07CoFIKVgAIYUK267E6ETICXS7cbtdKE3mUCvw5ST6b0ukRKk07ORX0qJ2+6IiwivIvrUtzbx2PuLArZ57P1F/PjsK8iwpobcf7DSQgCvv/56l/Mr20voLFmyhIULF7J06VK/NNDGjRsRQjBlyhRmz55Ndna2pvbtGTJkCC+99FKXoHHOOef4KyZramooLCzkwgsvBDwBzKcgceedd/LMM89wzz338Oijj1JcXMwbb7zBV199xQ9/+ENWrlzJ4cOHefrpp9m1axdWq5UrrriCJUuWcN1113UY87zzzmP27NkIIdi2bRtXXHEFX331lf/+f/7zH/Ly8jrYPPHEE6Smhv4z7A6V6UVA1sRRsRlIF/kRPaacTBq+3BvTgGfOyyFz/Ejyz5pC/plTyJ8xlexJY+gzo4SUwf1o3neI5v3lNO07hCEthaa9Bzm2agONX5dh7pND1sRRtB46SsOuvTibW3A7nDTuOUDT3kO0HDhC454yGr/ah72mHnttA476RpwNjbhaWnG1tOJubSP/tOKYPd+AJIvCexKxbPPHXTK8zjTbWlm25eOw+g9WWqipqYmnnnqK++/veNh8IkkLFRQUMGHCBHQBPkAvW7aMWbNmkZKSAhyXTJJS0tra6j8qbNeuXZx33nkAjBo1irKyMo4e9YhOO51OWltbcTqdtLS0dHsmaFpamr+v5ubmmB5BBirTi4iUAX1iNFLkvxSpQwdirwlOiqUz6YVDsQzoQ2tFJU2lB7pdnzLlZqEzGUEI0guH4Gq1Ubf1S2xVwQngOtupw0unE9uxGmzHjtu2VVSSfkpB6M6rYHPCUlEf3ClFwbZrTyjSQg888AA//elP/cHCRzJIC7VnyZIl3HnnnR2uXX/99bzzzjuMGTOG3/zmNwBMnDiR119/nTPPPJP169dz4MABysvLmTJlCnfddRdDhgzBarVy4YUX+rPGzrzxxhvce++9HDt2jH//+9/+60IILrzwQoQQ3Hrrrdxyyy0RP6/OqEwvArInj4m3C71izEgj7/RJ1G7Z1eG6MBlBpyO9cCj5Z0wm74zJZI4t6tDGOrAveadPoqnsMJWrNtC0pwxLvzws/fLImjCSnJJx5J46gbTCobhtdtoqKnHb7FSt2UzNhu2anxeqM8XmZBpFctA/M6/3RiG0a0+w0kJbt26ltLSUOXPmdLmXDNJCPioqKti+fXuXg69ffPFFjhw5wujRo1m61LMv+Z577qG2tpbi4mL+8Ic/MGnSJAwGA7W1taxYsYL9+/dz5MgRmpub+fvf/97teHPmzOGrr75i+fLlPPDAA/7rn376KZs3b+bdd9/lj3/8I6tWrYroeXWHCnoRcPQ/62I0UviVf2lFQ6n6bEuX61njitAZ9DSWHqDy081UfbqZht37ySkZR+bYIvJnTMXR0ETVZ1v8a2TgybjavqmibttuajbuoHr9NppKD/gzNXt1XdQ2YptyMrH0z49K34rkY+7kc0k1Bz5fM9VsZe6kc0PuO1hpoc8//5xNmzZRUFDAmWeeyddff83ZZ58NJIe0kI9XX32VOXPmYOxmT7Ber2fevHm89tprgGfa88UXX2Tr1q0sWrSIyspKhg0bxkcffcSwYcPIz8/HaDTyne98h88++yzguDNmzGDv3r1UVXmycd/z6NOnD3PmzGH9+vURPa/uUEEvAmo27YzNQBHs8dIZDFgG9PGvp+WdVkzu9GJqN+/yFHm0Qzqd1GzcQf3OPVSu2hCzzffBUrlqI20VlaQM7kefGVPj7Y4izmRa07h35rUB29w789qwiliClRa6/fbbOXLkCGVlZaxZs4ZTTjmFTz75BEgOaSEf//znP7nyyuMV6VJKSktL/Y/feustv2RSXV0ddrsdgL/97W/MmDGDjIwMhgwZwtq1a2lpaUFKycqVK7s9NLy0tNSfrW7evBm73U5ubi7Nzc00NjYCnrW+Dz74oEM1rFaoNb0IKH7sTup37Im68GokmZOjvhFnY3PynrjSDS2HvqHtmypySsZTs7GX/ZJBzPoY0lPJmTQGW209rpY27LX1Ya9/KmKLbx9e5316qWZrxPv0gpEWCkQiSQtt2LCBOXPmUFtby1tvvcWDDz7Izp2eD+1lZWUcOnSIb33rW/72Ukrmz59PQ0MDUkomTpzIs88+C3hUDa699lr0ej1jxozh+eefB2DatGnMnTvXvzVi0qRJ/jW5P//5zwDcdtttvPbaayxatAij0YjVamXp0qUIITh69Kh/mtjpdHLVVVdx0UUXhfXaB0JJC0VI3Y6v+WjG97HXRu9NMnd6MdVrwz+INf2UAiz98qj6bGuHqcpkJmPMCBr3HED2sm6YP2NqwIAvjAYMqSk42u33s/TLI234YNoqa2naU6aJvxMfvZOx996qSV8nA6FKCzW0NrNsS7sTWSadG1aG1x4lLZRYPPTQQ6SlpXHXXXd1uB6qtJCa3oyQrHGncP7qxZ49Z9Eiwn1mjV+XUblqI7nTJmjkUPxp2LWX9MKhvTeUsscKztRhg0grGNgh4AG0fVNF1WdbaDlwmMzxp2jhriLKZFhTueH0y7hv1vXccPplEQc86CgtlCycqAHvZz/7GX//+9812aungp4GZI0tYub6ZfSfNSMq/budGv3RnUCHHmeMHBbUiTKVqzeSUzIOQ0bHjcPpI4eDIKCqhNvuoH7716SPHIYpOyMyh9XWiaTkhhtuSBotvROZJ554gtLSUm6//faI+1JBTyMs+Tmc9dofwttL1gtaafYJw4mzhGvumxt025oN20kbPpj8M6dgyvWUoVv65tC8rzwo+8bd+7H0i6xqVMW80DkZl14UoRHO74gKehpisFo46/Vn6HvudE377VxlGS61X3zVe6MEJ39GCZnjR1K5KrQ12bqtX1K5ZhOmrAzyz5xC6+GjIdk3fLWPzPGnoDOHJ+ir9fmpJzoWi4Xq6moV+BQ9IqWkuroai8USkt2J89E/QcgaW8R5K1/m2JqNrPr2D7HX1EXcp8tbHhwRQpBRVEDNph29t01gHPVN1G8PXxmiae9BmvYeDN1QSuq3f+0pjFmzKfSpYpXqhcSgQYMoLy+nsrIy3q4oEhiLxcKgQYNCslFBL0r0ObOESU/ezbobfh5xX+42DTI9KdGnhvaJKBGZcNd0pPs0Vs8P//T8SKhctYH8GVOp+nxLr5WjivAxGo0MGzYs3m4oTkDU9GYUGXrFLE36cbXZNOknkk3u8Sa7uIizXr6GftMM5E7IIX1ErM497Urlqg1kF48mfeQw9ClWdBZzrzZaKWUoFIrIUJleFJEuN3qrBVdr78cZBSJSex9arQ3GitELLmHY7H5IlySjQIfO4JkitJoaGPvTC1n7g+7P9YsFNe1EhPUWc+97KdXspkKREKiPn1HEmJHGd6vXMeCSsyPqx23TYE0Pz0b6ZCJnTDaZw3RkFer9Ac+HOTtxpmpdbTaa9h6kz4xu98IqFIoEQmV6UcZgtTD9hUd5Z9Ll2GvqMVjN6CxmdGYjOqMJncmIzqBHZzQiDHqEXofQCc9p6giETsexT7Q52FpvteBq0SZrjDYF3/sWg8/rPrC11GSw7o6/xtijwNgqa3AXBdgsrwpZFIqEQAW9GGDpk8tZ//q957iyENfndFYL+WdOQbrdSKcLt8uFdLqQTiduuxO33YHb4cBls3set9l6lPRxNjSRNnwwTfsOdXs/kSi+q8ijlN4JiWDXHzfSVtnQjVV8afy6LN4uKBSKXlBBL0bknz6ZoVddStkrK0Kyc7e2eUrkQ0RnNqEzm9AbjQiTwZtZGkkZ3D9hg57Q6dBZzKQXDsSS01MrI3sXrYmlW0Fjr22gz4ypHOvurE+130yhSAhU0Ish4+67jcrVG2kui1zluDfcNjtum53OOZ8hNaXb9onAtD98j2GXpCLdstssD0BgZ8icEspejZWWYfBIl4u6HV9jyEjH2dDY8aaa3lQoEgJVyBJDMkYOp/jXd/XeMIq4WloT8jiy4deeS8HFnoDcU8ADkFgpf+eLWLkVMvaaejJOGQqdTmBRWxYUisRA/SXGmAGzZqAPYl9XNLAO6OuR40lAeaEBZw9EBJUNuUgd3OPcZ0JQs3EH2cWjOlwTevWnplAkAuovMcYYM9KY8eazmPOyYz62KTcr9mtLQmDpm4sxKx19irXLGZSm7AwGXTadviXBfRBwtFix1bVEw1NNaSo9iCkn0/+9OntToUgMIp7nEkIMBhYB/QA38JyU8vdCiBxgKVAAlAFXSClrvTb3AjcCLuAOKeX73utTgJcAK/AO8P+klFIIYfaOMQWoBuZJKcu8NvOB+73u/K+U8uVIn1O06X/BGVy0+Q1WDDk7puOastNjOh7A4MtP58zfjulwTbqlZ7O29BwaqwshCzKl1JN/6nAOrdissafa4mqzYco+HvR0BhX0FIpEQItMzwn8VEo5GpgO/FAIMQa4B1gppSwCVnq/x3vve8BY4CLgT0II3zvCs8AtQJH3y6cVfyNQK6UsBH4LPO7tKwd4EJgGnAo8KISIfQoVBimD+mHpE7w8jha0Hj4W0/EQgjG3jux62bsPUehESAHPxxm/n8YZz8/XwsOoYc7Nou2b44clC6Mxjt4oFAofEWd6UsoKoML7uFEI8SUwEPg2cLa32cvAJ8BC7/UlUkobsF8IUQqcKoQoAzKklJ8DCCEWAZcD73ptHvL2tQx4RngWgGYCH0opa7w2H+IJlP+M9HlFGyEEKUP603asOmZjth6tImfqeDwplgAp/fv+3A6HZ5+fzYHLZsNlsyMdzshOg5ESe4ML0DbLETgZfJ6Fod+dyoHXutkekICoNT2FIjHQtIxPCFEATALWAX29AREpZYUQwndC8EBgbTuzcu81h/dx5+s+m0PevpxCiHogt/31bmw6+3YLniySIUOGhPX8kh1XUwtNpQew14a2sVtnNKCzmBF6PXqzEZ3JhM5o8JwgY9Cj0+vRmUztTpTReUr0BRx87yiGlAHkjdM68LmZ/uTpVG7YT8vBKk37jgZqTU+hSAw0C3pCiDTgNeAnUsqGAJV43d2QAa6Ha9PxopTPAc8BlJSUJMRO4YzRI6jZGFt9O0u//JCDntvh9J/yEuqR1ZWrYMCMa9A62wPQiRYu/c+1LJ/6HPaaJs37jwSdqeN0ZqBtGAqFInZoMucihDDiCXiLpZSvey8fFUL0997vD/gWlMqBwe3MBwFHvNcHdXO9g40QwgBkAjUB+koKJj91D9b++TEd0x2H7Qqf3rqUirXaHJrdGb2+kVkf3oKlf1ZQ7YXBQP5ZJaSPHIYhI3qFPZZ+HX+uap+eQpEYRPyX6F1bex74Ukr5VLtbbwK+aoP5wIp2178nhDALIYbhKVhZ750KbRRCTPf2eW0nG19fc4GPpZQSeB+4UAiR7S1gudB7LSmw5OVQcM23Yzqmo7aRzHFFMR3TbbPz+Y/fpHRZdM7LTMlt5KJ3bsSUk9Zr27zTi6lcvZHG3fsx52WSNkL7qW5znxwcnU5kUdObCkVioMX05hnANcB2IYRPUOznwK+BV4UQNwIHgf8BkFLuFEK8CuzCU/n5Qymly2t3O8e3LLzr/QJPUH3FW/RSg6f6EylljRDiV4CvmuGXvqKWZCG9qCCm49mqasgYPTymYwLYKmvZ8dTHFM69PCr9W7MbOOO5q/jP3OcCtmuvKdi8r5ys4tHkzygBt6Rmyy5cza0YMtLQG41I6Ua6pWeLBaA3GdFbTYDwHADucoPb5e9PuiQSScboETTuOeDP9kxZ6ehMiXcKjkJxMqJF9eYaepbIPK8Hm0eAR7q5vhEY1831NrxBs5t7LwAvBOtvoqE3x6GUPQ6HH+dMGcn4O6dFdYx+JZLihy9n64PLe2zTdrRjtWzd1i/9jzNGDcfe0IQpI42Gr/Z1sQ12Yrjyvx0rStu+qUQ6XT20VigUsUQtNMQZ6XLH24WYYO2bSb9TTVEfZ9T8QQz73vRu7+XPmErz/vJu7wE0fLUP27GabgOeQqE4MVBBL84YszJiP2gciioGnDcMnTH6FYwCGyW/OqPDtZwpI8g/q4TK7iR/OpGI55IqFArtUEEvzvS94PSYBz6dUU/K0AFYB/bFlJ2BMSMt6oUWekvsCjkMxgb+p/Sn5J46gokPXMaMF+ZQuXpjzMbvFiUtpFAkBGp1Pc64GppwNsR2j5nb4aLlQMedHTkl46K3Z1AI+p8eWx0/g6GFc1/9PgaOUH8w1N2F2qO2LCgUiYEKenHm4GsfIN2xWdfTmU2YsjJwtbZ1uRfNQgtjeir6WKopCSuYcjHYPOt3DaX1MRxcoVAkMiroxRFbdS1fPv7XqPVvSEsha+IonM2tGCxm3C4XNRu203a067Fdbkf0siFHQxN7ltbgtruw1bYhdIL+3+pL/2lRKmwxZoM34NUfSuWzW/8QnXFCQU1vKhQJgQp6ceTox2tpKf8mav1njjuFqk+Dk+DRRVnY9ouHX/c/NqSlMP6HV2nTsWkASCfoTOBqAX0KOI4HdemWuEPIYrPGj2DQrLE0H2lE6AQHX1+Ls0kD/T4V9BSKhEAFvThyaPlHUe0/lJP9Y7nm5GppQ69VkuduAWfd8e+dHc8myBraQkZhPxr29P7hou85xZzx+xLMGQLoC0BucR823LkkYjeVyoJCkRiov8R44o7uJvHu1u56wl5d13sjrdDpkFosY5oHdgx4PZA2tHfdwoyRQzn1kSnegHecEZenYe2fF7aLPlQhi0KRGKhML45UR1thIcgpNWEy0rTvUO8NNcKcm4lOi0zPHpworqVfD4dRC4ExPZUR82cw6roBWHO7vl5CJ5j+9KVse2I9eI8lA6/6u5THD7dxu3E7XJ7jydxuj2Sh241PbcSQEv2N+QqFondU0IsTbocDW2WUjwkNsipU68rN3NOK0el0oNMhBLja7FSv3+a/3+fM0QSQngoOYfVssnf1XoBj7ZOG3mrBOjAPS34W+dOG0u/0fLKKDBitoLcE9qXfVCP9Xj0jYJte6dsvMnuFQqEJKujFCVerLTJV8iCo2/Y1lj45tB3rObhaBvQhvXAIbUcqQa/zBECdQOj06Iz644Kx+uNBDIT3v+PBQvr+L8Fe10j99t3+e9mTx5I1YSTS5cmC+p4xmIgx5YDtcOA2ulQQOvJPL2TUdYMxZcSzmERNbyoUiYAKenHCmJFG0Q+v5qvfRO+sbOlykTpscMCg13bkGOmFQ2ksPRA1P2o37+zwvSn9tMg7dQaQKdJZwZDpnf50078Eej4TPUYIFfQUikRA/SXGEeuAPlEfQ7oCT11a+uZStWZT1P1oT9+plgisDWDMB1djgCaZYP8GODkO81YoFMGjgl4cSR3SP+pjOBubA9532RwYMnoXX9WKrIlFmLOCyLp03mPLhBkMOWAe5AlmSHBUdm9jzG8X8BIN9aemUCQCanozjgy8/Hws/fNpq+jhTVwDWo4cI33kcBp3dy+X46hrIP+MydRu291rgNSCIZeOA32Gt7JUx/Fpx3aBUG/1BDZ9Krgdnr13ziCKfoSx54AYBE69hYbUPNp0TpzYvT71vK1EIJB0njjtPqD31QtieRKbQqHoHhX04ojeYCD31AkcXrEyamM4G5ux9M3tMegBVH66mczxI2k9cjSq+/XSTxlC0bxcCes+nAAAIABJREFUcAVYjwMgB9w2wBZcx8ZcT0ZoP9J72x5oSBtApb4aN+WB4lz4qANZFIqEQM25xBlTlGWFUocNouqzLb22q9++m9ShAwHQGaPzWahw/qnBVVDqQ1Rk0FnDDngSqE0fyFF9Be5gg2xYqKinUCQCKujFmQkP/xidyRi1/k05mUELo9Zu3kl64VCE0Uje6ZPJnjSG/DOnaOZL9ZajwTV01nmmKoNCgKM2LH9cwsSxrIFU6Q4TnfROoVAkGiroxZnUoQMpuHq25v1a+uaRP2MqtZt29t64HY2lB3C1tFL12WZqt+yics0mzQJfw+4gszEJQZ9TJvTgDn0tss2UxaFMMw2yl71+CoXihEIFvQRgxO1Xat6ndWAfKldt0KSvyk83Y+kX+fmTfc8qCq6huwl0wW5rCH3a0Km3UJ1ixiEDbHvQGKnJYaMKhSJSVNBLAPJKxpE6bJCmfRqskeyF60jmuCLc9sj19g69vZXG8iCnEfWpQfYa/K+wxFOwsj/NTosMcqpVM1TQUygSARX0EgAhBDmTxmjap1tDNXZzdib2msjVx/UmI662IP0K9kTqEM7wrEsfxFH9EeIRgKRaM1QoEgIV9BKEPmefqml/tirtth7YG5o06aex9BCbfrkJKYMJAMEGieB+hRtT+1OlKw+yz2igMj2FIhFQQS9BSBms7ekstqNVmHJ7kNQJEb1Zu+rSyjXbafkGHC29BDVXsFqAwWV6Dcb4Bh21pqdQJAYq6CUIbUerNO3P0dBEasFATfqq/3K/ZhWc0u3mzdP+xuc/2x64YVAHNAtkENObdmM6rbI6OAc1IEVm0Nc1sMOXPsqCwQqFIjhU0EsApJTse+E1zfu119ZjyEiPuB9nQ6Nn68KMqZhyMjXwDCo+2BS4qEXoA1jrcJr7UpGRRUNKFg5Dx6KXNnOO/7EbwdFUM5Lg9ipGilmm0r9VT0bT4Q5fJre2moUKhSI81DFkccYT8JZ1EFnViuZ95ehTLOSfVULl6o0R91f12RaMmdocTu12OCn/qIZR1+YgdN1ka9IF6IGOwULqzNSlZlOl8xwq3SRqIVWQLgZgdgma9Q5aZSV5pkGkttXRbMmkrbe9eL7Y20vSqJN6CtpykEKPW6dDogPh0RA0uBzoXM3gtiFkN/sGg1rHVCgU0UYFvTgjhGD/Kyui1r+rpQ1nc4s2nQltjyhzO9xIVw8zmW4b/oBnzPNkftJFkzmFKt3BTo0ljfIIjTr8AaxKV05VCkDPRThZ7jyy7Qb0jjrchnQOWupx48Qtul9/y5J56O1hbnWwfwMpI8KzVSgUmqGmNxOAETf9T1T7b9i9H0N6sPveekY6nFj65GHU4LzQnJJRjL05H52xh/TK1ew5isyYB44qsB8FRxVChj9NqJN6DNIEErJkPnmtdgy2bxDuNvT2SgoaXGSQ272xhOy2CPYq1n+OrF0VZOWqQqGIFirTSwCGff/bNOzez87/fTYq/buaWzWb4hx582TMWWdhq2nG0dSGq82Bs82J3qT3ZG5OF642Jy6bE5fDhbvNgaOxDWeLHWerHWdTG44mG8b03oR2XGDsA/aKiH32kUomfRubQFhANCNcHTNggYu8pgasKYNo0DXQLLxqEBKG2gegc4Sv4gBAw3pwNiDzLkII9aenUMQD9ZeXIEx4+A7stQ3s+ePiqPRfve4LLP3ysPTNo+6Lr8Lqo8+ZIxl+mS9jTPV+hYsOzAPAVkHndTs/9uPaeE5zP74xN+HgWEijmKSVDJlFZpsdncNbwRmofsZtI62pnDQEDWkDcQm31zbCgOej5SscZTbIviDqChsKhaIranozQRA6HSPvuAZzXrY2HWZakelm//u72+6g7ZsqGnaVkj+jBH2qNeQuj63ZTUuNVm/UbjzRJ9B05fGKS7dOT6towilCm2IcaMsgu7HieMALGklG0+EwbQNj1O1n288fovmgRoFUoVAEjcr0EoiMU4ZxwZp/8PH519NS/k1Yfbh+fgGto7PZM7iJBlcjRmHEpDNhESaMUo9B6tnvFqTaTiH/B+8jy0J7Q2855iQl1wTSQcRyPM4mMA3wVLIEyvjCHEkndejtsdufFwqO2mren3YF57z7V7KLR8fbHYXipEGcjAvrJSUlcuPGyNe3osXR/65n3Y330bS3c5UiiKI+tN55BsLuRjg9PzvhdCNsLnQtDg5dkEMZwa2DZejTmfTofvjgy6B9yxw9gFnvXYGQdjCkeKos3cGenhIAQyY4/z979x0eR3Uufvx7ZrZq1ZutYlnuDfeCO5iAbXro/dJyCSm0kOQXuOnkphdSSUhCLmlAQhJCNzYQbIN7x8ZGcpPlql5X2jLn98fKRrLaandWsyudz/PoQZqdOfPKSPvqzClv9/t7tiYVUmbv2zZiaUY2uQ3mLvo3Q11ZEm9d9wdaTtZjS05i4fM/I3/ZIqvDUpQBQwixRUo5q6vXVE8vDg05bw5LXv8db150B01XjiE4xINW5UU0+vGPyWDD2J7eyMOf+FEfbGDLo8OZsfIDRJh/+xQsnwzSB/hDScpZCK0m7GkpepvY0rc/zmzSSU5jfeTxxIjExab/9wotJ0OxBRqbeeeye5nz5DcZdec1FkenKAOfSnpxKmX0cIpWPsZvyv4POF33TQDmbSQN0BhsIvi15di+/nqv59qSXUx5oBhBu3G1CKuWdxJsAlsWBKoIfZ8dk5zsY928XCMbEYcFYg++VEXFutIOx2QgwIa7HqW57DjnfPUziD5UjlAUpW/URJY4Nn3UAj475X5cunm18bry/vka5Pe+OfX5z9yJOLtagNEEWvRrADGaQHeHlinY0sHRcQPuvvTzhBS4W0145Gqy5upUdn33tW5f3/X1n7PhE/+D4Y++dqGiKF1TSS+OCSGYnD2FR2Z9GZfe99mW4aoLNhC8s+fSRgUXTyV7cjeVAuzmVHPAXx16ZBqowUB0SHT1ujfsZpJINX3GZbRqD7r594wf0Xy0557xgaf+wTuX34u/wZxyToqidKQmsiSIFYdf55/7n49Z+8l6MiNq0sh6fAvynZKPXhAw7LLpnPujC7A7uhkj0zyhnppJJIJRv36TQ7XVAOR4PCS7Uxiams7Q1BSGpCaTm+oiN9VBdrKN7FSN7BSJu63ubKG/AHdz/DzaPLFJ8PZ1T/bpmsyZkzjvpV/jzsuNUVSKMnCpiSwDwNKiZWyr2MrB+gMxab8x2Miu1EYmPziTcS2SUbfNIXfuUJKyfQhagR4mhRhN5k1oAZ7c4z2T8AAqmpqoaGriYGXPyzhSXEnkp2Uyt3Aov106EZs0ac/RCEjhQTbZKX35EFu++Pc+X1+9ZTdvLvkvFv7z56RPHBODCBVlcFKPNxOEEIKrRl0d8/tUDw1w4XNLGHGZB092Q1vCC0OwAbQkU2LQI5zI0dDSzL6T5Ty9ZTObLV6pEDzgpeXXz0WU8E5zZKaxcsHNnPzPBhMjU5TBTSW9BDIuYzxzhpwb03uM9uREdmGgDjSHKTHcPcHN95ZdxNShkVeT//aajabEEqnA+x8SblX3ruhJLhpKy/DX1vP20rs59MzL5gWnKIOYSnoJ5voxNzI6LXaPu6a4e1sv1xMZmn0ZJYHBF6an8cVF8yJu4+W9e/EJc2r/hU04kMEMghUejA9Lez+/B86cTForQo94Db+f925+mD3fe1JVaVCUKKmkl2BSHCmcX7gEvcfK4pFriOY9NVDXVvzVHBcUZfKvm69nSHJkyWuvuUsaeyFo/uM6vI8/Q+sf/3H6UMTsqZ2/5+1f+hGbPvU1jED/VIFXlIFIJb0ENHvIHD479X6SbOaMobX3WmUZ+wJRtBuoBpPK5gyx13JFEQxLi2xJxNM7PzQljt5pBCuT4FRF76eGqW7Xh+QsmNHpeOlvnmPtdQ8Q8MbfOkRFSQQq6SWoiZmTuHfyp3Hq0TyO7Oykt4KfHN7KFn+kC84l2EyqFAEEhYvNRyObFfqLdRswRDcL+4UDGUxHkhLGFmg9EHaCxx20Pv2PyNvoRmtV113V8hdW8daFd9ByKr7WIipKIlBLFhLYuIzxfGvet/ln6T9Yf2IdMtqqB+38rmwracXTGa33fdp/EAcINxp+hAw9itta4+GtQ8fZevwkG8vLeWj+uZxXNIR6X4D/HDpOitPOfZM7J9qjXnvE34PfCPJBvZ1JKad7RQIQyGAqvjd3Edy1O3RYE+jTpuJYMg5B95teh5qwYTR6CJaeAH+A4OFyjEOHI46xJ/X7DpIxfSI12/Z0eq3yvW2smHs957/6JGnjR8Xk/ooyEKmkl+BSHWncMfEuZg+Zw692/YKAYc54j0RyxA+jIxg6/MXWUh565RU+c+65PH7BWH63u4ZPvdhxYf19L7/a4Wu7pnPf5Ks6HNvXmMrFf/5X3wNo55ltJXx9RBGytoHgoSMIj4fgrhUQaDf2aEiCW7fj3bodx7WXoRc7EbIJsCFlMhiC4KEaAlt3Yxw7DmGOqfmJchedXiatNB0sZ+WCmznvxV+Rs2BmdPdSlEFCJb0BYlLWOXx+xhd5fNtPaAmGv2VXT1ZWHmRY3og+9/bWlYfGtn65YQO/3BDeGjMDA4nosPnY0CSByx7dhJ3yg8fxbdoe9vm+59uWBjidEAyGleD8OKgzcjn4vheHx4Fm1/BWezm5oXMPra/q9pSSMraYhg8PdR1vdS1vXXgn8/70fYquXR71/RRloFNJbwAZkTqSuUPn8p+jb5vSXnVrDT8+XMeXhk+lKMy9L1sC8MoHu/p8r6AhOXtb6TStjj13n8en3yrj1xsjW3cXccGC1s6L8gM4KD01lJQ0DU2T+HyCk6X11B86SVPZus73tkX/6+XITDuzdKE7wZZW1l73ADMef5TxD9we9T0VZSBTSW+AuWb0dbx7fC1+w5yd+pPtyaSJ8B+ZVvsEzf6+zyzUNdHlDP8T/nS2H498obkezbqBNl5SKT2cQv2xWio3vxv2dTIYJGPmJPz1jTSWRDbu587PpWbL7rDO3frgt2k6fIzpP/gimh6bJS2KkujU7M0BxqE7uLT4ctPaq/fV8/c6X9iLovOTJPfNP6/P90l2dD2DstkvWX+kcwX5cNmjqE3XIjwcrC9mw8omDry4nsrNe/vWgBBodhu2JDfZXSw/aC99yjiy5k5D2Dv+HWpz962s1L6f/B/vXv+gWtKgKN1QSW8AWpi/yNT2Nlfvo1aGP63/u/OyGZbRt+3Mkuxdb2GW4Yyup2YTkf2IH24qZsObPnY+vYa6vYf6dG3y6OHkLJpF8ohCqtbvoHbHXirf3UrWuVOxnbXoPGP6RJJHDaN25z6q1m8nbVJotx13fi7Z86fTVH68z7Ef+ecbvHnB7bRU9vxYVFEGI5X0BiC/4acoZbipbT5xqhK/EV4Cctngv2f23LM5m1Pv+kn7toroKiVEunl12eYT1O7ue0WLrHOn0nz4KBVrNtO4v2MPtWrDDuzJSaRNGoOnuICMmZOo2baHxv1Hzpwj9NCvZNDbQs32vTQfOhZR/FXrt7Ny/k00HjKn8oWiDBQq6Q1AUkoW5S9mQsZEcpxD8fgKyJDDyBbDIm6zrOkYb7SE/6htUUFKn9r3ODuux5PCwa/3eLnyr9HVELRFMKZ3MlBE9a6+752puZw0HjiC4e9+DNR77BT1H+yn5VRVl2N1raeqSBlbTNrksQSbo5uF21ByiJXzb6R6a3hjgooyGKiJLANQljuLRfmLmZ+3gCU/+RRrSz+aTXn/5RfR4ur7IzOA3Y2VXJrU9U4tN7x6mHVlB3Ha7Nh1nWafr09t13q9rKt049Q1HLrGd9Zu5ZmdOyKKs71we3qGFNSSx94N9VRuXd/rGrmuZM06h4q1W3o9TxoGweaux9yaj5zAluLBCJizh6n3eAUrF93Con/8jPzli01pU1ESmUp6A5QQApuw8Ysbvsj0b//XmYkoP3tpJXdfeD62jKqIdnDxSQ2HMDoc8wclKU4n5bWRF7E7Wl/Pgt/9CYARWfkcrIrssd5pDt3OuSMm4bB1PxYppcTARqPIZv3zB2k5EXndupwFM8JKeOEINDQRbAmzjmEYgs1e3rn8U8x58puMuvMa09pVlESkHm8OcB6Hi4L0jpNKfr/qP9Qc86D3cWPoAw1H2BXovMvIkSbBu4cPRRNmB75g5+UWSY7wHq2OG1LE/JGTcdhsrCndzvf3bUImdX7UGsDB9tI8Xv7lYf7z8820nIh8H8v0qeOp3LAz4uvPZkvxkDp2hGntAchAgA13Pcr2L/0QaRi9X6AoA5RKegNcaUU5j11+DxOGFnc4/rd313OwRPZ5w+r/K99JQHZ8ZDgiRVKckRltqGfYz5rUMilvJP5ggPkjJ3c4npGUwqLRU1k8ZhqLR09jSsFoSk6V896BXTS2hsbD/NLg0JDCDte14mbrjmTKXt8U0WPM9jSXk0BjM9LEcj+BhiZaK2tMa6+9Pd/7Le/e9DlTe5KKkkhU0hvglk2cyx/Xv8aDF9zY6bXXt+3ghZWHcHnzSLOHV77Hb/iplR2XFwghqGhqNCVegHT3R9P6daFR01yPPxjgvQO7GJqaRXFWHkWZQ6hpbmBN6Q5Wl2xndel2dh4txZCdezFP1TZSaRRQ1lxMaU0xm9dpHF8d/XghQNacKZ1maZrBnurBmWPeHxLtlf3tNd5edje+2vqYtK8o8UyN6Q1wQggWjZ7G3pOHGDdkOPtOdtwZ5EhVFT97eSUAl8yYTlaKh12Hyrlk4Ugqfac6tVeQNJQ0Ok5S2V4l2Fre9+n9XZlaOKbDSOP8UVNYU/rR3pkn6vv+GPLXpz5k7rOtBGobTIjwI57iAirf3Wpqm6e58nJoiHAXl3CcWr2JN+bfyJLXfotneEHM7qMo8Ub19AaBFFcSP3v77zx4wQ3YtO63p3p16zb+9M5ath8+xJqtx0i2dxwLK0oqxu1N5R8H/fzrUJDXjhisOippDEgmDS2KOs7Fo6exo7yEHeUlZ47VNEffG/ESxHfu2KjbOZsjMw0ZNK9SfHtV67aTOXsytpRI6xr2rv6D/bwx7wZqdvZxpxlFSWCqpzcIvLl3E0EjyJ82vMYNsy7kLxtX9HrNmg/20ugdwezZoTfddEc6S4cvZcf+4zz44q851WDumFOqy8Pq0o7VEBaOmsra/eY8htw0XOdcU1oKyVk8m4rVm0xssSPDH6B60y5yFs+i7v0SHJnpNJaa3/PzHq9g1aJbWPzCLxmyZK7p7StKvFE9vQGuoqGGN/dtxqbpvHdgF3XeRhaMmhLWtdsOHSTHP4Evz/4qX5/7GLPz5vCJhVdS+s3nuXn2UlPjnFIwusPXi0dPMy3hAfxZL0NzRF6Qtj1nTibVG82brdmTitWbMXx+pBEke/70mNzDX9/I28s+wf6notsIQFESgUp6A9yfN77OvJGTGZIamhTx8q53WX8w/B06Vr9fwrCUIty2j5YqpLg8/PGOr4WdPHujCY2G1mYm549iRHYeC0ZOwdnNXpyRqpataLPHRd2O5naRVJTXr7MfA43NNB0op+nQ0dC+notnkzRsqKn3MPx+Ntz9P+z4yuNhby6uKIloQCQ9IcRyIcQ+IUSpEOJLVscTL6SU/GbNCyydMIdkZ9KZ40Ej/HGoqYWjuzyuazrfvvJeFo2eyvRhY/E4Oq7fy0/LZljGEGYPn8CsogkdXvvYuFmMHzKcoswhzBw2jjS3hx3lJTS2ejlYeZx3D+zE6zM/qbw2zYWIsuROxtRxYZf6MZv32Cmay45RsXoTvtoGchfPNv0eu7/1BBvufhTDxCUYihJPEn5MTwihA78ELgLKgU1CiBellNGXrU5wq0u2YddtzB4+kSVjZ/LSrrU8sfqf1HnDX15wwbhZ3b6W7Exi7f6dSCnJTk4nze0h3Z1CssvNlrJ9BI0gR2pOAjCtcAypLg+S0NrBw9UnACirDr0+f+TkDj3QloD5SW9VWi0Xfeki+N/XI7o+qSif+r0HTY6qbwxfaOF+oKGJU6s3kbNoFhVrNpt6jwN/+Cctp6pZ+NxPsHmSer9AURJIwic9YA5QKqU8ACCEeBa4EhjUSc8wDF7atZavXHInSyeGpnDMHzWF+5dcz3W/fZR394c3JvX67vUsGTuT9LZdTfZXlDMkJZO3P9zCfc/96MyjsMrGWgCO1XW9Fdn2djMyITRm19jqJdnppr6lifcOdKy2Xudtwq7b8AfN63FomuCd8xu55MP5NP39vT5f7x6aTXNZdNujma1q4070JHfUm1Of7dgr/+HNC+/kvH//CldulqltK4qVBsLjzQLgSLuvy9uOdSCEuEcIsVkIsbmioqLfgrOKpmn88Jr7uX7mhR2O56Vl8+KnfsCMYeGNbz259gW++ervz3y9/uD7ZH5+KVc88YUzvbVIrC7dztYj+1hdur1TQgQYkpJpasID0DUNhGDFJ2wkf6wPk0I0jez5M6jd9aGp8ZjBaPWhu/u2q064qtZvZ8Xc66n7YH9M2lcUKwyEpNfVNvqdRuKllE9KKWdJKWfl5PStwOlAk+lJ43+vvDescx+84EZ+cNV97CwvYf2B93nw74+bnoy6omnRFY/timj7UQnaJG98IYXk+ZN6j8PlJHPmRCrf20owDquRJxXl4auqjVn7TQfLeWP+jZyK4fIMRelPAyHplQPtC8UVAvH1DCoOLZ80j6dv/yrp7hQK0nNw2jrPlnTY7CwYNQVd18lJyWD5Lx488xgz1uqam84kKbPo+kc/7n675J1Hc0ka2/Oies3hQAbjd4Nm3RmbXl57/tp63rroTsr+/lrM76UosTYQkt4mYIwQYoQQwgHcCLxocUwJ4b/mXsLXLr2boGHw+HUPop+1W4sv4Of13esAGJqaxbN3P8aEocUdZoLGys5jpbgd5r6h28+audnoDrD12+NwDu1+zMozPJ+GDw+ZGoeZGkoOkbN4dkx3boHQBJq1NzzE3p8+HdP7KEqsiYGwJkcIcQnwOKADT0kp/7en82fNmiU3bzZ3xlsiq2qsIys5jafXvcIdf3zszPHROYVccs58dE3jh1ffj6Zp1DY30Oxr4d6/fo8jNSepaqo/M0PTTJPyRrD7uLkzJacNL2bOnOROxyccTGbIPSs6ldxxZKTiysuhfk/8j2llzp5MsLmFut2dx0fNNv7hu5j+/S8gtIHwN7MyEAkhtkgpu5x6PiB+aqWUr0opx0opR/WW8JTOspLTALh93qUc/NY/GZkdmgdU3VzPRRPmMDFvJP/esRqA9KQU0pNSqG6uZ3t5CfUtTYzJHdZt25HHFF7Vh74wuima+8GIRoJf6bjDTNKwoXiKCyxNeELTyJx1DmmTx+LISO3x3OpNu6jbXULO4u6XmJhl74+eUuWJlIQ1IJKeYp7irHz2fPUZZhaNp7qpnu+8/jR3zbuMhaOnnjknyeHisskLAajzNuJxuHrcyDoSvoCPyQVdL4yPlL+HBddrFjdhf+giAJLHFaM5HNRs+8DU+/dV8ugiqje/T92uD3EX5pE2eSw5i2f1uBuL4e+fReVlf3uNt5beha+mrl/upyhmUUlP6cRpd/Dyp3/En+74Gj+65gE0TSPVlUQwGOSNPRuoaarnnQ8/KqmzvbyEc0dM6lT8NRrrD+4mo4uK59EwenmUv+pSL0m3LYagjEmNvHB5hheQOXsyTWXHzxyr27WPul0fUrF6M4HGZlInjOryWs1uzv6i4ahYs5k3FtxE0+Gj/XZPRYmWSnoDiJnjs0PTsrj0nAX8besqih69ktwvXsLv3nuRBaOm8Py2t6lvaaIoY8iZ89/dv5OC9FwWj5nOglFTmD5sLJqI7MdrSsFoFo2eyrayfWZ9OwD4A71svyYEb9xqwLhcU+6Xs2gWWfOm9XnrM3taMtWbdmF08/jQV1NP/Qf7yV4wo/OLRv/ONK3/YD8r5t5A9VZrtmZTlL5SSW8AEUIQNLG+2w9W/pmfvPksR2pOUt/SxMoPNuJxupk/cjKjcgrJ9KRxzfQlZ84/VHWM1SXbeHf/TrYd+ZDJBaNIdrp7uENIbkoGC0dNZc7wiaQnJbPzaClrSnfQ2GruLiPhrC8M2gSvPuDCc828iO6ROmEUmtNB2uSxVG7cSdW67dhSPOQsnAmi5yUYnpGF5Cyahb8uvG3iKt/bRs6CGWjOj5abWDG5pOVEBasW38qx197p93srSl+ppDfA6G29CjN6fbkpmR2+Xl2ynaZWL5PyR/L07V/lt7c+gl3TmT9ycpfX7ygvYWhq5+UAQggm5Y1k8ZjpjBsynFMNNazdv4ONh/dQ2/zRG77sZuJJX9n62NOSOrx6r07SrYvCvkZzOrCnJmMEAgi7jYaSw8jWUIV5f209FWu3kHbOWFxDsgFIHTeCpOH5CE0jaXg+OQtm0HSgnIo1m8N/XCgl/poahl0Y2l0md/FsTpm8D2e4Ak3NvHP5p9j/+79bcn9FCddA2HtT6UYgEMBmi/x/cXbbrM4z7RlB3inZxiXnzEcIwcS8Eawu3UF1D9XNs5PTaPa14A8GGJM7DENK9p48xO7jB3q8t67pfaoG0d6wrCwykpOZN24kKR4nwt1EY42DY1W1QJgzDjXBa3fCMtcSWn73drenCV3HXZCLLclN/d4D+Ou776XV7Qo9rrWlJlO/7yDCbsOZnU7z4WM0H+77fgrpE0cw70I7PlpoOncyp1ZvQug6tpSkUG+xn5cjyWCQDZ/4Mk2HjzH5G/cjeunZKooVBsQ6vb4aDOv0gkYQr6+VZFfkC8l3lpdw9ZOPYNN0bJrOJxZcwQMX3NDhzWxneQnPbVnFD1f9FV/A32U7brsTr79v09udNjut3bTXk/suvYjUdKjwHe/95HBIydLnHbT++q1OL+UsmoX3RAVNh48hfX2PNRpp44qZt9SBk9AjYK9Io7wqHZtNUJxykKCwUx3MY92v+r6xthlG3H4Vc578JrrD3LqIihKOntbpqZ7eAKVrelQJD6AwI5f9FeWXBEb9AAAgAElEQVRnvj5aW9Hpr/cphWOYUjgGl93JV196sst2+prwAGyaTivhJ5KLpkxm+tgC6sRRKnwm/iEnBG9c62NZcAktv+3Y45OGQWPJYfPuFabUsUXMW+rCSdOZY25Zx5jM08sHBDYC5OpHmHrnInb8YU2/x3jw6X/hPXqSRf/4OfbUzhsCKIpV1Jie0q0kh6vD1/sry7s5Ex5dfjuZnp4XUPfF2Vui9WTRhPGMGieoFeWmjQN2IAQrbvCjf34pCEHO4lnkLJxJY2kZwhX7vS/bSxlVyLzlSR0SXk+Kkw+Rf960GEfVtROr3mPloptpLo+8GoeimE0lPaVbNc0NHb5uaGmmuqnrxci6pjM5v+u1Y5E4e5/M7ty+ZBETzrFhEOOp+kLw1sVe/N+6GH9DExVrt5AybgTS33+PNVNGFrDg0hRcMryEd1pWsbnrHfuiduc+Vsy9npqdey2LQVHaU0lP6daLOzs+Ftt0+IMzlc67kp9mXsmmcHp6d33sPFzZ/VP14bTdizUyHwntRlOxehP0UwWG5OJ85l+ahlOGX/X+tBEphxgyt/cySrHiPXqSlQtv5uR/NlgWg6KcppKe0q3HXn2qw9d13kaueOILXP6rhzlYeQzjrIXQeWnmVdjWe1lv9sllF2DLrIrN48wuDHXncFvhTL5VmMecOYKcc83dIq0nnqI85l+egYuG3k/ughDgyoxtFYbeBBqaeHvZ3Rz8iyqAolhLJT2lW1metE7HjtSc5OVd7zLyK1eT96VLKa85dea1exdfFfEuLGfTRfc9vc9eeiEy9VS3r5tpeHIB9xbN5Kt5mSxwNmEXIJDM+u4l/XJ/T9FQFnw8EzfdLwvplSaY9c1FjLgpsgX3ZjF8ftbd+gXe/98nTN09SFH6QiU9pUtSSg5W9bx27FRDDZMfu4Un3vkHAGNyi7h2xpIerwlXwAh2WUT2oSuX4UuK/cSIIk8+nyyayZdyk5lmb0I7a9Zq2ggfaRPyYx7HnCsLcMsoEh7guucGHNn1zP3OOVxX8jCXrn2QjCnmV8YI184vP86W+7+FYeLuQYoSLpX0lC4JIbj//OsZN2R4j+fVehv48ku/YW3pdqoa65hSMBq3PfoZjSfqq5haOJoFo6acOfbwx5fR5Ijt5sajU4v4TNFMHhmSwnR7U7cLrAVBzv3xVTGNBcAuoivf47rnBjTPR+OeNns9qYVNLHn2BpIKM3u4MrY+/MWfeffGhwg0NVsWgzI4qcXpSrcMw6A14GPkV67hRH1Vj+emuJIIGgaHHvsXTX4vn37mB6zYswFDdhz30zUdKWWn4z21Oyq7kAtnj6Te1v2SiWiNTyvmsoxMRuvhvwkbJPFc8U9jFhPA0vsmRfxo0/Xf16Oldn+tz5vKS/OewFdrXeJJnzKO81/9LUkFQ3o/WVHCpBanKxHRNA23w8WCUVP4x7but+KC0HKGJIeLdQd3ccXUxdwx71KGpGZS19yIXbex4oMNjM0toqKxlkNVx9CEhk3TcdhsOG0OkhwuBOCw2bFpOrqmowmBrmlctmAcpwxzq6gDaEJjRsYYLkxNolj3An1789doZt6vb2XdvX82PTaAJffNxCkr6OIpb6+cd13XY8IDcLjrOe+vd5Jc5KJiYxVr7/q/yAIVAmG3IXQdzW5D0zWEzYaw29AcNjS7HU3XQdfRdB3NYUfYdISuIYBNn/k60779MGkT+29ykDJ4qaSn9KqrCS3t2TSdqYVj2HV0P9f/7svMKZ5IdnIab+3bQlOrl6A08DhcbDq858w1hjTwBQ18QT+NrV6quln/B1BYmkz+SNO+HQSCOVnjuDjFxVC9BYi8msPw5elsy0un5bh5SydsHjdjrphJEsfQRN+fxDj/6xr0jPBmemZP9AE+8s9zUHjlImp3l4FhIIMG0jCQwWDoc2mAIZHBIEGfHxloOx4IgJRInx+JHyPCf8qKNVs576UnyJnfRbkkRTGRSnpKr+6cdxlPrn2h29fHDR3OlrKPFh+vKd3e6ZxoygT1tnwh7HaEzoLs8Vzk0cnRfUBL1G0KWsk9dxRlL2yJPsA2No+bsdmRbW/mvPUq9Jy+LV4HkH4DGfDRWNr/26oB+KpreevCO1nw3E8ovPwCS2JQBgeV9JRezSmeyIShxXxw4lCXrztMrJjelZU73udTY2bS4I9sbMsmbCzKmcBFHkGm5gPMnTXoHhrd9muOjFQmXzsVXTNAiNB/OdLndpw3fxx9SGR/XJS/7eXoK9YuHg96W1jz8c8w+4mvM/qeGyyNRRm41OxNpVeapnHPwo93+3o4hWKj0exr5Ud/fY9kf0FE15+TPoLrkn1tCc98Ux6egeaMLPE7MlJZcOsYCl2HyHOUkWc/TK4eQcK76Ur0vMh7rnZP32oOxoo0DDZ+8qtsf+RHakmDEhMq6SlhyUruflwvaPTPVlxJnshmGpc29r1WXV/Y7PUs+sPtfb7Onp7C/FvHkkp0C+0dV12Mnh/d0oa0UQ4cWelRtWGmPd99krXX3k/AG/0jaEVpTyU9JSxHayu6fe1YbWWnigyx0NIc2Y+rJnQaZGzruuXNd5E6Li/s8+2pHhbcNp40ut/LNCyZGdhG2aNrA/A3SzxFudg8se2190X5C6t4e9nd+Gq6n+SkKH2lkp4SlqO13fdGDlUfZ1rhmJjHEDT63tNLc6Tyhfxi0rTYVkMQ+Jj7s2vCOteW4mH+7RNJI/qdZeyzpoGM/rFtxhiNpX9bzMUrb2bU7fEzkaRizWZWLrqFpiMmFQVWBj2V9JSw7Dq6v8fXS06VMywjNguMdU3jkVsuoNnRt8eU6Y40Hi4Y0TZTM/ayxrUy7Mqep9zbkpOYf/sk0k1IeADCbV49P80uSM7XmPbwSOxp1pUjOlvd7hLemHs91dv29H6yovRCJT2lV4ZhsLms53podl2nqdVLcWb4j/jCFTQM6vzVfaqokOnM4HMFxeRq0Y119VX6+O6/f5vHzbw7JpMhzOu1yEbzd1NxpArG3H2e6e1Gw3vsFKsW3cKx196xOhQlwamkp/RK0zTmj5zc6bhAkOZOxm134rI5qG6uJ9nlJi812/QYUmX4yTTLmcHD+cP6PeEBTPrUCM75YucKDLrbxbw7p5ApTJ5U4zP/sa2UkgN/XWd6u9EKNDXzzuWfouSJv1odipLAVNJTwjKraEKnY2OHFFHnbcTrb+VAW0WG948dwOtvYdbw8abe/7t/f40sR+9FarNdmTycP4ysGC1P6I2glcmfLuDaDx8+k/x0l5N5d081P+EBWrH5PWsAd555tRHNJINBNn36G2x/9MeqPJESEZX0lLA8uvx2vnbp3QAsHDUVu25j38mud++o9TZyrLbS/CD8oRmiqfbQ8omza+7lurN5OK8wZuvx+sLuqOecTw9j9J2LOffuGWTFIOFh09HSzRvTO00IwbBLJ5rerpn2fOc3rL/jSxj+2E5QUgYelfSUsCS7knhgyQ0MzxyKpmn4g4Eezz9WV8mc4old1sSLVJM3gLsln/97eRdaQy67draS68hHQ2OIO4fP5eWRYVnCEyDsIGxI4UGSilHhZtJ0nRzN5OoQWaFemPOGKxCOGnPbbuPOiZ+lC905+McXeOfye/E39n3bNWXwUqWFlD55cs0LfGfF05RVnwyrPFBxVh5p7mR2lJdEfW+Py4VN06hr7jh547Ebr+SzRUkxX5bQFaMhjWDpMQJ7SpAnToCmg2HiTiIOO1pBAXpRAf4du7FNGIt9QSG0OhGOOiA2GwP4GiRbv7sf3alT9u/N+KrM21DbbBkzJnH+K7/BPbT3x9/K4NBTaSGV9JQ+8Qf8NPlauObJR3hrX/j/hmNzi8jypLH7+AHqW8z9y/zKSTP41+UmlmHokUC2piOlxP/OToLvx3YavTZuLM7Lz0EQxL+5CvusTFPW5fVFzT6DD54q4eirWwg0xGevyjO8gPNf/y1p40dZHYoSB1TSO4tKetHbe+IQU751a6+POc/msjuYUzyJ1SXbTIlDExqv3XoVFxWY9xi1K5I0jOMt+Dfuwig9ENN7nc1x/RXYhvlAOPo94bVXuTPAjh9u5dTqHZbF0BNHZjrnvfgrchbMtDoUxWI9JT01pqdEZPzQYr5w0S19vq7F72N1yTYWj56GQ49++yxDGnzr3T14/bH7481ozcD70+dp/eu/+zXhaQX5kJwcSnhgacIDyJ5iY9EvZjP1a1fjidGs0Wj4qmt582N3UPb316wORYljKukpEXvs8k8yY9i4iK5dXbqdFJeb3JSMqOO4duJI7DEqEmC0ZtDyq79BoJ93/Hc4cV57HkmfXNy/9+2FI1Uw8c4shizuvIQlHhitPtZe/yC7v/MbtaRB6ZJKekrENE1jxf0/ZeGoqRFdX9VUT1GUW5elu5O5dbQbmxaDx5uOPIyjteZOTAmDbd4c3PdejbA3EauJKtEae2ux1SH0aMejP2bzZ76BEejb43dl4FNJT4lKdnI6b9z/U66aFtm2VcfqKnvtLfb0GNQXDNAYjEHCs+eC7wS2UTbsyz9mfvvdcN55LfZ5IxH2GssfZ/YkfYyGI7P7clPxoOSJZ1h91WcJNEdWWFcZmFTSU6Lmdrj45ye/x+9ufZTirNBYT7IzidvOvZiphWPY+ujTvPKZH5OX1nl7smN1lZyor+o28X1RTuDxNQ4e0rteLK1rGo0xW6kgQfqxTcoGd+xLJ7nuug49sxEh4nd5wGlCCEbdttDqMHp17OW3WXX+bbScqrI6FCVOqNmbiunKa06R5vaQ4vJ0OP7+0f2c+/27afZ1Lgzq0O2MGzqcg5VHaWwN/WU+Mn0o3/zuB2eWt/vTkjBcdgy7hrTrSE0DKcmbM55Lv2vStmf2HBA2EBq0Hj1z2L+xDv+aGO5H6XCSdP+yuO7dna21VvLyBX/HVx3/9e6SRxWx5PXfkTJ6uNWhKP1Azd5U+lVhRm6nhAdwTsEoHr7w5g7HPA43OcnpCCE4UFHOkJRMZgwbR2FqNo/+9VSH/Vzsdc04T9bhLq8h6WAlnv2n8ByoYOqyYnMCd+SDvwJ8xzskPADb7OGhhecxYpszHWRibanlTBcUfXy21WGEpXF/GW/Mv5HKjTutDkWxmEp6Sr968IIbSHMnA3DVOTOpeuASDn3yY+z/9JX8dMwiPrFLZ3FjCg9vELjLwnsktf6hF3jrzk3RBeYsAF/3+2MK0YjIjc2OH/r0qTjm5kAfSifFi1HXF1kdQthaK6p58/zbOPLCKqtDUSxkszoAZXDJ9KTx2fOuZZynhhtG2rDrAnRw2yWL/Rqb/r2Don/3rU1/bQMn395O2ZvnUPSxCPaMdBZ06tl1ImzIhoa+tx0G++zxIOP/EWFXbEmx3RTAbEFvC2uu/iwzfvII4x+43epwFAuonp7S7x644HquKHaFEl47xZckkz458m2kNn3hJQLePvaWwkl4ALIFx4WLIgvsbG4X2rix6LOmo8+ajpaSmAkPwDNUgEisxIeUbH3w22x56NtIIz6XhCixo3p6Sr/LSclEekeCt+Mm1DaXYNkL57Pp60Uc+NPbfW7XV13H9h+XMeW+IhypYbwRh5vwTsc3WsD1l+P720t9C0zTsc2ejshKRx+WhZbSipQtbeOVgkR8rAmhYrOrbloDCToZbt/jT+M9epJ5f/w+usv8Ek1KfFKzNxVLyLr1ULu2y9eMgGTNZ3ZwbEVk43T29BQmfPZjjLslG5u7m+TnKABf+AnvIwLpT8f7s2e6flnT0QoLsM2fjl7ggqAAKRC22JQAstqepyrZ8c1/WR1GVHIWzmTxv3+FMzPd6lAUk6jZm0r88XZdgBZAswkWPD6V7LmTImraX9vAzm+9wIvn/ZN9f60h2HrWH3bOSBMegEQ4GnFcdzn67Olga5vRmZmB/fyFuD55Ha7rJmMraEVQh9BrB2zCA7AnO6wOIWoVa7ewcsFNNB4yue6hEpdUT0/pd1JKOPJTkD1vEeWrl1Tu9LHm7mcxWiNfv+YZnsekh85jxGXJaJ7CPj3S7I000kEK0GRCLCo3hwA0EIK6/QavL38Kw5dYyy264hqaw/mvPknm9PiuGq/0TpUWOotKetaSRisc+XnY5x94qZEN93XzOLEPRt91AbO+OhqRoGNoZjAa0giWVXQcRjz7PeD05A4pIWhAMEiwqgZjb0mX+5BWGgW8+8v1sQu6H9mSk1j0j5+TtzT+d5tRutdT0lMTWZT+F2wM+9TGcoPtj60w5balT71F2phMxtyUOWgTX/BIJf7X3zK1zWS9PjSDcwD8AR1obOY/l36Sc3/7GCPvuNrqcJQYUGN6Sv9r6X4R+NmSCzVG3DjXtM2NtzzyPCXPVQ/SlAcY5n7nUko+PJAyIBLeaTIQYP2dj7DrsV+q8kQDkEp6Sv9r2t2n06c9lM/Vm6+j8PK5pIyNfgeQLf/vefb/o37gJD7hRJKGlGlIUpEiud1HUrvPPeDtvO9pVLcWgsnFR0kuzje13Xiw66s/Y+M9X8HwJ/54pfIRNaanxJys3wzukQh7JtJ7CE49H3lbUrL2/t2UvxT95s9zfnIjI69KIcGWVndiNKfT8sSzlsZQ1lzMtt+vsTSGWMlbvoiFf/8p9uTO+8kq8UktWVAsI1uPQ906OPEssnE3VL4SVXtCCGY8Ogl7anLUsW186FkO/jv88cW4FQdZe5j7IGOumZ94u7OE4fjra1i1+Fa8x09ZHYpiApX0lNiq3wJGKxjNUPUaGNEX9HRmgL/enGS14YFn2P/PAfSo0yJCCHKGSIQeu0oUVqrZtoc35t1I3Z5Sq0NRoqSSnhJbGYshdY6pTdbs6zxtPhobP/ccB19qMrXNmBB2AmV2AgcEgYMagUM6gcM6gZ2HrI6sjUB3dl/lPtE1HT7KGwtu4uQ7G60ORYmCGtNTYkpKCc0lUPmieW0akt2/rWTXd14wrU2Aub+6lRGXRFCloT8IO61vlhHctsPqSHrkFWm88bP3rQ4jpjSHnblPf4/iGy+1OhSlG2pMT7FQEOrMXbgsNEHKCPMnFaz/9J85/Ear6e1GTdhpfftI3Cc8ALeso2hZl+81A4bh8/PeTZ/jgx89pZY0JCCV9JSYEsIGmvn7M+bOcHPpf+7m4lV3odnN22PhvXv+yOGVcZT4hI3Wd44S3LLd6kjC5muMo3+/GNr2+e+x+b7HMILmPm5XYkslPSX2ksaa3qQ7W5BarJE+WmfsvUtNbfu9//4j3tpUU9uMiHAgtTQcC3IRuQVWRxO2sdPd2FIGx/T+kl/+hXevf5Bgy+BI9AOBSnpK7LmGxbT5aZ/LZ+iFM2N6j36nuUBPQgSrEDoJtRQgQxyncPEkhK6jJ7msDifmjvzzDd5edje+2nqrQ1HCoJKeEnv2bBCxK0EjhGDhT6eTPe8c89q05DdDB8cQcOSF/r0CiVu1YcqIY1z6qWHMvXOa1aH0i1OrN7Fy4c00HjxidShKL1TSU2JOCBGTR5zt2T2C8387l+xzTSoLE4uOleZqS2pD233khT6chUAQfCfBdxyCHXsN+qjh6JOmxiCo2BBCoIsgR0p7Lh81kNTtLuGNeTdStXmX1aEoPVBJT+kf0oj5LezJgvN/P5+sOXFaD82W2pbUTrT7OB76aO25gKljXgqOi4b2U6DmmTCumdQx0e+XmihaTlay6rzbOPry21aHonRDJT2lf8R4XO+004kvc9b4frlf30TbfUyccb3TXLKBORdnkFQ4xOpQ+k2w2cvqKz9NyRN/tToUpQsq6Sn9pP/WMzlSBEueWkjquOGRXZ+ehO40OShTJO6asNbKxB2fjIQ0DDZ9+hts++L3kUbsn3Io4VNJT+kfSaNC41n9xJEqyJjWLukJwYSHLsXmOWvHFSEYsiQ02aLwsunccOBBrt5+B3ZHDGbiRbuQOUF/Ww8cdg/aKf0f/OD3vHfL5wm2+qwORWkT1a+REOIHQoi9QoidQoh/CSHS2732iBCiVAixTwixrN3xmUKIXW2v/UyI0FxsIYRTCPFc2/ENQojidtfcLoQoafu4vd3xEW3nlrRdG7spgkpUhO6BITeCs//qrrmyk0AI5j95C+M/s5xpD+Rz1eZbWPD7W9GcbT8qUuIpzGDer25m9G2z0LQmBPFZP00IAbp5C/H7S0rmwN2PMxyHn30ltKShps7qUBSi/9txJXCOlHIK8CHwCIAQYiJwIzAJWA78Sghxevv1J4B7gDFtH8vbjt8N1EgpRwM/Ab7X1lYm8DXgXGAO8DUhREbbNd8DfiKlHAPUtLWhxCmh2SF1Hoj+eeOecFchV6y7m+FLk5j++UIAbG5B0cfcTHxgOUIL/fgf+PN/yJzkJnde/BZCbX3tON4/74Jg4s2GTE82t3BtIjr1zkZWLryZpsNHrQ5l0Isq6Ukp35BSnv4tXA8Utn1+JfCslLJVSnkQKAXmCCHygFQp5ToZ2rTuj8DH213zdNvnzwMfa+sFLgNWSimrpZQ1hBLt8rbXLmg7l7ZrT7elxCmRNALy/xvSFwOxLUPjzhJ4hnY9+WPyp4cw7bGrQ4u+paT8zWp00Qi29C7Pt5pRW4M8mZhvmGnyOPb0FKvDsFzdnlLemHcDNds/sDqUQc3MUYK7gNfaPi8A2q/SLG87VtD2+dnHO1zTlkjrgKwe2soCatsl3fZtdSKEuEcIsVkIsbmioqLP35xiHmHzINLmQN5t/fq482zjb8lg2jeuxpacRPOJptDaOMMPIg5nsVizWt4UQghm3DSl83jqIOQ9XsHKRTdzbMXArDKfCHr9TRJCrBJCvN/Fx5XtzvkfIAD85fShLpqSPRyP5Jqe2ur8gpRPSilnSSln5eTkdHea0o+EIxtyrwW7df8/xt+WwTU7bmL659rWkukekDGadJF4Kw5MM9RWxrirBnb1hXAFGpt559JPsv+p53s/WTFdr4MrUsoLe3q9bWLJZcDH5Ed1NsqB9guzCoFjbccLuzje/ppyIYQNSAOq246ff9Y1/wEqgXQhhK2tt9e+LSVBCM2BzLkBKp4Df//3wIUQCF2EnrRq7lCF91jQkiAY+diWECKBFyyEJOAcnJiRwSAb7v4fmsqOM/lrn0Uk0N6qiS7a2ZvLgf8HXCGlbP9u8SJwY9uMzBGEJqxslFIeBxqEEHPbxuT+C/h3u2tOz8y8FnirLYmuAJYKITLaJrAsBVa0vfZ227m0XXu6LSWBCLsLci6L6f6cYTF8ocebZtNTAK3T1mLhkoZEemOUjPtRY3V8zoq10vvf+AUbPvE/GH71b9Nfoh0o+AWQAqwUQmwXQvwaQEq5G/gbsAd4HfiMlPJ00alPAb8jNLllPx+NA/4eyBJClAKfA77U1lY18Biwqe3jm23HIJRwP9d2TVZbG0oCEvYsSLX68VcQHCY/arWlgQyA0Rh5GwbIqpPmxWSRpLTBvXShOwee+gfvXPEp/A1R/IwoYRODsfLvrFmz5ObNm60OQzmLDNTBsT+EkoRldLBnAgL8p6JrypYBQS/I6Kbsy6DE+/iK6GKJA4YUvPZ0FYGGJqtDiUsZ0ydy/iu/wZ2Xa3UoCU8IsUVK2eVf0Yk7JUwZcIQtDTIusDiKYGhsMdpq7/YsCDRFnfCAATMBRhOSlOGJt2l2f6nZtocVc2+gdneJ1aEMaCrpKfHFMz5UgsdqreWhOoBaBMsX7LngrwVM2npqgCQ9gLypeVaHENeay46xcsFNnHhzndWhDFgq6SlxRWgOyLsdMpeGxsOs5K8Mzeh0FhB25nEMbZuFGuz11HANpJl9+VkNpI4dPKWGIuGva+Dt5Z/g4F9etDqUAUklPSXuCFsKImUK5F5tdSih6uWtR9sSXy87yDjyQzXyEn5xQex4ZDWjFkZW/WIwkYEA6279Anu+/1sG47yLWFJJT4lbwp5l6cL1DlrLQzM7nYVtMZ31q+MsAF9slokOtDe9xjrzesED3fb/90M23/cYRlD9m5lFJT0lvrkKez+nv/hOhJKfvwKc7camnAWh3mCsDLD3uwl5ZRQttXp5SuIo+eVfWHP1Zwk0Jf5azXigkp4S35z9U3G9z1qPhqpFOAtjm/CAATWThbbdZQZY7zXWjr74FisX34r3eJTLaBSV9JQ45+x2D3HraR6Q/tDyhJgaeAnCnR4HM3QTTM3W3aw493pq3//Q6lASmtoNT4lrwuZBZi6HmlUWL1rvQrAu9CHsoS3UpKqOHY5mkc7RDfutDiOu6W4XtmQ3No8Lm8dN3sfGM/62fGxusCWvQLa4Ea44fQoS51TSU+KeSDkHac+Ak89YHUrXbGmh5Q0xM7AebwaljaayE1aH0SdC09DdTjSXE93lQHc70J2h/2p2G7rbju6woTvtaC4butOG7tDRnDZsbhu624Y9yYbu1rG5dXSXhi1Jx+bWsLnA5hLYkgQ2N+iu3paptMLJ55HZlyA84/rt32CgUElPSQjCVYBMmw9171kdSmfBplDx2UBtbNofWDkP2cOoirC1JQ6HHaFrCF1Hs+sImx6qdK8JBAJh17G1JRxh19FsoXN0lx3NrqM7bWgOHf10AnLZ0F06mk2gu3R0h47u1NDdOrpdoDk1dIdAdwlsToHuFGgO0B2gO0Gzx9t6ySBUvoQMNiJSZ1odTEJRSU9JGCJ9PoZMIrhtVVspIBGqraqLUGIQoRJBwkboa022/ReEkKERbK3tNSFBk+a8kRleMGJUgw9CsUZ1vQgVoRUCNO2jz4UGunamQK3QtI7nnj5f09v+KxCaDklJCLcrNNRoBEHKtnbbrmlLVtjaPnStrXyTBhLcjZJzPAUEW4OkjU6jcEkSuh2EDkLr5v+HszA0c1bpqOZtZKAeMs6Ps6Qcv1TSUxKKljENX/lrBDe/aU6DQoBma3tTb/fGLUJfi9Nv+KcTALRLIKItIQiQEm3IUJxLR59u+KwbyXYfWudjZ2Yznj7Wlq2RoAVx3XMpZya0aOJMIhSCj5L4mf+2vd52LN7eDNOAyfOHWB3GwLWJhjcAACAASURBVNGwBYINyKyLEZqqZNEblfSUhKNPWWBe0pMSgv5u18L1pY8lbXYIZJoSVnsCECl9ueJ01PGV7JQYav4Qgo3InI8j9CSro4lrasmCknC0tGyrQ1D6lR7bx8cDResxOPFXpL/G6kjimkp6SsIR+SPRho+3Ogyl37QNxAr1YKpXgdpQ4mtNrNmx/UklPSXhaE4X9ov/y+owlH7jb6t4oR7bhcXwwslnkd4DVkcSl1TSUxKSNnY6zi/+Gtvci8GdbHU4Sqw58iBYb3UUiUMG4NS/kA07rI4k7qikpyQkzWbHVjga580P4370d7ju//FHsyutEmezJJXBTkL1SmTNarXXaTsq6SkJT0vLRh89Bfsld1gah6ytxrfZG/rY1EzwlPr1Mo36eyJy9Ruh8hVkvG3jZxE1MqwMGI6lN2Ps30Xwg02W3F/WVRN4550zX4uLL0TPVb9iplBv2NFp3gsnG5G5H0dog3uzb/WnqDKg2JfdYnUI7ajuiWnUkoXotZbDiWdDO7gMYirpKQOKNmIStvmXWB1GiMp55lFjUubwV4aWNPgGb10+lfSUAUUIgeO6+9EnzbU6FPyrN2JU61aHMUAYVgcwcAQbQz0+70GrI7GESnrKgCN0G847v4I2drq1gTTW0fqv9RhVKvFFTaqkZyrpg1P/RDbstDqSfqeSnjIgCYcT191fQ59xPiLHuurrsraKlufXElSJLzqa+vczn4TqN5C1awfVkgY1tUwZsIQ7GdcdX0YaBsF9W8DbSOtffgj+fp4U0VhHcG8NjMngdAUFLdlAJA2eN5qoGaqnFzN16yFQj8xahhAD/48LlfSUAU9oGrYJswEwjh/Cv+Iv/R5DYP17BNZ/9LV9+YXYJ6lfv/CppBdTTXsg2ITMuQKhOa2OJqbU401lULHNWGJ1CEokDL/VEQx8LYfbljQ0Wh1JTKmkpwwqIjkNPKlWh6H0mVqc3i/8FXDiL0hfpdWRxIxKesrg4nBhv+B6y/fpDH54BN+6Bvw7fJbGkTjU+Ge/CTbAyWeQLWVWRxITKukpg4px7CCytsLyiRHGgX0E3nuXwM69lsaRWNTbVb8xWuHk88jGPVZHYjr1U6QMKoGtbxNY82+rw/iI6sCEbxDMLIwvBlS9iqzbMKCWNKikpwwasrmBwLrXrA5DiZRKetaoXQPVq5ADZIMANWdaGTRkQy34WqwOowNZeYLm3zSBEAihgQBt5EicF2RbHVr8UUnPOo07INiAzL4MoTmsjiYqqqenDBrakGHYL/+E1WF0JA1orIOGWmR9NbKuGtnktTqqOKXerizlPQAnn0MGm6yOJCrqp0gZVOwX3oD98rvB7bE6lO6pCuxdE+rtynK+k3D8L0h/ldWRREz9FCmDihACx0U34X709+gzliDyR2A7dxkia6jVoX3E6yVYLgiUgv/9AMHj6tc0RP0xEBeC9XDiGWRLudWRREQMpFk54Zo1a5bcvHmz1WEoFpOGgaw+gZadj2ysw/fKHwhsWAGB+Nr9w7b4PByz3VaHYT1bJgSqrY5COUOH7EsQnnFWB9KJEGKLlHJWV6+pPyGVQUtoGlp2fujz5DScNzyI/fxrLI6qM+FU880A9Xgz7gSh8iVk/RarA+kT9VOkKO3o42ea0o79vAU4b7zClLaEU81aBEAGrY5A6Urd+t7PiSPqT0hFaS/afTk9Hlx3XIHmqgF8uB+8keChOnwvRL4+MLDrAIFSNyLJhWN+NsI5+IYkANXTi1daYj16V0lPUdqLcjzP/YkrELaaM18LvRbbKNAfvJHAnpNIr5fAus0QCH8DZeNw6ZnPg8OWYxsdVYgJTCW9uBSoRkojtM40AaikpyjtyNoIdpd3OqG1Fa24CGFv7HJrMaHXYp/sBJzYp1+HUdOK/811GMeO9zHAUBHaQUkt5YhfMpgwPXGV9BSlHeNkmDvL23Sc112G8DgR6QFAIGQLyN57isJeg54L2k2L8f7pLThVEV3Qg0ZivKkOOsIOInFSSeJEqij9wDjwfugThxM8STiXL0ZkuqElSPBIJf433g69rmloeW6EqI1402hBHUm3zcJoSMW/bjfBXe+HEeAg7umppBefpB+8JZA01upIwqKSnqK0kTKAfeEoHBeNAlsAQSPQGvpwgZbuxDbxJlpf3ohRup/Wf63HdfX4aO+KllKHc2khctkEIABBJ62vbMH4sCT6b2ogGay5PhFUvIhMnorIusjqSHql/nRSlNNaytFSahC2GgQNdNWFE3oNrisnYD9/IcbBQ8hghmm3F7IBIb0IrRbX5aNwP3wD2ohi09pPfCrrxbXGHQmxPZlKeopymj/csbUA9pnJ2BfPBz38WZh9JajDec0M7IvmnTk2CDdQakclvbiXAEVn1eNNRTnNcw60noDmfWGdbp+dBjTENCQhm7HPScM268bQAZuf/91cwW82buSh+XN4YLIbKUNDfTYttLeoolimfjPSNQzhLrY6km6ppKcobYTuRmZeAP4q8IezdKH/ul1Cqw19YkCNt4Xy2koefvVVHn71o3N23HMtY1LBpX6rFcsE4dTzyIwLIGUqIg5rIKpfD0VpR+gepDM/zKRnDa2bztzUJ59nxW3XcFGB9b09Q0r8QWg1BC1BaAkKfAZtn4M3IPEGJK2GJGBIfEFJS1DSEjBoDRo0B4L4g5KAYRAwJJ+dNY48u6ozmDBq3gJvKTLn43FXdFYlPUU5WxwnPOj5Eea64w20BJMRhPqhgtDAvWz7MKTknAydkW27rf1+r4/NJ2owJAQNSdAIEjAkfsPAHwziCwaRgE3TaGhtxR8MEpSSoGHQEvDTGgjQEvDhCwRoCfjx+lvxBQIY0jD1e75vZjH4a01tU4mxljJoLoHkSVZH0oFKeorSjjT80HrS6jB61F1PD+Drq97o9fprp8zivKKhBKTkQJ2P36xfY2J0seE3BvUMnsTV9IFKeooS17z7gfjezV9EOYvx+Z2beX5n6HObppOdnE5lY3z3onzx/b9E6U7LIaTvFMKRa3UkZ6glC4rSXpyNP3TFqZv3axswgkzMG2Fae7ESUD29xFW30eoIOlBJT1HaEe6R4JlodRg90k3e2HfficNocb5ZsEp6CSzYiDR5jDca8f2TrihWcMd3z0eavFTiZEM1s4dHu51abLUE4+dNU+mj1nKo+DcyTnZWUElPUc7WUm51BP2utQ/1/azQqpJeYvPuB+8Bq6MAVNJTlA6kNMBb2vuJForFKrzt5R9SnJUfg5bN4VNJL/E1xccWZSrpKUp7LWUQbLI6ih4ZMdoJpjAjJybtmkH19AaAOJkkppKeorTX9IHVEfRKi9HGy1vL9pHiTIpJ29HyBdWahcQXH1uSqaSnKG2kYUCgxuowLNPsa2HasPgsBPr+qWqrQ1Ci1bwXefwvyMrXkEHrtpRTSU9R2ghNg4zzIW0BJE+zOpxu6T1tyRKlsuoTMWs7GpuPHrc6BCVaRgv4jkPTbjjxF6TRakkYKukpSjvCmY9In4fIuhDy7wJX/C1fiGX1oMPVJ5gxbFzsbhChulZr3iCVGAnUwpGfI+u39vutVdJTlG4IeybkXAGa2+pQOtBiXDMvHmvy1be0WB2CEgs1byGrViKb9/fbOj5Tkp4Q4vNCCCmEyG537BEhRKkQYp8QYlm74zOFELvaXvuZaPsNE0I4hRDPtR3fIIQobnfN7UKIkraP29sdH9F2bknbtfExPUgZMIRmDz3y7NtVsQiln1qHLWV7KcoYEuO79E1tiyorNGA17oCKf4Uee/aDqDecFkIMAy4CytodmwjcCEwC8oFVQoixUsog8ARwD7AeeBVYDvz/9u48TIrqXPz49+3u2fcNGGaBGQZcUFEYBBQUF1SMEeMS0Rs1mptE4jUao0aDNyYmJj81LjHeYPRqXKIxiZq4XUMg7msEEUVE2WXfZmCYrbc6vz+qgBmYfbq7errfz/P0Mz2nTlW9dWj6nao6dc7LwLeAemNMjYjMBG4DzheRQuBmoBZ7dpSFIvK8MabeqXO3MeYpEbnf2cac/h6TUu1kHQJ188CTDilF9pmfLxvSyuzfrQB4MwGPfe3Rkwn+DdC6FiQVTMB+798YkXCifaYHMKyolC/r42e2idX1dRgEieHEvSrG6l/HpA6K+uDUkZhl4W7geuC5NmUzgKeMMX5gtYisAI4WkTVArjHmXQAReQw4CzvpzQB+6qz/NHCfcxZ4KjDPGFPnrDMPOE1EngJOBC501nnUWV+TnoooEQ+mfBbiSev5Sunl9muvYzHNK+zk17wCwrv7HM/mpuhf6vvwy2Vkp2XQ6I+PM6ywZbAkDa/Ry5wJy2qBpmUQz0lPRM4ENhhjFu93H6AM+0xuj/VOWdB5v3/5nnXWARhjQiKyCyhqW77fOkXATmNMqINtdRTrd7DPMKmsrOz5QSoFvUt4nW0jswYyazD5k2HnOxBuhMyRgNj/2VtW2WeK+VPAlwu+PAjtgsAW2PU+OGc5q+p29TuW7jQFWplSM4Y3VyyO+r56KmBSyUCTXsLyZIKkRH033SY9EZkPDOlg0Wzgx8ApHa3WQZnporwv63S1rQMXGPMA8ABAbW2tXiNRrhFPGhSe0L4w6yBMuAlMGPHl7itPHQSZIzEZNdC4mP9btoR/fhGb4Zw27IyvGeSbwh4y4uP5ZhVxXig5A0mP/glJtx1ZjDEnG2MO2/8FrAKqgMXOZcty4EMRGYJ91lXRZjPlwEanvLyDctquIyI+IA+o62Jb24F8p+7+21JqwBFvVvuE13ZZ2hCk6FSmTbiSr487KSbxrNq+gSPKamKyr57Y6dehyBJW0bSYJDzoR+9NY8wnxphBxpjhxpjh2MlprDFmM/A8MNPpkVkFjAT+bYzZBOwWkYnO/bqL2Xcv8HlgT8/Mc4FXjN2HdS5wiogUiEgB9pnlXGfZq05dnHXb3ldUKuGk+lJ5+OKb+N3M62IyB156Svx0iN7WHHQ7BBUNGSOR7MNitruo/K8xxnwK/AVYCvwDuMLpuQkwC/hfYAWwErsTC8BDQJHT6eUa4AZnW3XAz4EPnNctezq1AD8CrnHWKXK2oVTCm3X8Obxy9X2cO/bEqO5nwdplDM4tjOo+empHS8DtEFQkpQ6CvIlQfHpMdxuJ3psAOGd7bX+/Fbi1g3oLgAPSujGmFTivk20/DDzcQfkq4Oi+RazUwHb8qLEcO+IIfB4vTy2YF5V9WMZi1KAKtjS4P/ZlVX4m0PderyqOeDKg5GzElx37Xcd8j0qpiPF5fTx52S18+pM/ccKocVHZh0T9cfieqczWr6uEkD8ZSi9xJeGBJj2lBjwR4dDSKq45+YKIb3tKzZFs2rUj4tvtLQGyPfE9z6HqRu4EKL0IyZvoWsKDCF7eVEq5a8Lw0aR4fQTDoe4r91DICrN827ruK0bZaaMOQkzkjktFlxEfRlKwJIUQXkLeHLLzJ8fFuK6a9JRKECU5Bcysncbj77/cfeUecv8rynbayGq3Q0h4BgFJwYgPS3yE8RLCQ9AIAYSAZQgYQ7MVpsUK0xIO0xwOsjsUoCHUyq5gKzuDzdQFmwiZ9o+XHFZ0GFeWxsenSZOeUgnkumn/wT8+fY9tjb2fDLc8fxBVxaUYYzDG7sSyrn5rFKLsvWlVpUCD22EMeNs8xXze0kxTOERzKEB9qJWNLbvYGWymMRy96Zs2N8XPPI2a9JRKIIeX1TDnguv44TP3srYXE8KOH3YIn2/5Mq6GHWurOsfqYrwl1VOrW/38cX1s57BL9aQyfXhsH0voiiY9pRLMOWNP5KSDx/PNx37Oc4vf6LKuz+NlUvXhvLnioxhF13tfOeggUk2j22G4zsC+y4/4sMRDGC9hhKARgghBY1+CDFiGgGXRYoVptcK0hEO0WiGWNq6KedxDsko5qmRszPfbGU16SiWg/Mwc/vbd2/jjv//Bl3Wb+deyBby2/MN2E3UOzSsmLyM7rhMewDWTaoGBM3O6nZx8gBcjXizxEsJHAC8hAyFj1wkYCBiD37JoMWFaw2GawyEaQ352hwM0hwM0OvfLGkOtNIcH3sP5xenFfO+I/yIrxb3emvvTpKdUL5lgPfjy+9QTzRgDuxfac/OlDoaU4qj1aBMRLpowHYDZ0y+loaWJHz83h6cXvUJ9025agn427oqvQaU7cvSQNGKV9ILeXFaHfIQsQ8hYBIyF37JotUIELIugFSZgWfitEEETJmgsmkMBmsJ+msP2z5awDpcGMDhzCD+dcEtMhsvrDU16SvWAsSxoXg7BrdC0FPKnYDJrEE8vx6Y0IXv6lLr59vuMaoykgAlDhj31kHjT7arGQPMyCOyArMOR1Lx+HUNuRhb3zbyWu8+7mkfefYm3Vy5m2ea1rNi2nh1NHU9XNGH4aCxjyE7LYHBuIU3+FuZ+9j6BUPS/2IfnFzIkJ5vMDjqwWJ4MHti6HX84RKsVpCUcYHfIT8AKMWvY0eR6vYQBC0PYQIXPIi28r3OP3VPRt/dSoRHBwsPaoIe7Vr8T9WNLBv6wn9UNqxiRFz+DloMmPaV6xr8bwg3gzbB/b3gPgtug4PgDqprmleDNBqsZmlcgRdP2LhNPCuSMwVh+2PmGPYfeHi0roKEAU3KmPZ9euBm2v+RsNLh3OiLLsruDezwemvwtZKVl9OpQUrw+vj15Bt+ePGNv2fOL3+DCP9zMxROm861jzuSgwZUEwyHyM3MOOBNdumk1p/72KtZHsWdnVmoq/7z4LGoyO++x+dGujp8fvHfNuweUZXvT8ImHViuE3wpqn5gY2OmvZ93udZr0lBqIJCMPMsYDYHLGIm0u2RjLDzv+Ad5cCNW3T2RpFRhjDryEGexkLMtQPWx6lH1PyHkhrRQKpuyt0hRo4bPNa6jMH0x+Vk4Ejg7OHHMcC294hBElZfi8XX8tHFpaxQuzfs2xv/4O/lCQsBXusn5f/OYrp3Wa8Oo9xby0Y1OvthfN7viqc69veJWp5Sd0XzGGpO2N7WRRW1trFixY4HYYKoGY1nWw5WmggwSQNtS+f5c/Ze/lUOPfbJ/FhfZ7nk58kH0EZB9mv/dmIp706B9AH7y85B2+++RtrKvfEvFtXzBmDH+cPrLDh+Pv2lzHF03x8fyg6trPJ97KoMzBMd+viCw0xtR2tEzP9JSKAEmvwOQeBQ0d/DHl32i/Mg+CdHsOZUkbghl6GQTrwWoEKwSeNPDlIr7InL1F22mjJzGscEi/kt6UmiNZW7eJQCiERwR/KECjv4U/f/wJ1QUF3DJpKGLsXouWJx2/ZBIw2yJ1CCrKVu5a4UrS64omPaUiJe9YaFxq38trK2cc5I6zp1NpQ0QgtRCIj/nqektEuP6Ub/DWnL4/0C4IX9Z1nDRvfe013lpTxYvnn0CWNFBPDrO/eLPP+1Kxt60l/noHx1dfUqUGMPGkQOlFkDUafIV2J5chFyGFJyC+XHt5gjnj8MkcNHhYn9Y9ZMhwFq37vMs6r69ZzZC7/8iindm8FQezPajeeXvjm4Ss+BooXJOeUhEkvhykeDoMvRjJHY+kxdelnUgTEa6c2uHcz12aMmIMG3dtZ7e/udu6TYEA4+5/hIU7O36sQsWvnYGd1LXG1x8rmvSUigKR5Llz8PVxJ5Gdltnj+qNLq3lr1cfsaund0GJ3/vVVilOH9DY85bLtrfF1iVOTnlKqX0pyCqgpKe9x/YLMHPrSa7wlEOBP8z4my8UJSFXvfbpjidshtKNJTynVLwvXLuvxRLNHlNXw1sq+d3xZvXUriz9pxJtEZ9ID2YnlJ3NuzdfdDqMdTXpKqT6zLItzHriBJn9Lu/LCrFyOrT6C4UWl5KZnISKk+lIoyOz/4xivLFnC8s9CFJiKfm9LRdfuQAMmzsa/0T+XlFJ95vF4OOPwyTzw1t8Jhu1eeh7xcPaRU6koGExuehbHjTySioLBzPv0fX417zFSfSn9Hrtz/idLePXTpXzrnEMjcRgqSsImHHcDTsdXNEqpAeee866m/s55/PXbv2R0aTWWsTiqfBQTqkZz5QnnUVU0lJKcAi6ceBpvX/sgtZUHR2S/YcuixFtBSdoQUhLwcZBEYBnL7RAOoMOQKaUixrIs3lu9hAlVo/F6vB3W2dpQx8Tb/5PVOzZGbL+TDz6YCWML2R3sfIBqFXvfPORSJpUeG/P9djUMmZ7pKaUixuPxcMyIIzpNeACDcguZ+/17qCyM3OMHby1bxpMvf0ZeSn7Etqn6751N8TdNkyY9pVTMjRxUyTvXPcBxI4+K2DYPHjqUhqA+wB4vslKyOX/UTLfDOIAmPaWUK8ryB/Hq1f/DnAuuJzO1fzNJ+LxeJhw5KO56CiYrQZgy9DjKs+Ovh6323lRKucbj8XD5cWczftih/HnhPJ5aML9PszbMOu0E6gKboxCh6ospZcfxtRFnux1Gh/RMTynlunHDDub2s6/k0588yRXHn9urdQ8eOhQrO77Gd0xm6d4Mvlp1ptthdEqTnlIqbuSkZ3HfzGt56KLZjOjh0GbnTh1D0Orfc38qMrwhoSalktzUPLdD6ZRe3lRKxZ3LjvkqXz18Msfc8W1WbFvfZV1LAu1+t5/CMvZ8har3jCE94CWnJZWcJi+ZTR7SmyGtxZDSZJHSHCZlSyvyZT2msRVrdwvhxhZyairxpqRyyPXj3T6CLmnSU0rFpZKcAuZe+RuO+uXFNLQ2dVrvpdfX8mXdFgKhIP5QkJAV5rvTj8PK2pE0iU/CkBH0kh7wkhHwkub3kN7qIXM3pDcafK0WvlYLT0sYb3MIT3MQmoKwuxXT0IrV2EK4oYlQYwsmEMRYHT9UboCA82pr5BX/wdi7bsCbmhrtQ+03fThdqSRhrDAQRjy9/2La8z0hIhgT27OoFz5+kzPnXNfhsqKsPNJTUtmwc9sBy8ryS5h00EiKB4OV5u5D68YSPPjwWD6wvBjLB5aA2O3qCwlHrAqR0mpIaTX4Wgze1jAefxhPSxhpDiKNAWgKYFr8mOYAVrOfcGMzoYYmTNDdiVrF4+Gs9a+TUTrI1Tj26OrhdD3TUypZBLaANxP6kPQArnv2t7zw8VvUDCrniUt/Rn4EBo/uiTMOn8zKnz/Dym3r+e4Tt7F6x0ZGDaqkIDOHZVvWdpjwADbs3MbT79vLvlY7kSHlgCUYy35wXjwWxrK7NYgvhAl7wQjGEvsnYMKCZUE4DKGwwQpbhMKGYNgiFAoTDIcJtvkZCIVoDQZoDQZoDvhpCfhpCrTuHZe0M8Mkm0lz9s1UEXZeA4WxLDbPf4eqi85yO5Ru6ZmeUqpHmgOtfP3B2Xy47nOG5hVz8OBh3PLV71BdUhazGHY07uKEe75HQUYOb6z4KGb7jbZcUplzf+8f1YgXntQUpi/6O3mH1rgdCqDDkCmlIiAzNZ0Xr7iTDb96gfsv/BGvLf+QET85h+Gzz+KWlx5ie+POqMdQlJ3HS9+7C0ise3UNxu92CP0yevblcZPwuqNJTynVKyJC7bBDmHbI0QCsrdvMzS8+yLOLXovJ/isKB3P72Vdw4kEd/iE/MIngzcpwO4o+++Lex3nvWz+mcU3XPW3jgSY9pVSfbNrV/oHwHz5zL69/8WFM9j2h6jDmX/Vb7jr3qribr62vvPmxuUcaDf4dO1n1h2dpXLmu+8ouS4xPi1Iq5oYXlbb7vdHfzIUP38zSTatjsn8R4QcnXcATl/4sIR5NkJxMt0PoH2NoXh//Q8Fp702lVJ/cfe5VDC8qpa5pF8XZ+SzZuIrH33+Z/337Oe469+qYxTFz/DRWbl/PTc//Pmb7jAaTM3Avb+7RsqnjnrTxRJOeUqpPMlLTueHUi9uV/fDkC6ksGBzzWG489RLeXbWEl5a8HfN9R0ooO/4f7O5OYEe92yF0Sy9vKhXnlmxYyfXP/pYP1ix1O5RujSkfSUFWbsz36/F4eOSS/2ZoXknM9x0pwfQUt0Pot4zyyE0MHC2a9JSKc/e88hR3zHuCY+74NlPvmsV7q5a4HVJcKs7O54nLfoqvi1nb41kgbWDGvYc3M4Nh55/udhjd0qSnVJy7+7yr+eHJF1KSU8Dryxdx8r1X0uRvcTusuDR11DhunXG522H0iT9lYH8dlxw7lowh8X+mrff0lIpzOelZ/Pqc7/PLGbNYtX0DKV4fWWkDv9NDtFw37Ru8s+oTnlv8htuh9Io/ZWD3QG1as4H6jz6j4MhD3A6lSwP7TwulkkiqL4WDhwzv8TxzyUpEeOgbszm0tMrtUHql1Tewh4TcvXwNC3/wS6xQiHge3lLP9JRSCacoO49fzpjFhQ//hOZAq9vh9EjrwL6lB8C2Nxfy3jdvICU/l+EXfIUVD/6VUFMzQ06cyMhZF7odHqADTiulEpQxhpE3n8fKbiahjQdVksMdr3vxL43Ng/1uOHPlfLKrK2KyLx1wWimVdESEH5w4k7L8+O9c4YGETngAa558gaa1G9wOQ5OeUipxnX7YMZ3OtxdP6g+YizzxfPzfv6Fu0Wduh6FJTymVuKqKhw6I2RjqrYFx37G/io4+3O0QNOkppbpnjOHRd1/iX8s+cDuUXrv+lG+4HUK3jAie9DS3w4i6j370a7dD0KSnlOqeiFCUncflT97GloYd3a8QR46rOZJUX/wP8eXNSPykZ8KW2yFo0lNK9cwZh08mOy2TO+c/6XYovZKRms4RZfE/q7ckQdLzpLj/lJwmPaVUj/30jP/k1/Of5M8L5rkdSq9MHTnW7RC6lehJz5uexshZF7gdhiY9pVTPzRhzHFNHjmXWn+7giy1fuh1Ojx02tNrtELqXMfCnFuqMLyeLUxc8Q/HEI90ORZOeUqp3vjnpK9Q3N3D0bZfx949edzucHhk9AJKeSU/cpFd96dnkjx7pdhiAJj2lVC9dNGE6F02Yzq6WRs5/6CYef/9lt0PqVkvA73YI3TLexP06Du7c7XYIeyVuKyulokJEuPXMyxERAqEgFz/yM2556aG4HWR4864dacI+OAAAClhJREFUTP+fH7gdRresVPc7efRX7kFVpDvTC3nSUikcN5rjnp/D+N/d7HJk+wz8VlZKxVxF4WDeufZBLn70Zyzfuo6bX3yQL7Z+yWOX3IzHE19/Sxdm5XLokCo+WBvfM8+H0wfu17EvJ4vs4WVMeux2Co48BCscJrizgbSiArdDO8DAbWWllKsmVh/G29c+wDVP/4YTDhrHlBFj2NG0i5Kc+PqiS/Wl8OA3buTOeU+yZNNKFq37wu2QOhRK9cb1F3JGaQnZIyrJqione3gZ2SMqyR5RQU7NMNIHFyOybz5Aj9cblwkPNOkppfqhJKeAP1x0Ez5ffH+VjCguY1BuAVu/qHc7lE6FfB5Xv5C9GelkV5WTVV1BdnU5OSMqya6uILu6gqyqcnwZ6S5GFznx/UlVSsW9eE94AK2hAHfOf5I0X/z2kPTGYLSSjNIS+0ytqtw5U6t03leQUTqo3dlaoor/T6tSSvVTIBTk8LIRXD7la+SkZ3HpY78gbIXdDqudYIqH/g6W5klLtZOYc4aWPaJi3/vqCnyZGRGJdSDTpKeUSnhFWXmcfPB45rzxNyxj8buZ13HbPx9n1Xb353fbw5/qIbMH9VIL8uyztT2XIEdUkjNyGNkjKsksG4zEWUeieKNJTymV8NJSUvnamKncec5VNPqb+WTDSv5w8U0cf9cst0PbK+xcWRSfj6zKUvvSY/W+s7acmmFkVZWTmpfjbqADXL+TnohcCfwXEAJeMsZc75TfCHwLCAPfN8bMdcrHAY8AGcD/AVcZY4yIpAGPAeOAHcD5xpg1zjqXADc5u/yFMeZRp7wKeAooBD4ELjLGJP5sjEqpXpsy0h4CKyc9i4LMHEJWmLyMbHa1NMY8lvyMHKqLh1JdUsaI4jKqi8uYmFVG9a9+QWb5EDwD4D7pQNWvlhWRE4AZwBHGGL+IDHLKDwVmAqOBocB8ERlljAkDc4DvAO9hJ73TgJexE2S9MaZGRGYCtwHni0ghcDNQCxhgoYg8b4ypd+rcbYx5SkTud7Yxpz/HpJRKfIeUVvHBmqX88OQL+ckLD0R8+x7xUFk4mOriMqqLhzKipNxOcsV2kivIyo34PlXP9PfPiVnA/zPG+AGMMVud8hnAU075ahFZARwtImuAXGPMuwAi8hhwFnbSmwH81Fn/aeA+sbsSnQrMM8bUOevMA04TkaeAE4ELnXUeddbXpKeU6tYbKxb1qzNLbnrW3qRWXVzGiJKyvb8PKyolxatna/Gov/8qo4ApInIr0Apca4z5ACjDPpPbY71TFnTe71+O83MdgDEmJCK7gKK25futUwTsNMaEOtjWAUTkO9hnmFRWVvb6QJVSiWXVto387o1nOl3uEQ/lBYOoKSlnREkZVUVD9ya4quKhFGXlJUUX/0TTbdITkfnAkA4WzXbWLwAmAuOBv4hINdDRJ8F0UU4f1ulqWwcuMOYB4AGA2tra+BwkUCkVM1NqjuSJD+buS2R7klqJc7ZWWDogZlxXvdNt0jPGnNzZMhGZBTxr7JFm/y0iFlCMfdZV0aZqObDRKS/voJw266wXER+QB9Q55VP3W+c1YDuQLyI+52yv7baUUqpLXx93EjPHT3M7DBVj/X2g4+/Y99UQkVFAKnYyeh6YKSJpTg/LkcC/jTGbgN0iMtG5X3cx8JyzreeBS5z35wKvOMl0LnCKiBSISAFwCjDXWfaqUxdn3T3bUkqpLsXbwNgqNvp7T+9h4GERWQIEgEucZPSpiPwFWIr9KMMVTs9NsDu/PIL9yMLLzgvgIeBxp9NLHXbvT4wxdSLyc+ADp94tezq1AD8CnhKRXwCLnG0opZRSHZJ4nQMrmmpra82CBQvcDkMppVQUiMhCY0xtR8v0/F4ppVTS0KSnlFIqaWjSU0oplTQ06SmllEoamvSUUkolDU16SimlkoYmPaWUUklDk55SSqmkoUlPKaVU0tCkp5RSKmlo0lNKKZU0NOkppZRKGpr0lFJKJQ1NekoppZKGJj2llFJJQ5OeUkqppJGUk8iKyDZgbSeLi4HtMQxnINO26jltq57Ttuo5bauODTPGlHS0ICmTXldEZEFnM+6q9rStek7bque0rXpO26r39PKmUkqppKFJTymlVNLQpHegB9wOYADRtuo5baue07bqOW2rXtJ7ekoppZKGnukppZRKGgmd9ETkWhExIlLcpuxGEVkhIp+LyKltyseJyCfOsntFRJzyNBH5s1P+vogMb7POJSKy3Hld0qa8yqm73Fk3NTZH3HsicoeILBORj0XkbyKS32aZtlUEiMhpThuuEJEb3I4nWkSkQkReFZHPRORTEbnKKS8UkXnOv/E8ESlos07UP2PxTES8IrJIRF50fte2ijZjTEK+gApgLvbzeMVO2aHAYiANqAJWAl5n2b+BSYAALwPTnfLvAfc772cCf3beFwKrnJ8FzvsCZ9lfgJnO+/uBWW63RxftdArgc97fBtymbRXR9vU6bVcNpDpteqjbcUXpWEuBsc77HOAL53N0O3CDU35DrD9j8fwCrgGeBF50fte2inabux1AFD9MTwNjgDXsS3o3Aje2qTPX+bCUAsvalF8A/L5tHee9D/tBUGlbx1n2e6dMnDp7EskkYK7b7dHDNvsa8IS2VUTbtN0x7d+uifwCngOmAZ8DpU5ZKfB5rD5jbrdBN+1TDvwLOJF9SU/bKsqvhLy8KSJnAhuMMYv3W1QGrGvz+3qnrMx5v395u3WMMSFgF1DUxbaKgJ1O3f23Fe8uw/5LEbStIqWzY09ozqW0o4D3gcHGmE0Azs9BTrVYfMbi2T3A9YDVpkzbKsp8bgfQVyIyHxjSwaLZwI+xL9sdsFoHZaaL8r6s09W2XNFVWxljnnPqzAZCwBN7VuugfsK3VRQkwzG2IyLZwDPA1caYBucWU4dVOyiL9GcsLonIGcBWY8xCEZnak1U6KEuKtoq0AZv0jDEnd1QuIodjX/Ne7PxnKwc+FJGjsf+iqWhTvRzY6JSXd1BOm3XWi4gPyAPqnPKp+63zGvYlhHwR8Tl/XbXdlis6a6s9nBvZZwAnGed6B0naVlHQWTsmJBFJwU54TxhjnnWKt4hIqTFmk4iUAlud8lh8xuLVscCZInI6kA7kisgf0baKPrevr0b7Rft7eqNpfzN4FftuBn8ATGTfzeDTnfIraH8z+C/O+0JgNfaN4ALnfaGz7K+075zxPbfboYv2OQ1YCpTsV65tFZn29TltV8W+jiyj3Y4rSscqwGPAPfuV30H7zhm3x/IzFu8v7AS0556etlW029vtAGLwgVqDk/Sc32dj93z6HKeXk1NeCyxxlt3Hvgf3050v5hXYvaSq26xzmVO+Ari0TXm1U3eFs26a2+3QRfuswL6+/5Hzul/bKuJtfDp2T8aV2JeUXY8pSsc5Gfsy2cdtPk+nY99H+hew3PlZ2GadqH/G4v1F+6SnbRXll47IopRSKmkkZO9NpZRSqiOa9JRSSiUNTXpKKaWShiY9pZRSSUOTnlJKqaShSU8ppVTS0KSnlFIqaWjSU0oplTT+P/RI3VysqtXtAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "counties.plot(column='POP12_SQMI',\n", + " legend=True, \n", + " cmap=\"RdYlGn\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[50,100,200,300,400]},\n", + " ax=ax)\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we are customizing our plot, we can also edit our legend to specify and format the text so that it's easier to read.\n", + "\n", + "- We'll use `legend_labels_list` to customize the labels for group in the legend." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Population Density per Sq Mile')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "counties.plot(column='POP12_SQMI',\n", + " legend=True, \n", + " cmap=\"RdYlGn\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[50,100,200,300,400]},\n", + " ax=ax)\n", + "\n", + "# Create the labels for the legend\n", + "legend_labels_list = ['<50','50 to 100','100 to 200','200 to 300','300 to 400','>400']\n", + "\n", + "# Apply the labels to the plot\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's plot a ratio\n", + "\n", + "If we look at the columns in our dataset, we see we have a number of variables\n", + "from which we can calculate proportions, rates, and the like.\n", + "\n", + "Let's try that out:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
00KernCalifornia839631102.9851089104.2828704997664892112676...1513.538161.3506103San Bernardino,Tulare,Inyo2495935NoneNoneNoneNonePOLYGON ((193446.035 -244342.585, 194033.795 -...
10KingsCalifornia152982109.9155039111.42742183027110142562...1203.201391.3906089Fresno,Kern,Tulare2212260NoneNoneNoneNonePOLYGON ((12524.028 -179431.328, 12358.142 -17...
20LakeCalifornia6466548.66525349.0823345203312322049...72.311329.4606106None0NoneNoneNoneNoneMULTIPOLYGON (((-240632.150 93056.104, -240669...
30LassenCalifornia348957.4350397.4228562553228341234...120.924720.4206086None0NoneNoneNoneNonePOLYGON ((-45364.032 352060.633, -45248.844 35...
40Los AngelesCalifornia98186052402.399043412423.264150493659985687472828...187.944087.1906073San Bernardino,Kern2874841NoneNoneNoneNoneMULTIPOLYGON (((173874.519 -471855.293, 173852...
\n", + "

5 rows × 59 columns

\n", + "
" + ], + "text/plain": [ + " FID_ NAME STATE_NAME POP2010 POP10_SQMI POP2012 POP12_SQMI \\\n", + "0 0 Kern California 839631 102.9 851089 104.282870 \n", + "1 0 Kings California 152982 109.9 155039 111.427421 \n", + "2 0 Lake California 64665 48.6 65253 49.082334 \n", + "3 0 Lassen California 34895 7.4 35039 7.422856 \n", + "4 0 Los Angeles California 9818605 2402.3 9904341 2423.264150 \n", + "\n", + " WHITE BLACK AMERI_ES ... AVG_SALE07 SQMI CountyFIPS \\\n", + "0 499766 48921 12676 ... 1513.53 8161.35 06103 \n", + "1 83027 11014 2562 ... 1203.20 1391.39 06089 \n", + "2 52033 1232 2049 ... 72.31 1329.46 06106 \n", + "3 25532 2834 1234 ... 120.92 4720.42 06086 \n", + "4 4936599 856874 72828 ... 187.94 4087.19 06073 \n", + "\n", + " NEIGHBORS PopNeigh NEIGHBOR_1 PopNeigh_1 NEIGHBOR_2 \\\n", + "0 San Bernardino,Tulare,Inyo 2495935 None None None \n", + "1 Fresno,Kern,Tulare 2212260 None None None \n", + "2 None 0 None None None \n", + "3 None 0 None None None \n", + "4 San Bernardino,Kern 2874841 None None None \n", + "\n", + " PopNeigh_2 geometry \n", + "0 None POLYGON ((193446.035 -244342.585, 194033.795 -... \n", + "1 None POLYGON ((12524.028 -179431.328, 12358.142 -17... \n", + "2 None MULTIPOLYGON (((-240632.150 93056.104, -240669... \n", + "3 None POLYGON ((-45364.032 352060.633, -45248.844 35... \n", + "4 None MULTIPOLYGON (((173874.519 -471855.293, 173852... \n", + "\n", + "[5 rows x 59 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (15,6)) \n", + "\n", + "# Plot percent hispanic as choropleth\n", + "counties.plot(column=(counties['HISPANIC']/counties['POP2012'] * 100), \n", + " legend=True, \n", + " cmap=\"Blues\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[20,40,60,80]},\n", + " edgecolor=\"grey\",\n", + " linewidth=0.5,\n", + " ax=ax)\n", + "\n", + "legend_labels_list = ['<20%','20% - 40%','40% - 60%','60% - 80%','80% - 100%']\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "ax.set_title(\"Percent Hispanic Population\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What new options and operations have we added to our code?\n", + "1. Based on our code, what title would you give this plot to describe what it displays?\n", + "1. How many bins do we specify in the `legend_labels_list` object, and how many bins are in the map legend? Why?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.4 Point maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choropleth maps are great, but mapping using point symbols enables us to visualize our spatial data in another way. \n", + "\n", + "If you know both mapping methods you can expand how much information you can show in one map. \n", + "\n", + "For example, point maps are a great way to map `counts` because the varying sizes of areas are deemphasized.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------\n", + "Let's read in some point data on Alameda County schools." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYSiteAddressCityStateTypeAPIOrg
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933Public
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932Public
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853Public
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927Public
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894Public
\n", + "
" + ], + "text/plain": [ + " X Y Site Address \\\n", + "0 -122.238761 37.744764 Amelia Earhart Elementary 400 Packet Landing Rd \n", + "1 -122.251856 37.738999 Bay Farm Elementary 200 Aughinbaugh Way \n", + "2 -122.258915 37.762058 Donald D. Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org \n", + "0 Alameda CA ES 933 Public \n", + "1 Alameda CA ES 932 Public \n", + "2 Alameda CA ES 853 Public \n", + "3 Alameda CA ES 927 Public \n", + "4 Alameda CA ES 894 Public " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We got it from a plain CSV file, let's coerce it to a GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can map it." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Alameda County Schools')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf.plot()\n", + "plt.title('Alameda County Schools')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Proportional Color Maps\n", + "**Proportional color maps** linearly scale the `color` of a point symbol by the data values.\n", + "\n", + "Let's try this by creating a map of `API`. API stands for *Academic Performance Index*, which is a measurement system that looks at the performance of an individual school." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Alameda County, School API scores')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf.plot(column=\"API\", cmap=\"gist_heat\", edgecolor=\"grey\", figsize=(10,8), legend=True)\n", + "plt.title(\"Alameda County, School API scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you see that continuous color bar in the legend you know that the mapping of data values to colors is not classified.\n", + "\n", + "\n", + "### Graduated Color Maps\n", + "\n", + "We can also create **graduated color maps** by binning data values before associating them with colors. These are just like choropleth maps, except that the term \"choropleth\" is only used with polygon data. \n", + "\n", + "Graduated color maps use the same syntax as the choropleth maps above - you create them by setting a value for `scheme`. \n", + "\n", + "Below, we copy the code we used above to create a choropleth, but we change the name of the geodataframe to use the point gdf. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Alameda County, School API scores')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (15,6)) \n", + "\n", + "# Plot percent non-white with graduated colors\n", + "schools_gdf.plot(column='API', \n", + " legend=True, \n", + " cmap=\"Blues\",\n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[0,200,400,600,800]},\n", + " edgecolor=\"grey\",\n", + " linewidth=0.5,\n", + " #markersize=60,\n", + " ax=ax)\n", + "\n", + "# Create a custom legend\n", + "legend_labels_list = ['0','0 - 200','200 - 400','400 - 600','600 - 800','>800']\n", + "\n", + "# Apply the legend to the map\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "# Create the plot\n", + "plt.tight_layout()\n", + "plt.title(\"Alameda County, School API scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the syntax for a choropleth and graduated color map is the same,\n", + "although some options only apply to one or the other.\n", + "\n", + "For example, uncomment the `markersize` parameter above to see how you can further customize a graduated color map." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graduated symbol maps\n", + "\n", + "`Graduated symbol maps` are also a great method for mapping points. These are just like graduated color maps but instead of associating symbol color with data values they associate point size. Similarly,graduated symbol maps use `classification schemes` to set the size of point symbols. \n", + "\n", + "> We demonstrate how to make graduated symbol maps along with some other mapping techniques in the `Optional Mapping notebook` which we encourage you to explore on your own. (***Coming Soon***)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.5 Mapping Categorical Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mapping categorical data, also called qualitative data, is a bit more straightforward. There is no need to scale or classify data values. The goal of the color map is to provide a contrasting set of colors so as to clearly delineate different categories. Here's a point-based example:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf.plot(column='Org', cmap='bwr',categorical=True, legend=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.6 Recap\n", + "We learned about important data driven mapping strategies and mapping concepts and can leverage what many of us know about `matplotlib`\n", + "- Choropleth Maps\n", + "- Point maps\n", + "- Color schemes \n", + "- Classifications" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise: Data-Driven Mapping\n", + "\n", + "Point and polygons are not the only geometry-types that we can use in data-driven mapping!\n", + "\n", + "Run the next cell to load a dataset containing Berkeley's bicycle boulevards (which we'll be using more in the following notebook).\n", + "\n", + "Then in the following cell, write your own code to:\n", + "1. plot the bike boulevards;\n", + "2. color them by status (find the correct column in the head of the dataframe, displayed below);\n", + "3. color them using a fitting, good-looking qualitative colormap that you choose from [The Matplotlib Colormap Reference](https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html);\n", + "4. set the line width to 5 (check the plot method's documentation to find the right argument for this!);\n", + "4. add the argument `figsize=[20,20]`, to make your map nice and big and visible!\n", + " \n", + "Then answer the questions posed in the last cell.\n", + "\n", + "
\n", + "\n", + "\n", + "To see the solution, double-click the Markdown cell below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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BB_STRNAMBB_STRIDBB_FROBB_TOBB_SECIDDIR_StatusALT_bikeCAShape_lenlen_kmgeometry
0Heinz/RussellRUS7th8thRUS01E/WExistingNo101.1281660.101MULTILINESTRING ((562293.786 4189795.092, 5623...
1Heinz/RussellRUS8th9thRUS02E/WEzistingNo100.8140720.101MULTILINESTRING ((562391.553 4189820.949, 5624...
2Heinz/RussellRUS9th10thRUS03E/WExistingNo100.0373960.100MULTILINESTRING ((562489.017 4189846.721, 5625...
3Heinz/RussellRUS10thSan PabloRUS04E/WExistingNo106.5928780.107MULTILINESTRING ((562585.723 4189872.321, 5626...
4San PabloRUSHeinzRussellRUS05N/SExistingNo89.5634780.090MULTILINESTRING ((562688.854 4189899.267, 5627...
\n", + "
" + ], + "text/plain": [ + " BB_STRNAM BB_STRID BB_FRO BB_TO BB_SECID DIR_ Status \\\n", + "0 Heinz/Russell RUS 7th 8th RUS01 E/W Existing \n", + "1 Heinz/Russell RUS 8th 9th RUS02 E/W Ezisting \n", + "2 Heinz/Russell RUS 9th 10th RUS03 E/W Existing \n", + "3 Heinz/Russell RUS 10th San Pablo RUS04 E/W Existing \n", + "4 San Pablo RUS Heinz Russell RUS05 N/S Existing \n", + "\n", + " ALT_bikeCA Shape_len len_km \\\n", + "0 No 101.128166 0.101 \n", + "1 No 100.814072 0.101 \n", + "2 No 100.037396 0.100 \n", + "3 No 106.592878 0.107 \n", + "4 No 89.563478 0.090 \n", + "\n", + " geometry \n", + "0 MULTILINESTRING ((562293.786 4189795.092, 5623... \n", + "1 MULTILINESTRING ((562391.553 4189820.949, 5624... \n", + "2 MULTILINESTRING ((562489.017 4189846.721, 5625... \n", + "3 MULTILINESTRING ((562585.723 4189872.321, 5626... \n", + "4 MULTILINESTRING ((562688.854 4189899.267, 5627... " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What does that map indicate about the status of the Berkeley bike boulevards?\n", + "1. What does that map indicate about the status of your Berkeley bike-boulevard *dataset*?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_build/html/_sources/ran/06_Spatial_Queries-Copy1.ipynb b/_build/html/_sources/ran/06_Spatial_Queries-Copy1.ipynb new file mode 100644 index 0000000..a05f817 --- /dev/null +++ b/_build/html/_sources/ran/06_Spatial_Queries-Copy1.ipynb @@ -0,0 +1,1852 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 6. Spatial Queries\n", + "\n", + "In spatial analysis, our goal is not just to make nice maps,\n", + "but to actually run analyses that leverage the explicitly spatial\n", + "nature of our data. The process of doing this is known as \n", + "**spatial analysis**.\n", + "\n", + "To construct spatial analyses, we string together series of spatial\n", + "operations in such a way that the end result answers our question of interest.\n", + "There are many such spatial operations. These are known as **spatial queries**.\n", + "\n", + "\n", + "- 6.0 Load and prep some data\n", + "- 6.1 Measurement Queries\n", + "- 6.2 Relationship Queries\n", + "- **Exercise**: Spatial Relationship Query\n", + "- 6.3 Proximity Analysis\n", + "- **Exercise**: Proximity Analysis\n", + "- 6.4 Recap\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip'\n", + " - 'notebook_data/protected_areas/CPAD_2020a_Units.shp'\n", + " - 'notebook_data/berkeley/BerkeleyCityLimits.shp'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + " - 'notebook_data/transportation/bart.csv'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------\n", + "\n", + "We will start by reviewing the most\n", + "fundamental set, which we'll refer to as **spatial queries**.\n", + "These can be divided into:\n", + "\n", + "- Measurement queries\n", + " - What is feature A's **length**?\n", + " - What is feature A's **area**?\n", + " - What is feature A's **perimeter**?\n", + " - What is feature A's **distance** from feature B?\n", + " - etc.\n", + "- Relationship queries\n", + " - Is feature A **within** feature B?\n", + " - Does feature A **intersect** with feature B?\n", + " - Does feature A **cross** feature B?\n", + " - etc.\n", + " \n", + "We'll work through examples of each of those types of queries.\n", + "\n", + "Then we'll see an example of a very common spatial analysis that \n", + "is a conceptual amalgam of those two types: **proximity analysis**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.0 Load and prep some data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read in our census tracts data again." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/rPWIoCxbLi/2xlgtWbzQE6VsuU3tc11RE0/CTLZKZ9RgcrVCIqQ3hpF7EwF64gE0RaFY9w/JlgtETA1F+BnhL99daTzfozxQ1nBdSdFxmBhPM9oeZn9XhNuLBQoVh6P9cUqWw72l5r9T60i599lVQXl+PI0n/fpgumQRMrRGUJwcTFCoOpsSMw9WSliux2y2wmAqyFS6stVTEw34yaCTgwmuzeY2tdUtFmr0p0Jcn8tjDibY1x5CUQQDySCrJYulfK3RoqcIv7Hd9SRdMZPJ1TIRUyNiqvUhZEE0oNMdM1AUpeE3uX4ipfGaSX9brSiCidUyYUOjLWxyZSbXpMq+RmvGee+zq4IytK7jZs1a7tRgkrlchTemshysCyVPZyoUKnajBU7gr5rdscC2QRnUVbqigUfKcNxffjiz+dJoitcfpDncFyMe1AmZGla9i2cmU2E+V8XUBPeXS35nj5Ss1r9IchWb+VyVF3piTSt81FQbyuzgN7yPtIcZTIZYKdZQFdGYWgH/rDnaHuLBSrnx+L7Ek9n+tXj22FVB+aGDHRztj3N1XYLk0lSGk4MJNFWQCOkNQ9aDXRFm0mUO9yeYr/eznp/whY9jQZ0bc3le6IkxmS6Tq9gMpkJcnMwghO/kPNQWavTPHuyK+MPKtkfEVBkbSWE5Hn2JIEFN4cLk1oG8lgSyXYld1/HpiQcoVG08CXeXCoy0hxlf2Xpbvb8zylfXbXk7oiZjIykuTaRx618yUvqzmRLJg+VSo3e3xd5lVwUlsCkowXfJmk5XmrZza8meSxNpRtdp89xfd668PJ1ltD3MSFuI2WyF4wO+2PH5iUzjNfZ3Rri7WETiuyb/7a1lwN+eSikZbHuylenWQoFkSMfUVBbyVdoixsOg3CCwEzJUTg0lKdUc4kGNK9M5VupWB5fqMijFmt00y/muWp212BXsuqBcyDVLefguyfUAnMxssi93pe+WvPGsucaDlRLJkM5AMtRUxlhj/XRIMmxwuC+G5XjMZioc6onzNEJ22bLN2EiUnkSApXXOYOG6wDP4Q9ILuSoP6gHbFw9wsDvK1ZkcZ0cM+hLBR5rRtti77Lo2u//p+44w0v6wnzSgqawU/TKGJ/0t6qnB5va3qW1mHtfIlG2uzuZ4vjv6yN8TSN6azXNnsUjJcrm/XNhRX+1GkiGdi5MZLk9lm864B7qijA0nMTWF9ojZCEiA2VyVqzM53r+vjYVcddttakvNbu+z64KyMxbgC//8/XzX0R40RbC8hTHrZLrUlIV0d6hs/KgaX8RUKdVcxkZSDKZC7Ovw7REW8k92hjvYGaFie1u2w1mOx/mJDGFT23YVrLke3Y+w+BOt/OueZ8dBKYRQhRCXhRB/Wf85JYT4ohDibv3/T+/5toFoQOfXf/A4P/vR/WTLmyX/h9vDT9VsFjK2360Xay5vzeU5P55mKl3m/nIJy5UsFiy6Y4836AEIaAqJsNHkpRk1NZKhZrGr7WwMIqZKpmQ1ZEue64qQ2vBYZdd9jbZ4p3mSf+KfA26u+/kXgL+VUh4A/rb+8zuGrir87EcP8gc/Mbbpvk1CVTtcPMwt3KAfRbHqULFcBlOPF01+sTeGEH7vbtlyOD2U5Mxwks6Y37R+rD/eKI9stSUO6gov9MYaiaoLExluLxYJm9qOvxRa7A129CkVQvQD3wV8Zt3N3wt8tv7nzwIff2cvzefYQJwXe5u7Y1ZLOzfcWc9OBJ3XU7Jcjg8kmM9X/Uzp4NabgSN9MVaKNQ52R7kwkcFQlYbl3v3lElXH48pMjkLVb1iPmBqnh5JNq+ChnhjnxzObnns6U6FnndZrywpv77PT7OuvAz8PrM+UdEkp5wGklPNCiM6tHiiE+BTwKYDBwcEnvkBTU/nT//pl/vmfvMH1uTwSX1JyfXkkoKmcGkygKAJPSqSsn702fH4DurJJVHk7VAGdUQNdVTE1vyRzZ7GwyYXr9FCSfNWmOxbgyrRfZtmoWLC/I0w8ZPiXI/0APDeepjsWIBUWpEvWI4NtIV/b8XW3ePZ5bFAKIT4GLEkpLwkhPvKkLyCl/DTwaYDTp08/1acqYKj80ne/yLf92lce2ej9Ym+M648xeB1uCzGx+uhsLfjN6V+5u8rZkRQXJtJETI1CzSER1okFNfIVhxd6olyczKCIh67S8aBOdZ2t3r6OMFOZCtZyiWP98cbkyNn6iNdz3VE6owbFDSZA65nPVbes37bYm+xkpXw/8D1CiO8EAkBMCPE5YFEI0VNfJXuApXfzQgdSIX7pu1/gF/7s2ra/s6b/uh07CdqNSCSehOMD8boNga+Wnq84Dd3YYwMJLk9l2dcRZnK1RGfMpCceoDNqcm+p2BB4frBc5Gh/nIDu98h2xx9OmRzojBA21G3V8+4uFlGFf9Zusbd57L+wlPIXpZT9Usph4IeAv5NS/gjwBeDH6r/2Y8D/865dZZ1/eLKfH395eNv7NzZ8b+RJPtBrS3quPpNpuZILExkuTmS4MZfn4mS60bCgq4KTg3GWizUcz7+O/Z0RrszkmoKsUHO5OpPj/Hjab+NLBjnaH6ctbBAyVA71bF9HjQY0XNk6U74XeDsdPf8L8J+EED8FTAGfeGcuaXsMTeHnPnqAP/z6xJb3L+SrHOmLNfwlNzKXrTRUDB5HPKBTsBwU4CPPdVCxXDTFd1wOmypdUYP5vL9SBlQF3dTIV/ztZcVyuTmfpyceaGowX8/1uTzHBxJcnckR1BWuzOQ4O5JCVcSWNc61YGwtlHufJwpKKeUrwCv1P68CH33nL+nRxIL6I7d52iMKeZmyRcTUmjppHsWZ4SSe9Fv/bi0UODWU5M5CnuFUECGCtEct5rMVqraH2LCCrRQtuqJmwyF6PaeHkqyWLBbqLXiV+hn07mKBEwOJTQPVAENtIRby1R3b/bV4dtl1va+PQ1UE/923Pccv/+WNLe9/ay6HrootbQhsV9IWMXYclHcWC3TFAo0kzhtTGV7siXF/pUJv3CQVNphYLTOfr6Kpm4NlsVBjpD1MtJ4kAr9GObFaYqVoEQtodeNaDad+vZbrNbaxD59RkK/aPN8dJWq2XLf2Os9cUAK8NNq27X22Kx857PwkJrC5ikOuUkQRvip7QBPcXSpiuZLx1RKxgE48qNMTD1K2nKbs6hrjKyWO9PkqfImgzmhHmEv1UbB81SFf3bqRfjv2dT6ZW3WLZ49nMiij2zggnxxMkC3bCNj2bFaxnIbHY9lytlxRNxIPGpwbT3NqKMlq6WENMlexOdgVIagrXJ4q4EpJe8RoNNCvcW02z8v7UpRqbiMgW7TYjmcyKPuTQT7yXAf5io3leJQtl9WSRbHmNLam25U/DE1tFPcfVyLpiQfwPNkQ21IVQU88gK4KumIBClUH2/V4dSrbkPUYSoWatHrAt+67Npvn0GOmVHZC60S593kmg/LN6Syv3F5uuu2lkRSvrxM0vj6X5+xICiH8soauCiq21xSEjysvJEMGmioo15NKay7Qp4ceelyODScJ6Cr7OyN4rke1LltpaAr7O8JMrZaZz1X8OcotXi+oq4x2+JZ+A6lwk9P0VrRKInufZzIov3J3c59C0fITKfs7I9xf8pUEzo2nOT2UxJWSm7NbjUo9eut6Y94P7I1yHuvjIlO2OT4Qx3Y9HFdSsz1e6ImRCGp8/UE9wOpBvd7SL2j4eju6Krg+519byXI5MZDAdr2mK1s/cdYWaXb3arH3eCaDMl2yODuS4s3pDDVHEjRUBIJ4UCMV1tF7ojxYLnKkL8FioYqpbd3pU6w9vkG9bLm80BPD9ST5qt2QIYG6Y5flkC3bLBdq9CWDDWGuAxsSMhFTRa9bxOeqDuPLxU2emPmKw+Ut1BHWc6jn8dKVLZ5tnsmgvDqT5/JUloNdEWIBnbLlEjJUchWHG3N5qrbLoZ4YFyczvNAT4+ZCflNGtj1ikNkwbbK2AK5fpa6t04bd3+ELbIUMX1xLqbfKVWy3yePkeH+cUj2Yb8z7Al4PVoq8Vl85dUUwkAqxlK827Ba2IqQrjI22+efaokXIVFvNA+8Bnsmg/NCBDi5PZbmzWERTBO/f30ax5gsYKwKChsZEfcupawIpfSv1taBMhXRe7I3x5TvNCZm1YEyEdHRVYbnQHLQrpRpCQKHq4HogpYemQEhTGRtOIoEHyyXenMkRMTVcT/KRg+185e4KPfEAiZDBYr5Ktuyr3Y2NtlG1XCT+FtXQBFa9v1bgW+uVqjZhUycUUBHSF41usbd5JoPy5X1t/P2tJYKGiislX76zwthwikszD5Mkw20h4kENrX4AXD9ula3YTKyUGmY8G9nfEeHBSonDfTHuLxUZ7YigCIGqCAxVafiFnBhMcGOuwJG+eJOYF0Cx5vB8d4T5XJWXRttQhaTmSA52RajZHpemspRqDtGA1phaWRMFMzWFgVSIuWyFsuWyvzNCoWpzoDOK05re2vM8k0F5aijJ7cUCNcfjcH0A+tJkuqnEMbFaJhbQuDGf58ywb3+3lrCJBDTmclUm0xVODSYxdYVC1eHarK9KvlSoki5Zdf/IhN/LupDnxGCySTXg8lSWkfYwS+t0hPZ1hGkLmzieR8hQWSrUKFRt2sIGhir52v1VwC+TzOcqTKyWGWkPEdD9M+fYSIqlfJV7S0V6EwHKlktX1KQ7ZlCxXRx3580PLZ5Nnsmg1FSFn/rACK/cXuatehC6EgoVm7Mjfr9qvuowmAzyxZtLFKoOM5mHM5T5itNoFr+/UiRiaMxkK3zwQDuTq6XGNvetemO7psCpId+S/fhgnNNDSd6ayyEQDKWCCATJoO5b9El/3Gs2W2EoFSIa0ImaKldn84y0BRmr99POZisc6IyyVKgyvrL1fOdctspLoykUQAiFqzOr7FvnHN1ib/LMpg3+6QdHWcxXGBtONW6bylRYzNe4MJEhV7Zx6iWIeFCnWHPpSwQ4M5T0G8Lrq1u2bNNbtwLIlK0t2/Mcz1cisD2Joalcmc7SHjY5PZykZntoiodpKJRqDoamcGEiQ3vEwNRVLk1meOXOCqcGE+iaQtjQKFQt2iMG5yfSjx24rtkeNxbyXJ/LY7uy8XdqsXd5JldK8IWTj/QlqNhuU89pVyzAxGqZXNXm7mKBDx9ox/Ek0YBGbyJIyFCR0jfUOdYTo+p4jbRrYJvSSU/cZDJdoTtmcu7BKicGk9yYy5MqG5Rtl4ARxJOSwVSIquMxNpxCUwW1ukrC2rDzSrFGMmzQEw9wbgs9nq0oVG32d0QwdZWy5ZIKt+qUe51nNigB2iImf/rGDPs7wxzsinBnscilyTQfPtjOdNrvork6k6VkOaTCJrcWChzrT/Da/RUO98WZy1YQQqAK30JAIDk2EG9o7QAMpoJkyzb56sNz44WJDKeGklydziIUwb2lIv2JIJ0xkzemfKsEkIRNjRMDCQK6iqEJOqJmfRrE/xbQFYEQ/gD1ek4NJdEUgUQiEAQNlem67GV3fLMTV4u9xTO7fc1VbL58x2+1u7dUIlOyOdoXJxEymMlUWMhVWC7WyFQcLNcX2zrUHePN6SztEd9qrjMWoDvmOzcf6o4hhNIkXzk2nGIqXSFf3ayfo9aFrI72xev1ShNdVRoyINOZCtdm86iq4LUHq1iOh6mrKArcns9zuDeGpipEA3ojWQWQDOrcXihwbjzN+fEM58bTzGQqVCyX57ujfPn2Ml+7s7zpelrsHZ7ZlXImUyZXeTiNsVyssVyscagnys35wqYxqkLNBfGwVJEI6UylyxzqjqIqgpsLBbJlm/eNJvnwwQ5KNYegrnJmOAVINEXB8R5mPteyoBcnM4yNpNBVwav3VjnaH2epUGNsOMWr91epWA4fOtCOlB5BXWA7Cm1Rk9sLBY70x5ESFgu+YoIqFAztYclljXtLRbpiJrmKzcv7UvzB18dZKdb4+Mn+d/dNbvFN4ZkNyhd74/zHTx7nX/zJ5abbb84XiBgqseDmYeA3JtMc6Yszm60wn6vRnwwwlS6zkH+4Nc2UbW4tbD7v6apAV0SjA+fF3mhDbnIxVyFb8VdTKSXT6TL7OyP8V4c6KdsuNcclZGjcnMvTFQ8xuVqmI2o0eWXmKw6KYMtVGfyz8tWZHId6oiwXLP74/BR3lzjqlKQAACAASURBVIv8q297bpPqQYtnm2d2+wrw3cd6+dmPHth0e9FyuTyZIWSoPN8d4dRggo8c7KAjGiBTttBVleMDCVYKFvs6IoR0haN9cUK6woPlEi+NphgbSTU9576OSFNL3PW5AkuFGqam0B0PNsbBJH6Wdi5XoWzZ6KpC1faQHizkLQxNIWSqLORqhNeZ5BZrzrYBCTC+XORwX4yJlTJ3lwpMrpb5rb+/z899/s0nFplusbt5ZlfKNf6bjx6gK2byh69OEAlo6KrCW7M5nuuJki37DeSepK6yLpnJ+Lo405kyH9jfztfurRAL6FydzfFib4yoqXFpMoPtSoba/FUN/Na600NJJlfLTaZDxwYSaIp4KPlRz9n0xg0820VKD0UIMhWLnrhJoWqjAC+NJilUHQR1jxMhKNZsMmWbqKGRr9moikJXLICqwGK+xny2SrpscWYo1djifuHKHAu5Kr/7T06RbGVm9wRCPsKJ6p3m9OnT8uLFi+/Kc3/mqw/4q6vzm6YsuqImfckg12ZzdIQN5tZtVTcOOQsBo+3hhp/H2HBq0/luLcvblwiSCOkowp9OAYHluqhCYKoKivD/jFBYLFiEDJV7SyUSYV8pPRHU0DUVXYF81SUS0JhOl+mKBTi3zUxlW9hgqC20pUX8aHuYP/iJMwy1tZoLdgtCiEtSytNP+rhnevu6np/+4Cg/8f5hhtuazXgWCzUmVkoc74/TnWi2mLs+l2dfR5jh9hBnR1K82BtrMq29t1TY5JgVC+i8NJLC1BSSQY1rszk0ReHOYgHpecSCGoYOOP6qKSV0R03iAZ1jA3Hawzo98QAhQ0MRkC7bmJpCrmyRChtMrpY4OZjg5GCC9nWzky/0RHm+O7plQIJvjvs/fuE6l6d2Vv9ssXt55rev6/me4338/a0l0mWLfOXh+UxXFcZXy6RLFs93R7BdiSJ8Eaz7yyXChkq+7HCwO4K9zro5XbY53h+nLezXOR3P48ZcjoFUiMV8FVX4MpSlmsOhngiG4iE9D88TqJrm+9ZJgeNJVFWwUqzhSphbLGK7HrGgQX8ywP3lIsvrdH3WEk/RgMaZoSQly0EI8dhunosTGX7o06/zGz90gm8/3P0Ov7stvlHsmZVyjV/8zkMMpcJNI075qs1wWxhPwp3FIu0Rg2RIpy1s8L59bXTFTFzP5eZ8gaG25uHkN2dyGJrK+Yk0b0xlKdseYVPjxd4YBcttyIJ4rkSg4DkuqnDx8DOxX7q9zP3VMq/cWebOUon5rO8LMtgWZi5b4dx4pikg11OoOlQdlxvzhR3ZLfQlg9Qcj5/540t8dhvB6ha7nz0XlJ2xAP/zPzxCKmJwuM8vyu/viDCxWuIjBzs4NpDA8yQRU+VIf5zz42lChsZz3TG6YyZ3l4qcHkqir9NxXa+e1x0LoKsKuqZwpCfKt77QwYOVEoau4jgS27WwPVAUlZlsjcFUkMnVMrYrOdQdZbVk+ULNMZMXe2OcrVvjracvEWBsOMWZ4ST3lnamUQug1UdYpIRf+sJ1fvkvbmyp6Ndid7Ontq9rHO6L89mfGOP3Xx3n7mKBmusxkAyhKAIFCJuavxWUHgNJP2iO9sdpi5i8HDFJlyxeHm1juVjjxnwBU1d4vjtKLKhjOy7nxtMENKXR57pasihbLhFToNgajoS7SyWGUyGuzz/UBtIUQTTgZ2nDNa0x4ZIK6XxwfztSSmzP48JEhtlsFUX4mkPJkH+23ErweT1vzeXpTwYbniq//+o487kKv/aDxwk8xvyoxe5hz62Ua3TGAvzCdxzi333vYe4sFuvjVGU01R9WFoCuSJ7rCnOgO0K6VCNftTk/vkoypJOp2Mznqr7blu1xa6Hgr6p1u/SX9jULQr/2IE227OEIDUNVONQdxfUkh3tjvFDX1XE9f0vaETUbGVZdEXRFA5Qth2zFxnIk+zsjDKVCvk3CYpFz42nOjadZrVshbIehChbzzd4l/+WtBX7kM+fIbGPp3mL3sWeDco0fPD3ABw+0Y6gqS/kaVcsFIVgtWQhFo1hziRi+o1VAU3jfaBuu51sWCOFnNde8I5/rCvPWdIZESOfO4mZ1PKH4urK2J3FcF0VR+Oq9FcqWP7+ZDOn+tlj6CaIP7m/npX1tGLqCB9Rcl8vTvszJZLp5pOvkoF8PLVkOxwd8k6LeRIATgwnODPsO0ycGk1uKS1+czPD9v/11Jld3vhVu8c1jzwelEIKf+sAIV2ayZMo2roRMyUJVFBASIfwRq6Cuoiu+Lo8AzgwlGWoLE9AUrs/lebE3xnAq1Oj0mcv6K5KsdwskQzq5ss10tsLf31nFk4Kv3VtBUwSGptCfDJIpW0RNnUzJwvN8TZ58fUVWBdxdbA6aXMXG0BTGRpK8MZXlrbk8xZrLQq7MS6MplvI1Lk9lmVwtc7gvypWZ7ZXwHqyU+P7f/jpvzbaMZ3c7ez4owZ9n/NSHRjk9mCBoqEQDGvmqg+1AUFe4vVRkIVchbKokwjrzuTKmBroCIx1hPnygne6YSdWVVGy/Vnlm2B+WNlSFk4MJYkGd24tFwqbGQCrIarHGaHuY4/0J7iwWubNYIGyoHOiKkKnYlCyHy1M5ArqC43pkyjZ6PVHTnwjyQk+UZEhnMBXizXW1yWP9cZYKFq8/SDdKJEuFGkFda3KQ3oqVosUnf/c1vtKaMtnV7MlEz0Ze6I3zfHeUy1MZvnZvlcGUXzq4uVDgcE+Ewz1RshWHquNxdSZPKmxgOb45j6aqDLWFMHUFBXCE4PqD3CYBq1TYbzKIGCoyUrdaDxuUqi6nh5K8OZ3l6lyefMVXsjvSF+f2YoHxFUG6bJOum9M+1+1r1g61pyhWHQoVu2ne0vF8Z+mN3FsqPtIicI2y5fITf3iBX/n4YX5obPDp39QW7xrviZUSQFEUhtrCqAKm0hVsx2OoLYQjfX2fdMni8lSWsuXSGw9StF3aowHiAQ0BlC3JF2+tMJUubwrIzqjZ6I195c4KEnCl5Gt3V0H4Z7qg4Qf3+/e1M9Ie5st3lpHS951cz4PlIu8bbaNmexi60tCSBb/t71H1SmuHolquJ/mFP7vGr3/pDt/INssWO+M9E5QAYyMpTtU1fdJlu1F6yFdseuJBbNdjpD1EyFRIBHT6YwGSEYOy5XJ7wVcz3+jlMdoepjcepCceIFufFNEUhYip8XxXmJl0iY8814GhCa5M56g6LuMrJUbbw3WV9ywnBxMEdIWzIymiAZ1s2UYiubROtvJoX5zL09u30IVMjcO9cQaSQUxtZ/+sv/uVB/zal+60apm7jMf+6wkhAkKI80KIK0KI60KIf1e//bgQ4nUhxJtCiItCiLF3/3LfHrPZCjfm8nW5DggbKpqi0B03MVTJD5zs5z984hj/9mMv4kiJlA4hXaVYsxsr2lS63KQo1x41mEyXGEqFGzYEEslywW8ceK47xiu3l1kt+gF7b6mIEFCt1zttV/ornISq7ZIpWdxayJMp2Y1tqiJ8Ua+NmVVdFcSD/vl2NlPh8nSW6UyF4wMJjvXHH/t+9MYD/NmlWT71RxcpW9uPjbX4xvLYKRHhT9CGpZRFIYQOfA34OeCXgV+TUv4XIcR3Aj8vpfzIo57r3ZwS2Qnf8Rtf5eZ8nq6YyWAqxErRYnylxD841IGQ8F3Hevn4CX+afylf5X/4z1dBwsWpLIf7Ejiux1yuSl8igONJchWbgKZyZSbLB/a3M52pUKja9ERN4mGDN6eznBpMUrE9SpZDzXFJhUziQY0v31mmPxlCVxVChoqqCApVh1zVZigV5Nx4hqFUkHhQx5Vs2rZGTQ1DUxo2fVvxYq9vm/Cof+J4UMd1PQ52R/n9Hz9DItQa/3qneNemRKTPmhONXv9P1v9bE5eJA3NP+uLfSK7N5PjY0R5+50dO8lx3jAsTGWIBrW6X5wtYrWUz7y8X+etrs1gO2B4cH4jjOC6elBzoCGMISUhX6ImZxIM6AV2lbPvb0pWihVAgoHicGkyAEHhIrs/lubdU4vxEmsV8lZf3tfNgpcTtxQI3F/JcnMwQD+kkgjoL+Srv35eiYrukQgY35vKYWvO2eaQj/MiABD+Q+zZMxmwkV7E50OVPn3zid15r0sdt8c1hR/OUQggVuATsB35LSvmvhRCHgL/Gl2ZTgJellJNbPPZTwKcABgcHT01ObvqVbwie57ewmZqK60l+/A/Os1yoMZct80J3nFhA8GJfgo8e6uazrz4gU6pRsCTnJjJ89PkOXMcioGtYUqFiuQRUxW8EEOAhsF2v4U2iKfAtB9uwXQcXFaEofPn2Q9+So/1xrs3kONIf5+pMs3JeoeqgqwrPdUWoOR7T6TJl2yMe1DE0hWRIx3I8SjWHe8uPbwYIGiqVx2RkB5LBhvFuV8zksz85xvPdLXevt8u7Ok8ppXSllMeBfmBMCHEY+BngX0opB4B/CfzeNo/9tJTytJTydEdHx5Ne3zuGooiGJZ6qCP7Xf3SCg10Rhtoi3FnKI4RKdyJILGj4084Sbi3keN9oinTZxtBUXE+wkq81VPJcfHnI4gYZD8eDdNlB1zQqlsTdostGArfm803n065YoK6aXuPN6Szjq2WqjkeuYjOVLnNvqciFiQy3Fgps5wqvK80r6mDST0KtcbQ/zmCqOeNbrDkN38vFfI1P/M5rjzWvbfHu8UTZVyllFngF+Hbgx4A/q9/1fwO7PtGznkTI4D988jh/+jMv8/GT/dQcl+Vcjart8KufPEEiJDjaFyakK7QFNUCl6np4QHvYpGQ5VGwPx5Ms5KubZh09CdPpGrqmUKq5dMVMOqMmz3dHGyKWlivJlGy64wHGRnwd2eVijRODCQ71xtnfEUYAUVPldL2Vbmw4RdBQNxnZApwdSTHQFuJIX4y2ujTI7cViQwvo9FCSqzM5Vos1xkZSjLaHGWkPYzlek+9moerwI793jr++vvBOv+0tdsBOsq8dQohE/c9B4B8At/DPkB+u/9q3AHffrYt8t9BVX9Lx337sRb7nRB99yQCa4jGTLqELgfQknvQoWi4u/krbEdEZXylxd6nI1++v8ur9VVThDzDHA/6Hvy8R8KU+NIWQrqAoMNQWrtsilFHWrWZV22G4LcTESomaKxlfKXF5Kku2aJEpW6TLNoWay5WpLB8+2MHtxTzZeqPBesZGUpwbT/NgucS12TyaKhqjazXX4+RggouTfkmlZLmcH0/zYKXE+Eppy4YDy/H4mc9d4k/OT70bb32LR7CT7OtR4LOAih/E/0lK+ctCiA8Av4HfFVQF/pmU8tKjnuubnX19FJbt8s//6GtMzRX5rU99kD9+bZy7C6sois5c0SVbtjk2mEB4kprr8er9tD/OZWpMpEtoioKurmnDCko1hyN9MQR+AunNmRyeJ+lPBumMmn5ZxPZYLVncXy7RGTUxNcF05uGUhyJ8t+iIqVFzPK7M5IgFNA50RVkp1HA9j1TEpGq73Fsqbur0SYR02iMmqbDxtraj/+23HuRffMv+lpTlE/K0Z8o9I5z1TvBTv/dVCoU8/+TDR/nuEwP8yl9c495CHqEoFG3pC16FNBzP7wK6Urd3L1suA8kgsaBGZ1hnoWjjeRJDU7g2m+elkRRBXWUuVyEVMggYClOrZQo1l5rjcaDL97+8v1QgFjQaW9OzIynmc1Wm0ltnRN83miJXsbkxv3liJWyo9CaDOK5HIqijqn6C6t5SgfZIgNnsZiOjR/Gj7xvil777RVSlFZg75T0vnPU4qvbjtVHboiH0YIL37e8E4F99xwt84IUepHRIBgSuJ8mWHRRcXMflzECcaODh8PBCrsZs3mIxVyMS0H235pEU1+fzLJdqjLQFiBu+NcL9lTJ9CV8v9uJEhvPjaVZLNhXL4UBXhGP98frrWU0qCGuMjaS4Mp0lEtgsOh02VHoTQe4uFumIBrg8nePiRIYHyyWO9CWYz1Xqkps7549em+RnP3+5pTH7DeA90ZAO7GjyPqTrmFqF24t52qMdmJrKD5wY4D9fmqEt5GLqKqqiUHUlM7kKVctlMf+wVrhashq1w8KMzZnhFJbjUa45XJ/N0xlOoWkK1arD8f44npSMjaSwXQ9dVXBd//xarjkkAhr3losEdZW2iEm2bFGsOQQ0v4f24kQaT8L58TRn6+dJ8AOyJxHk7lKx6XaAiu0ihJ+EepoN0l9dnSdTsvj0j57eJGHS4p2j9c7WWSnWWCza5GqS/+/qLLlijfcf7CQeMijZHu1CJaBIhCKYylQYXylzqCdKtupsWQc8OZjk63XX5rMjKSQwkfZXS1VV0DUaoltr9MRN+hIh7i8ViYd0MmWbwVSQ8ZUSQV2hMxrA9TxChtZ0fqzUdwFrAXlvyRcH20o/tlhXPthq9d0JX7+/yj/+31/n93/8DO2R7VUQWjw975nt6+OQErIVi+vzRWayVf704ji//IW3mFwt8YlTfQihoSBQkBiqv+renC9wpM/vMc1V7UZP7dhw8wp1ZTrLSqHmqxhYHtmyTTKo80JPjEM9Uc6OJBkbTrJUsLg4mcHxJG/N5jnSF2/o7VRsj9lshYV8jTuLedb3nC/mqhiawqHeWKP/tnML2ZC+RJClQo1syUJKXz7kabg6k+OTv/sac094Lm2xM1pBWac9YvCJUwMMpkKkyzZS0VnJl/j3/+91AqogFjJZKbss5au0hw1ODMQ5OZggqKscG4gz0hYmGTJ4aXSzqvq+zjARUyUe1JnLVRGKADw8zyOoq+QqNm/O5AhoCusTnIamcHIwydmRFCcHEwzVa5CaqhALGpytqyB0RE0sx2uY1CqCprIL+AFZtf2uHduTXJ3NcbgvzrGBxzeub8WD5RLf97+9yq2Fx0tftngyWtnXDXzw3/8dc9kqJwYSJAIqAhfpQTwa5G+uLzGcMmmPmBQsj4uTm+U3jvbFubpOcqMjarKvI4yQAlNXeP3BKmMjKfJVh1RI4+9urzCYChINaFyfK3BiIMHl6SwdUZOeeIDbC3lq6wY4h9pCREzNnygp23THTJbyVV7sizfa/DaeJfsSQWqOy8o2+rIv9saYz1WRUta/FATgC1aHDNU3HpLgSV/8REroipkUaw6eJ/n0j57mxGDynXj79xStksg7xG/87R2+dGMRrd6WFzFVpGuBB6ppYDsemgJVx+P2QomlDR/0o31xwgEV1/U/xGsF+zU+dKCdr9xdQVcFZ0fbCOoKhYqDqSm8Pr5KMmwymAw1VtuTg4lNVgURQ6VkuwR0laFUiFzZYqlo4XqSF3pi3JzPr/kM0ZcIUHO8bQNyzc5vPQc6I7iey2KuRmkLiREhfC3du/WtcshQ+cyPnubl/e07fp/fC7RKIu8QHznYybXZPJencziepFBzcNEQiopj2ahCUq1WkG6N57ojmx5/dTbH9GqF8xPpTQHZm/Bdo/d1hBlKhbg5l+drd32lAkMVvH80xWAygKb6BkDgT3EEN0yIFC2Xo31xhlIhFAG9iSCnBhOcHUlRqtmNgPTXO7YNSPAdrts2uHXdWy6SCgc4WlfN28iZ4VQjIMGXGPmxPzjPX1zZ1YNCzwytoNxAvvqwhe3iZIay5fDK3VWEqoD0cF0bwwxg6BqaqjDa3tzcPdQWanJ8Xs+Bzghfvr1MW9ikPxmiNxHkaH8CTRUUai5FyyNmqBiKwv7OKJoCyZDB0YHmrWFAV6g5vhZtyNC4NJVF4q9gk+mHyZfh9nBDdW87SpZLoWpzat32U0oaCaf1jI2kSIX0LbuDbFfys5+/zOdbbXlvm1ZJZAMTq83dM3cXS5wdSVFxJEHdQEh/rlJRNMqWy/hK8+93Rs1NpQ6AI30xvn5/FQ84P5FGVwVhU9vUxxrQFAZTQfqTIU4OpTg/nqY7HmAgFWQ6XcHUFEbbI9yY9xMsax02FyYydNZLHWsKBdYOC/2WK/G2OMbcWSyiKwLb870657OVhsDXVkgJv/Bn11gtWfyzj+xrteU9Ja2VcgOHuqMI4ftanh1JcXwgwbnxNJbjUqx5VDwFWyosFx0sTzaavte4MOErBqxPfh7pi3FrodAk52G7kpCxuaGh6njcWSoxnSlzrZ4wWshVmU5XeGk0xYHOhwHZEzeZzvjyJAIYSIXQ1If/pFuVRbZjq8xCrmJzcihJfzLIUr7KdGZnJZBf/evb/Mpf3WyJcj0lrZVyA6eHU/z333GIv7w6x7nxNCcGfK/IsuUSDih89e4q0YDG2HCKdL3e9+EDbWTKNldn/WCJBnS6Y5JoUCcZ1Fgt2Zv1dRRBfzLESFuYYs3hykyzSPLdpRIvj6ZwJbiuRzxkcO7BCkXr4dY4bGqkwgYL2Qqnh5Ncmsww3Bam5niMtocbwbsT5rMVBpJBZrMVzgynKFsOhqZSc1zSxRqVx2jKbuQzXxunWHP4le870uqXfUJaQbkFP/zSICC5MpPj3lKBo/0JVEUQqiud98aDTGfK3F0scrQvTkBTmUrnCOkKZdtrGPeQq3KkL8b4Som+RJBU2KBsOSRCOgFNpWQ53Fouka/6VgT3FgsYusL+zgi5ssP5iQxH++NkKzYXp7J86EA7V2dzjS3vvaUSbWGbsKlxYSKDoSlYrsdAKsir91e21IfdjsVCjVhA47muKKvFKveW374syOcvTFOyXP7jJ4+hq61N2U5pvVNbEDI0fvqDo3zXkR4KNRdXShJBDduT7GsPY2oKHRGT/Z0RrszmWC5ZDLeHOTaQ4FBPtOm5ArqK40lmsxWuzea4X593DJsqAtEYeH5zOsuxgQSGqjCbqXJ3qYDjSd6YyhIxNV7oiVJzPDxPcqQvztF6J9G+zghT6TJhQ+VYf5zZbAXPkww/hc16vuqQrVhMZ6oNf5K3y19cmePf/Pm1x0qStHhIa6XcBiEEv/J9h5nPVXhzKsvpoTgVW+JKWC3W6IwFWCnWCOkKrifpjhl86dYKz28ok9zfQkfHcjz+5sYSABFTRVcFw21hVks1VEVQsV2O9MUxNbXuN+IQ0NWGb4mhKURNjZdGU0yslDjQGUZVFC5MZBgb8ZNDJwcTKMKfqUyXtk/OrEcRYKgqNafGdPrttdB1RU0G20IoQvDnl2e5v1zi93/8DPHg5qmWFs20VspHkAgZ/J//9CV+8gMjDLdFeHkkzs98eJgPHWhnNlPm5ECCofYw87kqxZrHoe4o+YrD2HCSM8NJxoZT9CcCj3yN57tjjLSHiQQ0bi0U6YkHSZcsrszkOD+RpuZ4XJ3NcWU6S7JuXntpMkPZcnn9QZrR9ghhQ+PWgj9TubYi3V8ucrQ/Qa5sN9rxHkVn1ORgV6Th9rWQr3Kga3MddqcMtYW5MJFpaNtemszwjz79OivF2uMf/B6ntVI+hoCu8vPf/nzTbUf7k7w+nkYT/mRGOBXClR7zuSqrJaspS3lmONko4m/FTLaMguDOYpGxEd8/JKAJqvXWupWixemhJK6UrJRs9nf6gSKl5ORgApDkqw4v9ES5MV9orKa5isOb01kMVfDGZIaxkST3loqbVs1YQGMgFeL6XH5TZ8/bGc9aX+9d48Z8nh/83df43E+fpSf+aOnL9zKtoHwKehIhvvd4H5cm00RMHYnk6/dXOTGYZNTz+0fLlstbc3luLxR43762xhjXelJhA1VRmK0HsWV73Kgr3K1te8dXSoQN9WHyCH+b2RMPEjZVOsMhAm0qiuKr4V3bYId3YjBJzXEpVh2e64r6zlwClgs1Vutn4aszW9vjrT6iE+hxlLc5Q95fLvEDv+0H5kj7k5973wu0gvIp+fiJPr5+b4WAoeI4HqcG/dWzI2JyYiDOYr7C2EgKz5Ncnc4yNpLkynSOmuOXFlIhnbChNq2qrvTv64oF0BTB7cU1/xJfq6e9rsdjaAohFVRV4EkHFbBchTuLBV+/50GaeFDjYFd0y5nKNXRFbBuQAN3xwLZSJI9CwCO3qbPZCp/4ndf4458+y3Pd0W1/771KKyifkmLVImRorJZq3F3I05cMcXYkhScltxYLZEo2k+mHAXF+PEMqZHC0P8z4csn3qSzbHOuPNwr+Ekk8qHFjLsfhvgQRU0erW6ZPrJb58EETT3ooQiFXc4gbAsT/396bR0l2VXe637lDzHPOc2bNKpVqHoQQQsx+bYOxcbcXYMBNG9osu71oP5pnPw80z7SH5+e2l9t+vBYNNhjb3WAb02Y00AiBhpKqVKWqklRz5TxnzHPce8/740ZE5TxVVmaWdL+1Qqq8cW/Ezsi745yzz96/LTBRqBgSVVG4MJziVF+U8VRx0cyi2VRW2DNZbUbQfI71RBfk/c5nOlviZx97mi988CQHOxfPsX214jjlOhmKF0kXyyRyJR7a1cAPr01zbSrHka4Ig0tELuP5MvH+Mve3hciVzXpwZjEMy6JsmpTN285jmJJ82SJbKjEUz/OaHTFUQENSEtDX4KMj4qFQsfC6lv7THuuOMJ4uoSh2FzGlXq5lt9OriUvfnMqxq9mPR1PxuTWmMiVifhdnl3E4XRErOmSNZL7Cez5zmr/41yc40btyMOrVguOU62QsmSPm0+2qDMuiwa8zmq4gsDsxDy9Tlf/iWJr724P2Zr+xeKZMMl/h8ngGAeyoqqg/WV2XnuiNEm4Pky6a+Fx2S/jGgBtNtWUuT9+yOzWf2hFlLFlEStvZZFWbR1OVFdXsjnVHGYjnaAl6eOZWvN4u79Z0ztapnVl8WluxJKoiVt1eL1syeN9nT/OZ9x/ndbu3TkF/O+FsiayRb1wY4cZ4mm+/NME/vzxFS9BL3pTkKxbHeiKMp4sMJwsrbkO8OJphT4vtmMshsXNfu6J2tPJkX4zn+hP10ci07PbqVyayJHJlLOD1uxtRFUE8W8Gra/Ui5cmM3XJhuXUmwJGuMGcHE0xny+TKxgIHa1hCm6cx4KI75ltzv8tixeLf/OUZ/tlRZAccp1wTJcPkvz87xO984yWwJA/vaiRZNKgY0Bj0kMhVGE3ZpVKVVXRV82nwHgAAIABJREFUvjSSYndzYFHHnJ3LXVMwf82OWL1sqjXsQQjw6iq7moPsbg4Q9Gh2BYuwzw37NHJlg13NgTkdoZejOejm+uTthIfzQ6kFie3nBhP1jKLZTGfLBNwrqwYuRtm0+MhfP89Xz4+s6/pXEo5TrgG3pvK2A612Zg8CVYFrExmmsyW7Hbqm1IW0SoaFT1/5431xNM2elsACdTk5b2dzKFGYU9/YGvbwXH+CTMng9K04L42lsaTdB+QH16bRFAXDtNjZ5F/Q9Gc5ehv9ZEpzGxa1huYmQFgSrkxkFtWOvTyeWXD+ajEtyUf/x3n+x3Ov7ppMxynXSKFiMZEuUqhY/OjaNA0BF2OpAhXLPn5tIsPxnqjdUNalcbBjZdHjSyNpDnbMjUAai7TVOjOQ4NG9TZzojeLWFDsxQcDx3ii7mgN4dIWwR+NYT9SuanHrpAoVhCJ4eFfDqn4/a5Gp53Ayz3y/LhkW/dM5OqNzkwAe6Agznl6+sHo5pIT/4+8v8hdP3lr3a9zrOE65RuzNeLsVgClhMF6gZEga/G4OdkZoDLqRSDoiXrIlA01VFty4i5Ep2RkwmmI3sG2LeDjaHeFId4RjPVH7310RErkyz/UneOZmnOf6E5wfSpLIlRECvnd5ikLF4rn+BBXTZDxVxK2pXBxKki5UVtVyfTEl+XiuwvHeGD3zWujlyuacXFavrtTT9O6UT/7TS3z68Rsb8lr3Gk70dY0c6AiRLlS4NZMj6FbJlG7LOv7gqh31HE4UONwZJuBSuTCcsqd5UjK8nDSHlBzriTKWKiAQPHn9dgaQrU53e5vhLfc11zf1a/1KZqqb9bemczyyu5Gnb87QGHCjCmgIuLgwkqY94uHRvU08fmVqSTPmS1PWqCmxz3e6F0fTtATdTGRKVExrgYTInfAH37pMxbRedc2FHKdcI27ttgq5IuwuyEOJAr55+4KpomF33uoMcW4wRczvoj3iWVIzx6WpS+7/Zeet8WZyZYQQeF0qIY/GoY4QLWEvqXyZZMGgZFic7I2BhIBHZXdLEI+ucW0ys0Akaz7xZVq2XxpJ0RhwLRDi8rlVDobCWNgi0hvJf/7OVXJlg1/7sX2vGsd0nHId/NSRDv7w21ewJLRFvFgScuXbjqMI0FWBKSWJXBlV2Df7yd4YAbdG0KNRMSUXhlMc7Azjd2k8fXNhbmyNzLxO0QKBqghcqr3PqauCVL7MrZk8k5kSr9/TRKlioCiCimURzxl0RD31YFLIqyEtWR/lOyNeAh6NkmGyXNA46NUZTy38UpmvU7TR/Ncf3KRYNvnE2+9fciR/JeE45Tr4pTfsIpEr84VnBnj2VhwhoDNqCyrvbg5wZTzD1YkshzrDFCom7RF7NH1hOFEXVvZoCgc7w8RzZdwrVOXX7kNFwJGuKEGvRjlrUagYaIrC84O381dP9cX4wdUphIDX7mzg5lSeBr+LeK6MpgqujWfIGxaNATct1UqNwXh+2WSHGjPZEm5NzBGH3iw+//QAJcPid3/qgVe8YzqBnnXymz+xn2/8ysPsbQkiJZy+FcfvUnl+MFnfVxyK57k6kSXg0XiwLzbnZg55bUmQ7piPs4MJYvOmlRHf7QBK2KdzrCdKS8jD2cEE6UKFiyMpLgyn671Darg0heM9UU70xAjoCjO5MpmSwfmhFEG3xv6OEEGPnTJ3fTLL9cnskllF86mYkkNLaMFuBv/9uSE+9uUXMFaxB3wvs+JIKYTwAE8A7ur5fyel/ET1uX8H/DJgAF+XUn78Ltq67djVHORvPnSKd/6/TzIUL5ArmXh1pS4yVZNjfLna1PVkn73OMyyLm9O5ugr6oc7wAuGsgx1hnr4xg8+tUSxbXJmwnz/aHeH80O3yrIolub89WE82MK3bqux9jT4yRYMD7W5aQhWuVh34aGeEx69Nr+t3ns6sv5xrI/iHcyOUTIs/+dnDr1jdn9VMX0vAG6WUWSGEDvxICPFNwAv8JHBQSlkSQjTfTUO3Kw0BN//x7ffzX39wk1zZwO9S6wEJKWEyU6Qx6KZQNpjJlLgxvVAe5PJ4muPzKiuevD6NKSFTvF3YfKwnyvODiTnZPoWyyYujttOf6I1QrrZVuK8thN+l0Rz0UKyY1Y5gApdqt/I7UN34n12nuRoqSwhNbyZfvzCGYVr8l3cfXTFN8V5kRaeUtnhnbY6kVx8S+Ajw+1LKUvW8ybtl5Hbnjfua+cq5EV4eK/HivJv8/vYQLwwmly2TKhmSF4aSHOuJ1iOwtdwBS9qCyyd6o0gJblWhuMR0M1+2aAt72NEU4OK8KOihrjA3JnOc6A3T0+AlX16fcw3FCzQF3ExtsazHt1+c4CNfPMufv/foqhoC30us6mtGCKEKIc4Dk8B3pJSngT3A64QQp4UQPxBCnLibhm5nhBB84u33884jHQuee3E0vWLdItjT0OcHEhzuiuBdRKS5loTeHPLMWW/Ofy/DkkR9Lk722RpBJ3tjHOuOMp0pc19rkDP9SbJFW+V9pcT0pahVrWw137s8yYf/6uwrTilvVU4ppTSllIeBTuCkEOIA9igbBR4E/gPwJbHIRpIQ4sNCiDNCiDNTU0tvWt/rNAXd/NKjuwh51h/QlthSk36XWk+hq6nT1V53MJ5fVj4yW6xwdiDOdKZMIl/m2f44ZwcTjCQLKIrds+TyRGbZWs6VSBYqC9Lutoonrk7xr//yWfJlY+WT7xHWNCGXUiaBx4EfA4aBf5A2zwIWsKAXmpTyMSnlcSnl8aamV3a9nKIIXrNzdTmmyzGdtVPpHtrRwGC1bjE9a6+ytom/GJYEw7KddyZX5kiX3Y2rLezGpSr4XCqZosH+tpVzcpfiynhmW7VWf+ZmnJ//3HPkSq8Mx1zRKYUQTUKISPXfXuDNwGXgH4E3Vo/vAVzA+kJ6ryA+9ta9q8p1XQ1P3pihp8G34LhhSXY0LS//aFiSeK5MuljhykSGsVSJH16fZn81wPNcf3zRKo/V0BJyk11ErW4rebY/zvs/9+yiKnr3GquZa7UBnxdCqNhO/CUp5deEEC7gc0KIS0AZ+IB0OrqwuyXI//dzx/iVvz1HuljBkiClhWEBUtrV/1AvBDYsCyHE7S2N6jk1BuN5vC51wbppdjXHbAnL+X+CW9M5hLAzgBRh/wF11X6/YsWkKehmJlua0+JAEba05vz9y9riJOzVmUiX6Ix60RSBRGJJ2ybB3J9Xc0PMt732GcHtz6v2a9V+18Uy7vJlg9/+x0t88h0HCC+x7r4XcDo53yW+dWmMX/zi8wAc7pq7tzibE71RJtLFJXV9AE72RTmziAjWSvGjB3fEuDKe4XBXhO9Xk9BP9EZXFNRaDS5V8EBHGEUR9dfTFHvqvFrmt4HfKA52hvmrD57acsd0OjlvMw51Req5pheHk+xuXny6WTYslBUTrYU98sx7rEQiVyGRr/DSrG2a+Xm066VsShRFMJy4nffaFFx9cbNbUzDv0oBwYTjFz332NMn81iY6rBfHKe8SbWEvH3/bPoIeDVPCWKrAke7bKWoneqP0NvjIlQx6Yt5lNX3WG+isdZSuaasKAaOp5XNcj/fYLRce2tnAyb4YJ6otGJZiLFXi/vZQXZ6ka5Xr6ZJh3dWtjIsjKd7zmdMklql62a44Cel3kQ89soN3n+rmz79/nU8/foNzg0lO9NqqAJdGUhQqFqf6Yrg0lUJl6QBF2bQ40h2hfzpHYplOyrMJe3Xawh68usoT1ZS6lpBn0SqPGke6I/WsotpWDEB3bGGwCahnLr04mmZ3c4CemJfvXl79tpciqDu8rP8HLCnRVGXRNu5r4aWxNO/+zDP8zYceXJBbvJ1xnPIuE3BrfOwtexhNFvjq+VF7q2NnA4WKhVuzW+FNZUrLFgefG7TXo/e1BVftlPe1BRmM5+mI+OiN+UCApool+5oc6Y7U30dXBWXDJOzVSBUMDMuiJeimYlr1fN6emK/eaRrg5lSWdMFWQFj9rFQsub4Ne3VO9EapmJLLY+kls5hW4vJ4hvd85hn++hdOLanCt91wpq+bgKoqvO/BHvqqm/5P3Zjhdbsb8bk0XhxNYWHnoO5qDnBwGcmOmG913/aHuyIoQtAR8aGqAkNK+mfyTKZLRHw6h7vCHOu5PZXe1exnYOZ2Tm7IozOSKJIq2OvP0WSRiUyJVNHgWE+EBzpCDMTzc6afpoSJTBltDVkFy0lRpgqVutzJcp/JarAd8/Q90/HLccpN4nhvjN9++34aA24UAT+8Nk1LyEN71EdTwEVHxMP1ySwXR1K8ZmcD+9tCzBO4w7Asjlene3tabAcOuFUCLpVjPRH8LhWXKtBUwVM3Znj65gyWJesBp3TRIODROD+U4uyA3Rn64V0NXJ/MzenGNZMr01dNpZvtZKYluTiSRlXs20YAe1sCPNAR4kB7iPYV2v7NR1nl3becGsJquTKR4b33iGM6WyKbTNmwuDqR4df+/gKXRtMc7gzz4miKpqCLtoifXMmop8DtbQ2SLRpMZYqUTUlLyE3JsOiO+fC5NJ6ZpVagq4L2sIeBeIGOiIegp7YdIPHpGromCLo1MsUK/TMFumM+JJKzA0lOzFo/1mgKuIn5NUJe14Ip5qFqcfZ0tkxhltDWci3/FuOBjvCcKfBS7GkJcHUiu+J5q2FvS5C//tCpTclIWu+WiLOm3GRcmsKBjjAP9sVs7R0h2NEc4Mp4ltHU3BHhyngGRUBn1Edb2MPl8QypQoVkPsWhrrlTuoopGU0VOV6tNJHcDui8dmcMTRFoAjy6wqF2L5ZQiecN7msL8Vz/bYfsbbBrMBsCOjcmc3jdJXY1B4j6dMqmxQtDKXRNmdMtrMZiDulzqdzXGmQgnl+g7bNayZ21TIlX4spEhnc/Zgd/moLbc43pTF+3iJIp0VWFl0eT+HSN1+5sWDRCaEk7q+f0rTipwu0p5tXxhWLIFdMucO5t9M9RMB9OFLg2mcNCogmJKTTSJQtNUfDoCoc7I/UGsXYnsTKXx7NULEm6YHB9Mstz/QleGErRGvYws0q19fvbQ4S9OmcHk4vWPQpYoL6+OBub/X5tMst7PrN9u0o7TrkFpIsVLo4kGYzn2NkcIOrV0RS7wuNEbxSfS6U15F62EqNQsUjmK2iKWHDerekcF0ZSHOwI0xx0ky4YjKaKZEoWJioSO/umaJg8P5gk7NXpjHo53BXh2uTy1SMCyeAio2QNVREc6Y7Q1+jnxdE0Y9UtmJaghyPdEXY3B9hVfXh1lXzZWDKxooZnFUrza+XaZJb3fuZ0XZpzO+E45RZQrJiEvDoPdES4OZ2nIi1mchXKpuS5/gT5si22FfG56Ip5OdEbZW/LwuaqI0m7lYElWfTGvjCSYjJTorUagEnkK+TKJtmSCUIQ9mjsaw2SK9vr2BtTWfoWSYCfTXfMj2nZYtOL0RxwcW4wya15CgvnhpKcG0xyraoLdH0yy3i6SLZkLruHeLAzzAtLpCjeKVcmMrz3v20/x3SccgtoDnr4rR/fj0tT6GnwE89V8LlUTvbaGTTHe6L0z+SI58qMJ+3mr1OZIu3hpaObIa/O0e4Ih7vCC86T0haRTubtkrBKRXJ9IstIqkijX+fqhD06ZooGUb97yeyitrCb65MZTvRGaQktPu0cS6/+Bl8p2NIa8jCcKLBIB4cN4/K47ZjxbeSYTvR1CzEtiSUlF4dTfPr7VxlLl5fVzLm/PbRAbmQ+Hl3hkV1NPDcQX5BoUEsAP9Yd4exgkq6ol2S+TE+jH79L48ZUlqjPRdiroypwbihVrxRRBPQ1+on57Wjsqb4Y54cSdyQ3ubcliMelMJ4s0hnzkS5UyJdNKqaFEHYCwUZFXVfiHYfa+eQ77ie6gZk/TvT1HkRVBCqCoz1Rwj43S+WK2+loMQzToiPiXbbh64H2MM8PJWgOukkWKnOya7Ilg1N9MbxVTRuXppApmXNUzf0ulWsTBUI+F0JK7msLolSnuuPpEhVToikwGM/RGfVxY+r2NFURq0uUr3GlOkLPFw2rMbGGUfdOGU8X+bnPnuZvfuHBLa8ucaav24SXxjKcGUjw5n3NeLTbkRtNERztjnL6Vpyzg0mms0UOtIc42h1ZNKJ5ZiDBjqYAVyayPLSzgX2twfrDtCSpQplkocLu5sCiLQwG4gVSRZOheIGSKXl5LMNMtoQpwe/WOD+UxKUqRH0uJmd11zrZG+Vo99KJ6zXc2sLo1ZmBBPtaF66ZNxVp5/C+73On50S5twJnpNwGVEyL/pkcYZ+OheRkX4yXx7L0Nvp4YShJ2bi9QV8yZH2Ke6A9RKFizhmtatjNX7NLjjb3twd5dhV1lYqA9oiX07fi9T6X+YpF2ZS0R314dbulu0tTGV1mBI/5Xexs8jOcKBBwawQ8GoZp1VX3/O4tvhWr3xUXhlP8yt+e4/MfPLllpjhOuQ04P5SgIeBiKF5gb3MAny7Z1xogVTSQkkWdDm5rtmoK7G8LkypWGJjJ49UVGgNuXhpbev25GllGgT21RAh72utSuTqewbDkAmX2ldjVFKiLT9eYrTOkbqESV8zvmqPksBFpfXeC45TbAE1RaAt7GIoXMKVF2RD88PoMvQ0+Ij7XihqrhmVvfwTdqq3Cjl2doioCa568SI2V4nuHusIkcpX6aCoEnOqN1dvHr5WRZAFF2MnyMb8LJDxzc5ruqAeJILtBxder5UhXmMlMCU0RDCUKnBmwHbEt7FnQVXuzcZxyG3CkO8qxnhilisWFoSQHOsM0+HX6Z/I8sruRxqyL4Xie3kY/g/F8vXpjPpmSybPVnia1fiYdEQ+WpL6JD3bq2/ODSY50RTg3bw9wV3OAmN/FxeHUnLzWw50RtDu4WUeSBR7Z3cgT16Zpj3jY2RSgI+on4tOrkiArNxjaKE4ukusLdoph/0ye3c0BLo0kOdCxNX1THKfcJiTzFS6MpAi4NMZSZWaqVRuTmVI9QX08VaI75uflsdSqtXBGkkXubw/SHvaCkCjCbqGXrxiEPCoHOkILRs1s0aC3wU5Ybwl5GEsVyZYNQl6NnU3++uhbm/F1xby2Hm3Mxw+vL2zpV5sG1254TRVcGknVt2yCHm3DZEpW4kRvdMni6f6ZPG1hD09cmybo0fnz9x7dFJvm4zjlNuHSSAop7ULkHY1+mgJ2m/ZaZkxX1MtYqshUtsTR7gjPDy6d5WLN87Jar5H5nOqLMVUNBLl0haFFxLvCXld9r7Al6CHic1E2LEaTBVpDHnxulZlsmaG4/fN8BHY1yHMDCY50Rbg6kSFfNtnR5K875Z7mAGeX+X02Cq9LXXJ9XsPv1mgNufn6xTHe8eI4b7u/9a7bNR9nS2Sb8N5T3QDsbwuRKtjK5ucGE3UR5rawt65O8PxgcsnWBbDQKZfi1nSO1rCHVLHCULxAc9DNyb4YR7oiHO4MEfVq6KrgSFeY4z0RfJpAE5JsyWAmVyZezRCqjeQC2NnkZ1dzgB2NftrCHo72RLlQLc86P5Tk/o4w7RHvHOe4NpnlcOfCqeKJ3iiaIjjZGyPk1fDeQQ5swK3RE/OuGMSx0//sL6qPfPEsXzk3vO73XC+OU24TRpIFjnZHUAVY1f6LIY9ez6iZ3fXtYGeY1DKyICur49lMZkqEvXq9DcJkpsSzt+KcG0pyfjjNgc4Iuqowky3T4HchhMTv0ShVDNojHnKlhcnkN6ZyXJ/McnM6x1iqWE+W9+kKR7ojPHsrviD5IV00uDCSpCdm59O2BN28fk8jz/UnMCzJs/1x0gWDfXeg6t7b4OPy+NoixpaEzz81sO73XC/O9HWb8PULY0xmShimScircaI3iqoIShULj66gCMGhrjABl2a3w1vmtdYSjkkXDUZTBdyaQmnWQnV/W4jBmRxNQXuKagGGKRhJFVAVhZaQh7Lfts2rCQrGQuHlzqgXr66iq4K+FRTdexv8xPw6LWEvN6eyXB7P0BXzzplSF8omBzpCIO3ZwFA8X28RvxyrLaZejPNDScZTRVqXyTveaByn3AaYlmRHkx9dVXBrSrW64/bG/sm+GGcH4pRNW9qjr9GPR1e5NpGhZFgLUtsW6bO0JBeHkwS9Ooe7InVh5J1NfuK5Eh1RH6oCEZ+LTMHAo0NfzIOJgmFK8sKkbJrc1x5GCBY0cd3Z6EdR4KEdDVyeyCxZoqUrAlNKzgzMXVce64nMccr5TYlO9cWomFb9fcdTRQbi+TnnRH36HddNfuHpfj7+Y/vu6DXWguOU2wBVEfy3D5wgla/wW1+9WF+DgS3zoSuCI91RMsUKIPC6VISw15mdUS9n+uPkK0uHY/e1BtFUey+wf2buTWtKW27j9K04e1uCXJvMMJkukimZ6KpCoWIS8+vcnMpxtDtKSZj4PYJCxSBdNAh59Xo088EdMXY3B1CEQAiJS1fIl0zcqmB/WxBjkUFNVwQ7mwOLdgGbzJSWjczOVlff32arG7SGPbSE7GZGHl3lxlSW0eT69lZrfPZHt/jYW/eibFKCg+OU24iwT+dnT3Tz+JUpumI+9rYGaQq4UBV7+nppNEUiV+bsQIIj3RGmsyXyFXv7ojHoQRFwbTKHqkCuOsKc6osxMJNnvJqnWpOSfKA9jNdlv26ubHCqL0rYq9ERcddlOhQhqFiS07cSeHQNRRGUDIunX56iI+plZF6xsyXtPdAXhlO8fk8TU5kyzQEdCzAtO+/1tTsbuDWdYyprJ7e3hDxLtuUbihfY1xpY1VqwNlkYTxXr2rY+l7pKZYPlcWsKZdPCo2xOc1qndGsbYlpy2bSzC8NJvvvSGIWiSbZikM6X6W0OUjEl331pkrJpMZywA0cNfp0Lwyn2t4eRUlKojqgCeOZWvD4SneyNUTIMmgMupLQQisC0FEZTBVyaiq4q9S7TAA/varCDULOmyh7dDgrFc2U6ol7cmoJXV1EBQ9oF3Omi3YLesCReXaU55F62PGu1vU+OdEWwpOSF4blrR59L5YGOMM8PJFbVvHcp3JrCn777yJq2SJzSrVcQK+WBHuyMcHDWFkK+ZOCrJnS/91Q3X3thjG9eGkdTBPmyxUSmTE/J4Nn+BMd7IgQ9OtmSQVfUWxUolugqTGUMGrwauq4Rz1VQFYnPpZEuGsT8anWLQsGwLEZTRW7O2/M71mXLj7hUBZeicH4wwb62EOWKScjromRYNPhd9DT4CHk0zg4kyRYNNAHzyzL3tQbxulTUVa6Pzw0lObBIa7982eT0rfgdBXvAbrPwb//qLP/nv9jHBx7qxa3dvVHTccpXAL5ZFRY9DQF+6Y276Wn086mvvcyOJj+v3dlAulDhWHcERbHXiS5NoVgxuT6RobvBRzJfAQl5A3yKhUs1uTlTYix1O0jS1+hnKJ5fUs29Ytl7mOeGkhzpjnCoK1pveutzaexqDlRzYAVnB5NkSgaZksGx7jCqqnKmP14PWvndWn1k7mv0L5AXWYzlxsGLI6kl0+vWwu9+4zLXJrL84b88dEevsxyOU75C+YmD7RxoD/OXT95iNJnDr7sYT5cZSuRxqSp7W/080BFCERKJQFMUioaJW1hYpokiLe5r9bGvOQBCkKoGW4aXmUlKqE81B2by9Y36gFulPeJmLFlgZ6O9J1qs5tU2+HXcusZTN2bY1xrk+mRmQQqhXUGTW3D8ZF+M0WTB7leJ3f5hOZ69FedEbxQpWbSoeiUiPh3DsPjq+VF+550HVlVpsx4cp3wF09vo5xcf3cHHv3wRtwotYY+96V82bUGtoAsp7BETaeFVJYVSBa9bxygVEYDL7UVBEvWqXBrNoArBkd4og4n8gmZBPtft2ymZL/Omfc1cnUijKYKQx8WeFoVMsYzXpbGvNYTPreJWFTRV0BHxEvBo7GgMcHUyW99rPdodwTQlR7ujXJ/KIqUti5IqVBiK5+ck2q+G2pfGUmoHy9ER8eJ1qfzWj++/aw4JjlO+4mkN+9jd4qd/IknRULk5naevwUdryIOQkmyxQsmw7AJmRUHTBEXD4ukhA8jy6E4FITSEInmgPUhFgmXBvpYAPpdC2ONiMlOgZEgKFYOYT0dVq2rspQp9DX5URXJmMMGeliBXJ3NIKbmvLczpm3OnkvmyURfTujye4fV7GnlpNE17xMuz/UlaQm4m0iWEEPhdGhdTy+sVLceZgQTHeqIk8+UV82HBjmKPpYp89M17ONR1d6tHVnRKIYQHeAJwV8//OynlJ2Y9/zHgD4EmKeX03TLUYf38/Gt38CffvUKqYBDy6NyczhHPV3iwuvnuc+moikAqoAsdTUjesq8BaVqUJGAYKKqFVFRUKdEVFQPBzqgHoSrEvEFyFZOIW6XF70JRhB24sSSKAkLCya4wUhEc7AyjKoIXhhYGXXY2B+odq7Mlg3zJpDvmQ1UVXrMjRjxXZiJdwlohOr1azg4kaFtFps4DHWHaIl4+/XPHNqWl3mpGyhLwRillVgihAz8SQnxTSvmMEKILeAsweFetfBVSrJhLTpGklJwbSrK/LbSqaVRXzMcfvOsQT12f4YlrU3z+6X4OdkZI5Cs0BNx29FZRGIwX8Lk1Gv12A9hiuUQ6k0cDfCEvSAUhJJZlggVoGlJKEOB3gWFWkFIBRUdIy/ZGS2IIu8uILhTCHp2SYRHzuxZMPUeTBdyaYHdzkMagmyvjGTojXtKFCmGvzpXq1smTN2Z4cMfSTXYX/wy8RLwuhFiYG7y3NUimaLf8E7WJc3Whqijwe+98gL13kHe7VlZ0SmlvZNY2kvTqoxbo+mPg48BX74p1r1K+f2WSzz/Vzwce6uUNe5sBO2F9Il3kaHcUS8J3X5rgw184S4PfxU8f7eDnX9vLf/r6yzyyu4k3728BbOetpdxpqsIje5t4ZG8TH3l0J99+cZyXxtJcHc/SGHTTEfFxuCeGX1e5ODjFTC5v/5FNKBjgRQHTQnGpCAlS2K+vClvdQCBAKCiqRrZkEnApWEKQzBs0+jRSxQqaV0FXBUJRyRXNIdApAAASCklEQVQrPLyzgZJp1ataPLpKT8xP2bSDQPvbQiTyZXwuFdOSHOu5Lczl0VRbqmQ2NV9bJAxbrJhr3hLRFOhrDNBTDU5tFqtaUwohVOAssAv4cynlaSHEO4ARKeULy+VaCiE+DHwYoLu7+84tfhXwhr3NPLqnCSEEgzN5fvVL5zkzkMCrq/zBzxzkxx9o46GdjTz2xE2msyX+6DtXuTKRoS3s4VhPFCklU5kSUb9rUWmLhoCb95zqAWAiXaRlXh3k5dEov/u1i6iKChGBUchTLhZQfUGwQNF1u8jZMtGFPXqq1SlrxbRwqRKJhWIJwm5IFMqMJovsdgWomBaqIuhu8CGE3YWstuHfGHAxnS1zqi+GZVkYkmXrRu82fU0B3nGw/a4GdRZjVU4ppTSBw0KICPAVIcRB4DeAt67i2seAx8DO6LkDW19V1L7ouht8vOm+Fs4MJChUTL5xYYw37G3i4d2NnP/EW5nO2I1gI/MayjYvUnC8GPMdEmBfe4xHdrfxgytjqJaF5VJA0VCRCEXBNCpoqo6lKKRLJrqiMDCTpSPqR1MUdE1gmrbol6pqhL0QdCkIVcGUkplcqfq+si5z6dEUKlUp9JlciZ6YD8OUvG53I+cGEmTLK1eDbDRv2tvML71h16a/75qir1LKpBDiceAngT6gNkp2As8LIU5KKcc33MpXOR9+ZAftEQ97W4Psa729tgm4tXq3rI3mJ4508dSNSUwsXMKLlAomJiBQhcDe3ZT4NIEUgu4GP6WKCVIipIY0LSzsKE+ubOLCQlNU0gU72juRKeHVVW5N5+iOeQl5dTRhN7x1ayrT2RIuTaVsWOxrD5HKl7k2uXKUdCOZSBc3LQl9NisWOQshmqojJEIIL/Bm4JyUsllK2Sul7AWGgaOOQ94dVEXwk4c75jjk3aY14qUt6kPTdTy6G11TEaqCQCAVUQ32SCzLQsWkZNiatIrQEAKuzxS5OJ6lbEiCHhdStdP1Il6dlpCH1pAHTVEoVUx6GvxcGklzayZPIlehYkqaAm4ibh1FCF4cSSOlXfy8mYRX2c5+o1mN8kAb8H0hxAXgOeA7Usqv3V2zHLaaQtnEMgVSCkzTtCOr1fxzYdl7lVgSoSoYUtjKd0KQLBpM5wyifhfJfIWCYXJjOmsLNquCimVRKBtkChVcKhzoCHN+MMHhrgipQoVsyeDsYMK+MYVFc9BFc9Blp9+JxRXW7xaGuUp1sg1mNdHXC8CRFc7p3SiDHLYHFdNCKgIUlaJpUSpLfuJgM9+7OoWuKCDAkBaiYqCodi9NiS0AdqIvRqYoaY/aWrZel4phSjRVUDHtiLDPDbqQVEw702hwJkfYq2NJu0omVTKQQNQt2NcWxDQsiqYkU6zwwvD6kwbWwp0ksN8JTkaPw6KEvDq//zOHOdc/zQsjKR7d20JvY4CH9rTxx9+5TNkwUSyFbBkCmi1daZoWD3SEUYXAp6uEPX4UYWGYEo9mqwsIVDTFXmdeGE6yo8FPoWyyqyVIIlcmVagQ9ekUKxZel0rZskjlTRp9Oj4d3Jobe9fz7nKwM4xHVymUTbyuzY2+OsJZDstypLeRn3/tTnobbSmPPW1hPvjwTjy6hlQkfq8GQuDWBFG/StSrcWM6yzO34igK6KqKpiqUTIkQCrmywaXRFPmSwf62ENmyyUiyaAtqJfL0NdrSkzVd2YoFxYpFRULZhJFEgRO9a0scWCsneqMUyiaHuyKb7pDgOKXDOjixo5HffscBdjSFUFC5PpHFhYU0TUqGCRJ2NfkZTRb57uUpNEWgYicJRLw6WJJLoxm8ulqVOLHJVyxO34rT4HcRz5WYzpQYThQYr6riFSoWQa+OEPZIdjfobfBxtSraFfJuTUs8xykd1kVL2Mdvvv0gH3zdDnobgyiKghSCTMmiO+Yj5NW5NpmlI+LlqZtx0iULRUoyhRK9VWW7W9O5uiDz7pYAhzsjnOyN0hLyIIVgKlOifyZPPF/mR9enyZWNuuzkheEUh7sidxyRjflc9deohZBSRYPxVGnLnNJZU77KmJ16txE8tKuRlqCLP/rWy5jSViG/PpGhrynAke4IEa+LZN5NpmQylSmxp9VPsWyvCEdTRZqDbjRVcG2Fjs0lQzKRKnGwI8xEuoiqCM4PJWkMuDjcFcalqesqYI7nb4sz720N1vWC9rQEeM/JrclAc0bKVxnj6aXrD0uGya/9/QUevzI5pzXcSuxsCfHTJ3poDvjw6AqdDX5GkkV0RfDcrWl0VdAQcNEc9pIrWkxmSpzojfLgjhgRr85MdnWt56ayJS6MpPDpdg8UgOlsmfNDKZ69FefkHa41/dX1o6oI/sPb9m5Zez5HOMthDolcmb9/fpi/OzvMh163g3cd61z1tcWywX/6p5f4q+eG6sf2t4Xwu1VM00IKu84yXzYxTIuKJVGwk9trVRl+l0aubNgR1pqkQFV8WWJXeDQGXEgJyUKFqF8nXnXq2Xdy7XpLyrq2l32r33a0WqMiiaRiSiqGxbHql8W7T/as49ObiyOc5bAhRP0u3rCvmU99/WV+4x8vsq81yP0dqwuqeFwav/zmPYxkSvyvy5MA9ca1b7u/mbIhSeQr9WlnX6N/gVJdY8BF1Ofi2jJNaQ91hRFScKGayF6TzQTwaoKDXREuj2eWbBm4HA/rjRvikHeCM311WEBPzMf//pY9fOA1vXzx9Np6abSEPTz2vmM8urdpzvFixbKFuiaz7GzyE/Hq5Msmu+appk9ny9yazvFAx9IphVLCC8NJeht8gJ2wUIvG7u+IYEpoDLgJedc+5sy3ZytwnPIVxEiywMf/7gUu3WEmiqYq/Ls37ebX/8V9/N5PH1zX9X/8rw7XpT1evzuGpgj6p3Psag6QL5vEc2UiPp1ErsyRrrkjsWHJemOjxbAseypbK6mqmJKrE3ZBtADO9CfwuzWagx4Ca9xn/OeXJihsQUXKbBynfAXRGvJwqCvC5568tWV5mzWifhe/+eP7eMOeBjRVQRN2GVq6UKG3wUfZtLg0kiKZLzMwr5UCUO9EvRi1tePl8QxHu229nGLFoj3q5Uo1enphOEU8V16xsdB8zg8lV91K8G7hOOUrCFURvPdUD//5Xx1GU7f+T/vOI53sbvbbwRwkQZdGV9TL+cEEh7oixPxujvfGiC/S1i+87B7hbaeZSJdmnSvJlG6vIw3Lquu9rpayYa26leDdYuv/cg6vaN7/UB+FsoUpASHRVHi4L8qF4RS3pnMUyibHuqMEPdqcLYgXR9PsaQmw2K6ElLcPjiQL3N8e4lBnmESuTMx/25kb/Pb0+exAgl3Nq5P0ONUX25LUutk40VeHu0pnLMC+9iCD0zkSRZOQW8VSBIc7w5QMC5eq8NTNGRr9bnobvMRzBm1hD6oQXJ3MLGjzB3Z/kNk8dWOGlqCbiN9FpmhwtDuCotgJ8jV5lFxpdZFYw5JYltyS4uYazkjpUGciXeTc4NqVw1ficGcEA9uZpnMVNFUhVzbpn8nh0RUe2tnAjia/XdwcdPHiaJrJbKmegjefxVZ8E5mSrUvkc/H8YBJpSS5PZDk7kOD5weSSrRbmc3Ygwce+/AJ/8eQt/nqNkeeNwnFKhzotIQ9ffGaw3lJgo3hoVyMuVSGeK9Pkt0W3dAUyJZMXRlIIIbAk3JjMcn4oRUfEs2A0XA3xXJndzQF2NvnxulTaZ2m6NgRWryLwD+dG+OQ/vcRvfOUSN6bW1pJ9I3Cc0mEOb9zXzO9/8/Ka0uxWIhbw4NPVaps9EyEg4NY50hkmWzR58vo0L4+lqPW9vT6VY6oqCLZWJjJFbkzluDGVYyRRwK8rBNwqidzio+5K/PDq1LquuxMcp3SYw5v3N/P0jRn+r6+9tKGOGXArmJatITuTq1ComJQMi95GHyd6Y+xqDnKgLcju5gDHe6JMZkq0BN00LjLCLbfaU4XgeE+UqUyJsmnRHPaSLdnvtR6Gk4WVT9pgHKd0mINbU/m9dz3A1y6M8kffucJG5EYPxXNMZ4rotooIXpeC36UQ9Sq0ht0E3RpuXSEW8HBtMkuuZNAWcuPWVBQhONEbZV9roC5HuZRFr9kRI+DRSRbKtIU97GsN0R72sK81uOpATw1FQG/MxwOrTDHcSJzoq8MCjnZH+dZHHyHi1TekzKs97MWl6ZSMIl5doGCr4UlZVRSQEk0IBuJZeht8SGA6V2Ysbbe2m8mVMS3Jid4oM7nFK0pCXo1Lo2kyRYO+Rj/3tQb555cnOdkbRVXs16h1rV6J5qAbj67y6L5mfvJwxx3//mvFGSkdFqUx4N6wBARVVQi6FaSUGJVKVRnPRFVd3JzOkysb/OjGDJZlawNdHs/UhZnBFtJaiXTBYG9rsF4RkihU6Ip58egqk+kyuiLqpVnL8WMHWumK+djfFuJn1lAhs5E4I6XDphD2e8iULRKZHJYlkIpqK93paj2DJlMyll3D1XxzqbXumf4ED+6I8fJYmlvTOe5rDfLEtWmEsHtalioWfY0BGgIuGgP2erUh4KYx4Cbmd9EYcNEc9DjJAw6vDn75TbtJFQx++ysXKBfzuFSd3kY/U5lSfduhMeC227wvwVSmxN6WQD3SE3RrNAVtp2oKuWkKuOlt8PGuo500Bd00BVw0BT3E/K5tkXa4WhyndNgUPLqGR9doCrkZNwzSZQu3DueG7DrIgFvDo9mO43OptIY9tAQ9tITctIQ8NAXt/zcH3bRUn9vqEe1u4Tilw6bSGPRgWhYtikbM7+LRPc2EvDptEdvhmkMegm5tQ3WE7jUcp3TYVD765r1bbcK2596ZaDs4vEpwnNLBYZvhOKWDwzbDcUoHh22G45QODtsMxykdHLYZjlM6OGwzHKd0cNhmOE7p4LDN2NQGP0KIKWC9akSNwPQGmnOv2gDbww7HhpVt6JFSNi3x3JJsqlPeCUKIM+vpYPRKs2G72OHYcPdscKavDg7bDMcpHRy2GfeSUz621QawPWyA7WGHY4PNhttwz6wpHRxeLdxLI6WDw6sCxykdHLYZ29IphRD/UgjxohDCEkIcn3X8LUKIs0KIi9X/v3GRa/+nEOLSZtsghPAJIb4uhLhcve73N9uG6nPHqsevCyH+VNyhrsYyNjQIIb4vhMgKIf5s3jXvrtpwQQjxLSFE4xbY4BJCPCaEuFr9m7zrTmxYrx2zzln9fSml3HYP4D5gL/A4cHzW8SNAe/XfB4CRedf9NPA3wKXNtgHwAW+o/tsF/BD43zb7cwCeBV6Drfn2zbtogx94GPhF4M9mHdeASaCx+vP/DfzHzbSh+twngU9V/63U7NlsO9ZzX25LjR4p5cvAAvEkKeW5WT++CHiEEG4pZUkIEQB+Ffgw8KUtsCEPfL96TlkI8TxwR2q+a7UBiAEhKeXT1eu+ALwT2zk32oYc8CMhxK55l4jqwy+EmAFCwPX1vv86bQD4ILCvep7FBmT+rMeO9dyX23L6ukreBZyTUpaqP/8O8EdAfgttAEAIEQHeDnxvk23oAIZnPTdcPbZpSCkrwEeAi8AosB/47GbaUP38AX5HCPG8EOLLQoiWzbRhFmu+L7dspBRCfBdoXeSp35BSfnWFa+8H/gB4a/Xnw8AuKeW/F0L0boUNs45rwN8CfyqlvLnJNiy2flxxz+tObFjktXRspzwC3AT+C/DrwKc2ywbs+7oTeFJK+atCiF8F/h/gfStduMGfxbruyy1zSinlm9dznRCiE/gK8H4p5Y3q4dcAx4QQ/di/U7MQ4nEp5aObaEONx4BrUso/Wc1rbbANw8ydMndij1Z3xYYlOFx9zRtVO78E/Nom2zCDPTJ9pfrzl4F/s5oLN9iOdd2X99T0tTot+Trw61LKJ2vHpZSfllK2Syl7sRfcV1f6xTfahupznwLCwEfvxnuvZIOUcgzICCEerEZd3w+sdZS5U0aA/UKIWnXEW4CXN9MAaUdX/gl4tHroTcBLm2lD1Y713Zd3GpG6Gw/gp7C/9UvABPDt6vHfBHLA+VmP5nnX9rIx0dc12YA9KknsG7B2/Bc2+3MAjgOXgBvAn1HN2tpoG6rP9QNxIFs9Z3/1+C9WP4cL2M7RsAU29ABPVG34HtB9t+6J5exYz33ppNk5OGwz7qnpq4PDqwHHKR0cthmOUzo4bDMcp3Rw2GY4TungsM1wnNLBYZvhOKWDwzbj/wc2n3hD8QTLWwAAAABJRU5ErkJggg==\n", 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\n", + "
" + ], + "text/plain": [ + " STATEFP COUNTYFP TRACTCE AFFGEOID GEOID NAME LSAD \\\n", + "0 06 001 400300 1400000US06001400300 06001400300 4003 CT \n", + "1 06 001 400900 1400000US06001400900 06001400900 4009 CT \n", + "2 06 001 402200 1400000US06001402200 06001402200 4022 CT \n", + "3 06 001 402800 1400000US06001402800 06001402800 4028 CT \n", + "4 06 001 404800 1400000US06001404800 06001404800 4048 CT \n", + "\n", + " ALAND AWATER geometry \n", + "0 1105329 0 POLYGON ((-122.26416 37.84000, -122.26186 37.8... \n", + "1 420877 0 POLYGON ((-122.28558 37.83978, -122.28319 37.8... \n", + "2 712082 0 POLYGON ((-122.30403 37.80739, -122.30239 37.8... \n", + "3 398311 0 POLYGON ((-122.27598 37.80622, -122.27335 37.8... \n", + "4 628405 0 POLYGON ((-122.21825 37.80086, -122.21582 37.8... " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we'll grab just the Alameda Country tracts." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "census_tracts_ac = census_tracts.loc[census_tracts['COUNTYFP']=='001'].reset_index(drop=True)\n", + "census_tracts_ac.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.1 Measurement Queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll start off with some simple measurement queries.\n", + "\n", + "For example, here's how we can get the areas of each of our census tracts." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " census_tracts_ac.area\n" + ] + }, + { + "data": { + "text/plain": [ + "0 0.000113\n", + "1 0.000045\n", + "2 0.000071\n", + "3 0.000041\n", + "4 0.000063\n", + " ... \n", + "356 0.000098\n", + "357 0.002275\n", + "358 0.000033\n", + "359 0.000139\n", + "360 0.000316\n", + "Length: 361, dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay! \n", + "\n", + "We got... \n", + "\n", + "numbers!\n", + "\n", + "...?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What do those numbers mean?\n", + "1. What are our units?\n", + "1. And if we're not sure, how might be find out?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at our CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83\n", + "Axis Info [ellipsoidal]:\n", + "- Lat[north]: Geodetic latitude (degree)\n", + "- Lon[east]: Geodetic longitude (degree)\n", + "Area of Use:\n", + "- name: North America - NAD83\n", + "- bounds: (167.65, 14.92, -47.74, 86.46)\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ah-hah! We're working in an unprojected CRS, with units of decimal degrees.\n", + "\n", + "**When doing spatial analysis, we will almost always want to work in a projected CRS\n", + "that has natural distance units, such as meters!**\n", + "\n", + "Time to project!\n", + "\n", + "(As previously, we'll use UTM Zone 10N with a NAD83 data.\n", + "This is a good choice for our region of interest.)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10 = census_tracts_ac.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83 / UTM zone 10N\n", + "Axis Info [cartesian]:\n", + "- E[east]: Easting (metre)\n", + "- N[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: North America - 126°W to 120°W and NAD83 by country\n", + "- bounds: (-126.0, 30.54, -119.99, 81.8)\n", + "Coordinate Operation:\n", + "- name: UTM zone 10N\n", + "- method: Transverse Mercator\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's try our area calculation again." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1.105797e+06\n", + "1 4.355184e+05\n", + "2 6.930523e+05\n", + "3 4.003615e+05\n", + "4 6.183936e+05\n", + " ... \n", + "356 9.653980e+05\n", + "357 2.230584e+07\n", + "358 3.197167e+05\n", + "359 1.355161e+06\n", + "360 3.087534e+06\n", + "Length: 361, dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks much more reasonable!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What are our units now?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may have noticed that our census tracts already have an area column in them.\n", + "\n", + "Let's do a sanity check on our results." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1105796.6056938928" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculate the area for the 0th feature\n", + "census_tracts_ac_utm10.area[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1105329" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the area for the 0th feature according to its 'ALAND' attribute\n", + "census_tracts['ALAND'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "356 False\n", + "357 False\n", + "358 False\n", + "359 False\n", + "360 False\n", + "Length: 361, dtype: bool" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check equivalence of the calculated areas and the 'ALAND' column\n", + "census_tracts_ac_utm10['ALAND'].values == census_tracts_ac_utm10.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What explains this disagreement? Are the calculated areas incorrect?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also sum the area for Alameda county by adding `.sum()` to the end of our area calculation." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1948917581.1122904" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.area.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can actually look up how large Alameda County is to check our work.The county is 739 miles2, which is around 1,914,001,213 meters2. I'd say we're pretty close!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As it turns out, we can similarly use another attribute\n", + "to get the features' lengths.\n", + "\n", + "**NOTE**: In this case, given we're\n", + "dealing with polygons, this is equivalent to getting the features' perimeters." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 5357.060239\n", + "1 2756.937555\n", + "2 5395.895162\n", + "3 2681.974829\n", + "4 3710.388859\n", + " ... \n", + "356 4331.600289\n", + "357 32004.773556\n", + "358 2353.624225\n", + "359 4718.701537\n", + "360 8176.643793\n", + "Length: 361, dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.length" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.2 Relationship Queries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GBP2Co-TutCH" + }, + "source": [ + "\n", + "[Spatial relationship queries](https://en.wikipedia.org/wiki/Spatial_relation) consider how two geometries or sets of geometries relate to one another in space. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jgUkeehpCqnS" + }, + "source": [ + "Here is a list of the most commonly used GeoPandas methods to test spatial relationships.\n", + "\n", + "- [within](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.within)\n", + "- [contains](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.contains) (the inverse of `within`)\n", + "- [intersects](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.intersects)\n", + "\n", + "
\n", + "There several other GeoPandas spatial relationship predicates but they are more complex to properly employ. For example the following two operations only work with geometries that are completely aligned.\n", + "\n", + "- [touches](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.touches)\n", + "- [equals](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.equals)\n", + "\n", + "\n", + "All of these methods takes the form:\n", + "\n", + " Geoseries.(geometry)\n", + " \n", + "For example:\n", + "\n", + " Geoseries.contains(geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------------\n", + "\n", + "Let's load a new dataset to demonstrate these queries.\n", + "\n", + "This is a dataset containing all the protected areas (parks and the like) in California." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "pas = gpd.read_file('./notebook_data/protected_areas/CPAD_2020a_Units.shp')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Does this need to be reprojected too?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83 / California Albers\n", + "Axis Info [cartesian]:\n", + "- X[east]: Easting (metre)\n", + "- Y[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: USA - California\n", + "- bounds: (-124.45, 32.53, -114.12, 42.01)\n", + "Coordinate Operation:\n", + "- name: California Albers\n", + "- method: Albers Equal Area\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pas.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes it does!\n", + "\n", + "Let's reproject it." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "pas_utm10 = pas.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One common use for spatial queries is for spatial subsetting of data.\n", + "\n", + "In our case, lets use **intersects** to\n", + "find all of the parks that have land in Alameda County." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 POLYGON ((564744.993 4188317.651, 564946.532 4...\n", + "1 POLYGON ((562861.148 4188278.725, 563070.421 4...\n", + "2 POLYGON ((561264.509 4184672.770, 561409.095 4...\n", + "3 POLYGON ((563734.437 4184562.158, 563961.943 4...\n", + "4 POLYGON ((568821.460 4184008.066, 569030.992 4...\n", + " ... \n", + "356 POLYGON ((591097.402 4154398.989, 591400.070 4...\n", + "357 POLYGON ((578528.935 4151915.982, 578732.686 4...\n", + "358 POLYGON ((563141.438 4184274.978, 563293.747 4...\n", + "359 POLYGON ((572695.844 4175004.761, 572801.274 4...\n", + "360 POLYGON ((581072.943 4169465.752, 581136.259 4...\n", + "Name: geometry, Length: 361, dtype: geometry" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.geometry.squeeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "pas_in_ac = pas_utm10.intersects(census_tracts_ac_utm10.geometry.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we scroll the resulting GeoDataFrame to the right we'll see that \n", + "the `COUNTY` column of our resulting subset gives us a good sanity check on our results." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ACCESS_TYPUNIT_IDUNIT_NAMESUID_NMAAGNCY_IDAGNCY_NAMEAGNCY_LEVAGNCY_TYPAGNCY_WEBLAYER...MNG_AG_LEVMNG_AG_TYPPARK_URLCOUNTYACRESLABEL_NAMEYR_ESTDES_TPGAP_STSgeometry
63Open Access185Augustin Bernal Park87321257Pleasanton, City ofCityCity Agencyhttp://www.cityofpleasantonca.gov/City...CityCity Agencyhttp://www.cityofpleasantonca.gov/services/rec...Alameda217.388Augustin Bernal Park0.0Local Park4POLYGON ((595746.574 4165882.573, 595740.013 4...
145Open Access366San Antonio Park248321228Oakland, City ofCityCity Agencyhttp://www2.oaklandnet.com/Government/o/opr/in...City...CityCity AgencyNoneAlameda10.619San Antonio Park0.0Local Park4POLYGON ((566704.422 4182789.292, 566827.750 4...
217Open Access586Quarry Lakes Regional Recreation Area305942032East Bay Regional Park DistrictSpecial DistrictRecreation/Parks Districthttp://www.ebparks.org/Special District...Special DistrictRecreation/Parks DistrictNoneAlameda254.616Quarry Lakes Reg. Rec. Area2001.0Local Recreation Area4MULTIPOLYGON (((588060.979 4158338.499, 587843...
393Open Access1438Tennis & Community Park262431257Pleasanton, City ofCityCity Agencyhttp://www.cityofpleasantonca.gov/City...CityCity AgencyNoneAlameda15.595Tennis & Community Park0.0Local Park4POLYGON ((596761.389 4170334.335, 597109.868 4...
408Open Access48353Sean Diamond Park329171090Dublin, City ofCityCity Agencyhttp://www.ci.dublin.ca.us/index.aspx?nid=1458City...CityCity Agencyhttps://www.dublin.ca.gov/Facilities/Facility/...Alameda4.986Sean Diamond Park2018.0Local Park4POLYGON ((601693.284 4175288.100, 601695.836 4...
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5 rows × 22 columns

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" + ], + "text/plain": [ + " ACCESS_TYP UNIT_ID UNIT_NAME SUID_NMA \\\n", + "63 Open Access 185 Augustin Bernal Park 8732 \n", + "145 Open Access 366 San Antonio Park 24832 \n", + "217 Open Access 586 Quarry Lakes Regional Recreation Area 30594 \n", + "393 Open Access 1438 Tennis & Community Park 26243 \n", + "408 Open Access 48353 Sean Diamond Park 32917 \n", + "\n", + " AGNCY_ID AGNCY_NAME AGNCY_LEV \\\n", + "63 1257 Pleasanton, City of City \n", + "145 1228 Oakland, City of City \n", + "217 2032 East Bay Regional Park District Special District \n", + "393 1257 Pleasanton, City of City \n", + "408 1090 Dublin, City of City \n", + "\n", + " AGNCY_TYP \\\n", + "63 City Agency \n", + "145 City Agency \n", + "217 Recreation/Parks District \n", + "393 City Agency \n", + "408 City Agency \n", + "\n", + " AGNCY_WEB LAYER ... \\\n", + "63 http://www.cityofpleasantonca.gov/ City ... \n", + "145 http://www2.oaklandnet.com/Government/o/opr/in... City ... \n", + "217 http://www.ebparks.org/ Special District ... \n", + "393 http://www.cityofpleasantonca.gov/ City ... \n", + "408 http://www.ci.dublin.ca.us/index.aspx?nid=1458 City ... \n", + "\n", + " MNG_AG_LEV MNG_AG_TYP \\\n", + "63 City City Agency \n", + "145 City City Agency \n", + "217 Special District Recreation/Parks District \n", + "393 City City Agency \n", + "408 City City Agency \n", + "\n", + " PARK_URL COUNTY ACRES \\\n", + "63 http://www.cityofpleasantonca.gov/services/rec... Alameda 217.388 \n", + "145 None Alameda 10.619 \n", + "217 None Alameda 254.616 \n", + "393 None Alameda 15.595 \n", + "408 https://www.dublin.ca.gov/Facilities/Facility/... Alameda 4.986 \n", + "\n", + " LABEL_NAME YR_EST DES_TP GAP_STS \\\n", + "63 Augustin Bernal Park 0.0 Local Park 4 \n", + "145 San Antonio Park 0.0 Local Park 4 \n", + "217 Quarry Lakes Reg. Rec. Area 2001.0 Local Recreation Area 4 \n", + "393 Tennis & Community Park 0.0 Local Park 4 \n", + "408 Sean Diamond Park 2018.0 Local Park 4 \n", + "\n", + " geometry \n", + "63 POLYGON ((595746.574 4165882.573, 595740.013 4... \n", + "145 POLYGON ((566704.422 4182789.292, 566827.750 4... \n", + "217 MULTIPOLYGON (((588060.979 4158338.499, 587843... \n", + "393 POLYGON ((596761.389 4170334.335, 597109.868 4... \n", + "408 POLYGON ((601693.284 4175288.100, 601695.836 4... \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pas_utm10[pas_in_ac].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So does this overlay plot!" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = census_tracts_ac_utm10.plot(color='gray', figsize=[12,16])\n", + "pas_utm10[pas_in_ac].plot(ax=ax, column='ACRES', cmap='summer', legend=True,\n", + " edgecolor='black', linewidth=0.4, alpha=0.8,\n", + " legend_kwds={'label': \"acres\",\n", + " 'orientation': \"horizontal\"})\n", + "ax.set_title('Protected areas in Alameda County, colored by area', size=18);" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# color by county?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise: Spatial Relationship Query\n", + "\n", + "Let's use a spatial relationship query to create a new dataset containing Berkeley schools!\n", + "\n", + "Run the next two cells to load datasets containing Berkeley's city boundary and Alameda County's\n", + "schools and to reproject them to EPSG: 26910.\n", + "\n", + "Then in the following cell, write your own code to:\n", + "1. subset the schools for only those `within` Berkeley\n", + "2. plot the Berkeley boundary and then the schools as an overlay map\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYSiteAddressCityStateTypeAPIOrggeometry
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933PublicPOINT (-122.23876 37.74476)
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932PublicPOINT (-122.25186 37.73900)
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853PublicPOINT (-122.25892 37.76206)
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927PublicPOINT (-122.23484 37.76525)
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894PublicPOINT (-122.23808 37.75396)
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" + ], + "text/plain": [ + " X Y Site Address \\\n", + "0 -122.238761 37.744764 Amelia Earhart Elementary 400 Packet Landing Rd \n", + "1 -122.251856 37.738999 Bay Farm Elementary 200 Aughinbaugh Way \n", + "2 -122.258915 37.762058 Donald D. Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org geometry \n", + "0 Alameda CA ES 933 Public POINT (-122.23876 37.74476) \n", + "1 Alameda CA ES 932 Public POINT (-122.25186 37.73900) \n", + "2 Alameda CA ES 853 Public POINT (-122.25892 37.76206) \n", + "3 Alameda CA ES 927 Public POINT (-122.23484 37.76525) \n", + "4 Alameda CA ES 894 Public POINT (-122.23808 37.75396) " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the Alameda County schools CSV\n", + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "\n", + "# coerce it to a GeoDataFrame\n", + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "# define its unprojected (EPSG:4326) CRS\n", + "schools_gdf.crs = \"epsg:4326\"\n", + "\n", + "# transform to EPSG:26910\n", + "schools_gdf_utm10 = schools_gdf.to_crs( \"epsg:26910\")\n", + "\n", + "# display\n", + "schools_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.3 Proximity Analysis\n", + "\n", + "Now that we've seen the basic idea of spatial measurement and relationship queries,\n", + "let's take a look at a common analysis that combines those concepts: **promximity analysis**.\n", + "\n", + "Proximity analysis seeks to identify all features in a focal feature set\n", + "that are within some maximum distance of features in a reference feature set.\n", + "\n", + "A common workflow for this analysis is:\n", + "\n", + "1. Buffer (i.e. add a margin around) the reference dataset, out to the maximum distance.\n", + "1. Run a spatial relationship query to find all focal features that intersect (or are within) the buffer.\n", + "\n", + "---------------------------------\n", + "\n", + "Let's read in our bike boulevard data again.\n", + "\n", + "Then we'll find out which of our Berkeley schools are within a block's distance (200 m) of the boulevards." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, we need to reproject the boulevards to our projected CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10 = bike_blvds.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create our 200 meter bike boulevard buffers." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_buf = bike_blvds_utm10.buffer(distance=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's overlay everything." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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gHy7Fa7WWvm8pzh4YBVYvQGxZEy7dCGP6PxyF7lHjgZSi4M4P34L2V8bQpC7AJ//4Lvx8gQ8AEoGWsdehttT9ptEMsoiIiIqjZoMsAOjo6MDU1FRGoHHu3DlompYxbWjWSyomgJgCbOrUcC2k410LvfjejRl8+UIIQiIRYKn1KtrevxRDzwQSexOGLoRw6u9PAQB8oSgiPgWhJh8aroRw7NljiWApJoDVTR5cmY1BSMQ7ys/p7Os0DaqElPHWDVlqs6TM1uCBiIiIclHTa/c9Ho/pyjpd13H+/PmUAETXdYTDYTQ2NqacG1YEgj4PVizVcF9XAwL/chnnvn4CNxbU4WJXPBumdWi4//P34+IPLiYCrHTaVAQxVcF0YzywM/ppjQ6O4qu/8328eeA03jgRxLHvnoMx2tHBURz68CF8655v4dCHD2F0cBSQEv6ZKBZPR7BwJgrBIIqIiGhe1HQmCwAaGxvR0dGB8+fPpxyfmJjAhQsXEIvFEAqFMDs7a34BIRDyCoS8CkYHR3HkC0ehz+i49RfXcXalHw/UnUf3H96NW/s68epnX7UchzYdhRKTmG2M/0p8fh8O/vrBxB6HnnfEENUlDn7pKN6tKdA9Ko58/khKVuzI54/Ao8fQ8b4OjAbDaPUpWNoUQ9ijIgYgogpMepWU7NbExATGx8fR2tqax7NIRERE6Wo+yAKA1tZWhEIhTExMYMGCBWhoaMDly5czityzOfbssUTQs3R0ApN+H677FBz/L8dx68YVpo1IDdp0FNfbNUw3+aB4BSKTkZSC9sYbEQgJTHsVvPYfA1AaPBlZMX1Gx7H/8gbuWbcIf3okiPVL67DAp+LsrI53LPThgysaEFUEZjw3gywpZUZ2joiIiPJX09OFyTo6OrBs2TIsXboULS0tWL58Odrb211dI3nLnEXnJzFb78Hl5U2Ymju+ZvsaqPWpPanUehW3/85KtLbVoWUsBNlWD6/mSQmwAECbDEPRY5hu8mI2GEncV0wA11vrcOodC/DaAx34h54l+Df/dxyjIR1//ctp/ODKLF58axqvXY1nxETa7KEQAj6fz9XjJCIiouyYyZqjKErK6sH6+nosWbIEExMT1lOFaZIzVQuvziBc78HZ2xfgtjevYXRw1Ho14IZbcVswjG8NXcaKexZg9vDpzGtPRSAVgTOrFuLS8iiurfJjOgbcWFgHqQhMLKyP74EoJX6txQuvEoXfq+BzdzahrckD1HsQUpWULBYA+Hw+dnsnIiIqAgZZSWZmZjAzM4Pm5mYA8SxPW1tbSr1WfX09YrGY6WbRa7avwav/Pl53JQDcdnwMF1c0QwI4Prda0PifouvQohL1ER31E2H4dImOehWXQjG0rmjC2zGBYGs9Jv0+TLX4cGbVAihS4uj7bkH75WmIxRraj17F4nOTaL42i5brs2ifnMWGT3fj3vsW4OnjNzClS3Q1eXGmxYuwRzFdaWjWeJWIiIjyxyAriaqquH79OpqbmyGlTNRptbS0oKWlBU1NTZidncXZs2dNb9/Z15kIsgBg4VgIx9+9FNcW1UNcDGFZcAa+GOCRgCqBazNRnJqO4a2pKN6a0nFqMoIbEeDI/3cHYtNRKFJCm4yg5dos7vjpVZxb2YJbfjqGP//VNtz3kaU45tPxwz0nMHE5hMYlGu753H3QNnZiaiKMNyai8CkCUVVYBlhAfNUkERERFR6DrCQejwfXr19HOBzG9PR0YsuZ2267LdEjKxQK2Tbw1DpuThl2nLqBxuAszq3wo2VBHf7xTAhnp3Wcntbx84kovAqgCMArBDwCaPAomNRj+PM7W/DDz72KBWMz0EI37+u7/2oVvGEdaz9yC95a4IP28dvx4Y/fnjkIIdAenMFbv5zElz/zzxCKgIzJxP9rHTebljLIIiIiKg4GWUk8Hg+klJicnERDQwMaGhrg9/tRX1+fOEfTNDQ2NqKtrQ0XLlxAJHJz78D6iI73bb0D3/vLAMYbPDjf2YzZehWvP9CB4/UqfvSLKXRoChbXKfi1dh9u1RTcqnmwqtmDpfUK/voXUzh6PYIPLK7DpYiOiVBqMOcN60BrHRQAUrFes/DGd97Gmdev4eztfgCAjMmU/zfaPQDAyt9aWZDnjoiIiFIxyEpi7E8YjUaxcqV58OHxeNDQ0ICWlhb4fD689dZbiMVi8OoSrbM6frhqIX7yqbW4eGUWYQnURWOYafTiL7pbcI/fi64mD4QQCCtAVAFmVYFpVcFlPYaehV68dD6E169H8O7H78A/fimQ0qbBG9bhW9kMxaK/6OjgaLyo/kII6m8sx4KrIcxoHtSHMjNv+oye6Cqv6zpUVTW5IhEREeWKQVaahoYGTE9P254TjUYRDodRX1+PtrY2XLlyBcpcZ/VGr8C9nY1Ys8aPdQt9+NmNCJ75+RQ6Gz1obfXhLU1FVFWApEzU6OAojj97DDeuzGD24Tvx6kwEOzbcgl/+LJjYdgcAfDM6Lo2F8cZ3z8H78ZUZ10huTrr43CSO/8pSTCyoMw2ygHjLiSVTYUTOXYS6vCPlekRERJQfBllpGhoasjYh1TQN4XAYPp8v0fZhVhWY8ij48K0N+M3l8SxVWAD31ykYff0GXhkL47al9Yh6U5/y5ODIA8B7fRY/OT+Jkx6Jiz+4mHKub1bHTJ2Kf/ybE9jw212IKqnZq2RNwVksPjuBGS2eoZIAZupVzDR4MKt5MdugYrqlDts/8WO869I0PvjUr6H7iffk9+QRERFRAoOsNPX19ViwYIHtOQsWLEj0lkr01hICwXoPgnXqzZV8UqIrOov3L/bh7ZCOej0W39w5aaVfcpd4AGicmMWNljr8YM8JhC6nBk51oSiiXgVXr4ehhaM49b/P4LUv/hTRpNtHPAqut9Xj8rJGXL61GQEp8ca6JYh5BCQEdI+AqktMN3ow0aqh4/QN3HH0Kg7+8feBliZ0b+7O5+kjIiKiOY6DLCGECmAYwDkp5UfTfvZOAF8HcD+AJ6WUX0762XYAf4B466j/KqUcKMTAi0UIgUWLFtmeoyRNq3m9Xni93psF8MmtEoRARFFwW6MHL56exp/f1Yy6aAyz3pv1T8ld4gGgKRjG2NJGjI/NoGWphumkDFVdKApdFVA7m7B4KoY9e9/Cuc5m3FhYh6mWOpxf2YLGGxFE6hRIXULRYwj7VCy9MomGG2HUzeqom46iPqzDNxmBForCNzeVGAlFMfS5oUSQpes6xsbGIKXEkiVLcnouiYiIapmbTNZ2AG8CaDH52TiAPwLwUPJBIcQaxAOsXwEQBvBtIcSglPJkbsMtTw0NDQgGg6Y/m/EAv9bmw763p/H6eAR3NnpwJSnISt/PsCk4i5gioK/y40P/qguDXw7gWp2K6+31uLS8EaEmL36y6XY89uo1nHjvLZhu8kJIiZZrs2i7NI3Fb0+i+doMWsZn8cMHb0NvRz12PXYbphVAVwUiisBz7z0Ynz9MEzwbxJtvvgkgtX9We3s7C+OJiIhcchRkCSGWA+gDsAvAp9N/LqW8DOCyEKIv7Ud3AvgXKeX03HX+CcDHADyTz6DLjV2QNeVRcN9CL2RE4vk9P8ed3xtF0xINa/7dnbh9/a14zyfuwA++GEB0VocE4AnrCGse/PLB2/Dflmj48Y57oZ6dwKRHgVcI3FWn4I5lGrqavGg7fhWe0xNoGZuBJ5YaNbUs0XDvUg3n2uoRFhLnm+sQ9cQzcFYbVWtLNdO+WT/95k/xTzv/CcHRIPydfvTu6uW0IhERURZOM1kDAD4DoNnl9Y8B2CWEaAMQArAB8SnHDEKIrQC2AkBnZ6fLu5lfRqNSMzqAUy9fQOOZafz8lia0djQicjWEkb84iqapGLCuHTOfugevvxnEhRYfri5rQhtiGIaCM+dmMDEZxX2vX8FyAA89uALv+kgHJIBZARzdsAzf/1IAelKApdaruP/z9+P+3+jAgZ/dwF++MYlxHfHJ2jlrtq9JWYlo3G7N9jUZ4x8dHMVr/a8hOjetGDwTxMGtBwGAgRYREZGNrEGWEOKjAC5LKUeEEO93c3Ep5ZtCiC8B+C6ASQBHAZj2E5BS7gGwBwB6enosOkGVp/r6eiiKkugQb1BjEm1hif//8DnM3N2OGwvr8MMNXdA9ChaMhfA3EQWr35zACali42904N11ClY0enBXswexV6/giz+6gjNdzVj1xjUIAN976wbGmz3o3LgCANDy27fjfs2bueF0XydCYR3NDR74vQJXJNCYNC7Ljar7MoPbY88eSwRYhsh0BENPDjHIIiIisuEkk/UAgN8SQmwAUA+gRQjxTSnlw07uQEr5twD+FgCEEF8A8Haugy1XQgjU19dn9NfyxCQUSFyWQMONMJb/Mojm6zMItmmY0VSsfeU8dvzx3bjH70OzV0FUASZUIOxV8fePn4BvWRMAgbBPQV04Fm8g+tfHE0EWgMSG0wZVVXH20FkE/tNPMXlhCluWNiBUdxcaNqYGUMbtjBYQr372VRx79hju+dQ9eOfvvBOTk5MAYDqtCMQzWkRERGQta5AlpfwsgM8CwFwm60+cBlhzt1kspbwshOgEsAlAVTZjMguywqrAjCLwK+cmMXnkSsZtmpdouGN5A656FZzzpDYonbgcQt2CeoiYRKjRi7rwLID4akSjP5emaYmC9Lq6OtTV1eGN//4Ghp8aRmQ6vtpx4sI0/vELr+E+D9D50ZvBmaIoOPftczjSfwR6KD5tGLoQwsjnR7Bs2TLc/tDtEEJAqAJSz0wsCtV8w2kiIiKKy7lPlhDicQCQUj4vhFiKeK1VC4CYEGIHgLuklDcAfGuuJisC4JNSymsFGHfZMVt9J4XAuObBmu1349WnX8uogXrnp9bgaqPP9HpNSzTUh6IQMYnZBg9wLR5kNXY0YvXq1Yk+XckCewPYv2V/RlAUDek4/tdv4Ff+7bvR0NCAuro6qKqK72z4TiLASpw7HcX3n/w+7tl8T/wxmARYdseJiIgozlWQJaV8GcDLc/9+Pun4RQDLLW7z67kPr3JIaRF0CIFbNq6ArohEDVTTsib0/PserPnXa3D16tWUTaYN9227ExeePwEBYEaL/5rUehX3/sm9lgHWwa0HLYOf6QvTaG9vTzkWHDWf8ks+7l/hN50a1No0DHQNcMUhERGRBXZ8L5DZ2VnT46qqoqmpCe/Z+h586NMfgtfrBQAMbhvEV5Z8BVKXEIpA1+904f4/uz9xuxUbbsVHQzH8+PwsZus9EIqAPqPj9b96HUsWx5uDDj05lAhywpPhxBShGX+n3/SYWQCVfG7vrl4c3How5dqKV0F4IozQWLxeiysOiYiIMjHIKgApZUo9Vl1dHRYtWpSonUrPPA1uG8Twczc7WciYTGwEbQRaYVXgPR9ehgXfvoipRRrkXJuGqXNTeOn3XoKUErFIfDVjtiJ01aciPBlGv9KfknUyC6C8DV707upN/LcRNKUHdEaAZeCKQyIiolQMsgpASonGxka0tLTg2rVr6OzstO2QPrJnxPT46f91Guv/ej08Hg8unj8PoUQQOxnEhJb6a9LDmQ1DrQhFQEppm3VKDqDMpv26N3enHOtX+k3vy2r6kYiIqBYxyCoARVESDVT9fr9pzVQyu2JyY9/EmZkZzAbPo+HcJIRFcXw23gYvPJrHNuuUHkA54WSakYiIqNYp2U8hN+wCrMDeAAa6nO2PvWjRIjSuWI5fPzOBd3//rKPbaG0a/Cv8gIgXrG/csxGhcYs+V3lknXp39cLb4E05lj7NSEREVOuYySoRY/WfXXF6Mo/HA3QsQu/OX8PBT38fkaSu66pPTanJAuJBzvpn12dkpYaeHCp41snpNCMREVEtY5BVIkNPDmUNsPwr0gIfRUH34+8BmpsyAhrjmtmCHCfF7bnIZZqRiIioljDIKpFs03N2gY9VQOMkyClE1imwN4DD2w8naru0Ns00a0ZEREQ3McgqEaticSCewSrmdFs+WafA3gAOPHYgZWoyNBbC/i37E9cmIiKiTCx8LxGrYvFN39yEHad3pAQrRoF8v9KPga4BBPYGSj3chKEnh1ICLIPUJQ5vPzwPIyIiIqoMzGSViBFEJU+7ebTMpz+9QD54Joh9j+zD6Cuj6Nvdl9cYAnsDrl58ukYAACAASURBVKcN7aY501tDEBER0U3MZJVYNGmVYGgshINbD6ZkqkwL5CUw/PxwXhktI3gLngkC8mZT0mzXLGTvq3LK0BERERUbg6wSMgugItMR7H90P/pFP/pFv/UWOTJ++0Lfd7ZrZluF6DRQyjXIIyIiqlQMskrIaurN2Jcw19sns8oWWd022zW7N3ej54key587DfxyDfKIiIgqFWuySshuhaHT21sZ3DaIka+OpARsyfsU5rMVTt/uvpQNrZM57Ryfa5BHRERUqZjJKiGzFYZO2fXRGtw2iOHnhk0zYka2KN+tcDIapRrHHdZsWZ3H/Q6JiKhaMcgqoe7N3di4Z2Nif0Gh2m8knSCAtVvWWq4EHNkzYnvz4Jkghp4cwtotazP2NnTa5yrfIG31htVA2sPlfodERFTNOF1YYsmNQQN7A3jp916CHtbtbySBk4dOWrZgkHr2mq7gmSCOvnDUVWCVPm4gt87xgb0BHH3hKJA8zCyBIxERUaVjkDWPjABj/6P7sxa/G/2yjEAlud5KqMJRoGVMHeYa2OTaOd6qLcXJQydzGgcREVEl4HThPOve3A0pna0uRNppRtC0bus6x/cXPBPE056n0S9K16uKRe9ERFSLGGSVgXyKv4OjQfTt7rNts5DOyHqVqlcVi96JiKgWMcgqA6arDh3WxBuBSt/uPmz65ibXqxdL0asq36J5IiKiSsQgqwykrzr0r/Bj0zc2WbZNMKQHKunX0do0aG1a1vsv9rSd2ePLtQCfiIioUgjH9UAl1NPTI4eHzZtf1pL0zaIBxDNcMh6oOF3dN9A1YNsE1b/Cjx2ndxRgxERERLVHCDEipcyo2+HqwjKWT9uEZL27ejODNYOY62FlY3DbIEb2jEDqEkIVWLd1Hfp297kaAxERUa1hkFWm0ntibfrGJsvgyqp/liElWEvPaEng6AtH0flAp+n1X/zgizg1dOrm6bpMbLHDQIuIiMgaa7LKkDFNGDwTBKT9KkCn53Zv7saO0ztM67ysit8DewMpAVayka/ad5knIiKqdQyyypBZ806rQMjNuYC7nlV2qw6zNU8lIiKqdQyyypCbQMhto083PavsiuWJiIjIHoOsMuQmEHLb6NNpz6rA3oBtry5vo7t+XE4E9gYw0DWAfqV03eiJiIiKhUFWGbJqTmq2CtBto0+nPauGnhzK2MYneSwbv7rR8eMx2AVRburQiIiIKgFXF5aR9JV8KdJWASavKNRaNXg0D0LjIUdtHpxs9GzXoNRupaOV9J5fyRtcd2/utq0tY9NSIiKqRAyyyoRtgDUnuaA9OWAJjYXgbfDm1eYhnb/Tb1qT5V/hzynoyRZEcRNpIiKqNpwuLBPZAixD8EzQ9YrCXKbiCr3fYLYgiptIExFRtWGQVWGEKqwDljNB03ont0EZUNj9BgN7AxCKeRW9EURxE2kiIqo2nC6sMFKX8K8wn8qLn5BZ75TrVJyT2q1sjCya1DOr6JODqEJtIURERFQuGGSVkF1d1MrelY6mDI2NoS33IpyTXO9kWV9Vgqk4sywaEM/IpWfGChHUERERlQtOF5ZItrqoR7/3KFb2rky5TfoUm5H5SZ/Ks2Jkqgo9Feemn5VVtkzGJAMqIiKqasxklYiTFgWPfu/RlJ/bZb6Ssz7PtD+D0Fgo4z61Vi1xrjGG4JkghCpSarLcBDvZWjGkc5tFc7sKkoiIqFwxyCqRbHVRVsFFoQIM4zpuAiQzbvtZmU1tWmXR3AZwRERE5YxBVolYZXR8jT48rT6dsuGyXXBhFoyFxjOzWAAyjhei4afbInonBe2Jx2Ty/LAhKRERVSoGWQVkN9VlltFRPArCk2HTa5kFF1aZHq1VM50uTJ+SK0TDz1yK6O0ycumPKd/xERERlQsWvheIk4afHu1mTKu1aYjpMdtrpgcXVpkoAI4K2wvR8LPQRfRWqw9zHR8REVG5YJBVIHZTcUYAlpxtioai1hswz3GaiQqNhxw1Di1EgOSkSWkhVh/mOj4iIqJywenCArFqDmq3DY4dq0yU2f0YrR52nN5he003DT+drmw0u10hVh8CN3uCsR6LiIgqkZAySzplHvT09Mjh4eH5HoYrT3ueNu1qLlQRL2p38TR7G73Y+NXMTJRd/ZK3wZvTtjdmwRQA0xWBG/dsBGAfpA10DVhuLG0WBJo9plwfCxER0XwQQoxIKXvSjzOTVSBmAZZx3HYbHBOfm/yc6XEj6Ni/ZX/G/eWyCs8q6+TRPKaZt8PbDyMaitpmqYqx+pCIiKgSMcgqEKtAyuk2OMnn2+ne3I19D+8z/ZnbVXhW05hW4zRbwZge3NmtPrTrBcagioiIqg0L3wvErqg8pVjchpMi78DegOVWOm5X4RWqNULydayeh9UbVmesvjzw2AE80/6MowJ5K26K7ImIiEqJQVaBZFt11725GztO74DWppne3mzDZDNDTw5Z1ne5XYVnFZR5G70ZgZy3wWs59uTrWD0PJw+dzMiQxSKxeHbMouVFNk7aZhAREc0XThcWULZpr8DeAGZvzGYcV30qHvzag46mzOxqu0ZfGXU17WY2jan6VOhhPTWQE8DaLWvR+UCnoy1yzJ6HfY+YT3Emc1tXVogO9kRERMXCTFYJDT05hFgkswGpr9nnOCgQqsVcIYCRPSOuxmOWdfI1+zLHKIGTh05mzdbZTd05ncp0M4WZawd7q3Fy6pGIiAqJmawSsmsm6pTVKsZsP7OSnnXqV/pNzzPGbpWtM1upeOCxAzi8/TBC4yForRoUr2IaZCZzU1eWyxY/VisqR18ZxdEXjnJzaiIiKhhmskqoENva2BbPC8QLyUU/+kU/nml/xnU2Jtcxmk3dJddchcZC8QBrLhGntWlQfWrK+W67u6/esNrVcatxRqYjGNkzYjn1SERElAsGWSVUiG1tenf1Wv7WhBApbRZCYyHse3gfBrcNFn2Mjqf5ZPx6659djwe/9mDWrYDsnDx00vFxYyrQqqbNKgvIzamJiChXnC4soUI03jTOPfiJg4hMxTMvQhHwNngRngyb3mb4uWF0PtDp6H5yHaPd9jjpjAzRjtM78pqKc1qTZdcp3yBUYRpocXNqIiLKFbfVqRL9Sr/t1j1amwZfkw/BM8FEQFHIvQGdBDIpBLAzttP0R4PbBjGyZwRSlxCKSHSgTw/4nG7hY5fBAuKZtbVb1qbUZBnHub0PERFlY7WtDqcLq0S2jEtoLJQINIyMTaH6Sg1uG8T+LftTAhSzmisn4x3cNojh54YTY5QxGc/YmfTBcjq1aTflZ0xT9u3us105SURE5BanC6tE765ey+127OTbV8oIitLd/fG70flAZ3x1Ydp2PHY1XtnaUCSP1+nUpuUqxLSMF7f3ISKiQmKQVSW6N3dj9JVR04Anm3yKu62CopE9I+jb3Yfuzd2WexaacdKGInm8TgIjs6arbhccEBERucUgq4r07e7LyB4ZW+GYbe5sEIpAv9KfCIAA54XvVkFR8nE3GSKrAvRkbovRC7HggIiIyC0WvtcAN0Xpqk+FlDKlaahdAfjTnqdNgyKhCjwVfcrR2JKDn9ZVrTg1dMr6BgKAhKOi/ZQCelVg3dZ16Nvdl3VMREREbrDwvYalbIeDm1vzmG3Ro4f1jK7sdk05121d5+p4MrMNnt/+8dtY2bvy5hgVEd+wGkgEWED83Jd+76V481WTbXAyCuh1ieHnhl31DCMiIsoHM1nzwE2NUjFla/uQbtM3N5mOM9eMkdMWDHbnJkvOuOWbYSMiInLKKpPFmqwSs9o7Dyj9HnluGogCsBxn3+6+lKAq0V09SxDpZoNnJ8X5ySsPndSKERERFROnC0vMau+8+dgjz6zPlOpToXjN/yycjNNsCnDfw/vQL/rx4gdfTDnXzT6JTovdjWDMbCrU7jgREVGhMcgqMTfZm2JLqdWaa8D54NcexENff8jyNtnGaRZEGk4NnUoJtNzsk2h2rhkjGMunVoyIiKgQOF1YYpaNMedpjzyr9gpDTw6ZjlNr1Wyvly0IS1456Ka1Qvq5WquW2ZZCQSJAM6YvubqQiIjmCwvfSyywN4CXfu8l6GE9cUz1qXjwaw+WVd+mwN4ADjx2IGOloTHW0VdGEwGMwb/Cj/Bk2LYnFwDslOZ7Frph1Wm+54keBlJERFRSbOFQRtID23IMdLs3d6OupS7juB7W8Q+P/0NKewRD8EwQszdmbfcsLBS7TvNERETlgNOFJTb05FBGdigWieHw9sPz2tbBrK1EaNw8IxWeDFteJxaJQWvTEJ4KQ5/RM36+sndlXmMynhOuHiQionLHIKvErGqWQmOhxDRbqds6WLWVMK17ciA0FsKmb27Ca19/LaN7+6mhU+gX/VjZuxKPfu9R12MCgNFXRi1vx9WDRERULjhdWGJOVsgBpW3rYNVWAnA+3nQHHjuA+x67DzvlTtPsVfpKQ6djOviJg7abYHP1IBERlQsGWSU0uG0Qkans+wcaStXWwTK7Nh5KafHgRiwSSwSJVnsR2u1RaDUmu+ePRe9ERFROOF1YQm6LskvV1sGurYTR4iGwN4B9D+9zdd18gkS33egBmAZYTrb8KZdtjoiIqLowk1VCboqyrZpyFkO2pqBGfZRb+QSJVmMSivNO7k42iTbrUH9w68GUzaaJiIhywSCrhJwWZQtVJDY6LgWzzu/J92/Xxd3O6g2rAdisKBSwDGasxrTuE+Y1V17Ni36lHwNdA4lrOmnzUE7bHBERUXXhdGEJJE9ZOSFjsuTTVVad34Hcp/2OvnAUnQ904tHvPYq/qPsLxMKprSsggX0P78MPvvADfPL4J02n7Xac3pExTuBmJ3cIQFGVRFuJ5FWITto8lNM2R0REVF2YySqy9CkrJ9Kn2QJ7AxjoGsjI1JRKrtN+yRmh9N5gya6+cRV/dctfZW4s/ci+lKk9UxKIRVOvbdyvk02i3WxSTURE5AaDrCJzW+yeXotVDjVDVvVRPU/0ZG3xYGSEsgUtk+cnM6ckJTD8/HDKY3UatAZHg442iXazSTUREZEbDLKKzE0GS2vTMmqxyqFmyKo+qm93383jFozgyiyYcUQi5bE6DVr9nX707e5DzxM9icyVUEVGm4ds9WhERES5Yk1WkQlVOA60fE2+jA/3QtUM5dumwKpmK7nFQ3KHdiA1I2Tc1m0bCCD1sTp5LpPvt293X9beWXb1aERERLlikFVk67aus+1QnswscLLrYWXIFkDZbVEDoCA9opJXIlpdq3tzN37whR/g6htXM27ftKwJkxcmAZMYKvmx2gatAon7BYCBrgH2viIionnDIKvIjCxKckNMT73HtHO5Wd1S765e2wyRXQBl14IhMh3B4e2HEQ1FbW/rhpOM0CePfxJfufsrGYGW6lWx8gMrcer7p1ICrfT6KKugNXka0MlzQkREVGxCSuc1Q6XS09Mjh4edZX8qkdXUmlUtkF2maqBrwDzTtcKfaH/Qr/SbZoisJN+2WKyeg7Vb1uLkoZO2GahsXdytnhMg/tjyyWo56SBPRES1RQgxIqXsST/OTNY8cDK1ln6+2x5WwdFgIjhzE2DZXbOQrLJrJw+dzBrgZauzshu/WVbLab2asbLRYHSQN8ZERESUzHGQJYRQAQwDOCel/Gjaz94J4OsA7gfwpJTyy0k/+xSAf4v4R30AwGNSypkCjL2iFarY2qpmS2vVcOCxA5b9qYwtaowmnunXNBRrXz83Bf0vfvDFlM2kV/auxKPfe9Ty2tn2PTRWZ5oV7NtNLdp1kGeQRURE6dy0cNgO4E2Ln40D+CMAX04+KIS4Ze54j5RyDQAVwO/mME6yYNXnKToTtQywtDYNEDANsFSfmlHvVYweXU6bgKYHWABwaugUXvzgi5bX7t3VC2TZwcgI5ty0yHDSQZ6IiMjgKMgSQiwH0Afgb8x+LqW8LKV8FYDZBnceAJoQwgOgAcD5HMdKJro3d2PtlrUpvaDWbllrWlhviIailj/3NfuyFswXokeX0yag6QFWtuPAXAYqS9xjBHNuMmqWHeQtNq0mIqLa5jSTNQDgMwCs90YxIaU8h3h2axTABQBBKeV3zM4VQmwVQgwLIYavXLni5m5qWmBvAEdfOJrIpkhdYvh5+0UDdps9h8ZDiX8Xc1+/QjQB7Rf9lhktuwapikdBcDSIfmG9IMAs02bVQV5ClnyrIyIiKn9ZgywhxEcBXJZSutsfJn7bhQAeBLASwDIAjUKIh83OlVLukVL2SCl7Fi1a5PauapZZtsltoXuy5OCi2Pv6dW/uxo7TO7AzthM7Tu/Iqdbr1NApfOXur2Qct+owr9ar8b0OszxH4clwRuDUt7sP3kaTrvUxlLQDPxERVQYnmawHAPyWEOI0gP8O4ANCiG86vP4HAZySUl6RUkYA7APw3pxGSqbcZJVUnxqvx7KQPl1XDvv6rexdmfWcq29czQiIzDJlm765yXaj6mShsZBp/ZlVFrAUKzKJiKiyZA2ypJSflVIul1J2IV60/n0ppWk2ysQogF8VQjQIIQSAXlgXz1MOnGaV/Cv8ePBrD2L9s+tNMzxm+ybO975+gb0BjP9i3NG5Zpmk5EzZ6g2rsX/LfldF6pHpCPZtSd0GqNjZPSIiqh45bxAthHhcCPH43L+XCiHeBvBpAH8mhHhbCNEipfy/AP4XgCOIt29QAOwpwLhpjpONl43mokbbCLMMz2eufsZyb8Idp3dg0zc2AQD2PbIPA10DRa9BSlnZ6IDdeUZ/q5xWAepImY4sh+weERFVBlfNSKWULwN4ee7fzycdvwhgucVtdgLYmfMIyVZKY9MzwXjrApttaYzbuMlGDW4bjBfTz123FNvUmNaa2bBa+QdY97dyKnkLIKtGsgD3SiQiolTs+F4FkoOmQjcPDewNpARYhuSGnsXgtsbJLktV6D5W6UEq90okIiIzDLKqTKE6yRvstuUpZrG3Vdd2oQrToMmuZYPVbYQq8LEXPpaxh6Jbdv3EGGQREdWunGuyqDbYBVLFLPa2qn1at3Wd65ooq/5W67auS61Rs9B+V7vtWIvZT4yIiCoXM1mUIXnKUSjmWSAIFLXY224T7c4HOlOOt65qxf4t+7Hv4X0QqsC6retS9hI0/j2yZwRSlxnnJGf/vnL3V1JqsNrvasf7Pvc+23orq6yb2yC0WPtEEhHR/BBSlt++az09PXJ42L5rORVHen2RKQH0PN5TFpsiGysH0/U80QPAOrByyuz58DZ4U1pZODmnEPdDRETlSQgxIqXsyTjOIIuSDXQN2LZDyDVYKZanPU+7Kmxf2bsS478Yd5wtsno+jLYYhnyzUE7vh4iIyo9VkMXpQkqRrY5I6hJHXziKzgc6yyLD4nblYPLG0k5WAVoFnOnH811wwLouIqLqw8J3SuGkjshYOVcO7PpjOVEuj4Wd5ImIqg+DLErhpIM8UD4ZFquVg3ARe5XDY2EneSKi6sPpQkqRvqrPanVhuWRYrFYOAjAtiDdj91jsemwVkt1qSiIiqkwsfCdblbzqbXDbYErw1fX+Lrz947czHsvy9yzH6ZdPJ85ru6MNYyfGbOu9ep6wX13JdgxERLWDqwspZ9UUMKQ/ltZVrSnF8Jbm9oR0srqykgNTIiJyj0EWOVZNQVU2TltACFXgqehTjq7JdgxERLWFLRzIkVrb7NhpCwg3rSLYjoGIiACuLqQ0dpsdV5vA3oDjc90UurMdAxERAQyyKE2tZGGMjJ1Tlq0iTLAdAxERAQyyKE2tZGHMMnaG9rvaE5kroYqsKwnTdW/uxsY9G+Ff4QdEvBaLRe9ERLWHNVmUondXr+nKuGrLwlhm5gTwyeOfzPv6+W6zQ0RElY9BVo1wumKwVppi+jv95isAqyxjR0RE84dBVg1wu2KwWrMwyc1JIQDFoyAWjSV+Xo0ZOyIimj8MsmqA3YrBcg+m0ru2Z2sEanedlG12JBCLxuBr8iE8Fc47Y1dLvcWIiMgZBlk1INuKwXINENIDI6nLxH+7DbRG9oyYHo+EItgZ25n7IFF7vcWIiMgZri6sAXYrBo0AIXgmCMibAYKbHlLFYhUYjewZQWBvAANdA+hX+jHQNZB1vFbNRN00GbVSS73FiIjIOQZZNcCub1M5Bwh2gZHbwNCqmaibJqNWaqW3GBERucMgqwbY9W0qRYDgNutksAuA3AaGVs1EnTQZzTb+WuktRkRE7rAmq0ZYrRgsdiuDfOqV1m1dl1qsnoVdYGjUcLktoncy/lrpLUZERO4wyKpxxQ4Q8lnZaBUYnTx0MqfAsG93n+uCeSfjr5XeYkRE5A6DrBpX7AAh3+lIs8AoPbsEFC9z5HT81dpbjKiSlevKacpPJf1eGWRRUQOEYkxHOgkMC/UizDb+SnqxE9UStlapTpX2exVS5r+EvdB6enrk8LDzWhwqX1ZZp2JumGx2nwCgtWlY/+x6V/drN34AJX9sROTMQNeA+RekFX7sOL1jHkZEhVCuv1chxIiUsif9OFcXUlHZrWwsFrM6KgAIjYVc9wCzG385t78gqnVsrVKdKu33yulCSlGM6a9S1yvZvdhy2U7IavyV9mInqiXcBL46Of29lkspBzNZlGDW/X3fI/swuG1wvofmSrY30UIFQeyPRdUg1z525c6uCTNVLie/13LayYRBFiWYTrNJYPj54Yp64832Juo0CMr24cM3cap05fRhVGjzUapAxefk91pOpRwsfKeEfqUfsPhzmO+iQreeaX8GobFQ5g8EsOkbm7K+0Tot2C+XlDRRLqyKiIH4a55/z1SJLD/LBLAztrMo92lV+M6aLEqwmusGKq/OaP2z6zNXGAqg5/EeRx8aTpuosj8WVTK713Xy0niAzXapcpRTPR6nCymhd1cvYLFdYKXVGZmllDd9Y5Pjju8saqdakO11HZmO4PD2w1U7pWio1rq0WlVOpRzMZFFC9+ZujL4yiuHnh1NSrZVaZ5RPlqmcvgkRFYvZtlrpzKbdc1mlW64qrbklxdmVapTTVmcMsihF3+4+dD7QWRZ/nPOp0Hs6snaLylHKh5FFqYCVasnq5rO/Ks0PJ4FxuZRyMMiiDOXyxzmf3HwTyhZA8ZsylTPj9W612MOjeUyzWdWS1WVpQOWppMCYQRaRBSfBplkAdeCxAzi8/TBC4yH4O/0IT4bL7g2hEJk1ZueqS3pWS6gCkekIPJoHildBLBJLnFupJQSG5L9doQhIPXMpWrUEkdWokgJjFr4T5cHsG1UsEot/858rEjZtJYH5e0MoRG+kau6vVMu6N3cnioaNwCM0FoIQAlqbBoj4HqAezYN9j+yryCLx9L9dswCr0oPIaldJjaCZySJC7lmZfAKl+XpDKESqvZLS9ZQp+e9da9UAwDbzqod1+Jp8Ga1RKnHq22pvU6EKyJhkVrYCFLpmtpgYZFHNy7VmKrA3YDnVkM18viEUItVeSel6SpX+956cabUrfg+OBqsiuLb6G5UxWbRGlVRY5bR6MBsGWVTTAnsD2L9lf0aglO2Dw/igchNg+Vf4y+INoRDtKcqxxQVrxJyxyuRk4+/0V0VwXY5/u+RepSzQYk0W1axsgZLdB4fbDyqtTUPvrt7EB9XQk0N517Lk2kDRrFEfALSuanV83+XU7A9gjZgbuQRExu92vmthCtE0tNz+dim7Sm4WyyCLala2QMnug8Pug0r1qRn/fffH7y5oEJBPUNG9uRvL37M84/ipoVMY3Dbo6P7LbfPdctoQttw5CYi0Ns30d2saoIv431+xP/wKFUiX298u2av0L1CcLqSaZRcoZftmaznlMLepbvq0VaFrWfK93umXT5seH9kz4njroXJK11fDNFapZOvy7m3wYv2z601/t8axw9sP36zlmksEF7sIvpCvoXL62yV7lV4HyCCLapZVoCRUkfWbrd3qFrM38H2P7DO9Tq5BQL5BhdUUaS5F/OWAdTbOpRcNp68udFLLFg1FTY8X88OPgXRtqvTfO4MsqllWgZKTqQO3q1sKHQTkez2hmq+KFKrFDuFlrpKWdJeDfDI52abZi/Xhx0C6NlX6751BFtWsbIFSttVqbj6oCh0E5Hu9dVvXYfi5YdPjbgT2BnDwEwcRmYqPQygC6z6xzvGUY6FU0pLuSpctiCrWhx8D6dpU6b93BllU06wCpULvN1joICDf6xlB0MiekZSM1slDJxHYG3B0HbP2FzImE8FbeqBV7BYLrLMpDavMAlDcDz8G0rWp0n/vQsryq8Ho6emRw8OZ37KJSmWga8CysH3H6R3zMKLisNoU2GzKND1Imr46nchgpROqwFPRp2zvR/EqqGupc1ULRPPP7HcJxFckWhXME1U7IcSIlLIn/TgzWUQmKr3Y0imnK3fMMnt20uu9bPd4RGVuz1JuBrcNJjKTQhVYt7U407aVnlkgKiUGWVTxijENVenFlk45DSbdNl9NL6B3EpxW0rLscjO4bTClxk7q1tO2hcCpWSJn2IyUKlqxGtXVSldopx283Wbw0gvonQan1ZYpLJWRPSOujlNlqORO5xTHIIsqWrE6fZdbV+jA3gCeaX8G/aIf/aIfz7Q/U5A3XLtgMvkNXijOWzv0PNGTkT2x2sonXbVlCkul2vqeUeV3Oqc4ThdSRStm7VS5TIkE9gbw0u+9BD2sJ46FxkI48NgBAPnVMFnV1wBIqcFy8mFt12PMrAFmeCKc8piqMVNYKtXW94wqv9M5xTHIoopWC7VTQ08OpQQjhlgklvMbbrY6toGuAdMaLKEKyJiEv9OP1RtW4+Shk1lr4dLva9M3NqF7c3fRWzrUkkL1PaPyUSuLb6odgyyqaJXeqM4JuzfVXN5wnfQAs7qujEnsjO0s2H0xqCqM9L5nxVxdSKVRC18gawGDLKpo5bKcvJhZGbvmj7m84TqZhrDc11ERWZuVJj8XQsmcxrKa8mBmKz99u/sYVFWRWvgCWQsYZFHFm++MSKG7w6fr3dWbUZMFp1ODhgAAIABJREFUxJt5On3DTQ5gYFFelZy9MnuDB+K1WXaPLf25sKrlSs+UFfs5JKo05fIFkvLDIIvIpfSMS3gyXNQCVeMah7cfTjTvdNNd26pDd7rkrJhx3fRtcwD7x+a0n1Z6Bq5YRb7MjlElm+8vkJQ/BllEJqw+nN10Pi9UgaoxltB4CP4V7gMFJ4GP2TRE9+Zu7Htkn+n5+RTlmt1XMYp8mR0jovnGIIsojd2Hs5vO54UoUC1EoGAbqAjYZnjcFt9a1nIlrUo0u69iFPlyCTw5wWwnFRODLKI0dh/OTjMruRaoFmMq0jKAcbDZtdviW6vzszVyLUaRL5fA1y6zwAnI3g+O2U57DEjdY5BFlMbuw9kqYNHaNPiafHm9+RRrKjKfAMZt8W2uxbrFKPLlEvjKUOgPbrPX0YHHDkAIkVg8YgRTHs3DbKdDnH7PjZCy/LZd6OnpkcPDmY31iEphoGvAMvOTa6Ymn/s14yQLlawWv4GaFfwX6ndFhVGM35Gb15ElAVf94OZLKV/Xdu+Lbt6LqpUQYkRK2ZN+nJksojRW7QvCk2EAwMY9G4vyxlbMqchaXKXEJfDlrxh1c4WYDq6EbGepM0ucfs8NgyyiNGYtE4D4foEHtx7Exj0bi/LNrZhTkbWqFoPLSlKMD2675r3ptDYN0VC0Iht+lnphh+XzKuNZLr4vmVPmewBE5ah7czd8Tb6M48abmCGwN4CBrgH0K/0Y6BpAYG8g5/vs3dULb4M35Zi3wYv1z67HjtM7sDO2EztO7+AbGVUNu1WquTJ7HSleBapPTTlmvLY27tkI/wp/fKXtCn/FTCeXOrNk9rwm7nMui5bP+x9Q2PfTcsFMFpEFyzexM8HEi98qXQ+UR/G3W7nWeNRizRflrxirSq1eR2bHjHMr8W+11As7Up5Xk/vNN4tWrYX1LHwnsmBXQOtt8MKjeVKmEw1WUxBuvyEnBy5aqwYA8YakRQpici1CZoE55SOwN5Dzbga1LN/XXT5fjPqVfvPtufJYMOC2sL7cvthZFb5zupDIgl16PDIdMQ2wgHjtllWthFPGG2jwTHyvwdBYKH5/snCp+XR2NR7FuB3VjmzTQNFQNPFvo/axGqaKiql7c3fOU53p7y9O3lOSf4dCEeYnzdVn5fK7czP9mcv45wunC4ksGG9W+x4231rGLTe1Etk6yxejwDXXGg+uOiI72aaB2Jk/d7ku7HD7nDvd+B3IfZrPzfRnJf3NMJNFZKN7c3f8m6IJrU0zLVTX2jTT893USjgJUAodxORahFyM4mWqHtkynQzSS8/tc271pU+o5hmtyHQE+7fsd5VZslr4Y1afV0l/MwyyiLKwW/Vnlq5f/+x6x28WVpwEKEIRBU2Pu3mTK8TtqDZk+0BkkF56bp9zq9+hjEnAauZQl6ZTeHZTxx7t5uSa1qZZTn9W0t8MpwuJssi26s8qPZ1PUaZVQ9RkxpuY3RjcKKctcai8uSk6zjYNVIwVhmTP7XNu1yPLTvoUntXU8egrozj6wtGU8STX6eU7/vnE1YVEZSp9deHMtZn4N8c0lb6tRbmtEiJ7ble1OTmffwOl5+Y5N/sdOpa04tBqBaFQhWmdl917W7n9zVitLmSQRVRkhWrFUIxl0/ON7R8qTy572JXbByK5l/w7zJbBSpb8d2H5HmZDqALrtq5D3+4+dzcsMe5dSDQP0oOI5LYPblfhlLr5YClU0iohisul6JjbG1W+5N9hv+h3dJv0KTyr9zCrTBYQL4sYfi6edCn3QMsMC9+JishpKwYnqrHAvJJWCVFcuRQdV+MWLJXCalWhUIRt3y6r97B1W9dZ9iQ0jOwZyX/g84CZLKIiKmQrhmosMK/G7Fy1K4ei42rdgqXYCjVtu27rukR2KeX4J+yn9ezewzof6LTcsgew781VzhhkERWR5aqctHOcymfapdB1MYPbBjGyZwRSlznXTZTDBza5Uw7BPqeZ3StkYGq8znN5/Vu9hxnHn/Y8bRpQWWXPyh2DLKIictKKYfWG1UUfR6G/+Q9uG0z5Jptr3UQ5fGCTe6WqsUr/YrB6w2qcPHTS8osLp5kzJX8ZSpdPYNq3u8/Va93plzzLLNnWda7HWA4YZBEVUbad6wHg5KGTAKzfhAqRgSr0N3+r+oiRPSOus1ksiq5MxV4xaPbFwOzDN1l6VthsjEDtBPXpX4bMlCIwdfMlL58sWTlyHGQJIVQAwwDOSSk/mvazdwL4OoD7ATwppfzy3PE7APx90qm3AXhKSjmQ78CJyoGTKTMjiLBavhwcDTpu0pdrBqrQBeZ2K4Go+pWiJirbopF06dPMZmM88NgBCCGgh/WijbucOCkWL0X9o9sveW6zZOXMzerC7QDetPjZOIA/AvDl5INSyhNSynullPcCWAdgGsD+XAZKVG6Mb4lGYGFMmQ1uGzQ9325VltWb0MieEdt935wq5Iow21VcAlzxVQOy7UdYCG6+AJitZDMbYywSSwRYhkKPu5xk+9JjBKbFXqlZy6uIHQVZQojlAPoA/I3Zz6WUl6WUrwKw+9rRC+CXUsozrkdJVIasviUOPzds+iZl14LBcm8wizdJt29OrataM47lUmBuZAesKKoSnxaVN7MEVm/YXIJfuUrxoen0C4DR7DI9I+JmLNX6YW9XLG4EpgBwcOtBx6/bXJRL24/54DSTNQDgMwBiedzX7wL4O6sfCiG2CiGGhRDDV65cyeNuiErD7lui2ZtU9+Zu0w2luzd3w9foM72O1ZukmzenwW2DODV0KuP48vcsdzVFEtgbwP4t+y2ncHxNPsSiqW8RVlkCI1gr5hs7FU8pPjTNvpSks/qiENgbgFCcr0ar1g97q2Lxnid6EoFpKbKS1djjz6msQZYQ4qMALkspc+4EJoTwAfgtAP/T6hwp5R4pZY+UsmfRokW53hVRydh9S7R6k+re3I0dp3dgZ2xn4k1ucNsgwpPhjHMVj2LapM/tm5NVxu30y6cdX8MIiiwDSwGEpzIfA2CeJSjFGzsVTyk+NM2+lPQ80WPb7BKw/1tVvApUn1rUcZeLwN5AYlGNQagCPU/0pNQ7lSIrafcFs9o5KXx/AMBvCSE2AKgH0CKE+KaU8mEX97MewBEp5aVcBklUjqyWGhucvklZBUExPYa+3X03m/TNrYZqXdWK/Vv2Y9/D+xytvClEkXq2ImQjE+C0sWgt12hUg1K13shl5anV36pQBR76+kOJc6p5daGbPUFL1RC4VlcRZw2ypJSfBfBZABBCvB/An7gMsADgX8NmqpCoEhmBjVWg5WQ5effmbutgZ+5w8ptTLv2prPYFc9Pczy74Sc4EOG0syk7vla9cPzQt6xtjMjHechx3IblZzceGwMWV896FQojHhRCPz/17qRDibQCfBvBnQoi3hRAtcz9rAPAhAPsKMWCictK3uw+bvrkp69SJXQ2S5T5gJsft+lNZsarLkLp0XHBuFfwIVSS+HbuZEqjlGg0qrlousjZYZorPBDNe87U8lVcKrpqRSilfBvDy3L+fTzp+EcByi9tMA2jLeYREZc7J1IndN0s3HY5zmfpLb+6XzGmfIKtvu+lvxk6zG+z0TsXCzIz9dl5mr/lsr9tiN56tZkLK8mse2NPTI4eH7bvUElUSq0akEMDO2E7H+wDa7ev1VPSprOMY6Bown6abWwZvh2+0VClq/W/VrCYrnZPXvNW1rOq7apkQYkRK2ZN+nNvqEJVAthokpx2O893XK5+C83KtwSFKV+t/q06283K6yISbcecn55osInKuEDVITpdk22G9ClFtMNrF+Ffk95rnSuD8MMgiKoF8i0tTCufneBu8+NgLH3O1x1c5FJyz0ztR6eT7mrcKxrRWja9jB1iTRVQB8qmlSjef9Sqs76BSyfZ3Xkt1W/k8VrPXrOJVUjbaBvg6tqrJYpBFVAGyFc7no5QfNoUMFomsZAvmGey7k/4eEZ4MIzQWyjivll/HLHwnqmB2hfOF/JbqtKVDrljfQYVk9rcPAPu37M9YhZtcrM1ibnfSFxL0K/2m5/F1nIlBFlEFsOr9s3rDaldBktk30lJ+2LDTOxWK2ReEA48dgBDmOxwAN4MABvv54evYORa+E1UAq8L5k4dOOt5o2azrvFnKHyjeh005FN5TdTDLRsUisZQ6oXRGEJBtlS0XZ9jj69g5ZrKIKoRZ7599j5jvVmUWJGXb5DlZ8odNIeu12OmdCsXtF4HkIMCuK3ypp9ArEV/HzjHIIqpgbtL2Tj+Uiv1hM5+NImtpRVm1s9s6xkxyUbtdkDDQNcB6LQdqveGrUwyyiCqYm33abD+UBAAZn4as1g8bZiiqQyJQPhNM/N06kf47tgoSWK9FhcSaLKIK5qbJqVkdRYK8ufzauG21fdjYrSijypDRlFciHmgh/vcrFGF6O6GaHzfDXRGokJjJIqpwTtP2xjn7HnZWx1VtK4iqLWisRaZ1hUlfEAa3Dea1tyfgLjtMlA0zWUQ1pHtzN7Q2zfRnWmvqcacriCplJRYzFJUvW6Dct7sPPU/0JDJXbvf2BPLfAosoGTNZRGTKyQqiSqpzYoai8jnJrvbt7nMVVJlhUTcVCoMsohoTGjfvjWV2PNuHTSV1zuay88rHQJkqDYMsohrjptYqW8uDSqtzYoaisjFQpkrDIIuoxjjNBjiZCqy24ngqfwyUqZKw8J2oxjgt7HXS8oDbaxARWWMmi6gGOckGOJkK5PQNEZE1BllEZMrpVCCnb4iIzHG6kIhMcSqQiCg/zGQRzZNy3Kw4fUxrt6zF8f9xHKGxeHsHj+bsLcPssQFI7DknVAGpy5S9EomIqo2Q0uHumiXU09Mjh4czt0YgqhbpK/eAeJZoPjtLm41J8SoQQkAP64lj2cZpdh078/24iYjyJYQYkVL2pB/ndCHRPCjHzYrNxhSLxFICLCA+zsPbD7u6jp35ftxERMXCIItoHpRjE0839x0aC1nuUZjLYyjX5qVERPlgTRbRPChkE89carvMbmM1JitWW+e4vY5xGyKiasNMFtE8KNTKPaP+KXgmCMh4V/Z9j+xDv+jHQNeAabbJ7DYHtx7E6g2rM8akeK3fIqwCKbPHZocrFomoWjHIIpoHTruuZ2Na/zS3lsUq4LKqBzt56GTGmB76+kPQ2jTL+zcL5lIemwWhCAC5P24iokrA1YVEFaxf6U8EVdkYq/j2PbLP/DYC2Bn7f+3df4xcZ3XG8efseh02gSyxCaUiODZqCkUdubBbBEqhgq1CncUkdaGiWhRDVFnECLJULQK5wnIqS5S20qaqktSipCm4LS2KgXRxS1hEK6EU2G2SblugCcQJLgWbuHUprFhn9/SPubM7P+47c2fm3rl3Zr4fyfL4nTszd95E3uP3nPe8RyXVphPHd4xvtnBo9d71wdLKyRV9+tZP1xTPj24f1U0fvYnACsDAYHchMIDaqWWq7OILvaYyXp9ObBVgVb93vcUjiw27E9fX1tlNCGAoEGQBfazd+qeLT14sB0/W+Nza/61trmC104Jh871jdggWcRclAPQKQRbQxxrqn2KCp1gx6cLVp1e3VrA6ELdC1mrVDAAGGUEW0OdKsyXNnZnTUT+qAx870H7AVeXSjy7JRuNfOL5zPPjeoR2CnH8IYJjRJwsYIKXZ0mZBeXXxetLieEnyddfY5WMNR/7su3Nf7Hs3681VGSvaGY0A0AvsLgSGwPzu+cRpwMqhzQRGAJBMaHchK1nAEJg+Pt1waPPo9lG5uzYubWyOVVJ51StiAIDOEGQBQyCUtosbI7gCgHSQLgQAAOgC6UIAiSUtbO/kcGoAGBYEWQBqVDq+V+q3KgdIS6oJoJJeBwDDiiALQM2KlI2YfL22jKBybE518BQ6aLr+ul7cM6toAIqIIAsYAs0CkvoVqfoAq6L+KJw8j8xhFQ1AP6DjOzDg6g98rgQkKydXJMWvSMWpPwonzyNzmq2iAUBREGQBA65VQJJk5SnuKJw8j8zh4GkA/YAgCxhwrQKSJCtP+0/sb0jD1RxObeVO8XHXZYGDpwH0A2qygAE3sWsi9kidSkAS1w2+5rprJ4KBU16d4ePumYOnARQNK1nAgGuV1qusSI3vHG94bVEDlzxX0QAgKTq+A0OA5qIAkJ1Qx3eCLAAAgC6EgizShQAAABmg8B1AXwqlNhcOL2j5xLJ83WWjpslDk5q5aybv2wUwhAiyAPSdUMf3h+99WE8sPrF5na+7lu4ulx4QaAHoNdKFAPpOqMFqdYBVbfnEci9uCwBqEGQB6DvtdnYPnccIAFkiyALQd9rt7G6jltGdAEAYQRaAvhNqsLpnek/s9ZOHJntxWwBQgyALQN8JdXy/5fO3aOq2qc2VKxs1Td02RdE7gFzQjBQAAKALNCMFAADoIfpkARhInMMIIG8EWQAGTqhZqSQCLQA9Q7oQwMAJNSs9ffvpnO4IwDAiyAIwcELNSlefXtXKyZUe3w2AYUWQBWDgNGtWunhksYd3AmCYUZMFoDDSKlafPj6t+992f+xz1atcFMcDyBIrWQAKoVKsfvHJi5JvFat3kt4rzZY0vnM89rnKKleanwcAcQiyABRCqFi90/Tevjv3NRy9IysHU/O753X69tOpfh4A1CNdCKAQQsXqofFWKmm/xSOL5dUqkxQdcHHxyfB7dvp5AFCPlSwAhRAqVm9WxN5KabakuTNz5TMOE54g1s3nAUA1giwAhTB9fLohvTd2+Zimj093/d5JV6fS+jwAkEgXAiiImvReCrv9qncO2ojJ1xuXssZ3jmv7s7ezuxBAJgiyALQly7YHpdlSKu9Vf6xOXIA1dvmY9t25j6AKQGYIsgAk1i9nAsbtVJQkGzX5hrNq1efob4Z+QZAFILFmbRaK9EMuVIPlG66jG0d7fDdIU78E+oBE4TuANqTdZiEroR2CDX2z0HfS7qcGZIkgC0BLKydXNL97PtgGIc+2B5V7OzZyTPO757VycqW8QzDmb7dLP7ykhcMLvb9JpKZfAn1AIsgC0ELN8TMxsmh7EBc4tbw3r00dmSz2NcsnllO9V/RWFv3UgKwQZAFoKlRELkkT105o/4n9qdbCtHOmYLPUkW/EL7vF7TRE/8iynxqQNoIsAE0F0zAmzZ2ZS73YuJ2am2apIxuNX8mykfhx9IfSbEn7T+wvd/G3bAJ9IC3sLgTQ1MSuidhUYVbpmXZqbprd23U3Xqelu5ca38jKq2X8UO5fafVTA7LGShaApnqdnmmn5qbZvc3cNaOxKxp3E/q6sxMNQE8QZAFoKi49s/fgXi0eWWxZmN6JdoK6VqmjUC0ZO9EA9ALpQgAtVadnsm4G2e4Zhs1SR71OdQJAtcRBlpmNSlqS9J/u/sa6514q6V5Jr5B0xN3/oOq550r6iKSfVbnLzq3u/lAK9w4gB73o+p5Wzc308emagFBiJxqA3mlnJet2SV+TdGXMcxckvUfSzTHP3Snp79z9zWa2XdLlbd8lgMLop2aQ7a6KAUCaEgVZZnaNpBlJxyX9Zv3z7n5O0jkzm6l73ZWSXivp7dF1a5LWurtlAHnqtxQcO9EA5CVp4fu8pPdJ2mjz/V8s6byke83sYTP7iJldEXehmR0ysyUzWzp//nybHwOgV2gGCQDJtAyyzOyNks65eydnUWxTuU7rbnd/uaQfSnp/3IXufsLdp9x96uqrr+7gowD0QlrNIJMenQMA/SpJuvB6SW8ysxslPUvSlWb2cXd/W4LXnpV01t2/HP35kwoEWQD6R7cpuKx3KAJAEbRcyXL3D7j7Ne6+W9JbJX0hYYAld/+upG+b2UuioWlJ/97pzQIYDO0cnTMoWLkDhk/HfbLM7J2S5O73mNkLVG7vcKWkDTObk/Qyd/9fSe+WdDLaWfgtSe/o/rYB9LN+2qGYBlbugOHUVpDl7l+U9MXo8T1V49+VdE3gNY9Imur4DgEMnKx2KK6cXClku4Ze9BYDUDwcqwOg57LYoVhZLbr45EXJt1aLipCWy2LljvQjUHwEWQB6Lq0ditWKXOfVzqHXSRQ5oASwhSALQC5KsyXNnZnT0Y2jmj4+3fWB00Wu80p75a7IASWALRwQDaBr3dRCpVUUXuRO9Gkf71PkgBLAFoIsAF3pNkhKqyi86IdBp3m8T5EDSgBbSBcC6Eq3qau0VmWyqPMqKo42AvoDK1kAutJtkJTmqkynq0VFbf0Qknb6EUA2CLIAdKXbICnvNF+/NgpNM/0IIBukCwF0pdvUVd5pPnbqAcgKK1kAOlKdYhvfMa5t49u0emG1o9RVnqsy7NQDkBWCLABtq0+xrT69qrHLx3TgYwcKn8Kqr78a3zGu1adXG65jpx6AbpEuBNC2fk2xxXVKX/vBmkbGav8qZKcegDSwkgWgbf2aYosLDtfX1mUjpvGd4x2nO3uh33ZAAiDIAtCBojbDbBWIhIJA33A9s/pMYdOd/boDEhh2pAsBtK2IzTCTHJrcLAhMI925cnJF87vnuzqDMe49Tx081ZfpWWDYEWQBaFsWbRe6DVCS1InFBYfVukl3tgryOvl+lff0dU/9fgFkj3QhgI6k2XYhjXRYkjqxynuduuWUfKMxcBnfMd7WfVdrFeR18v3i3rNa/cocdVtAsbCSBSB3aexWDKUC68dLsyU966pntX+TLTQL8jr9fq1Wqq678brNx0nSpQB6iyALQO7S2K3YTp3Y6oXGvljNxqXW6b5mQV6n36/VRoLHPvvY5uN+basBDDKCLAC5S7oK1Uw7dWLB93XFBlBJVomaBXmdfr92asj6ta0GMMioyQKQu7QOiU5aJxb3eRVx9VKhVaLTt5/evKb62riaqE6+32YN2cFTscXv1UFaUdtqAMOMIAtA7loFKJl+XkxgUkmzVa4LrQatPr2qlZMrNYFW3D138/2SBmlpBaoA0mPu8VuD8zQ1NeVLS0t53waAIXBs5JgU99egSUc3jkoqpxDjgjGpnJacOzOX4R2WJdk5yO5CIB9mtuzuU/XjrGQBGGpJ0mzTx6d1/9vuj319r2qekqRC02yrAaB7FL4D6AtZdFOXku1KLM2WNL4zvofWxK4JLRxe0B3b7tAxO6Y7tt2hhcMLqdwbgP7GShaAwkujWWl1Kq3SdLRyIPTeg3v12Gcfa5pm23fnvtiapx0/tUNLd2+VN/i6b/555q6Zzr90ikgjAvmgJgtA4TWriZKkPdN7dMvnbwk+Xx+k1Ru7fCzRsUBxwUpo55+Nmj74zAebvl8vxH33pN8XQDKhmiyCLACFFyxOr9Is0GoVpEmdF7Afs2PB54760bbfL22h7z6+c1zbn72d1S0gBaEgi5osAIWXpNfTE4tPBJ9LUpzeaQG7jVpb473WrP0ER/AA2SLIAlAIzQrbW3U+byVJkNZp087JQ5Ntjfda0u/FETxA+giyAOSu1bE1lSNzOtUqSOumaefMXTOaum1qc+XKRk1Tt00Vpui9nQC1etUrq92cwDBhdyGA3DU73Li6m/pTX3qqZidftT3Te4LvX99xvX53Yat6pNDuvH7YtVf/3W3EYgv1pa1VrzR2cwKg8B1AASTpul6xcHihIdBqtbuwG6HdeXsP7tWj9z3ad7v2mm0iOPDxAyrNloLF8r3qbg/0Gzq+Ayisdg43nrlrpqepuNAq2/KJ5YYVofrVtyIKzfX4zvGWZzX2qrs9MCgIsgDkLu5wY5l03Y3XBV8Tl6p76ktPbQY/NmqaPDTZdUAWCixCKbcsApE005Kxc131OaXZUltBL4AwCt8B5K40W9Leg3ul6q4HLj1636OxBddxhfKnDp7S0t1Lm8FPpfN6t0fchAKLUIuGtAORVpsC2lXZRFB/TNDq06ub75vkqCEArRFkASiExz77WEOtUKitQFwKL7SytHxiuav7CgUck4cm2wpEOt2t12xTQKdKsyVtf/b2hvHqdOf+E/s1ce2EZOVarKLXmgFFRLoQQCG0UwfUTkrO113zu+c7TrHV786rTtftun5XojReN7v1sqqPavW+pdkSQRXQJYIsAIXQqg6oui6pWRuCOEmDmlDtUyjgSBqIJGlRETK+Y1yrT682jHeblqTuCsge6UIAhdCsDqi+Lil0IHMzrVJsadU+LRxe0B3b7tAxO6Y7tt2hhcMLwXMTW61GrZxc0doP1hrGR8ZGuq6Pou4KyB4rWQAKoVlabn73fOxuOBs1+YbH7i6M0yyo6Wa1qaK+h1el+D6k1arR4pFFra+tN4xfduVlbaXymu1OLHozVaCfEWQBKIRmgUCwjcKG1zQrLc2WNHPXTLiZZpOgJo3ap7aK7E0tV42ChztfaEwfhrSqB+s2qOqHrvdAXkgXAshdq1RdKDgKjXeSCmv3M+K0Uycmb130nsY9ZbE7sSLt9hLAoCHIApC7VoFAu0FTqAWBpGAbhTRqlFrVhVWbuLZ1oJTGPWXZvf307aczC+CAQUC6EEDukrQTkNqrH6pPhSVNm3WT+po8NBlfgzUiaWPrj0kDpTTuKatdhCsnV2J3PUrluZ3fPU8KEUOPA6IB5K4XBxK38xnd1BktHF5oONonaT+tLIQOuO62uWhoPuP0w8HZQDc4IBpAYcWdp5d2O4GkabNOG4e2CszyCjCy2kXYTrqxHw7OBrJAkAUgd71oJ5A0bdZJK4duOrr3Qhbd20PzGZLFwdlA0RFkASiErI9xabZaVr0KVX9+YkXWPbaSiktHztw1k+pnJDF9fFqfevuntPHMRuuLRSd5DCeCLACFlWYPptBqmaSG4CtOGj22uq3XatbsNI9ASzGbKfdM79HZh85mmvoF+gVBFoBCyiIFF7daFuomXy1Jj61WqchQgLT0J0ubOw9bfcdQs9PlE8s9D7IWjyxq41LjKtaFxy9o/4n9NCgFRJAFoKB6lYJrWitkShQkJCncD3aDr4tTmn3HULPTtpqgpqTZ6l3WqV+gXxBkASikLJtoVguuQrXRPiJJ4X47gVDoO9qodXQhy5D8AAAKtklEQVQ4dhay6r8FDBI6vgMopDSOlEkija7qUjnQmjszp6MbRzV3Zq5hJaetbvCB7zh5aLKt8SylNW/AICPIAlBIvfohHjqCJ+10VzAQqvtbuPo7rpxcqTkGaNf1uzR129RmwGajpqnbpnIpeu/VvAH9jI7vAAorzd2FRbindnYXZtWpHUD6Qh3fCbIAIKG4wEcmycsrOZUVqDQCw7SPGipiwAoMCoIsAOhSq/P6RrePyt1rWhvErT6FAp4kTVGlrQL4Zs1Iq99rfMe41n6wpvW19ab3BaAzoSCLmiwASKjVzsb1tfWG3lGVlgwVldWwi0+WA6lKb6yFwws1481UdhhWem0tHF6oeb7+M1afXq0JsOLuC0D6aOEAAAm1e15fRXVwFur/VanV6sTS3Utaumdpc1Us7jNa3ReA9LGSBQAJxe14TKK6JUMosGkaYEW795qqWhVLGgjS0wrIFkEWACRU07ZAaji7b3T7qEbGav9arW87EQpsQn20Jq6d2Oy9laTX1qUfXUp0HT2tgOwRZAFAGzabjvpRHfjYgZo+UTd99CbdfO/NTXtHhfp/TR6abNkXLGnTUV/3hvcaGRvR+M5xeloBPcTuQgDosSS7C0NtFqp7bYVU2knQsgHoDVo4AMCAoWEpUAyhIIvdhQDQp5IcTA0gPwRZANDHSrMlgiqgoCh8BwAAyABBFgAAQAYIsgAAADJATRYAoKkkrSUANCLIAgAE1beJqBzdI4lAC2iBdCEAICh0oPXikcWc7gjoHwRZAICg0IHWoXEAWwiyAABBoQOtQ+MAthBkAQCCQgdaVx9cDSAehe8AgCCO7gE6R5AFAGiKo3uAzpAuBAAAyABBFgAAQAYIsgAAADJAkAUAAJABgiwAAIAMEGQBAABkgCALAAAgAwRZAAAAGUgcZJnZqJk9bGZ/G/PcS83sITP7sZn9Vt1zZ8xsxcweMbOlNG4aAACg6Nrp+H67pK9JujLmuQuS3iPp5sBrX+fu32/z3gAAAPpWopUsM7tG0oykj8Q97+7n3P2rki6leG8AAAB9K2m6cF7S+yRtdPAZLulzZrZsZodCF5nZITNbMrOl8+fPd/AxAAAAxdEyyDKzN0o65+7LHX7G9e7+Ckn7JL3LzF4bd5G7n3D3KXefuvrqqzv8KAAAgGJIspJ1vaQ3mdkZSX8l6fVm9vGkH+Du34l+PyfplKRXdnCfAAAAfaVlkOXuH3D3a9x9t6S3SvqCu78tyZub2RVm9pzKY0k3SPrXLu4XAFAAKydXNL97XsdGjml+97xWTq7kfUtA4bSzu7CGmb1Tktz9HjN7gaQllXcebpjZnKSXSXqepFNmVvmsv3D3v+v6rgEAuVk5uaIHDj2gSz8q73W6+ORFPXDoAUlSabaU560BhWLunvc9NJiamvKlJVpqAUARze+e18UnLzaMT1w7obkzczncEZAvM1t296n6cTq+AwDacvGpxgCr2TgwrAiyAACJrZxckY1Y7HMTuyZ6fDdAsRFkAQASqdRi+XpjmcnY5WOaPj6dw10BxUWQBQBIZPHI4maxezUbNe0/sZ+id6AOQRYAIJFQzZVvOAEWEIMgCwCQSKjmilosIB5BFgAgkenj0xq7fKxmjFosIKzjZqQAgOFSSQkuHlnUxacuamLXhKaPT5MqBAIIsgAAiZVmSwRVQEKkCwEAADJAkAUAAJABgiwAAIAMEGQBAABkgCALAAAgAwRZAAAAGSDIAgAAyABBFgAAQAYIsgAAADJAkAUAAJABgiwAAIAMEGQBAABkgCALAAAgAwRZAAAAGSDIAgAAyABBFgAAQAYIsgAAADJg7p73PTQws/OSnszp458n6fs5ffawY+7zwbznh7nPD3Ofj0Gd92vd/er6wUIGWXkysyV3n8r7PoYRc58P5j0/zH1+mPt8DNu8ky4EAADIAEEWAABABgiyGp3I+waGGHOfD+Y9P8x9fpj7fAzVvFOTBQAAkAFWsgAAADJAkAUAAJCBgQqyzOyMma2Y2SNmtlQ1/m4z+4aZ/ZuZfbhq/ANm9nj03Buqxiej93nczP7IzCwav8zMPhGNf9nMdle95qCZPRb9Otibb1wMcfMezdMj0a8zZvZI1fXMe0oCc/9zZvZPlTEze2XV9cx9SgJzv9fMHorGHzCzK6uuZ+5TYGbPNbNPmtnXzexrZvZqM9thZg9G8/GgmV1VdT3znpLA3L/Fyj9bN8xsqu565t7dB+aXpDOSnlc39jpJn5d0WfTn50e/v0zSo5Iuk7RH0jcljUbPfUXSqyWZpNOS9kXjhyXdEz1+q6RPRI93SPpW9PtV0eOr8p6PPOe97vk/lPRB5r1n/89/rmrubpT0Rea+Z3P/VUm/GD2+VdLvMvepz/t9kn4jerxd0nMlfVjS+6Ox90v6Pea9Z3P/M5JeIumLkqaqrmXu3QdrJSvgNkkfcvcfS5K7n4vGb5L0V+7+Y3d/QtLjkl5pZj8p6Up3f8jL/3X/XNLNVa+5L3r8SUnTUQT+BkkPuvsFd/9vSQ9K+uVefLmii+bn1yT9ZTTEvGfPJVVWUCYkfSd6zNxn7yWS/jF6/KCkX40eM/cpiFYGXyvpTyXJ3dfc/X9UO1f3qXYOmfcUhObe3b/m7t+IeQlzrwFLF6r8w+VzZrZsZoeisZ+W9Jpo6fEfzOzno/EXSvp21WvPRmMvjB7Xj9e8xt2fkXRR0s4m7zUs4ua94jWSvufuj0V/Zt7TFTf3c5J+38y+LekPJH0gGmfu0xU39/8q6U3R47dIelH0mLlPx4slnZd0r5k9bGYfMbMrJP2Eu/+XJEW/Pz+6nnlPT2juQ5h7DV6Qdb27v0LSPknvMrPXStqm8vLiqyT9tqS/jiJji3m9NxlXh68ZBnHzXvHr2lrFkpj3tMXN/W2S3uvuL5L0XkX/8hRzn7a4ub81erws6TmS1qJrmft0bJP0Ckl3u/vLJf1Q5fRgCPOeHua+AwMVZLn7d6Lfz0k6JemVKke893vZVyRtqHxA5Vlt/StTkq5ROa1yNnpcP67q15jZNpVTMReavNdQCMx7ZY4OSPpE1eXMe4oCc39Q0v3RJX8TjUnMfari5t7dv+7uN7j7pMr/uPhmdDlzn46zks66+5ejP39S5R/834vSUIp+P1d1PfOejtDcN7t+6Od+YIIsM7vCzJ5TeSzpBpWX7j8l6fXR+E+rXKz3fUmfkfTWaDfDHknXSfpKtNT8AzN7VbTidYukT0cf8xmVf4BJ0pslfSHKKf+9pBvM7KpoV8sN0djAazLvkvRLkr7u7tVLw8x7SprM/Xck/WJ02eslVVK1zH1KQnNvZs+PxkYk/Y6ke6KXMPcpcPfvSvq2mb0kGpqW9O+qnauDqp1D5j0FTeY+hLmXBmd3ocr54kejX/8m6Yhv7YD4uMo/fP5Z0uurXnNE5X9pfkPR7oZofCq6/puS/lhbnfGfpfLKwOMq7454cdVrbo3GH5f0jrznI+95j577M0nvjHkN857h3Ev6BUnL0fiXJU0y9z2b+9sl/Uf060OVeWTuU537n5O0JOlfVP5H9FUq1+0sqvwPikVJO5j3ns39r6i80vRjSd+T9PfM/dYvjtUBAADIwMCkCwEAAIqEIAsAACADBFkAAAAZIMgCAADIAEEWAABABgiyAAAAMkCQBQAAkIH/BzUUxtNKwlKhAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "berkeley_utm10.plot(color='lightgrey', ax=ax)\n", + "bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "bike_blvds_utm10.plot(ax=ax)\n", + "schools_gdf_utm10.plot(color='purple',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Looks like we're all ready to run our intersection to complete the proximity analysis.\n", + "\n", + "\n", + "**NOTE**: In order to subset with our buffers we need to call the `unary_union` attribute of the buffer object.\n", + "This gives us a single unified polygon, rather than a series of multipolygons representing buffers around each of the points in our multilines." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'berkeley_schools' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschools_near_blvds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mberkeley_schools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwithin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbike_blvds_buf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munary_union\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mblvd_schools\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mberkeley_schools\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mschools_near_blvds\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'berkeley_schools' is not defined" + ] + } + ], + "source": [ + "schools_near_blvds = berkeley_schools.within(bike_blvds_buf.unary_union)\n", + "blvd_schools = berkeley_schools[schools_near_blvds]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's overlay again, to see if the schools we subsetted make sense." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "berkeley_utm10.plot(color='lightgrey', ax=ax)\n", + "bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "bike_blvds_utm10.plot(ax=ax)\n", + "berkeley_schools.plot(color='purple',ax=ax)\n", + "blvd_schools.plot(color='yellow', markersize=50, ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to find the shortest distance from one school to the bike boulevards, we can use the `distance` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley_schools.distance(bike_blvds_utm10.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise: Proximity Analysis\n", + "\n", + "Now it's your turn to try out a proximity analysis!\n", + "\n", + "Run the next cell to load our BART-system data, reproject it to EPSG: 26910, and subset it to Berkeley.\n", + "\n", + "Then in the following cell, write your own code to find all schools within walking distance (1 km) of a BART station.\n", + "\n", + "As a reminder, let's break this into steps:\n", + "1. buffer your Berkeley BART stations to 1 km (**HINT**: remember your units!)\n", + "2. use the schools' `within` attribute to check whether or not they're within the buffers (**HINT**: don't forget the `unary_union`!)\n", + "3. subset the Berkeley schools using the object returned by your spatial relationship query\n", + "\n", + "4. as always, plot your results for a good visual check!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the BART stations from CSV\n", + "bart_stations = pd.read_csv('notebook_data/transportation/bart.csv')\n", + "# coerce to a GeoDataFrame\n", + "bart_stations_gdf = gpd.GeoDataFrame(bart_stations, \n", + " geometry=gpd.points_from_xy(bart_stations.lon, bart_stations.lat))\n", + "# define its unprojected (EPSG:4326) CRS\n", + "bart_stations_gdf.crs = \"epsg:4326\"\n", + "# transform to UTM Zone 10 N (EPSG:26910)\n", + "bart_stations_gdf_utm10 = bart_stations_gdf.to_crs( \"epsg:26910\")\n", + "# subset to Berkeley\n", + "berkeley_bart = bart_stations_gdf_utm10[bart_stations_gdf_utm10.within(berkeley_utm10.unary_union)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "----------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.4 Recap\n", + "Leveraging what we've learned in our earlier lessons, we got to work with map overlays and start answering questions related to proximity. Key concepts include:\n", + "- Measuring area and length\n", + "\t- `.area`, \n", + "\t- `.length`\n", + "- Relationship Queries\n", + "\t- `.intersects()`\n", + "\t- `.within()`\n", + "- Buffer analysis\n", + "\t- `.buffer()`\n", + "\t- `.distance()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_build/html/_sources/ran/07_Joins_and_Aggregation-Copy1.ipynb b/_build/html/_sources/ran/07_Joins_and_Aggregation-Copy1.ipynb new file mode 100644 index 0000000..fe4a697 --- /dev/null +++ b/_build/html/_sources/ran/07_Joins_and_Aggregation-Copy1.ipynb @@ -0,0 +1,2380 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 7. Attribute and Spatial Joins\n", + "\n", + "Now that we understand the logic of spatial relationship queries,\n", + "let's take a look at another fundamental spatial operation that relies on them.\n", + "\n", + "This operation, called a **spatial join**, is the process by which we can\n", + "leverage the spatial relationships between distinct datasets to merge\n", + "their information into a new, synthetic dataset.\n", + "\n", + "This operation can be thought as the spatial equivalent of an\n", + "**attribute join**, in which multiple tabular datasets can be merged by\n", + "aligning matching values in a common column that they both contain.\n", + "Thus, we'll start by developing an understanding of this operation first!\n", + "\n", + "- 7.0 Data Input and Prep\n", + "- 7.1 Attribute Joins\n", + "- **Exercise**: Choropleth Map\n", + "- 7.2 Spatial Joins\n", + "- 7.3 Aggregation\n", + "- **Exercise**: Aggregation\n", + "- 7.4 Recap\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/census/ACS5yr/census_variables_CA.csv'\n", + " - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip'\n", + " - 'notebook_data/alco_schools.csv'\n", + " \n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7.0 Data Input and Prep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read in a table of data from the US Census' 5-year American Community Survey (ACS5)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NAMEc_racec_whitec_blackc_asianc_latinxc_race_moec_white_moec_black_moec_asian_moe...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0Census Tract 4012, Alameda County, California24561287476259283213191116124...0.8149510.1033500.0584150.0102120.0130720.5513700.0643840.1890410.0835620.058219
1Census Tract 4013, Alameda County, California39838451348827796680186411283...0.6118650.2800400.0633480.0226240.0221220.3411530.1089930.3914960.0180840.104594
2Census Tract 4014, Alameda County, California43407131902593981644314440198...0.8076830.1637390.0178030.0063250.0044510.4708460.0213170.2557990.1166140.102194
3Census Tract 4015, Alameda County, California20805631064215190369222283116...0.8413460.1014420.0538460.0033650.0000000.5020370.0906310.2301430.0478620.017312
4Census Tract 4016, Alameda County, California1889324960247274400135376164...0.8306450.0795700.0822580.0021510.0053760.5704810.1227200.1774460.0630180.000000
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" + ], + "text/plain": [ + " NAME c_race c_white c_black \\\n", + "0 Census Tract 4012, Alameda County, California 2456 1287 476 \n", + "1 Census Tract 4013, Alameda County, California 3983 845 1348 \n", + "2 Census Tract 4014, Alameda County, California 4340 713 1902 \n", + "3 Census Tract 4015, Alameda County, California 2080 563 1064 \n", + "4 Census Tract 4016, Alameda County, California 1889 324 960 \n", + "\n", + " c_asian c_latinx c_race_moe c_white_moe c_black_moe c_asian_moe ... \\\n", + "0 259 283 213 191 116 124 ... \n", + "1 827 796 680 186 411 283 ... \n", + "2 593 981 644 314 440 198 ... \n", + "3 215 190 369 222 283 116 ... \n", + "4 247 274 400 135 376 164 ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.814951 0.103350 0.058415 0.010212 0.013072 0.551370 \n", + "1 0.611865 0.280040 0.063348 0.022624 0.022122 0.341153 \n", + "2 0.807683 0.163739 0.017803 0.006325 0.004451 0.470846 \n", + "3 0.841346 0.101442 0.053846 0.003365 0.000000 0.502037 \n", + "4 0.830645 0.079570 0.082258 0.002151 0.005376 0.570481 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.064384 0.189041 0.083562 0.058219 \n", + "1 0.108993 0.391496 0.018084 0.104594 \n", + "2 0.021317 0.255799 0.116614 0.102194 \n", + "3 0.090631 0.230143 0.047862 0.017312 \n", + "4 0.122720 0.177446 0.063018 0.000000 \n", + "\n", + "[5 rows x 66 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read in the ACS5 data for CA into a pandas DataFrame.\n", + "# Note: We force the FIPS_11_digit to be read in as a string to preserve any leading zeroes.\n", + "acs5_df = pd.read_csv(\"notebook_data/census/ACS5yr/census_variables_CA.csv\", dtype={'FIPS_11_digit':str})\n", + "acs5_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Brief summary of the data**:\n", + "\n", + "Below is a table of the variables in this table. They were combined from \n", + "different ACS 5 year tables.\n", + "\n", + "NOTE:\n", + "- variables that start with `c_` are counts\n", + "- variables that start with `med_` are medians\n", + "- variables that end in `_moe` are margin of error estimates\n", + "- variables that start with `_p` are proportions calcuated from the counts divided by the table denominator (the total count for whom that variable was assessed)\n", + "\n", + "\n", + "| Variable | Description |\n", + "|-----------------|-------------------------------------------------|\n", + "|`c_race` |Total population \n", + "|`c_white` |Total white non-Latinx\n", + "| `c_black` | Total black and African American non-Latinx\n", + "| `c_asian` | Total Asian non-Latinx\n", + "| `c_latinx` | Total Latinx\n", + "| `state_fips` | State level FIPS code\n", + "| `county_fips` | County level FIPS code\n", + "| `tract_fips` |Tracts level FIPS code\n", + "| `med_rent` |Median rent\n", + "| `med_hhinc` |Median household income\n", + "| `c_tenants` |Total tenants\n", + "| `c_owners` |Total owners\n", + "| `c_renters` |Total renters\n", + "| `c_movers` |Total number of people who moved\n", + "| `c_stay` |Total number of people who stayed\n", + "| `c_movelocal` |Number of people who moved locally\n", + "| `c_movecounty` |Number of people who moved counties\n", + "| `c_movestate` | Number of people who moved states\n", + "| `c_moveabroad` |Number of people who moved abroad\n", + "| `c_commute` |Total number of commuters\n", + "| `c_car` | Number of commuters who use a car\n", + "| `c_carpool` | Number of commuters who carpool\n", + "| `c_transit` |Number of commuters who use public transit\n", + "| `c_bike` |Number of commuters who bike\n", + "| `c_walk` |Number of commuters who bike\n", + "| `year` | ACS data year\n", + "| `FIPS_11_digit` | 11-digit FIPS code\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're going to drop all of our `moe` columns by identifying all of those that end with `_moe`. We can do that in two steps, first by using `filter` to identify columns that contain the string `_moe`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['c_race_moe', 'c_white_moe', 'c_black_moe', 'c_asian_moe',\n", + " 'c_latinx_moe', 'med_rent_moe', 'med_hhinc_moe', 'c_tenants_moe',\n", + " 'c_owners_moe', 'c_renters_moe', 'c_movers_moe', 'c_stay_moe',\n", + " 'c_movelocal_moe', 'c_movecounty_moe', 'c_movestate_moe',\n", + " 'c_moveabroad_moe', 'c_commute_moe', 'c_car_moe', 'c_carpool_moe',\n", + " 'c_transit_moe', 'c_bike_moe', 'c_walk_moe'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "moe_cols = acs5_df.filter(like='_moe',axis=1).columns\n", + "moe_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df.drop(moe_cols, axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And lastly, let's grab only the rows for year 2018 and county FIPS code 1 (i.e. Alameda County)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df_ac = acs5_df[(acs5_df['year']==2018) & (acs5_df['county_fips']==1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------------------\n", + "Now let's also read in our census tracts again!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001']\n", + "tracts_gdf_ac.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7.1 Attribute Joins\n", + "\n", + "**Attribute Joins between Geodataframes and Dataframes**\n", + "\n", + "*We just mapped the census tracts. But what makes a map powerful is when you map the data associated with the locations.*\n", + "\n", + "- `tracts_gdf_ac`: These are polygon data in a GeoDataFrame. However, as we saw in the `head` of that dataset, they no attributes of interest!\n", + "\n", + "- `acs5_df_ac`: These are 2018 ACS data from a CSV file ('census_variables_CA.csv'), imported and read in as a `pandas` DataFrame. However, they have no geometries!\n", + "\n", + "In order to map the ACS data we need to associate it with the tracts. Let's do that now, by joining the columns from `acs5_df_ac` to the columns of `tracts_gdf_ac` using a common column as the key for matching rows. This process is called an **attribute join**.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "The image above gives us a nice conceptual summary of the types of joins we could run.\n", + "\n", + "1. In general, why might we choose one type of join over another?\n", + "1. In our case, do we want an inner, left, right, or outer (AKA 'full') join? \n", + "\n", + "(**NOTE**: You can read more about merging in `geopandas` [here](http://geopandas.org/mergingdata.html#attribute-joins).)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, here we go!\n", + "\n", + "Let's take a look at the common column in both our DataFrames.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 06001400300\n", + "1 06001400900\n", + "2 06001402200\n", + "3 06001402800\n", + "4 06001404800\n", + "Name: GEOID, dtype: object" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tracts_gdf_ac['GEOID'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8323 06001441501\n", + "8324 06001404700\n", + "8325 06001442500\n", + "8326 06001450300\n", + "8327 06001450607\n", + "Name: FIPS_11_digit, dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acs5_df_ac['FIPS_11_digit'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Note that they are **not named the same thing**. \n", + " \n", + " That's okay! We just need to know that they contain the same information.\n", + "\n", + "Also note that they are **not in the same order**. \n", + " \n", + " That's not only okay... That's the point! (If they were in the same order already then we could just join them side by side, without having Python find and line up the matching rows from each!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------------------\n", + "\n", + "Let's do a `left` join to keep all of the census tracts in Alameda County and only the ACS data for those tracts.\n", + "\n", + "**NOTE**: To figure out how to do this we could always take a peek at the documentation by calling\n", + "`?tracts_gdf_ac.merge`, or `help(tracts_gdf_ac)`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STATEFPCOUNTYFPTRACTCEAFFGEOIDGEOIDNAME_xLSADALANDAWATERgeometry...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0060014003001400000US06001400300060014003004003CT11053290POLYGON ((-122.26416 37.84000, -122.26186 37.8......0.8405420.0450690.0584070.0315280.0244540.4208400.0594960.2806720.0678990.057479
1060014009001400000US06001400900060014009004009CT4208770POLYGON ((-122.28558 37.83978, -122.28319 37.8......0.9061610.0656870.0057120.0224400.0000000.5557180.0689150.2133430.0608500.044721
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2 rows × 54 columns

\n", + "
" + ], + "text/plain": [ + " STATEFP COUNTYFP TRACTCE AFFGEOID GEOID NAME_x LSAD \\\n", + "0 06 001 400300 1400000US06001400300 06001400300 4003 CT \n", + "1 06 001 400900 1400000US06001400900 06001400900 4009 CT \n", + "\n", + " ALAND AWATER geometry ... \\\n", + "0 1105329 0 POLYGON ((-122.26416 37.84000, -122.26186 37.8... ... \n", + "1 420877 0 POLYGON ((-122.28558 37.83978, -122.28319 37.8... ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.840542 0.045069 0.058407 0.031528 0.024454 0.420840 \n", + "1 0.906161 0.065687 0.005712 0.022440 0.000000 0.555718 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.059496 0.280672 0.067899 0.057479 \n", + "1 0.068915 0.213343 0.060850 0.044721 \n", + "\n", + "[2 rows x 54 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Left join keeps all tracts and the acs data for those tracts\n", + "tracts_acs_gdf_ac = tracts_gdf_ac.merge(acs5_df_ac, left_on='GEOID',right_on=\"FIPS_11_digit\", how='left')\n", + "tracts_acs_gdf_ac.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check that we have all the variables we have in our dataset now." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['STATEFP',\n", + " 'COUNTYFP',\n", + " 'TRACTCE',\n", + " 'AFFGEOID',\n", + " 'GEOID',\n", + " 'NAME_x',\n", + " 'LSAD',\n", + " 'ALAND',\n", + " 'AWATER',\n", + " 'geometry',\n", + " 'NAME_y',\n", + " 'c_race',\n", + " 'c_white',\n", + " 'c_black',\n", + " 'c_asian',\n", + " 'c_latinx',\n", + " 'state_fips',\n", + " 'county_fips',\n", + " 'tract_fips',\n", + " 'med_rent',\n", + " 'med_hhinc',\n", + " 'c_tenants',\n", + " 'c_owners',\n", + " 'c_renters',\n", + " 'c_movers',\n", + " 'c_stay',\n", + " 'c_movelocal',\n", + " 'c_movecounty',\n", + " 'c_movestate',\n", + " 'c_moveabroad',\n", + " 'c_commute',\n", + " 'c_car',\n", + " 'c_carpool',\n", + " 'c_transit',\n", + " 'c_bike',\n", + " 'c_walk',\n", + " 'year',\n", + " 'FIPS_11_digit',\n", + " 'p_white',\n", + " 'p_black',\n", + " 'p_asian',\n", + " 'p_latinx',\n", + " 'p_owners',\n", + " 'p_renters',\n", + " 'p_stay',\n", + " 'p_movelocal',\n", + " 'p_movecounty',\n", + " 'p_movestate',\n", + " 'p_moveabroad',\n", + " 'p_car',\n", + " 'p_carpool',\n", + " 'p_transit',\n", + " 'p_bike',\n", + " 'p_walk']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(tracts_acs_gdf_ac.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "It's always important to run sanity checks on our results, at each step of the way!\n", + "\n", + "In this case, how many rows and columns should we have?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows and columns in the Alameda County Census tract gdf:\n", + "\t (361, 10)\n", + "Row and columns in the ACS5 2018 data:\n", + "\t (361, 44)\n", + "Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\n", + "\t (361, 54)\n" + ] + } + ], + "source": [ + "print(\"Rows and columns in the Alameda County Census tract gdf:\\n\\t\", tracts_gdf_ac.shape)\n", + "print(\"Row and columns in the ACS5 2018 data:\\n\\t\", acs5_df_ac.shape)\n", + "print(\"Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\\n\\t\", tracts_acs_gdf_ac.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's save out our merged data so we can use it in the final notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_acs_gdf_ac.to_file('outdata/tracts_acs_gdf_ac.json', driver='GeoJSON')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Choropleth Map\n", + "We can now make choropleth maps using our attribute joined geodataframe. Go ahead and pick one variable to color the map, then map it. You can go back to lesson 5 if you need a refresher on how to make this!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------\n", + "# 7.2 Spatial Joins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! We've wrapped our heads around the concept of an attribute join.\n", + "\n", + "Now let's extend that concept to its spatially explicit equivalent: the **spatial join**!\n", + "\n", + "\n", + "
\n", + "\n", + "To start, we'll read in some other data: The Alameda County schools data.\n", + "\n", + "Then we'll work with that data and our `tracts_acs_gdf_ac` data together." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check if we have to transform the schools to match the`tracts_acs_gdf_ac`'s CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "schools_gdf CRS: epsg:4326\n", + "tracts_acs_gdf_ac CRS: epsg:4269\n" + ] + } + ], + "source": [ + "print('schools_gdf CRS:', schools_gdf.crs)\n", + "print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes we do! Let's do that.\n", + "\n", + "**NOTE**: Explicit syntax aiming at that dataset's CRS leaves less room for human error!" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "schools_gdf CRS: epsg:4269\n", + "tracts_acs_gdf_ac CRS: epsg:4269\n" + ] + } + ], + "source": [ + "schools_gdf = schools_gdf.to_crs(tracts_acs_gdf_ac.crs)\n", + "\n", + "print('schools_gdf CRS:', schools_gdf.crs)\n", + "print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we're ready to combine the datasets in an analysis.\n", + "\n", + "**In this case, we want to get data from the census tract within which each school is located.**\n", + "\n", + "But how can we do that? The two datasets don't share a common column to use for a join." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'AFFGEOID', 'GEOID', 'NAME_x', 'LSAD',\n", + " 'ALAND', 'AWATER', 'geometry', 'NAME_y', 'c_race', 'c_white', 'c_black',\n", + " 'c_asian', 'c_latinx', 'state_fips', 'county_fips', 'tract_fips',\n", + " 'med_rent', 'med_hhinc', 'c_tenants', 'c_owners', 'c_renters',\n", + " 'c_movers', 'c_stay', 'c_movelocal', 'c_movecounty', 'c_movestate',\n", + " 'c_moveabroad', 'c_commute', 'c_car', 'c_carpool', 'c_transit',\n", + " 'c_bike', 'c_walk', 'year', 'FIPS_11_digit', 'p_white', 'p_black',\n", + " 'p_asian', 'p_latinx', 'p_owners', 'p_renters', 'p_stay', 'p_movelocal',\n", + " 'p_movecounty', 'p_movestate', 'p_moveabroad', 'p_car', 'p_carpool',\n", + " 'p_transit', 'p_bike', 'p_walk'],\n", + " dtype='object')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tracts_acs_gdf_ac.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['X', 'Y', 'Site', 'Address', 'City', 'State', 'Type', 'API', 'Org',\n", + " 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_gdf.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, they do have a shared relationship by way of space! \n", + "\n", + "So, we'll use a spatial relationship query to figure out the census tract that\n", + "each school is in, then associate the tract's data with that school (as additional data in the school's row).\n", + "This is a **spatial join**!\n", + "\n", + "---------------------------------\n", + "\n", + "### Census Tract Data Associated with Each School\n", + "\n", + "In this case, let's say we're interested in the relationship between the median household income\n", + "in a census tract (`tracts_acs_gdf_ac['med_hhinc']`) and a school's Academic Performance Index\n", + "(`schools_gdf['API']`).\n", + "\n", + "To start, let's take a look at the distributions of our two variables of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tracts_acs_gdf_ac.hist('med_hhinc')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf.hist('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh, right! Those pesky schools with no reported APIs (i.e. API == 0)! Let's drop those." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf_api = schools_gdf.loc[schools_gdf['API'] > 0, ]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf_api.hist('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much better!\n", + "\n", + "Now, maybe we think there ought to be some correlation between the two variables?\n", + "As a first pass at this possibility, let's overlay the two datasets, coloring each one by\n", + "its variable of interest. This should give us a sense of whether or not similar values co-occur." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = tracts_acs_gdf_ac.plot(column='med_hhinc', cmap='cividis', figsize=[18,18],\n", + " legend=True, legend_kwds={'label': \"median household income ($)\",\n", + " 'orientation': \"horizontal\"})\n", + "schools_gdf_api.plot(column='API', cmap='cividis', edgecolor='black', alpha=1, ax=ax,\n", + " legend=True, legend_kwds={'label': \"API\", 'orientation': \"horizontal\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatially Joining our Schools and Census Tracts\n", + "\n", + "Though it's hard to say for sure, it certainly looks possible.\n", + "It would be ideal to scatterplot the variables! But in order to do that, \n", + "we need to know the median household income in each school's tract, which\n", + "means we definitely need our **spatial join**!\n", + "\n", + "We'll first take a look at the documentation for the spatial join function, `gpd.sjoin`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function sjoin in module geopandas.tools.sjoin:\n", + "\n", + "sjoin(left_df, right_df, how='inner', op='intersects', lsuffix='left', rsuffix='right')\n", + " Spatial join of two GeoDataFrames.\n", + " \n", + " Parameters\n", + " ----------\n", + " left_df, right_df : GeoDataFrames\n", + " how : string, default 'inner'\n", + " The type of join:\n", + " \n", + " * 'left': use keys from left_df; retain only left_df geometry column\n", + " * 'right': use keys from right_df; retain only right_df geometry column\n", + " * 'inner': use intersection of keys from both dfs; retain only\n", + " left_df geometry column\n", + " op : string, default 'intersects'\n", + " Binary predicate, one of {'intersects', 'contains', 'within'}.\n", + " See http://shapely.readthedocs.io/en/latest/manual.html#binary-predicates.\n", + " lsuffix : string, default 'left'\n", + " Suffix to apply to overlapping column names (left GeoDataFrame).\n", + " rsuffix : string, default 'right'\n", + " Suffix to apply to overlapping column names (right GeoDataFrame).\n", + "\n" + ] + } + ], + "source": [ + "help(gpd.sjoin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like the key arguments to consider are:\n", + "- the two GeoDataFrames (**`left_df`** and **`right_df`**)\n", + "- the type of join to run (**`how`**), which can take the values `left`, `right`, or `inner`\n", + "- the spatial relationship query to use (**`op`**)\n", + "\n", + "**NOTE**:\n", + "- By default `sjoin` is an inner join. It keeps the data from both geodataframes only where the locations spatially intersect.\n", + "\n", + "- By default `sjoin` maintains the geometry of first geodataframe input to the operation. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Which GeoDataFrame are we joining onto which (i.e. which one is getting the other one's data added to it)?\n", + "1. What happened to 'outer' as a join type?\n", + "1. Thus, in our operation, which GeoDataFrame should be the `left_df`, which should be the `right_df`, and `how` do we want our join to run?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alright! Let's run our join!" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts = gpd.sjoin(schools_gdf_api, tracts_acs_gdf_ac, how='left')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking Our Output\n", + "\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "As always, we want to sanity-check our intermediate result before we rush ahead.\n", + "\n", + "One way to do that is to introspect the structure of the result object a bit.\n", + "\n", + "1. What type of object should that have given us?\n", + "1. What should the dimensions of that object be, and why?\n", + "1. If we wanted a visual check of our results (i.e. a plot or map), what could we do?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(325, 64)\n", + "(550, 10)\n", + "(361, 54)\n" + ] + } + ], + "source": [ + "print(schools_jointracts.shape)\n", + "print(schools_gdf.shape)\n", + "print(tracts_acs_gdf_ac.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYSiteAddressCityStateTypeAPIOrggeometry...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933PublicPOINT (-122.23876 37.74476)...0.9016940.0531200.0133140.0235340.0083380.6807450.0776500.1072930.0047220.019150
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932PublicPOINT (-122.25186 37.73900)...0.9016940.0531200.0133140.0235340.0083380.6807450.0776500.1072930.0047220.019150
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853PublicPOINT (-122.25892 37.76206)...0.8451200.0902400.0326400.0320000.0000000.6010570.0429330.2470280.0330250.011889
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927PublicPOINT (-122.23484 37.76525)...0.9393130.0324920.0230930.0000000.0051020.5618230.0774930.1726500.0188030.036467
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894PublicPOINT (-122.23808 37.75396)...0.9344160.0311220.0107790.0214060.0022770.6455320.0675320.1503980.0150400.031849
\n", + "

5 rows × 64 columns

\n", + "
" + ], + "text/plain": [ + " X Y Site Address \\\n", + "0 -122.238761 37.744764 Amelia Earhart Elementary 400 Packet Landing Rd \n", + "1 -122.251856 37.738999 Bay Farm Elementary 200 Aughinbaugh Way \n", + "2 -122.258915 37.762058 Donald D. Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org geometry ... \\\n", + "0 Alameda CA ES 933 Public POINT (-122.23876 37.74476) ... \n", + "1 Alameda CA ES 932 Public POINT (-122.25186 37.73900) ... \n", + "2 Alameda CA ES 853 Public POINT (-122.25892 37.76206) ... \n", + "3 Alameda CA ES 927 Public POINT (-122.23484 37.76525) ... \n", + "4 Alameda CA ES 894 Public POINT (-122.23808 37.75396) ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.901694 0.053120 0.013314 0.023534 0.008338 0.680745 \n", + "1 0.901694 0.053120 0.013314 0.023534 0.008338 0.680745 \n", + "2 0.845120 0.090240 0.032640 0.032000 0.000000 0.601057 \n", + "3 0.939313 0.032492 0.023093 0.000000 0.005102 0.561823 \n", + "4 0.934416 0.031122 0.010779 0.021406 0.002277 0.645532 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.077650 0.107293 0.004722 0.019150 \n", + "1 0.077650 0.107293 0.004722 0.019150 \n", + "2 0.042933 0.247028 0.033025 0.011889 \n", + "3 0.077493 0.172650 0.018803 0.036467 \n", + "4 0.067532 0.150398 0.015040 0.031849 \n", + "\n", + "[5 rows x 64 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_jointracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confirmed! The output of the our `sjoin` operation is a GeoDataFrame (`schools_jointracts`) with:\n", + "- a row for each school that is located inside a census tract (all of them are)\n", + "- the **point geometry** of that school\n", + "- all of the attribute data columns (non-geometry columns) from both input GeoDataFrames\n", + "\n", + "----------------------------\n", + "\n", + "Let's also take a look at an overlay map of the schools on the tracts.\n", + "If we color the schools categorically by their tracts IDs, then we should see\n", + "that all schools within a given tract polygon are the same color." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = tracts_acs_gdf_ac.plot(color='white', edgecolor='black', figsize=[18,18])\n", + "schools_jointracts.plot(column='GEOID', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assessing the Relationship between Median Household Income and API\n", + "\n", + "Fantastic! That looks right!\n", + "\n", + "Now we can create that scatterplot we were thinking about!" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'API')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6,6))\n", + "ax.scatter(schools_jointracts.med_hhinc, schools_jointracts.API)\n", + "ax.set_xlabel('median household income ($)')\n", + "ax.set_ylabel('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wow! Just as we suspected based on our overlay map,\n", + "there's a pretty obvious, strong, and positive correlation\n", + "between median household income in a school's tract\n", + "and the school's API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7.3: Aggregation\n", + "\n", + "We just saw that a spatial join in one way to leverage the spatial relationship\n", + "between two datasets in order to create a new, synthetic dataset.\n", + "\n", + "An **aggregation** is another way we can generate new data from this relationship.\n", + "In this case, for each feature in one dataset we find all the features in another\n", + "dataset that satisfy our chosen spatial relationship query with it (e.g. within, intersects),\n", + "then aggregate them using some summary function (e.g. count, mean).\n", + "\n", + "------------------------------------\n", + "\n", + "### Getting the Aggregated School Counts\n", + "\n", + "Let's take this for a spin with our data. We'll count all the schools within each census tract.\n", + "\n", + "Note that we've already done the first step of spatially joining the data from the aggregating features\n", + "(the tracts) onto the data to be aggregated (our schools).\n", + "\n", + "The next step is to group our GeoDataFrame by census tract, and then summarize our data by group.\n", + "We do this using the DataFrame method `groupy`.\n", + "\n", + "To get the correct count, lets rejoin our schools on our tracts, this time keeping all schools\n", + "(not just those with APIs > 0, as before)." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='left')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now for the `groupy` operation.\n", + "\n", + "**NOTE**: We could really use any column, since we're just taking a count. For now we'll just use the school names ('Site')." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Counts, rows and columns: (263, 2)\n", + "Tracts, rows and columns: (361, 54)\n" + ] + }, + { + "data": { + "text/html": [ + "
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\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Above we selected three columns from the input GeoDataFrame to create a subset as input to the dissolve operation. Why?\n", + "1. Why did we run a new spatial join? What would have happened if we had used the `schools_jointracts` object instead?\n", + "1. What explains the dimensions of the new object (361, 2)?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "You responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mapping our Spatial Join Output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also, because our `sjoin` plus `dissolve` pipeline outputs a GeoDataFrame, we can now easily map the school count by census tract!" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "\n", + "# Display the output of our spatial join\n", + "tracts_schoolcounts.plot(ax=ax,column='Site', \n", + " scheme=\"user_defined\",\n", + " classification_kwds={'bins':[*range(9)]},\n", + " cmap=\"PuRd_r\",\n", + " edgecolor=\"grey\",\n", + " legend=True, \n", + " legend_kwds={'title':'Number of schools'})\n", + "schools_gdf.plot(ax=ax, color='black', markersize=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------\n", + "\n", + "# Exercise: Aggregation\n", + "\n", + "#### What is the mean API of each census tract?\n", + "\n", + "As we mentioned, the spatial aggregation workflow that we just put together above\n", + "could have been used not to generate a new count variable, but also\n", + "to generate any other new variable the results from calling an aggregation function\n", + "on an attribute column.\n", + "\n", + "In this case, we want to calculate and map the mean API of the schools in each census tract.\n", + "\n", + "Copy and paste code from above where useful, then tweak and/or add to that code such that your new code:\n", + "1. joins the schools onto the tracts (**HINT**: make sure to decide whether or not you want to include schools with API = 0!)\n", + "1. dissolves that joined object by the tract IDs, giving you a new GeoDataFrame with each tract's mean API (**HINT**: because this is now a different calculation, different problems may arise and need handling!)\n", + "1. plots the tracts, colored by API scores (**HINT**: overlay the schools points again, visualizing them in a way that will help you visually check your results!)\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "----------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.4 Recap\n", + "We discussed how we can combine datasets to enhance any geospatial data analyses you could do. Key concepts include:\n", + "- Attribute joins\n", + "\t- `.merge()`\n", + "- Spatial joins (order matters!)\n", + "\t- `gpd.sjoin()`\n", + "- Aggregation\n", + "\t-`.groupby()`\n", + "\t- `.dissolve()` (preserves geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_build/html/_sources/ran/08_Pulling_It_All_Together-Copy1.ipynb b/_build/html/_sources/ran/08_Pulling_It_All_Together-Copy1.ipynb new file mode 100644 index 0000000..6591414 --- /dev/null +++ b/_build/html/_sources/ran/08_Pulling_It_All_Together-Copy1.ipynb @@ -0,0 +1,449 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 08. Pulling it all Together\n", + "\n", + "For this last lesson, we'll practice going through a full workflow!! We'll answer the question:\n", + "## What is the total grocery-store sales volume of each census tract?\n", + "\n", + "\n", + "### WORKFLOW:\n", + "\n", + "
\n", + "Here's a set of steps that we will implement in the labeled cells below:\n", + "\n", + " 8.1 Read in and Prep Data\n", + "- read in tracts acs joined data\n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- transform it to match the CRS of `tracts_acs_gdf_ac`\n", + "- take a peek\n", + "\n", + "8.2 Spatial Join and Dissolve\n", + "- join the two datasets in such a way that you can then...\n", + "- group by tract and calculate the total grocery-store sales volume\n", + "- don't forget to check the dimensions, contents, and any other relevant aspects of your results\n", + "\n", + "8.3 Plot and Review\n", + "- plot the tracts, coloring them by total grocery-store sales volume\n", + "- plot the grocery stores on top\n", + "- bonus points for devising a nice visualization scheme that helps you heuristically check your results!\n", + "\n", + "\n", + "\n", + "### INSTRUCTIONS:\n", + "**We've written out some of the code for you, but you'll need to replace the ellipses with the correct\n", + "content.**\n", + "\n", + "*You can check your answers by double-clicking on the Markdown cells where indicated.*\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'outdata/tracts_acs_gdf_ac.json'\n", + " - 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: N/A\n", + " - Exercises: 30 minutes\n", + "\n", + "\n", + "\n", + "\n", + "-----------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "---------------------------------------\n", + "\n", + "\n", + "### Install Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------------------\n", + "\n", + "## 8.1 Read in the Prep Data\n", + "\n", + "We first need to prepare our data by loading both our tracts/acs and grocery data, and conduct our usual steps to make there they have the same CRS.\n", + "\n", + "- read in our tracts acs joined data \n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- transform it to match the CRS of `tracts_acs_gdf_ac`\n", + "- take a peek\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m tracts_acs_gdf_ac = gpd.read_file(..)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "# read in tracts acs data\n", + "\n", + "tracts_acs_gdf_ac = gpd.read_file(..)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read our grocery-data CSV into a Pandas DataFrame\n", + "\n", + "grocery_pts_df = pd.read_csv(...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# coerce it to a GeoDataFrame\n", + "\n", + "grocery_pts_gdf = gpd.GeoDataFrame(grocery_pts_df, \n", + " geometry=gpd.points_from_xy(...,...))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define its CRS (NOTE: Use EPSG:4326)\n", + "\n", + "grocery_pts_gdf.crs = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# transform it to match the CRS of tracts_acs_gdf_ac\n", + "\n", + "grocery_pts_gdf.to_crs(..., inplace=...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# take a peek\n", + "\n", + "print(grocery_pts_gdf.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "-----------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.2 Spatial Join and Dissolve\n", + "\n", + "Now that we have our data and they're in the same projection, we're going to conduct an *attribute join* to bring together the two datasets. From there we'll be able to actually *aggregate* our data to count the total sales volume.\n", + "\n", + "- join the two datasets in such a way that you can then...\n", + "- group by tract and calculate the total grocery-store sales volume\n", + "- don't forget to check the dimensions, contents, and any other relevant aspects of your results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# join the two datasets in such a way that you can then...\n", + "\n", + "tracts_joingrocery = gpd.sjoin(..., ..., how= ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# group by tract and calculate the total grocery-store sales volume\n", + "\n", + "tracts_totsalesvol = tracts_joingrocery[['GEOID','geometry','SALESVOL']].dissolve(by= ...,\n", + " aggfunc=..., as_index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# don't forget to check the dimensions, contents, and any other relevant aspects of your results\n", + "\n", + "# check the dimensions\n", + "print('Dimensions of result:', ...)\n", + "print('Dimesions of census tracts:', ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check the result\n", + "print(tracts_totsalesvol.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "----------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.3 Plot and Review\n", + "\n", + "With any time of geospatial analysis you do, it's always nice to plot and visualize your results to check your work and start to understand the full story of your analysis.\n", + "\n", + "- Plot the tracts, coloring them by total grocery-store sales volume\n", + "- Plot the grocery stores on top\n", + "- Bonus points for devising a nice visualization scheme that helps you heuristically check your results!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create the figure and axes\n", + "\n", + "fig, ax = plt.subplots(figsize = (20,20)) \n", + "\n", + "# plot the tracts, coloring by total SALESVOL\n", + "\n", + "tracts_totsalesvol.plot(ax=ax, column= ..., scheme=\"quantiles\", cmap=\"autumn\", edgecolor=\"grey\",\n", + " legend=True, legend_kwds={'title':...})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subset the stores for only those within our tracts, to keep map within region of interest\n", + "\n", + "grocery_pts_gdf_ac = grocery_pts_gdf.loc[..., ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# add the grocery stores, coloring by SALESVOL, for a visual check\n", + "\n", + "grocery_pts_gdf_ac.plot(ax=ax, column= ... , cmap= ..., linewidth= ..., markersize= ...,\n", + " legend=True, legend_kwds={'label': ... , 'orientation': \"horizontal\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "scrolled": false + }, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "-------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "***\n", + "\n", + "# Congrats!! Thanks for Joining Us for Geospatial Fundamentals!!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_build/html/_static/__init__.py b/_build/html/_static/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/_build/html/_static/__pycache__/__init__.cpython-38.pyc b/_build/html/_static/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..478d75f Binary files /dev/null and b/_build/html/_static/__pycache__/__init__.cpython-38.pyc differ diff --git a/_build/html/_static/basic.css b/_build/html/_static/basic.css new file mode 100644 index 0000000..9f93524 --- /dev/null +++ b/_build/html/_static/basic.css @@ -0,0 +1,768 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; 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+} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: relative; + left: 0px; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/_build/html/_static/clipboard.min.js b/_build/html/_static/clipboard.min.js new file mode 100644 index 0000000..02c549e --- /dev/null +++ b/_build/html/_static/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.4 + * https://zenorocha.github.io/clipboard.js + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return function(n){var o={};function r(t){if(o[t])return o[t].exports;var e=o[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,r),e.l=!0,e.exports}return r.m=n,r.c=o,r.d=function(t,e,n){r.o(t,e)||Object.defineProperty(t,e,{enumerable:!0,get:n})},r.r=function(t){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(r.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)r.d(n,o,function(t){return e[t]}.bind(null,o));return n},r.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return r.d(e,"a",e),e},r.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},r.p="",r(r.s=0)}([function(t,e,n){"use strict";var r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t},i=function(){function o(t,e){for(var n=0;n + + + + diff --git a/_build/html/_static/copybutton.css b/_build/html/_static/copybutton.css new file mode 100644 index 0000000..75b17a8 --- /dev/null +++ b/_build/html/_static/copybutton.css @@ -0,0 +1,67 @@ +/* Copy buttons */ +a.copybtn { + position: absolute; + top: .2em; + right: .2em; + width: 1em; + height: 1em; + opacity: .3; + transition: opacity 0.5s; + border: none; + user-select: none; +} + +div.highlight { + position: relative; +} + +a.copybtn > img { + vertical-align: top; + margin: 0; + top: 0; + left: 0; + position: absolute; +} + +.highlight:hover .copybtn { + opacity: 1; +} + +/** + * A minimal CSS-only tooltip copied from: + * https://codepen.io/mildrenben/pen/rVBrpK + * + * To use, write HTML like the following: + * + *

Short

+ */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: 2px; + top: 0; + left: -.2em; + background: grey; + font-size: 1rem; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} diff --git a/_build/html/_static/copybutton.js b/_build/html/_static/copybutton.js new file mode 100644 index 0000000..65a5916 --- /dev/null +++ b/_build/html/_static/copybutton.js @@ -0,0 +1,153 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for two seconds, then changes it back +const temporarilyChangeTooltip = (el, newText) => { + const oldText = el.getAttribute('data-tooltip') + el.setAttribute('data-tooltip', newText) + setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const codeCells = document.querySelectorAll('div.highlight pre') + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + const pre_bg = getComputedStyle(codeCell).backgroundColor; + + const clipboardButton = id => + ` + ${messages[locale]['copy_to_clipboard']} + ` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) { + + var regexp; + var match; + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match) { + promptFound = true + if (removePrompts) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + } else { + if (!onlyCopyPromptLines) { + outputLines.push(line) + } + } + } + + // If no lines with the prompt were found then just use original lines + if (promptFound) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + return formatCopyText(target.innerText, '', false, true, true) +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy_success']) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_build/html/_static/copybutton_funcs.js b/_build/html/_static/copybutton_funcs.js new file mode 100644 index 0000000..57caa55 --- /dev/null +++ b/_build/html/_static/copybutton_funcs.js @@ -0,0 +1,47 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) { + + var regexp; + var match; + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match) { + promptFound = true + if (removePrompts) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + } else { + if (!onlyCopyPromptLines) { + outputLines.push(line) + } + } + } + + // If no lines with the prompt were found then just use original lines + if (promptFound) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/_build/html/_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css b/_build/html/_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css new file mode 100644 index 0000000..7fd19a7 --- /dev/null +++ b/_build/html/_static/css/index.f658d18f9b420779cfdf24aa0a7e2d77.css @@ -0,0 +1,6 @@ +/*! + * Bootstrap v4.5.0 (https://getbootstrap.com/) + * Copyright 2011-2020 The Bootstrap Authors + * Copyright 2011-2020 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + 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+/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + +/** + * make the code below compatible with browsers without + * an installed firebug like debugger +if (!window.console || !console.firebug) { + var names = ["log", "debug", "info", "warn", "error", "assert", "dir", + "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", + "profile", "profileEnd"]; + window.console = {}; + for (var i = 0; i < names.length; ++i) + window.console[names[i]] = function() {}; +} + */ + +/** + * small helper function to urldecode strings + */ +jQuery.urldecode = function(x) { + return decodeURIComponent(x).replace(/\+/g, ' '); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} + +/** + * Small JavaScript module for the documentation. + */ +var Documentation = { + + init : function() { + this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { + this.initOnKeyListeners(); + } + }, + + /** + * i18n support + */ + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + LOCALE : 'unknown', + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated === 'undefined') + return string; + return (typeof translated === 'string') ? translated : translated[0]; + }, + + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated === 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; + }, + + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash && $.browser.mozilla) + window.setTimeout(function() { + document.location.href += ''; + }, 10); + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; + if (terms.length) { + var body = $('div.body'); + if (!body.length) { + body = $('body'); + } + window.setTimeout(function() { + $.each(terms, function() { + body.highlightText(this.toLowerCase(), 'highlighted'); + }); + }, 10); + $('

' + _('Hide Search Matches') + '

') + .appendTo($('#searchbox')); + } + }, + + /** + * init the domain index toggle buttons + */ + initIndexTable : function() { + var togglers = $('img.toggler').click(function() { + var src = $(this).attr('src'); + var idnum = $(this).attr('id').substr(7); + $('tr.cg-' + idnum).toggle(); + if (src.substr(-9) === 'minus.png') + $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); + else + $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); + }).css('display', ''); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { + togglers.click(); + } + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords : function() { + $('#searchbox .highlight-link').fadeOut(300); + $('span.highlighted').removeClass('highlighted'); + }, + + /** + * make the url absolute + */ + makeURL : function(relativeURL) { + return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; + }, + + /** + * get the current relative url + */ + getCurrentURL : function() { + var path = document.location.pathname; + var parts = path.split(/\//); + $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { + if (this === '..') + parts.pop(); + }); + var url = parts.join('/'); + return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + }, + + initOnKeyListeners: function() { + $(document).keydown(function(event) { + var activeElementType = document.activeElement.tagName; + // don't navigate when in search box or textarea + if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' + && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) { + switch (event.keyCode) { + case 37: // left + var prevHref = $('link[rel="prev"]').prop('href'); + if (prevHref) { + window.location.href = prevHref; + return false; + } + case 39: // right + var nextHref = $('link[rel="next"]').prop('href'); + if (nextHref) { + window.location.href = nextHref; + return false; + } + } + } + }); + } +}; + +// quick alias for translations +_ = Documentation.gettext; + +$(document).ready(function() { + Documentation.init(); +}); diff --git a/_build/html/_static/documentation_options.js b/_build/html/_static/documentation_options.js new file mode 100644 index 0000000..7ad534e --- /dev/null +++ b/_build/html/_static/documentation_options.js @@ -0,0 +1,11 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '', + LANGUAGE: 'None', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '', + NAVIGATION_WITH_KEYS: true +}; \ No newline at end of file diff --git a/_build/html/_static/file.png b/_build/html/_static/file.png new file mode 100644 index 0000000..a858a41 Binary files /dev/null and b/_build/html/_static/file.png differ diff --git a/_build/html/_static/images/logo_binder.svg b/_build/html/_static/images/logo_binder.svg new file mode 100644 index 0000000..45fecf7 --- /dev/null +++ b/_build/html/_static/images/logo_binder.svg @@ -0,0 +1,19 @@ + + + + +logo + + + + + + + + diff --git a/_build/html/_static/images/logo_colab.png b/_build/html/_static/images/logo_colab.png new file mode 100644 index 0000000..b7560ec Binary files /dev/null and b/_build/html/_static/images/logo_colab.png differ diff --git a/_build/html/_static/images/logo_jupyterhub.svg b/_build/html/_static/images/logo_jupyterhub.svg new file mode 100644 index 0000000..60cfe9f --- /dev/null +++ b/_build/html/_static/images/logo_jupyterhub.svg @@ -0,0 +1 @@ +logo_jupyterhubHub diff --git a/_build/html/_static/jquery-3.4.1.js b/_build/html/_static/jquery-3.4.1.js new file mode 100644 index 0000000..773ad95 --- /dev/null +++ b/_build/html/_static/jquery-3.4.1.js @@ -0,0 +1,10598 @@ +/*! + * jQuery JavaScript Library v3.4.1 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2019-05-01T21:04Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var document = window.document; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var concat = arr.concat; + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + return typeof obj === "function" && typeof obj.nodeType !== "number"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.4.1", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }, + + // Support: Android <=4.0 only + // Make sure we trim BOM and NBSP + rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a global context + globalEval: function( code, options ) { + DOMEval( code, { nonce: options && options.nonce } ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // Support: Android <=4.0 only + trim: function( text ) { + return text == null ? + "" : + ( text + "" ).replace( rtrim, "" ); + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return concat.apply( [], ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), +function( i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); +} ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.4 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2019-04-08 + */ +(function( window ) { + +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ({}).hasOwnProperty, + arr = [], + pop = arr.pop, + push_native = arr.push, + push = arr.push, + slice = arr.slice, + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[i] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier + identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + + whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ), + funescape = function( _, escaped, escapedWhitespace ) { + var high = "0x" + escaped - 0x10000; + // NaN means non-codepoint + // Support: Firefox<24 + // Workaround erroneous numeric interpretation of +"0x" + return high !== high || escapedWhitespace ? + escaped : + high < 0 ? + // BMP codepoint + String.fromCharCode( high + 0x10000 ) : + // Supplemental Plane codepoint (surrogate pair) + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + (arr = slice.call( preferredDoc.childNodes )), + preferredDoc.childNodes + ); + // Support: Android<4.0 + // Detect silently failing push.apply + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + push_native.apply( target, slice.call(els) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + // Can't trust NodeList.length + while ( (target[j++] = els[i++]) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + + if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) { + setDocument( context ); + } + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) { + + // ID selector + if ( (m = match[1]) ) { + + // Document context + if ( nodeType === 9 ) { + if ( (elem = context.getElementById( m )) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && (elem = newContext.getElementById( m )) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[2] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( (m = match[3]) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + (!rbuggyQSA || !rbuggyQSA.test( selector )) && + + // Support: IE 8 only + // Exclude object elements + (nodeType !== 1 || context.nodeName.toLowerCase() !== "object") ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && rdescend.test( selector ) ) { + + // Capture the context ID, setting it first if necessary + if ( (nid = context.getAttribute( "id" )) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", (nid = expando) ); + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[i] = "#" + nid + " " + toSelector( groups[i] ); + } + newSelector = groups.join( "," ); + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return (cache[ key + " " ] = value); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement("fieldset"); + + try { + return !!fn( el ); + } catch (e) { + return false; + } finally { + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split("|"), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[i] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( (cur = cur.nextSibling) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return (name === "input" || name === "button") && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction(function( argument ) { + argument = +argument; + return markFunction(function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ (j = matchIndexes[i]) ] ) { + seed[j] = !(matches[j] = seed[j]); + } + } + }); + }); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem.namespaceURI, + docElem = (elem.ownerDocument || elem).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9-11, Edge + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + if ( preferredDoc !== document && + (subWindow = document.defaultView) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert(function( el ) { + el.className = "i"; + return !el.getAttribute("className"); + }); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert(function( el ) { + el.appendChild( document.createComment("") ); + return !el.getElementsByTagName("*").length; + }); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert(function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + }); + + // ID filter and find + if ( support.getById ) { + Expr.filter["ID"] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute("id") === attrId; + }; + }; + Expr.find["ID"] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter["ID"] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode("id"); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find["ID"] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode("id"); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( (elem = elems[i++]) ) { + node = elem.getAttributeNode("id"); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find["TAG"] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( (elem = results[i++]) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( (support.qsa = rnative.test( document.querySelectorAll )) ) { + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert(function( el ) { + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll("[msallowcapture^='']").length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll("[selected]").length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push("~="); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll(":checked").length ) { + rbuggyQSA.push(":checked"); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push(".#.+[+~]"); + } + }); + + assert(function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement("input"); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll("[name=d]").length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll(":enabled").length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll(":disabled").length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll("*,:x"); + rbuggyQSA.push(",.*:"); + }); + } + + if ( (support.matchesSelector = rnative.test( (matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector) )) ) { + + assert(function( el ) { + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + }); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + )); + } : + function( a, b ) { + if ( b ) { + while ( (b = b.parentNode) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) { + + // Choose the first element that is related to our preferred document + if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) { + return -1; + } + if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + return a === document ? -1 : + b === document ? 1 : + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( (cur = cur.parentNode) ) { + ap.unshift( cur ); + } + cur = b; + while ( (cur = cur.parentNode) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[i] === bp[i] ) { + i++; + } + + return i ? + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[i], bp[i] ) : + + // Otherwise nodes in our document sort first + ap[i] === preferredDoc ? -1 : + bp[i] === preferredDoc ? 1 : + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + // Set document vars if needed + if ( ( elem.ownerDocument || elem ) !== document ) { + setDocument( elem ); + } + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch (e) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + // Set document vars if needed + if ( ( context.ownerDocument || context ) !== document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + // Set document vars if needed + if ( ( elem.ownerDocument || elem ) !== document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + (val = elem.getAttributeNode(name)) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return (sel + "").replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( (elem = results[i++]) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + // If no nodeType, this is expected to be an array + while ( (node = elem[i++]) ) { + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[1] = match[1].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape ); + + if ( match[2] === "~=" ) { + match[3] = " " + match[3] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[1] = match[1].toLowerCase(); + + if ( match[1].slice( 0, 3 ) === "nth" ) { + // nth-* requires argument + if ( !match[3] ) { + Sizzle.error( match[0] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) ); + match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" ); + + // other types prohibit arguments + } else if ( match[3] ) { + Sizzle.error( match[0] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[6] && match[2]; + + if ( matchExpr["CHILD"].test( match[0] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[3] ) { + match[2] = match[4] || match[5] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + // Get excess from tokenize (recursively) + (excess = tokenize( unquoted, true )) && + // advance to the next closing parenthesis + (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) { + + // excess is a negative index + match[0] = match[0].slice( 0, excess ); + match[2] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { return true; } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + (pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) && + classCache( className, function( elem ) { + return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" ); + }); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + }; + }, + + "CHILD": function( type, what, argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( (node = node[ dir ]) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || (node[ expando ] = {}); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + (outerCache[ node.uniqueID ] = {}); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( (node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + (diff = nodeIndex = 0) || start.pop()) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + // Use previously-cached element index if available + if ( useCache ) { + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || (node[ expando ] = {}); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + (outerCache[ node.uniqueID ] = {}); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + // Use the same loop as above to seek `elem` from the start + while ( (node = ++nodeIndex && node && node[ dir ] || + (diff = nodeIndex = 0) || start.pop()) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || (node[ expando ] = {}); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + (outerCache[ node.uniqueID ] = {}); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction(function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[i] ); + seed[ idx ] = !( matches[ idx ] = matched[i] ); + } + }) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + // Potentially complex pseudos + "not": markFunction(function( selector ) { + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction(function( seed, matches, context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( (elem = unmatched[i]) ) { + seed[i] = !(matches[i] = elem); + } + } + }) : + function( elem, context, xml ) { + input[0] = elem; + matcher( input, null, xml, results ); + // Don't keep the element (issue #299) + input[0] = null; + return !results.pop(); + }; + }), + + "has": markFunction(function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + }), + + "contains": markFunction(function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + }), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + // lang value must be a valid identifier + if ( !ridentifier.test(lang || "") ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( (elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( (elem = elem.parentNode) && elem.nodeType === 1 ); + return false; + }; + }), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected); + }, + + "selected": function( elem ) { + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos["empty"]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo(function() { + return [ 0 ]; + }), + + "last": createPositionalPseudo(function( matchIndexes, length ) { + return [ length - 1 ]; + }), + + "eq": createPositionalPseudo(function( matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + }), + + "even": createPositionalPseudo(function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + }), + + "odd": createPositionalPseudo(function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + }), + + "lt": createPositionalPseudo(function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + }), + + "gt": createPositionalPseudo(function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + }) + } +}; + +Expr.pseudos["nth"] = Expr.pseudos["eq"]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || (match = rcomma.exec( soFar )) ) { + if ( match ) { + // Don't consume trailing commas as valid + soFar = soFar.slice( match[0].length ) || soFar; + } + groups.push( (tokens = []) ); + } + + matched = false; + + // Combinators + if ( (match = rcombinators.exec( soFar )) ) { + matched = match.shift(); + tokens.push({ + value: matched, + // Cast descendant combinators to space + type: match[0].replace( rtrim, " " ) + }); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] || + (match = preFilters[ type ]( match ))) ) { + matched = match.shift(); + tokens.push({ + value: matched, + type: type, + matches: match + }); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[i].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( (elem = elem[ dir ]) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( (elem = elem[ dir ]) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( (elem = elem[ dir ]) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || (elem[ expando ] = {}); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {}); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( (oldCache = uniqueCache[ key ]) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return (newCache[ 2 ] = oldCache[ 2 ]); + } else { + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[i]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[0]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[i], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( (elem = unmatched[i]) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction(function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( (elem = temp[i]) ) { + matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( (elem = matcherOut[i]) ) { + // Restore matcherIn since elem is not yet a final match + temp.push( (matcherIn[i] = elem) ); + } + } + postFinder( null, (matcherOut = []), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( (elem = matcherOut[i]) && + (temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) { + + seed[temp] = !(results[temp] = elem); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + }); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[0].type ], + implicitRelative = leadingRelative || Expr.relative[" "], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + (checkContext = context).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( (matcher = Expr.relative[ tokens[i].type ]) ) { + matchers = [ addCombinator(elementMatcher( matchers ), matcher) ]; + } else { + matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[j].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" }) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( (tokens = tokens.slice( j )) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find["TAG"]( "*", outermost ), + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1), + len = elems.length; + + if ( outermost ) { + outermostContext = context === document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && (elem = elems[i]) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + if ( !context && elem.ownerDocument !== document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( (matcher = elementMatchers[j++]) ) { + if ( matcher( elem, context || document, xml) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + // They will have gone through all possible matchers + if ( (elem = !matcher && elem) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( (matcher = setMatchers[j++]) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !(unmatched[i] || setMatched[i]) ) { + setMatched[i] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[i] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( (selector = compiled.selector || selector) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[0] = match[0].slice( 0 ); + if ( tokens.length > 2 && (token = tokens[0]).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) { + + context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[i]; + + // Abort if we hit a combinator + if ( Expr.relative[ (type = token.type) ] ) { + break; + } + if ( (find = Expr.find[ type ]) ) { + // Search, expanding context for leading sibling combinators + if ( (seed = find( + token.matches[0].replace( runescape, funescape ), + rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context + )) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split("").sort( sortOrder ).join("") === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert(function( el ) { + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement("fieldset") ) & 1; +}); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert(function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute("href") === "#" ; +}) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + }); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert(function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +}) ) { + addHandle( "value", function( elem, name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + }); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert(function( el ) { + return el.getAttribute("disabled") == null; +}) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + (val = elem.getAttributeNode( name )) && val.specified ? + val.value : + null; + } + }); +} + +return Sizzle; + +})( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +}; +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( typeof elem.contentDocument !== "undefined" ) { + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the master Deferred + master = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + master.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( master.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return master.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); + } + + return master.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + +var swap = function( elem, options, callback, args ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.apply( elem, args || [] ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // Support: IE <=9 only + option: [ 1, "" ], + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
" ], + col: [ 2, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + + _default: [ 0, "", "" ] +}; + +// Support: IE <=9 only +wrapMap.optgroup = wrapMap.option; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; +} )(); + + +var + rkeyEvent = /^key/, + rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, + rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Don't attach events to noData or text/comment nodes (but allow plain objects) + if ( !elemData ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = {}; + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + // Make a writable jQuery.Event from the native event object + var event = jQuery.event.fix( nativeEvent ); + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + return result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + + which: function( event ) { + var button = event.button; + + // Add which for key events + if ( event.which == null && rkeyEvent.test( event.type ) ) { + return event.charCode != null ? event.charCode : event.keyCode; + } + + // Add which for click: 1 === left; 2 === middle; 3 === right + if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { + if ( button & 1 ) { + return 1; + } + + if ( button & 2 ) { + return 3; + } + + if ( button & 4 ) { + return 2; + } + + return 0; + } + + return event.which; + } +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + /* eslint-disable max-len */ + + // See https://github.com/eslint/eslint/issues/3229 + rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi, + + /* eslint-enable */ + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.access( src ); + pdataCur = dataPriv.set( dest, pdataOld ); + events = pdataOld.events; + + if ( events ) { + delete pdataCur.handle; + pdataCur.events = {}; + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = concat.apply( [], args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + } ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html.replace( rxhtmlTag, "<$1>" ); + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + // Support: IE 9-11 only + // Also use offsetWidth/offsetHeight for when box sizing is unreliable + // We use getClientRects() to check for hidden/disconnected. + // In those cases, the computed value can be trusted to be border-box + if ( ( !support.boxSizingReliable() && isBorderBox || + val === "auto" || + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue && type !== false ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + var doc = this.ownerDocument || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = Date.now(); + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) { + xml = undefined; + } + + if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { + jQuery.error( "Invalid XML: " + data ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ) + .filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ) + .map( function( i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + + +jQuery._evalUrl = function( url, options ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " +{% endmacro %} \ No newline at end of file diff --git a/_build/html/genindex.html b/_build/html/genindex.html new file mode 100644 index 0000000..a2ffd30 --- /dev/null +++ b/_build/html/genindex.html @@ -0,0 +1,314 @@ + + + + + + + + Index — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Index

+ +
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+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

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+ + +
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+ + + + + + + + \ No newline at end of file diff --git a/_build/html/index.html b/_build/html/index.html new file mode 100644 index 0000000..30cd13a --- /dev/null +++ b/_build/html/index.html @@ -0,0 +1,2 @@ + + diff --git a/_build/html/lessons/01_Overview_Geospatial_Data.html b/_build/html/lessons/01_Overview_Geospatial_Data.html new file mode 100644 index 0000000..935fb8c --- /dev/null +++ b/_build/html/lessons/01_Overview_Geospatial_Data.html @@ -0,0 +1,529 @@ + + + + + + + Lesson 1. Overview of Geospatial Data — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 1. Overview of Geospatial Data

+

Before diving into any coding, let’s first go over some core concepts.

+
    +

  • 1.1 Geospatial Data

    • +

  • 1.2 Coordinate Reference Systems

    • +

  • 1.3 Types of Spatial Data

    • +

  • 1.4 Other Resources

    • +
+

Note that this Jupyterbook covers a lot! There’s so much to learn and understand about the world of doing geospatial work. But we want you to keep in mind that this really only the start of your journey. All the authors who contributed to this are still learning too :)

+
+

1.1 Geospatial Data

+

So there are a couple of terms that get confused when we’re trying to talk about work in this area:

+
    +

  • Geographic Information Systems (GIS)

    • +

  • Geographic Data

    • +

  • Geospatial Data +We’ll walk through each of these term-by-term.

    • +
+

Geographic Information Systems (GIS) is probably a term that you’ve heard of before and it integrates many types of data, which includes spatial location. You can think of it as a framework to analyze spatial and geographic data.

+
+

Note: GIS can also be an acronym for Geographic Information Science, which is the study of the study of geographic systems.

+
+

Geographic data can answer the questions “where” and “what”. To make this a little bit more concrete, let’s use this sign in Anatone, WA, USA as an example.

+ +

Image Credit: Dsdugan at English Wikipedia

+

Dsdugan at English Wikipedia

+

Here, our answer to the question to “where” is Anatone, WA. The “what” question is answered by all the details on the sign, for example we know that the number of dogs in Anatone is 22. These types of details are also called attributes.

+

Another component of geographic data is metadata. This component includes things such as when the data was taken, by whom, how, the quality, as wel as other information about the geographic data itself.

+

Geospatial Data is a location that is given by a set of coordinates. For example, the location for Anatone could be specified with a specific latitude and longitude (\(46.135570\), \(-117.132659\)).

+
+
+

1.2 Coordinate Reference Systems

+

A Coordinate Reference System or CRS is a system for associating specific numerical coordinates with a position on earth. So depending on the CRS that is used the numbers for the latitude and longitude could differ.

+ +

Image Credit: Wikimedia Commons

+

There are many CRSs because our understanding and ability to measure the surface of the earth has evolved over time. We can think of these different reasonings as an orange peel or a lamp.

+

Think if we take a regular orange as our earth:

+ +

Image Credit: ESRI project package by j_nelson

+

And the first assumption we make is that it is spherical:

+ +

Image Credit: ESRI project package by j_nelson

+

Assuming that it’s spherical will introduce some distortion, as well as how I choose to draw all of my continents on it. Plus when I decide to peel it, depending on how I do that, It’ll look like different maps on a flat surface:

+ + +

Image Credit: ESRI project package by j_nelson

+

Another way to think about this is by thinking about our planet earth as a lamp in a dark room.

+ +

Image Credit: Brando

+

Depending on factors such as how we tilt the lamp and if our walls our flat the image that we project onto the wall will be different.

+

In short, since our earth isn’t flat, our earth is distorted to make it feasible to show it on a flat surface.

+

There are two types of coordinate reference systems.

+
    +

  • Geographic CRS

    • +

  • Projected CRS

    • +
+

Geographic CRS are great for storing data and has units of degrees and are widely used. WGS84 is the most commonly used CRS and is basd on satellites and used by cellphones and GPS. It has the best overall fir for most places on earth. Another common one is NAD83 which is based on both satellite and survey data. It’s a great fit for USA based work and is utilized in a lot of federal data products such as the census data. Both of these CRS have EPSG codes, which a 4+ digit number used to reference a CRs. For WGS84 the code is 4326, while for NAD83 its 4269. You’ll be using these codes when you’re using CRS in Python.

+

Projected CRS are good for mapping and spatial analysis. They transform the geographic coordinates (latitude, longitude) to be 2D (X, Y) with units such as meters. All map projections include some type of distortion, whether that be in area, shape, distance or direction. Depending on the CRS it’ll probably be minimizing distortion for one of these characteristics. For example, the Mercator projection places importance on shape and direction, but in turn has distorted area as you move away from the equator.

+ +

Image Credit: QGIS Documentation

+

Of course some projections are worse than others. This joke projection has somehow made all continents look like South America! This story of distortion tells us that some projections are better than others.

+ +

Image Credit: xkcd comics

+
+

Note: Here are some videos related to the concept of CRS.

+
    +

  • Drawing projections on fruits: Link

    • +

  • West Wing discussion on using specific projections: Link

    • +

  • Vox on why world maps are wrong: Link

    • +
+
+
+
+

1.3 Types of Spatial Data

+

As you start to gather geospatial data, you’ll encounter two types: vector and raster data.

+

Vector data can be thought of as that that you can connect the dots with. This type of data includes points, lines, and polygons.

+ +

As an example, we can look at these different types of vector data by looking at different data in San Francisco.

+ +

Each of these geometry types can be used for different types of information. Point geometries are great for showing where crimes have occurred historically. Lines can show us the location and length of the freeways in the city. Polygons could help us show information such as population per square mile in different neighborhoods.

+

Now let’s think about what this vector data could look like when you open it up.

+ +

You might get something like this. Each row represents one geospatial feature. So for our second attribute we have the ID number 2, the plot size 20, vegetation type, and a vegetation class of deciduous. Those additional information like the plot size, are attributes. These help describe our features.

+

Furthermore, each of these features have an associated geometry or geometry collection. So in our first table our geometry is a point,

+

One last thing about vector data– each group of features is called a layer. So you could have all three of these data, and each dataset would be its own layer.

+

Raster data on the other hand is continous. Each location is represented by a grid cell, which are usually all the same size. There a fixed number of rows and columns, and each cell has a value that represents the attribute of interest.

+ +

Image Credit: Humboldt GSP

+

Raster data should feel familiar to you since images are basically raster data!

+

Now that we know we have these two types of datasets, we can talk about when to use each. Vector data are better for when you have discreetly bounded data. This could be for counties, rivers, etc. On the other hand, raster data is better for continuous data (like the image we just looked at), or maybe something like temperature, elevation or rainfall.

+

Now these two datasets come in different file formats, so you’ll know what it is before you pull it in for whatever GIS software you’re using. Some common ones I use are shapefile and geojsons for vector data, and geotiffs for raster data.

+ + + + + + + + + + + + + + + + + + + + +

Vector

Raster

Shapefile (.shp…)

GeoTIFF

GeoJSON, JSON

netCDF

KML

DEM

GeoPackage

+

Although these two types of data look different, and come in different formats, you can still use a combination of raster and vector data to answers questions that you’re probably aiming to answer through your own work.

+
+
+

1.4 Other Resources

+

This is really only a brief introduction to geospatial concepts! If you want to dive a little deeper, here are a couple of resources you can check out:

+ +
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+ + + + +
+ + +
+ + Welcome to Geospatial Fundamentals in Python + Lesson 2. Introduction to Geopandas + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/02_Introduction_to_GeoPandas.html b/_build/html/lessons/02_Introduction_to_GeoPandas.html new file mode 100644 index 0000000..d6aa274 --- /dev/null +++ b/_build/html/lessons/02_Introduction_to_GeoPandas.html @@ -0,0 +1,917 @@ + + + + + + + Lesson 2. Introduction to Geopandas — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ + + + + + +
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+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 2. Introduction to Geopandas

+

In this lesson we’ll learn about a package that is core to using geospatial data in Python. We’ll go through the structure of the data (it’s not too different from regular DataFrames!), geometries, shapefiles, and how to save your hard work.

+
    +

  • 2.1 What is GeoPandas?

    • +

  • 2.2 Read in a shapefile

    • +

  • 2.3 Explore the GeoDataFrame

    • +

  • 2.4 Plot the GeoDataFrame

    • +

  • 2.5 Subset the GeoDataFrame

    • +

  • 2.6 Save your data

    • +

  • 2.7 Recap

    • +

  • Exercise: IO, Manipulation, and Mapping

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/california_counties/CaliforniaCounties.shp’

      • +

    • ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 30 minutes

      • +

    • Exercises: 5 minutes +

      • +
    +
    • +
+
+

2.1 What is GeoPandas?

+ +
+

GeoPandas = pandas + geo

+

GeoPandas gives you access to all of the functionality of pandas, which is the primary data analysis tool for working with tabular data in Python. GeoPandas extends pandas with attributes and methods for working with geospatial data.

+
+
+

Import Libraries

+

Let’s start by importing the libraries that we will use.

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+
+

2.2 Read in a shapefile

+

As we discussed in the initial geospatial overview, a shapefile is one type of geospatial data that holds vector data.

+
+

To learn more about ESRI Shapefiles, this is a good place to start: ESRI Shapefile Wiki Page

+
+

The tricky thing to remember about shapefiles is that they’re actually a collection of 3 to 9+ files together. Here’s a list of all the files that can make up a shapefile:

+
+

shp: The main file that stores the feature geometry

+

shx: The index file that stores the index of the feature geometry

+

dbf: The dBASE table that stores the attribute information of features

+

prj: The file that stores the coordinate system information. (should be required!)

+

xml: Metadata —Stores information about the shapefile.

+

cpg: Specifies the code page for identifying the character set to be used.

+
+

But it remains the most commonly used file format for vector spatial data, and it’s really easy to visualize in one go!

+

Let’s try it out with California counties, and use geopandas for the first time. gpd.read_file is a flexible function that let’s you read in many different types of geospatial data.

+
+
+
# Read in the counties shapefile
+counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')
+
+
+
+
+
+
+
# Plot out California counties
+counties.plot()
+
+
+
+
+

Bam! Amazing! We’re off to a running start.

+
+
+

2.3 Explore the GeoDataFrame

+

Before we get in too deep, let’s discuss what a GeoDataFrame is and how it’s different from pandas DataFrames.

+
+

The GeoPandas GeoDataFrame

+

A GeoPandas GeoDataFrame, or gdf for short, is just like a pandas dataframe (df) but with an extra geometry column and methods & attributes that work on that column. I repeat because it’s important:

+
+

A GeoPandas GeoDataFrame is a pandas DataFrame with a geometry column and methods & attributes that work on that column.

+
+
+

This means all the methods and attributes of a pandas DataFrame also work on a Geopandas GeoDataFrame!!

+
+

With that in mind, let’s start exploring out dataframe just like we would do in pandas.

+
+
+
# Find the number of rows and columns in counties
+counties.shape
+
+
+
+
+
+
+
# Look at the first couple of rows in our geodataframe
+counties.head()
+
+
+
+
+
+
+
# Look at all the variables included in our data
+counties.columns
+
+
+
+
+

It looks like we have a good amount of information about the total population for different years and the densities, as well as race, age, and occupancy info.

+
+
+
+

2.4 Plot the GeoDataFrame

+

We’re able to plot our GeoDataFrame because of the extra geometry column.

+
+

Geopandas Geometries

+

There are three main types of geometries that can be associated with your geodataframe: points, lines and polygons:

+

+

In the geodataframe these geometries are encoded in a format known as Well-Known Text (WKT). For example:

+
+
    +

  • POINT (30 10)

    • +

  • LINESTRING (30 10, 10 30, 40 40)

    • +

  • POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))

    • +
+

where coordinates are separated by a space and coordinate pairs by a comma

+
+

Your geodataframe may also include the variants multipoints, multilines, and multipolgyons if the row-level feature of interest is comprised of multiple parts. For example, a geodataframe of states, where one row represents one state, would have a POLYGON geometry for Utah but MULTIPOLYGON for Hawaii, which includes many islands.

+
+

It’s ok to mix and match geometries of the same family, e.g., POLYGON and MULTIPOLYGON, in the same geodatafame.

+
+

Question What kind of geometry would a roads geodataframe have? What about one that includes landmarks in the San Francisco Bay Area?

+

You can check the types of geometries in a geodataframe or a subset of the geodataframe by combining the type and unique methods.

+
+
+
# Let's check what geometries we have in our counties geodataframe
+counties['geometry'].head()
+
+
+
+
+
+
+
# Let's check to make sure that we only have polygons and multipolygons 
+counties['geometry'].type.unique()
+
+
+
+
+
+
+
counties.plot()
+
+
+
+
+

Just like with other plots you can make in Python, we can start customizing our map with colors, size, etc.

+
+
+
# We can run the following line of code to get more info about the parameters we can specify:
+
+?counties.plot
+
+
+
+
+
+
+
# Make the figure size bigger
+counties.plot(figsize=(6,9))
+
+
+
+
+
+
+
counties.plot(figsize=(6,9), 
+              edgecolor='grey',  # grey colored border lines
+              facecolor='pink' , # fill in our counties as pink
+              linewidth=2)       # make the linedwith a width of 2
+
+
+
+
+
+
+
+

2.5 Subset the GeoDataframe

+

Since we’ll be focusing on Berkeley later in the workshop, let’s subset our GeoDataFrame to just be for Alameda County.

+
+
+
# See all county names included in our dataset
+counties['NAME'].values
+
+
+
+
+

It looks like Alameda county is specified as “Alameda” in this dataset.

+
+
+
counties
+
+
+
+
+

Now we can create a new geodataframe called alameda_county that is a subset of our counties geodataframe.

+
+
+
alameda_county = counties.loc[counties['NAME'] == 'Alameda'].copy().reset_index(drop=True)
+
+
+
+
+
+
+
# Plot our newly subsetted geodataframe
+alameda_county.plot()
+
+
+
+
+

Nice! Looks like we have what we were looking for.

+

FYI: You can also make dynamic plots of one or more county without saving to a new gdf.

+
+
+
bay_area_counties = ['Alameda', 'Contra Costa', 'Marin', 'Napa', 'San Francisco', 
+                        'San Mateo', 'Santa Clara', 'Santa Cruz', 'Solano', 'Sonoma']
+counties.loc[counties['NAME'].isin(bay_area_counties)].plot()
+
+
+
+
+
+
+

2.6 Save your Data

+

Let’s not forget to save out our Alameda County geodataframe alameda_county. This way we won’t need to repeat the processing steps and attribute join we did above.

+

We can save it as a shapefile.

+
+
+
alameda_county.to_file("outdata/alameda_county.shp")
+
+
+
+
+

One of the problems of saving to a shapefile is that our column names get truncated to 10 characters (a shapefile limitation.)

+

Instead of renaming all columns with obscure names that are less than 10 characters, we can save our GeoDataFrame to a spatial data file format that does not have this limation - GeoJSON or GPKG (geopackage) file.

+
    +

  • These formats have the added benefit of outputting only one file in contrast tothe multi-file shapefile format.

    • +
+
+
+
alameda_county.to_file("outdata/alameda_county.json", driver="GeoJSON")
+
+
+
+
+
+
+
alameda_county.to_file("outdata/alameda_county.gpkg", driver="GPKG")
+
+
+
+
+

You can read these in, just as you would a shapefile with gpd.read_file

+
+
+
alameda_county_test = gpd.read_file("outdata/alameda_county.gpkg")
+alameda_county_test.plot()
+
+
+
+
+
+
+
alameda_county_test2 = gpd.read_file("outdata/alameda_county.json")
+alameda_county_test2.plot()
+
+
+
+
+

There are also many other formats we could use for data output.

+

NOTE: If you’re working with point data (i.e. a single latitude and longitude value per feature), +then CSV might be a good option!

+
+
+

2.7 Recap

+

In this lesson we learned about…

+
    +

  • The geopandas package

    • +

  • Reading in shapefiles

    +
      +

    • gpd.read_file

      • +
    +
    • +

  • GeoDataFrame structures

    +
      +

    • shape, head, columns

      • +
    +
    • +

  • Plotting GeoDataFrames

    +
      +

    • plot

      • +
    +
    • +

  • Subsetting GeoDatFrames

    +
      +

    • loc

      • +
    +
    • +

  • Saving out GeoDataFrames

    +
      +

    • to_file

      • +
    +
    • +
+
+
+

Exercise: IO, Manipulation, and Mapping

+

Now you’ll get a chance to practice the operations we learned above.

+

In the following cell, compose code to:

+
    +

  1. Read in the California places data (notebook_data/census/Places/cb_2018_06_place_500k.zip)

    2. +

  2. Subset the data to Berkeley

    3. +

  3. Plot, and customize as desired

    4. +

  4. Save out as a shapefile (outdata/berkeley_places.shp)

    5. +
+

Note: pulling in a zipped shapefile has the same syntax as just pulling in a shapefile. The only difference is that insead of just putting in the filepath you’ll want to write zip://notebook_data/census/Places/cb_2018_06_place_500k.zip

+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE
+
+
+
+
+
+
+

Double-click to see solution!

+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + Lesson 1. Overview of Geospatial Data + Lesson 3. Coordinate Reference Systems (CRS) & Map Projections + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/03_CRS_Map_Projections.html b/_build/html/lessons/03_CRS_Map_Projections.html new file mode 100644 index 0000000..b84f4d2 --- /dev/null +++ b/_build/html/lessons/03_CRS_Map_Projections.html @@ -0,0 +1,1093 @@ + + + + + + + Lesson 3. Coordinate Reference Systems (CRS) & Map Projections — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 3. Coordinate Reference Systems (CRS) & Map Projections

+

Building off of what we learned in the previous notebook, we’ll get to understand an integral aspect of geospatial data: Coordinate Reference Systems.

+
    +

  • 3.1 California County Shapefile

    • +

  • 3.2 USA State Shapefile

    • +

  • 3.3 Plot the Two Together

    • +

  • 3.4 Coordinate Reference System (CRS)

    • +

  • 3.5 Getting the CRS

    • +

  • 3.6 Setting the CRS

    • +

  • 3.7 Transforming or Reprojecting the CRS

    • +

  • 3.8 Plotting States and Counties Togther

    • +

  • 3.9 Recap

    • +

  • Exercise: CRS Management

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/california_counties/CaliforniaCounties.shp’

      • +

    • ‘notebook_data/us_states/us_states.shp’

      • +

    • ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 45 minutes

      • +

    • Exercises: 10 minutes +

      • +
    +
    • +
+
+

Import Libraries

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+

3.1 California County shapefile

+

Let’s go ahead and bring back in our California County shapefile. As before, we can read the file in using gpd.read_file and plot it straight away.

+
+
+
counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')
+counties.plot(color='darkgreen')
+
+
+
+
+

Even if we have an awesome map like this, sometimes we want to have more geographical context, or we just want additional information. We’re going to try overlaying our counties GeoDataFrame on our USA states shapefile.

+
+
+

3.2 USA State shapefile

+

We’re going to bring in our states geodataframe, and let’s do the usual operations to start exploring our data.

+
+
+
# Read in states shapefile
+states = gpd.read_file('notebook_data/us_states/us_states.shp')
+
+
+
+
+
+
+
# Look at the first few rows
+states.head()
+
+
+
+
+
+
+
# Count how many rows and columns we have
+states.shape
+
+
+
+
+
+
+
# Plot our states data
+states.plot()
+
+
+
+
+

You might have noticed that our plot extends beyond the 50 states (which we also saw when we executed the shape method). Let’s double check what states we have included in our data.

+
+
+
states['STATE'].values
+
+
+
+
+

Beyond the 50 states we seem to have American Samoa, Puerto Rico, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands included in this geodataframe. To make our map cleaner, let’s limit the states to the contiguous states (so we’ll also exclude Alaska and Hawaii).

+
+
+
# Define list of non-contiguous states
+non_contiguous_us = [ 'American Samoa','Puerto Rico','Guam',
+                      'Commonwealth of the Northern Mariana Islands',
+                      'United States Virgin Islands', 'Alaska','Hawaii']
+# Limit data according to above list
+states_limited = states.loc[~states['STATE'].isin(non_contiguous_us)]
+
+
+
+
+
+
+
# Plot it
+states_limited.plot()
+
+
+
+
+

To prepare for our mapping overlay, let’s make our states a nice, light grey color.

+
+
+
states_limited.plot(color='lightgrey', figsize=(10,10))
+
+
+
+
+
+
+

3.3 Plot the two together

+

Now that we have both geodataframes in our environment, we can plot both in the same figure.

+

NOTE: To do this, note that we’re getting a Matplotlib Axes object (ax), then explicitly adding each our layers to it +by providing the ax=ax argument to the plot method.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+counties.plot(color='darkgreen',ax=ax)
+states_limited.plot(color='lightgrey', ax=ax)
+
+
+
+
+

Oh no, what happened here?

+

Question Without looking ahead, what do you think happened?

+
+
+If you look at the numbers we have on the x and y axes in our two plots, you'll see that the county data has much larger numbers than our states data. It's represented in some different type of unit other than decimal degrees! +

In fact, that means if we zoom in really close into our plot we’ll probably see the states data plotted.

+
+
+
%matplotlib inline
+fig, ax = plt.subplots(figsize=(10,10))
+counties.plot(color='darkgreen',ax=ax)
+states_limited.plot(color='lightgrey', ax=ax)
+ax.set_xlim(-140,-50)
+ax.set_ylim(20,50)
+
+
+
+
+

This is a key issue that you’ll have to resolve time and time again when working with geospatial data!

+

It all revolves around coordinate reference systems and projections.

+
+
+
+

3.4 Coordinate Reference Systems (CRS)

+

Question Do you have experience with Coordinate Reference Systems?

+



As a refresher, a CRS describes how the coordinates in a geospatial dataset relate to locations on the surface of the earth.

+

A geographic CRS consists of:

+
    +

  • a 3D model of the shape of the earth (a datum), approximated as a sphere or spheroid (aka ellipsoid)

    • +

  • the units of the coordinate system (e.g, decimal degrees, meters, feet) and

    • +

  • the origin (i.e. the 0,0 location), specified as the meeting of the equator and the prime meridian(

    • +
+

A projected CRS consists of

+
    +

  • a geographic CRS

    • +

  • a map projection and related parameters used to transform the geographic coordinates to 2D space.

    +
      +

    • a map projection is a mathematical model used to transform coordinate data

      • +
    +
    • +
+
+

A Geographic vs Projected CRS

+
+

There are many, many CRSs

+

Theoretically the number of CRSs is unlimited!

+

Why? Primariy, because there are many different definitions of the shape of the earth, multiplied by many different ways to cast its surface into 2 dimensions. Our understanding of the earth’s shape and our ability to measure it has changed greatly over time.

+
+
+

Why are CRSs Important?

+
    +

  • You need to know the data about your data (or metadata) to use it appropriately.

    • +

  • All projected CRSs introduce distortion in shape, area, and/or distance. So understanding what CRS best maintains the characteristics you need for your area of interest and your analysis is important.

    • +

  • Some analysis methods expect geospatial data to be in a projected CRS

    +
      +

    • For example, geopandas expects a geodataframe to be in a projected CRS for area or distance based analyses.

      • +
    +
    • +

  • Some Python libraries, but not all, implement dynamic reprojection from the input CRS to the required CRS and assume a specific CRS (WGS84) when a CRS is not explicitly defined.

    • +

  • Most Python spatial libraries, including Geopandas, require geospatial data to be in the same CRS if they are being analysed together.

    • +
+
+
+

What you need to know when working with CRSs

+
    +

  • What CRSs used in your study area and their main characteristics

    • +

  • How to identify, or get, the CRS of a geodataframe

    • +

  • How to set the CRS of geodataframe (i.e. define the projection)

    • +

  • Hot to transform the CRS of a geodataframe (i.e. reproject the data)

    • +
+
+
+
+

Codes for CRSs commonly used with CA data

+

CRSs are typically referenced by an EPSG code.

+

It’s important to know the commonly used CRSs and their EPSG codes for your geographic area of interest.

+

For example, below is a list of commonly used CRSs for California geospatial data along with their EPSG codes.

+
+

Geographic CRSs

+

-4326: WGS84 (units decimal degrees) - the most commonly used geographic CRS

+

-4269: NAD83 (units decimal degrees) - the geographic CRS customized to best fit the USA. This is used by all Census geographic data.

+
+

NAD83 (epsg:4269) are approximately the same as WGS84(epsg:4326) although locations can differ by up to 1 meter in the continental USA and elsewhere up to 3m. That is not a big issue with census tract data as these data are only accurate within +/-7meters.

+
+
+
+

Projected CRSs

+

-5070: CONUS NAD83 (units meters) projected CRS for mapping the entire contiguous USA (CONUS)

+

-3857: Web Mercator (units meters) conformal (shape preserving) CRS used as the default in web mapping

+

-3310: CA Albers Equal Area, NAD83 (units meters) projected CRS for CA statewide mapping and spatial analysis

+

-26910: UTM Zone 10N, NAD83 (units meters) projected CRS for northern CA mapping & analysis

+

-26911: UTM Zone 11N, NAD83 (units meters) projected CRS for Southern CA mapping & analysis

+

-102641 to 102646: CA State Plane zones 1-6, NAD83 (units feet) projected CRS used for local analysis.

+

You can find the full CRS details on the website https://www.spatialreference.org

+
+
+
+
+

3.5 Getting the CRS

+
+

Getting the CRS of a gdf

+

GeoPandas GeoDataFrames have a crs attribute that returns the CRS of the data.

+
+
+
counties.crs
+
+
+
+
+
+
+
states_limited.crs
+
+
+
+
+

As we can clearly see from those two printouts (even if we don’t understand all the content!), +the CRSs of our two datasets are different! This explains why we couldn’t overlay them correctly!

+
+

The above CRS definition specifies

+
    +

  • the name of the CRS (WGS84),

    • +

  • the axis units (degree)

    • +

  • the shape (datum),

    • +

  • and the origin (Prime Meridian, and the equator)

    • +

  • and the area for which it is best suited (World)

    • +
+
+

Notes:

+
    +

  • geocentric latitude and longitude assume a spherical (round) model of the shape of the earth

    • +

  • geodetic latitude and longitude assume a spheriodal (ellipsoidal) model, which is closer to the true shape.

    • +

  • geodesy is the study of the shape of the earth.

    • +
+
+

NOTE: If you print a crs call, Python will just display the EPSG code used to initiate the CRS object. Depending on your versions of Geopandas and its dependencies, this may or may not look different from what we just saw above.

+
+
+
print(states_limited.crs)
+
+
+
+
+
+
+
+

3.6 Setting the CRS

+

You can also set the CRS of a gdf using the crs attribute. You would set the CRS if is not defined or if you think it is incorrectly defined.

+
+

In desktop GIS terminology setting the CRS is called defining the CRS

+
+

As an example, let’s set the CRS of our data to None

+
+
+
# first set the CRS to None
+states_limited.crs = None
+
+
+
+
+
+
+
# Check it again
+states_limited.crs
+
+
+
+
+

…hummm…

+

If a variable has a null value (None) then displaying it without printing it won’t display anything!

+
+
+
# Check it again
+print(states_limited.crs)
+
+
+
+
+

Now we’ll set it back to its correct CRS.

+
+
+
# Set it to 4326
+states_limited.crs = "epsg:4326"
+
+
+
+
+
+
+
# Show it
+states_limited.crs
+
+
+
+
+

NOTE: You can set the CRS to anything you like, but that doesn’t make it correct! This is because setting the CRS does not change the coordinate data; it just tells the software how to interpret it.

+
+
+

3.7 Transforming or Reprojecting the CRS

+

You can transform the CRS of a geodataframe with the to_crs method.

+
+

In desktop GIS terminology transforming the CRS is called projecting the data (or reprojecting the data)

+
+

When you do this you want to save the output to a new GeoDataFrame.

+
+
+
states_limited_utm10 = states_limited.to_crs( "epsg:26910")
+
+
+
+
+

Now take a look at the CRS.

+
+
+
states_limited_utm10.crs
+
+
+
+
+

You can see the result immediately by plotting the data.

+
+
+
# plot geographic gdf
+states_limited.plot();
+plt.axis('square');
+
+# plot utm gdf
+states_limited_utm10.plot();
+plt.axis('square')
+
+
+
+
+
+
+
# Your thoughts here
+
+
+
+
+
+ +
+
+
+

Questions

+
+
    +

  1. What two key differences do you see between the two plots above?

    2. +

  2. Do either of these plotted USA maps look good?

    3. +

  3. Try looking at the common CRS EPSG codes above and see if any of them look better for the whole country than what we have now. Then try transforming the states data to the CRS that you think would be best and plotting it. (Use the code cell two cells below.)

    4. +
+
+
+
# YOUR CODE HERE
+
+
+
+
+

Double-click to see solution!

+
+
+
+

3.8 Plotting states and counties together

+

Now that we know what a CRS is and how we can set them, let’s convert our counties GeoDataFrame to match up with out states’ crs.

+
+
+
# Convert counties data to NAD83 
+counties_utm10 = counties.to_crs("epsg:26910")
+
+
+
+
+
+
+
counties_utm10.plot()
+
+
+
+
+
+
+
# Plot it together!
+fig, ax = plt.subplots(figsize=(10,10))
+states_limited_utm10.plot(color='lightgrey', ax=ax)
+counties_utm10.plot(color='darkgreen',ax=ax)
+
+
+
+
+

Since we know that the best CRS to plot the contiguous US from the above question is 5070, let’s also transform and plot everything in that CRS.

+
+
+
counties_conus = counties.to_crs("epsg:5070")
+
+
+
+
+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+states_limited_conus.plot(color='lightgrey', ax=ax)
+counties_conus.plot(color='darkgreen',ax=ax)
+
+
+
+
+
+
+

3.9 Recap

+

In this lesson we learned about…

+
    +

  • Coordinate Reference Systems

    • +

  • Getting the CRS of a geodataframe

    +
      +

    • crs

      • +
    +
    • +

  • Transforming/repojecting CRS

    +
      +

    • to_crs

      • +
    +
    • +

  • Overlaying maps

    • +
+
+
+

Exercise: CRS Management

+

Now it’s time to take a crack and managing the CRS of a new dataset. In the code cell below, write code to:

+
    +

  1. Bring in the CA places data (notebook_data/census/Places/cb_2018_06_place_500k.zip)

    2. +

  2. Check if the CRS is EPSG code 26910. If not, transform the CRS

    3. +

  3. Plot the California counties and places together.

    4. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE
+
+
+
+
+
+
+

Double-click to see solution!

+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + Lesson 2. Introduction to Geopandas + Lesson 4. More Data, More Maps! + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/04_More_Data_More_Maps.html b/_build/html/lessons/04_More_Data_More_Maps.html new file mode 100644 index 0000000..e49900d --- /dev/null +++ b/_build/html/lessons/04_More_Data_More_Maps.html @@ -0,0 +1,914 @@ + + + + + + + Lesson 4. More Data, More Maps! — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 4. More Data, More Maps!

+

Now that we know how to pull in data, check and transform Coordinate Reference Systems (CRS), and plot GeoDataFrames together - let’s practice doing the same thing with other geometry types. In this notebook we’ll be bringing in bike boulevards and schools, which will get us primed to think about spatial relationship questions.

+
    +

  • 4.1 Berkeley Bike Boulevards

    • +

  • 4.2 Alameda County Schools

    • +

  • Exercise: Even More Data!

    • +

  • 4.3 Map Overlays with Matplotlib

    • +

  • 4.4 Recap

    • +

  • Exercise: Overlay Mapping

    • +

  • 4.5 Teaser for Day 2

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/transportation/BerkeleyBikeBlvds.geojson’

      • +

    • ‘notebook_data/alco_schools.csv’

      • +

    • ‘notebook_data/parcels/parcel_pts_rand30pct.geojson’

      • +

    • ‘notebook_data/berkeley/BerkeleyCityLimits.shp’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 30 minutes

      • +

    • Exercises: 20 minutes +

      • +
    +
    • +
+
+

Import Libraries

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+

4.1 Berkeley Bike Boulevards

+

We’re going to bring in data bike boulevards in Berkeley. Note two things that are different from our previous data:

+
    +

  • We’re bringing in a GeoJSON this time and not a shapefile

    • +

  • We have a line geometry GeoDataFrame (our county and states data had polygon geometries)

    • +
+
+
+
bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')
+bike_blvds.plot()
+
+
+
+
+

As usual, we’ll want to do our usual data exploration…

+
+
+
bike_blvds.head()
+
+
+
+
+
+
+
bike_blvds.shape
+
+
+
+
+
+
+
bike_blvds.columns
+
+
+
+
+

Our bike boulevard data includes the following information:

+
    +

  • BB_STRNAM - bike boulevard Streetname

    • +

  • BB_STRID - bike boulevard Street ID

    • +

  • BB_FRO - bike boulevard origin street

    • +

  • BB_TO - bike boulevard end street

    • +

  • BB_SECID- bike boulevard section id

    • +

  • DIR_ - cardinal directions the bike boulevard runs

    • +

  • Status - status on whether the bike boulevard exists

    • +

  • ALT_bikeCA - ?

    • +

  • Shape_len - length of the boulevard in meters

    • +

  • len_km - length of the boulevard in kilometers

    • +

  • geometry

    • +
+
+ +
+
+
+

Question

+
+

Why are there 211 features when we only have 8 bike boulevards?

+
+
+
fig,ax = plt.subplots(figsize=(10,10))
+bike_blvds.plot(ax=ax)
+bike_blvds.head(1).plot(color='orange',ax=ax)
+
+
+
+
+

And now take a look at our CRS…

+
+
+
bike_blvds.crs
+
+
+
+
+

Let’s tranform our CRS to UTM Zone 10N, NAD83 that we used in the last lesson.

+
+
+
bike_blvds_utm10 = bike_blvds.to_crs( "epsg:26910")
+
+
+
+
+
+
+
bike_blvds_utm10.head()
+
+
+
+
+
+
+
bike_blvds_utm10.crs
+
+
+
+
+
+
+
+

4.2 Alameda County Schools

+

Alright! Now that we have our bike boulevard data squared away, we’re going to bring in our Alameda County school data.

+
+
+
schools_df = pd.read_csv('notebook_data/alco_schools.csv')
+schools_df.head()
+
+
+
+
+
+
+
schools_df.shape
+
+
+
+
+

Questions

+

Without looking ahead:

+
    +

  1. Is this a geodataframe?

    2. +

  2. How do you know?

    3. +
+
+
+This is not a GeoDataFrame! A couple of clues to figure that out are.. +
    +

  1. We’re pulling in a Comma Separated Value (CSV) file, which is not a geospatial data format

    2. +

  2. There is no geometry column (although we do have latitude and longitude values)

    3. +
+
+

Although our school data is not starting off as a GeoDataFrame, we actually have the tools and information to make it one. Using the gpd.GeoDataFrame constructor, we can transform our plain DataFrame into a GeoDataFrame (specifying the geometry information and then the CRS).

+
+
+
schools_gdf = gpd.GeoDataFrame(schools_df, 
+                               geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))
+
+
+
+
+
+
+
print(schools_gdf.crs)
+
+
+
+
+
+
+
schools_gdf.crs = "epsg:4326"
+schools_gdf.head()
+
+
+
+
+

You’ll notice that the shape is the same from what we had as a dataframe, just with the added geometry column.

+
+
+
schools_gdf.shape
+
+
+
+
+

And with it being a GeoDataFrame, we can plot it as we did for our other data sets. +Notice that we have our first point geometry GeoDataFrame.

+
+
+
schools_gdf.plot()
+
+
+
+
+

But of course we’ll want to transform the CRS, so that we can later plot it with our bike boulevard data.

+
+
+
schools_gdf_utm10 = schools_gdf.to_crs( "epsg:26910")
+schools_gdf_utm10.plot()
+
+
+
+
+

In Lesson 2 we discussed that you can save out GeoDataFrames in multiple file formats. You could opt for a GeoJSON, a shapefile, etc… for point data sets it is also an option to save it out as a CSV since the geometry isn’t complicated

+
+
+

Exercise: Even More Data!

+

Let’s play around with another point GeoDataFrame.

+

In the code cell provided below, compose code to:

+
    +

  1. Read in the parcel points data (notebook_data/parcels/parcel_pts_rand30pct.geojson)

    2. +

  2. Transform the CRS to 26910

    3. +

  3. Plot and customize as desired!

    4. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+
+

4.3 Map Overlays with Matplotlib

+

No matter the geometry type we have for our GeoDataFrame, we can create overlay plots.

+

Since we’ve already done the legwork of transforming our CRS, we can go ahead and plot them together.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+bike_blvds_utm10.plot(ax=ax, color='red')
+schools_gdf_utm10.plot(ax=ax)
+
+
+
+
+

If we want to answer questions like “What schools are close to bike boulevards in Berkeley?”, the above plot isn’t super helpful, since the extent covers all of Alameda county.

+

Luckily, GeoDataFrames have an easy method to extract the minimium and maximum values for both x and y, so we can use that information to set the bounds for our plot.

+
+
+
minx, miny, maxx, maxy = bike_blvds.total_bounds
+print(minx, miny, maxx, maxy)
+
+
+
+
+

Using xlim and ylim we can zoom in to see if there are schools proximal to the bike boulevards.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+bike_blvds_utm10.plot(ax=ax, color='red')
+schools_gdf_utm10 .plot(ax=ax)
+plt.xlim(minx, maxx)
+plt.ylim(miny, maxy)
+
+
+
+
+
+
+

4.4 Recap

+

In this lesson we learned a several new skills:

+
    +

  • Transformed an a-spatial dataframe into a geospatial one

    +
      +

    • gpd.GeoDataFrame

      • +
    +
    • +

  • Worked with point and line GeoDataFrames

    • +

  • Overlayed point and line GeoDataFrames

    • +

  • Limited the extent of a map

    +
      +

    • total_bounds

      • +
    +
    • +
+
+
+

Exercise: Overlay Mapping

+

Let’s take some time to practice reading in and reconciling new datasets, then mapping them together.

+

In the code cell provided below, write code to:

+
    +

  1. Bring in your Berkeley places shapefile (and don’t forget to check/transform the crs!) (notebook_data/berkeley/BerkeleyCityLimits.shp)

    2. +

  2. Overlay the parcel points on top of the bike boulevards

    3. +

  3. Create the same plot but limit it to the extent of Berkeley city limits

    4. +
+

BONUS: Add the Berkeley outline to your last plot!

+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+
+

Double-click the see the solution!

+ +
+
+
+

4.5 Teaser for Day 2…

+

You may be wondering if and how we could make our maps more interesting and informative than this.

+

To give you a tantalizing taste of Day 2, the answer is: Yes, we can! And here’s how!

+
+
+
ax = schools_gdf_utm10.plot(column='Org', cmap='winter', 
+                               markersize=35, edgecolor='black',
+                               linewidth=0.5, alpha=1, figsize=[9, 9],
+                               legend=True)
+ax.set_title('Public and Private Schools, Alameda County')
+
+
+
+
+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + Lesson 3. Coordinate Reference Systems (CRS) & Map Projections + Lesson 6. Spatial Queries + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/05_Data-Driven_Mapping.html b/_build/html/lessons/05_Data-Driven_Mapping.html new file mode 100644 index 0000000..7f52821 --- /dev/null +++ b/_build/html/lessons/05_Data-Driven_Mapping.html @@ -0,0 +1,1207 @@ + + + + + + + Lesson 5. Data-driven Mapping — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 5. Data-driven Mapping

+

Data-driven mapping refers to the process of using data values to determine the symbology of mapped features. Color, shape, and size are the three most common symbology types used in data-driven mapping. +Data-driven maps are often refered to as thematic maps.

+
    +

  • 5.1 Choropleth Maps

    • +

  • 5.2 Issues with Visualization

    • +

  • 5.3 Classification Schemes

    • +

  • 5.4 Point Maps

    • +

  • 5.5 Mapping Categorical Data

    • +

  • 5.6 Recap

    • +

  • Exercise: Data-Driven Mapping

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/california_counties/CaliforniaCounties.shp’

      • +

    • ‘notebook_data/alco_schools.csv’

      • +

    • ‘notebook_data/transportation/BerkeleyBikeBlvds.geojson’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 30 minutes

      • +

    • Exercises: 15 minutes +

      • +
    +
    • +
+
+

Types of Thematic Maps

+

There are two primary types of maps used to convey data values:

+
    +

  • Choropleth maps: set the color of areas (polygons) by data value

    • +

  • Point symbol maps: set the color or size of points by data value

    • +
+

We will discuss both of these types of maps in more detail in this lesson. But let’s take a quick look at choropleth maps.

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+
+

5.1 Choropleth Maps

+

Choropleth maps are the most common type of thematic map.

+

Let’s take a look at how we can use a geodataframe to make a choropleth map.

+

We’ll start by reloading our counties dataset from Day 1.

+
+
+
counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')
+
+
+
+
+
+
+
counties.head()
+
+
+
+
+
+
+
counties.columns
+
+
+
+
+

Here’s a plain map of our polygons.

+
+
+
counties.plot()
+
+
+
+
+

Now, for comparison, let’s create a choropleth map by setting the color of the county based on the values in the population per square mile (POP12_SQMI) column.

+
+
+
counties.plot(column='POP12_SQMI', figsize=(10,10))
+
+
+
+
+

That’s really the heart of it. To set the color of the features based on the values in a column, set the column argument to the column name in the gdf.

+
+

Protip:

+
+
    +

  • You can quickly right-click on the plot and save to a file or open in a new browser window.

    • +
+

By default map colors are linearly scaled to data values. This is called a proportional color map.

+
    +

  • The great thing about proportional color maps is that you can visualize the full range of data values.

    • +
+

We can also add a legend, and even tweak its display.

+
+
+
counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True)
+plt.show()
+
+
+
+
+
+
+
counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True,
+                    legend_kwds={'label': "Population Density per mile$^2$",
+                                 'orientation': "horizontal"},)
+plt.show()
+
+
+
+
+
+ +
+
+
+

Question

+
+

Why are we plotting POP12_SQMI instead of POP2012?

+
+
+

Note: Types of Color Maps

+

There are a few different types of color maps (or color palettes), each of which has a different purpose:

+
    +

  • diverging - a “diverging” set of colors are used so emphasize mid-range values as well as extremes.

    • +

  • sequential - usually with a single color hue to emphasize changes in magnitude, where darker colors typically mean higher values

    • +

  • qualitative - a diverse set of colors to identify categories and avoid implying quantitative significance.

    • +
+

+

Image Credit: Dsdugan at English Wikipedia

+
+

Pro-tip: You can actually see all your color map options if you misspell what you put in cmap and try to run-in. Try it out!

+
+
+

Pro-tip: Sites like ColorBrewer let’s you play around with different types of color maps. If you want to create your own, The Python Graph Gallery is a way to see what your Python color options are.

+
+
+
+
+

5.2 Issues with Visualization

+
+

Types of choropleth data

+

There are several types of quantitative data variables that can be used to create a choropleth map. Let’s consider these in terms of our ACS data.

+
    +

  • Count

    +
      +

    • counts, aggregated by feature

      +
        +

      • e.g. population within a census tract

        • +
      +
      • +
    +
    • +

  • Density

    +
      +

    • count, aggregated by feature, normalized by feature area

      +
        +

      • e.g. population per square mile within a census tract

        • +
      +
      • +
    +
    • +

  • Proportions / Percentages

    +
      +

    • value in a specific category divided by total value across in all categories

      +
        +

      • e.g. proportion of the tract population that is white compared to the total tract population

        • +
      +
      • +
    +
    • +

  • Rates / Ratios

    +
      +

    • value in one category divided by value in another category

      +
        +

      • e.g. homeowner-to-renter ratio would be calculated as the number of homeowners (c_owners/ c_renters)

        • +
      +
      • +
    +
    • +
+
+
+

Interpretability of plotted data

+

The goal of a choropleth map is to use color to visualize the spatial distribution of a quantitative variable.

+

Brighter or richer colors are typically used to signify higher values.

+

A big problem with choropleth maps is that our eyes are drawn to the color of larger areas, even if the values being mapped in one or more smaller areas are more important.

+

We see just this sort of problem in our population-density map.

+

Why does our map not look that interesting? Take a look at the histogram below, then consider the following question.

+
+
+
plt.hist(counties['POP12_SQMI'],bins=40)
+plt.title('Population Density per mile$^2$')
+plt.show()
+
+
+
+
+
+ +
+
+
+

Question

+
+

What county does that outlier represent? What problem does that pose?

+
+
+
+
+

5.3 Classification schemes

+

Let’s try to make our map more interpretable!

+

The common alternative to a proportionial color map is to use a classification scheme to create a graduated color map. This is the standard way to create a choropleth map.

+

A classification scheme is a method for binning continuous data values into 4-7 classes (the default is 5) and map those classes to a color palette.

+
+

The commonly used classifications schemes:

+
    +

  • Equal intervals

    +
      +

    • equal-size data ranges (e.g., values within 0-10, 10-20, 20-30, etc.)

      • +

    • pros:

      +
        +

      • best for data spread across entire range of values

        • +

      • easily understood by map readers

        • +
      +
      • +

    • cons:

      +
        +

      • but avoid if you have highly skewed data or a few big outliers

        • +
      +
      • +
    +
    • +

  • Quantiles

    +
      +

    • equal number of observations in each bin

      • +

    • pros:

      +
        +

      • looks nice, becuase it best spreads colors across full set of data values

        • +

      • thus, it’s often the default scheme for mapping software

        • +
      +
      • +

    • cons:

      +
        +

      • bin ranges based on the number of observations, not on the data values

        • +

      • thus, different classes can have very similar or very different values.

        • +
      +
      • +
    +
    • +

  • Natural breaks

    +
      +

    • minimize within-class variance and maximize between-class differences

      • +

    • e.g. ‘fisher-jenks’

      • +

    • pros:

      +
        +

      • great for exploratory data analysis, because it can identify natural groupings

        • +
      +
      • +

    • cons:

      +
        +

      • class breaks are best fit to one dataset, so the same bins can’t always be used for multiple years

        • +
      +
      • +
    +
    • +

  • Manual

    +
      +

    • classifications are user-defined

      • +

    • pros:

      +
        +

      • especially useful if you want to slightly change the breaks produced by another scheme

        • +

      • can be used as a fixed set of breaks to compare data over time

        • +
      +
      • +

    • cons:

      +
        +

      • more work involved

        • +
      +
      • +
    +
    • +
+
+
+

Classification schemes and GeoDataFrames

+

Classification schemes can be implemented using the geodataframe plot method by setting a value for the scheme argument. This requires the pysal and mapclassify libraries to be installed in your Python environment.

+

Here is a list of the classification schemes names that we will use:

+
    +

  • equalinterval, quantiles,fisherjenks,naturalbreaks, and userdefined.

    • +
+

For more information about these classification schemes see the pysal mapclassifiers web page or check out the help docs.

+
+
+
+

Classification schemes in action

+

Let’s redo the last map using the quantile classification scheme.

+
    +

  • What is different about the code? About the output map?

    • +
+
+
+
# Plot population density - mile^2
+fig, ax = plt.subplots(figsize = (10,5)) 
+counties.plot(column='POP12_SQMI', 
+                   scheme="quantiles",
+                   legend=True,
+                   ax=ax
+                   )
+ax.set_title("Population Density per Sq Mile")
+
+
+
+
+

Note: For interval notation

+
    +

  • A square bracket is inclusive

    • +

  • A parentheses is exclusive

    • +
+
+
+

User Defined Classification Schemes

+

You may get pretty close to your final map without being completely satisfied. In this case you can manually define a classification scheme.

+

Let’s customize our map with a user-defined classification scheme where we manually set the breaks for the bins using the classification_kwds argument.

+
+
+
fig, ax = plt.subplots(figsize = (14,8)) 
+counties.plot(column='POP12_SQMI',
+                    legend=True, 
+                    cmap="RdYlGn", 
+                    scheme='user_defined', 
+                    classification_kwds={'bins':[50,100,200,300,400]},
+                    ax=ax)
+ax.set_title("Population Density per Sq Mile")
+
+
+
+
+

Since we are customizing our plot, we can also edit our legend to specify and format the text so that it’s easier to read.

+
    +

  • We’ll use legend_labels_list to customize the labels for group in the legend.

    • +
+
+
+
fig, ax = plt.subplots(figsize = (14,8)) 
+counties.plot(column='POP12_SQMI',
+                    legend=True, 
+                    cmap="RdYlGn", 
+                    scheme='user_defined', 
+                    classification_kwds={'bins':[50,100,200,300,400]},
+                    ax=ax)
+
+# Create the labels for the legend
+legend_labels_list = ['<50','50 to 100','100 to 200','200 to 300','300 to 400','>400']
+
+# Apply the labels to the plot
+for j in range(0,len(ax.get_legend().get_texts())):
+        ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])
+
+ax.set_title("Population Density per Sq Mile")
+
+
+
+
+
+
+

Let’s plot a ratio

+

If we look at the columns in our dataset, we see we have a number of variables +from which we can calculate proportions, rates, and the like.

+

Let’s try that out:

+
+
+
counties.head()
+
+
+
+
+
+
+
fig, ax = plt.subplots(figsize = (15,6)) 
+
+# Plot percent hispanic as choropleth
+counties.plot(column=(counties['HISPANIC']/counties['POP2012'] * 100), 
+                        legend=True, 
+                        cmap="Blues", 
+                        scheme='user_defined', 
+                        classification_kwds={'bins':[20,40,60,80]},
+                        edgecolor="grey",
+                        linewidth=0.5,
+                        ax=ax)
+
+legend_labels_list = ['<20%','20% - 40%','40% - 60%','60% - 80%','80% - 100%']
+for j in range(0,len(ax.get_legend().get_texts())):
+        ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])
+
+ax.set_title("Percent Hispanic Population")
+plt.tight_layout()
+
+
+
+
+
+ +
+
+
+

Questions

+
+
    +

  1. What new options and operations have we added to our code?

    2. +

  2. Based on our code, what title would you give this plot to describe what it displays?

    3. +

  3. How many bins do we specify in the legend_labels_list object, and how many bins are in the map legend? Why?

    4. +
+
+
+
+
+

5.4 Point maps

+

Choropleth maps are great, but mapping using point symbols enables us to visualize our spatial data in another way.

+

If you know both mapping methods you can expand how much information you can show in one map.

+

For example, point maps are a great way to map counts because the varying sizes of areas are deemphasized.

+
+

Let’s read in some point data on Alameda County schools.

+
+
+
schools_df = pd.read_csv('notebook_data/alco_schools.csv')
+schools_df.head()
+
+
+
+
+

We got it from a plain CSV file, let’s coerce it to a GeoDataFrame.

+
+
+
schools_gdf = gpd.GeoDataFrame(schools_df, 
+                               geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))
+schools_gdf.crs = "epsg:4326"
+
+
+
+
+

Then we can map it.

+
+
+
schools_gdf.plot()
+plt.title('Alameda County Schools')
+
+
+
+
+
+

Proportional Color Maps

+

Proportional color maps linearly scale the color of a point symbol by the data values.

+

Let’s try this by creating a map of API. API stands for Academic Performance Index, which is a measurement system that looks at the performance of an individual school.

+
+
+
schools_gdf.plot(column="API", cmap="gist_heat", 
+                 edgecolor="grey", figsize=(10,8), legend=True)
+plt.title("Alameda County, School API scores")
+
+
+
+
+

When you see that continuous color bar in the legend you know that the mapping of data values to colors is not classified.

+
+
+

Graduated Color Maps

+

We can also create graduated color maps by binning data values before associating them with colors. These are just like choropleth maps, except that the term “choropleth” is only used with polygon data.

+

Graduated color maps use the same syntax as the choropleth maps above - you create them by setting a value for scheme.

+

Below, we copy the code we used above to create a choropleth, but we change the name of the geodataframe to use the point gdf.

+
+
+
fig, ax = plt.subplots(figsize = (15,6)) 
+
+# Plot percent non-white with graduated colors
+schools_gdf.plot(column='API', 
+                        legend=True, 
+                        cmap="Blues",
+                        scheme='user_defined', 
+                        classification_kwds={'bins':[0,200,400,600,800]},
+                        edgecolor="grey",
+                        linewidth=0.5,
+                        #markersize=60,
+                        ax=ax)
+
+# Create a custom legend
+legend_labels_list = ['0','< 200','< 400','< 600','< 800','>= 800']
+
+# Apply the legend to the map
+for j in range(0,len(ax.get_legend().get_texts())):
+        ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])
+
+# Create the plot
+plt.tight_layout()
+plt.title("Alameda County, School API scores")
+
+
+
+
+
+
+
schools_gdf['API'].describe()
+
+
+
+
+

As you can see, the syntax for a choropleth and graduated color map is the same, +although some options only apply to one or the other.

+

For example, uncomment the markersize parameter above to see how you can further customize a graduated color map.

+
+
+

Graduated symbol maps

+

Graduated symbol maps are also a great method for mapping points. These are just like graduated color maps but instead of associating symbol color with data values they associate point size. Similarly,graduated symbol maps use classification schemes to set the size of point symbols.

+
+

We demonstrate how to make graduated symbol maps along with some other mapping techniques in the Optional Mapping notebook which we encourage you to explore on your own. (Coming Soon)

+
+
+
+

5.5 Mapping Categorical Data

+

Mapping categorical data, also called qualitative data, is a bit more straightforward. There is no need to scale or classify data values. The goal of the color map is to provide a contrasting set of colors so as to clearly delineate different categories. Here’s a point-based example:

+
+
+
schools_gdf.plot(column='Org', categorical=True, legend=True)
+
+
+
+
+
+
+

5.6 Recap

+

We learned about important data driven mapping strategies and mapping concepts and can leverage what many of us know about matplotlib

+
    +

  • Choropleth Maps

    • +

  • Point maps

    • +

  • Color schemes

    • +

  • Classifications

    • +
+
+
+
+

Exercise: Data-Driven Mapping

+

Point and polygons are not the only geometry-types that we can use in data-driven mapping!

+

Run the next cell to load a dataset containing Berkeley’s bicycle boulevards (which we’ll be using more in the following notebook).

+

Then in the following cell, write your own code to:

+
    +

  1. plot the bike boulevards;

    2. +

  2. color them by status (find the correct column in the head of the dataframe, displayed below);

    3. +

  3. color them using a fitting, good-looking qualitative colormap that you choose from The Matplotlib Colormap Reference;

    4. +

  4. set the line width to 5 (check the plot method’s documentation to find the right argument for this!);

    5. +

  5. add the argument figsize=[20,20], to make your map nice and big and visible!

    6. +
+

Then answer the questions posed in the last cell.

+
+

To see the solution, double-click the Markdown cell below.

+
+
+
bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')
+bike_blvds.head()
+
+
+
+
+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+ +
+
+
+

Questions

+
+
    +

  1. What does that map indicate about the status of the Berkeley bike boulevards?

    2. +

  2. What does that map indicate about the status of your Berkeley bike-boulevard dataset?

    3. +
+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/06_Spatial_Queries.html b/_build/html/lessons/06_Spatial_Queries.html new file mode 100644 index 0000000..8f19b59 --- /dev/null +++ b/_build/html/lessons/06_Spatial_Queries.html @@ -0,0 +1,1087 @@ + + + + + + + Lesson 6. Spatial Queries — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
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+ + + + +
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+ + + + + + +
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+ + + +
+ +
+ + + +
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+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 6. Spatial Queries

+

In spatial analysis, our goal is not just to make nice maps, +but to actually run analyses that leverage the explicitly spatial +nature of our data. The process of doing this is known as +spatial analysis.

+

To construct spatial analyses, we string together series of spatial +operations in such a way that the end result answers our question of interest. +There are many such spatial operations. These are known as spatial queries.

+
    +

  • 6.0 Load and prep some data

    • +

  • 6.1 Measurement Queries

    • +

  • 6.2 Relationship Queries

    • +

  • Exercise: Spatial Relationship Query

    • +

  • 6.3 Proximity Analysis

    • +

  • Exercise: Proximity Analysis

    • +

  • 6.4 Recap

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/census/Tracts/cb_2013_06_tract_500k.zip’

      • +

    • ‘notebook_data/protected_areas/CPAD_2020a_Units.shp’

      • +

    • ‘notebook_data/berkeley/BerkeleyCityLimits.shp’

      • +

    • ‘notebook_data/alco_schools.csv’

      • +

    • ‘notebook_data/transportation/BerkeleyBikeBlvds.geojson’

      • +

    • ‘notebook_data/transportation/bart.csv’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 45 minutes

      • +

    • Exercises: 20 minutes +

      • +
    +
    • +
+
+

We will start by reviewing the most +fundamental set, which we’ll refer to as spatial queries. +These can be divided into:

+
    +

  • Measurement queries

    +
      +

    • What is feature A’s length?

      • +

    • What is feature A’s area?

      • +

    • What is feature A’s perimeter?

      • +

    • What is feature A’s distance from feature B?

      • +

    • etc.

      • +
    +
    • +

  • Relationship queries

    +
      +

    • Is feature A within feature B?

      • +

    • Does feature A intersect with feature B?

      • +

    • Does feature A cross feature B?

      • +

    • etc.

      • +
    +
    • +
+

We’ll work through examples of each of those types of queries.

+

Then we’ll see an example of a very common spatial analysis that +is a conceptual amalgam of those two types: proximity analysis.

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+

6.0 Load and prep some data

+

Let’s read in our census tracts data again.

+
+
+
census_tracts = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip")
+census_tracts.plot()
+
+
+
+
+
+
+
census_tracts.head()
+
+
+
+
+

Then we’ll grab just the Alameda Country tracts.

+
+
+
census_tracts_ac = census_tracts.loc[census_tracts['COUNTYFP']=='001'].reset_index(drop=True)
+census_tracts_ac.plot()
+
+
+
+
+
+
+

6.1 Measurement Queries

+

We’ll start off with some simple measurement queries.

+

For example, here’s how we can get the areas of each of our census tracts.

+
+
+
census_tracts_ac.area
+
+
+
+
+

Okay!

+

We got…

+

numbers!

+

…?

+
+ +
+
+
+

Questions

+
+
    +

  1. What do those numbers mean?

    2. +

  2. What are our units?

    3. +

  3. And if we’re not sure, how might be find out?

    4. +
+

Let’s take a look at our CRS.

+
+
+
census_tracts_ac.crs
+
+
+
+
+

Ah-hah! We’re working in an unprojected CRS, with units of decimal degrees.

+

When doing spatial analysis, we will almost always want to work in a projected CRS +that has natural distance units, such as meters!

+

Time to project!

+

(As previously, we’ll use UTM Zone 10N with a NAD83 data. +This is a good choice for our region of interest.)

+
+
+
census_tracts_ac_utm10 = census_tracts_ac.to_crs( "epsg:26910")
+
+
+
+
+
+
+
census_tracts_ac_utm10.crs
+
+
+
+
+

Now let’s try our area calculation again.

+
+
+
census_tracts_ac_utm10.area
+
+
+
+
+

That looks much more reasonable!

+
+ +
+
+
+
+

Question

+
+

What are our units now?

+

You may have noticed that our census tracts already have an area column in them.

+

Let’s do a sanity check on our results.

+
+
+
# calculate the area for the 0th feature
+census_tracts_ac_utm10.area[0]
+
+
+
+
+
+
+
# get the area for the 0th feature according to its 'ALAND' attribute
+census_tracts['ALAND'][0]
+
+
+
+
+
+
+
# check equivalence of the calculated areas and the 'ALAND' column
+census_tracts_ac_utm10['ALAND'].values == census_tracts_ac_utm10.area
+
+
+
+
+
+ +
+
+
+
+

Question

+
+

What explains this disagreement? Are the calculated areas incorrect?

+

We can also sum the area for Alameda county by adding .sum() to the end of our area calculation.

+
+
+
census_tracts_ac_utm10.area.sum()
+
+
+
+
+

We can actually look up how large Alameda County is to check our work.The county is 739 miles2, which is around 1,914,001,213 meters2. I’d say we’re pretty close!

+

As it turns out, we can similarly use another attribute +to get the features’ lengths.

+

NOTE: In this case, given we’re +dealing with polygons, this is equivalent to getting the features’ perimeters.

+
+
+
census_tracts_ac_utm10.length
+
+
+
+
+
+
+
+

6.2 Relationship Queries

+

Spatial relationship queries consider how two geometries or sets of geometries relate to one another in space.

+

+

Here is a list of the most commonly used GeoPandas methods to test spatial relationships.

+ +
+There several other GeoPandas spatial relationship predicates but they are more complex to properly employ. For example the following two operations only work with geometries that are completely aligned. + +

All of these methods takes the form:

+
Geoseries.<predicate>(geometry)
+
+
+

For example:

+
Geoseries.contains(geometry)
+
+
+
+

Let’s load a new dataset to demonstrate these queries.

+

This is a dataset containing all the protected areas (parks and the like) in California.

+
+
+
pas = gpd.read_file('./notebook_data/protected_areas/CPAD_2020a_Units.shp')
+
+
+
+
+

Does this need to be reprojected too?

+
+
+
pas.crs
+
+
+
+
+

Yes it does!

+

Let’s reproject it.

+
+
+
pas_utm10 = pas.to_crs("epsg:26910")
+
+
+
+
+

One common use for spatial queries is for spatial subsetting of data.

+

In our case, lets use intersects to +find all of the parks that have land in Alameda County.

+

But before we do that, let’s take another look at our geometries.

+
+
+
census_tracts_ac_utm10.geometry.type.unique()
+
+
+
+
+
+
+
census_tracts_ac_utm10.plot()
+
+
+
+
+

Because we nave census tracts, each of these rows is either a Polygon or a MultiPolygon. For our relationship query we can actually simplify our geometry to be one polygon by using unary_union

+
+
+
census_tracts_ac_utm10.geometry.unary_union
+
+
+
+
+
+
+
print(census_tracts_ac_utm10.geometry.unary_union)
+
+
+
+
+

Now we can go ahead and conduct our operation intersects

+
+
+
pas_in_ac = pas_utm10.intersects(census_tracts_ac_utm10.geometry.unary_union)
+
+
+
+
+

If we scroll the resulting GeoDataFrame to the right we’ll see that +the COUNTY column of our resulting subset gives us a good sanity check on our results.

+
+
+
pas_in_ac
+
+
+
+
+
+
+
pas_utm10[pas_in_ac].head()
+
+
+
+
+

So does this overlay plot!

+
+
+
ax = census_tracts_ac_utm10.plot(color='gray', figsize=[12,16])
+pas_utm10[pas_in_ac].plot(ax=ax, column='ACRES', cmap='summer', legend=True,
+                          edgecolor='black', linewidth=0.4, alpha=0.8,
+                          legend_kwds={'label': "acres",
+                                       'orientation': "horizontal"})
+ax.set_title('Protected areas in Alameda County, colored by area', size=18);
+
+
+
+
+
+
+
# color by county?
+
+
+
+
+
+
+

Exercise: Spatial Relationship Query

+

Let’s use a spatial relationship query to create a new dataset containing Berkeley schools!

+

Run the next two cells to load datasets containing Berkeley’s city boundary and Alameda County’s +schools and to reproject them to EPSG: 26910.

+

Then in the following cell, write your own code to:

+
    +

  1. subset the schools for only those within Berkeley

    2. +

  2. plot the Berkeley boundary and then the schools as an overlay map

    3. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# load the Berkeley boundary
+berkeley = gpd.read_file("notebook_data/berkeley/BerkeleyCityLimits.shp")
+
+# transform to EPSG:26910
+berkeley_utm10 = berkeley.to_crs("epsg:26910")
+
+# display
+berkeley_utm10.head()
+
+
+
+
+
+
+
# load the Alameda County schools CSV
+schools_df = pd.read_csv('notebook_data/alco_schools.csv')
+
+# coerce it to a GeoDataFrame
+schools_gdf = gpd.GeoDataFrame(schools_df, 
+                               geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))
+# define its unprojected (EPSG:4326) CRS
+schools_gdf.crs = "epsg:4326"
+
+# transform to EPSG:26910
+schools_gdf_utm10 = schools_gdf.to_crs( "epsg:26910")
+
+# display
+schools_df.head()
+
+
+
+
+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+
+
+

6.3 Proximity Analysis

+

Now that we’ve seen the basic idea of spatial measurement and relationship queries, +let’s take a look at a common analysis that combines those concepts: promximity analysis.

+

Proximity analysis seeks to identify all features in a focal feature set +that are within some maximum distance of features in a reference feature set.

+

A common workflow for this analysis is:

+
    +

  1. Buffer (i.e. add a margin around) the reference dataset, out to the maximum distance.

    2. +

  2. Run a spatial relationship query to find all focal features that intersect (or are within) the buffer.

    3. +
+
+

Let’s read in our bike boulevard data again.

+

Then we’ll find out which of our Berkeley schools are within a block’s distance (200 m) of the boulevards.

+
+
+
bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')
+bike_blvds.plot()
+
+
+
+
+

Of course, we need to reproject the boulevards to our projected CRS.

+
+
+
bike_blvds_utm10 = bike_blvds.to_crs( "epsg:26910")
+
+
+
+
+

Now we can create our 200 meter bike boulevard buffers.

+
+
+
bike_blvds_utm10.crs
+
+
+
+
+
+
+
bike_blvds_buf = bike_blvds_utm10.buffer(distance=200)
+
+
+
+
+
+
+
bike_blvds_utm10.head()
+
+
+
+
+
+
+
bike_blvds_buf.head()
+
+
+
+
+

Now let’s overlay everything.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+berkeley_utm10.plot(color='lightgrey', ax=ax)
+bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)
+bike_blvds_utm10.plot(ax=ax)
+berkeley_schools.plot(color='purple',ax=ax)
+
+
+
+
+

Great! Looks like we’re all ready to run our intersection to complete the proximity analysis.

+

NOTE: In order to subset with our buffers we need to call the unary_union attribute of the buffer object. +This gives us a single unified polygon, rather than a series of multipolygons representing buffers around each of the points in our multilines.

+
+
+
schools_near_blvds = berkeley_schools.within(bike_blvds_buf.unary_union)
+blvd_schools = berkeley_schools[schools_near_blvds]
+
+
+
+
+

Now let’s overlay again, to see if the schools we subsetted make sense.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+berkeley_utm10.plot(color='lightgrey', ax=ax)
+bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)
+bike_blvds_utm10.plot(ax=ax)
+berkeley_schools.plot(color='purple',ax=ax)
+blvd_schools.plot(color='yellow', markersize=50, ax=ax)
+
+
+
+
+

If we want to find the shortest distance from one school to the bike boulevards, we can use the distance function.

+
+
+
berkeley_schools.distance(bike_blvds_utm10.unary_union)
+
+
+
+
+
+
+

Exercise: Proximity Analysis

+

Now it’s your turn to try out a proximity analysis!

+

Run the next cell to load our BART-system data, reproject it to EPSG: 26910, and subset it to Berkeley.

+

Then in the following cell, write your own code to find all schools within walking distance (1 km) of a BART station.

+

As a reminder, let’s break this into steps:

+
    +

  1. buffer your Berkeley BART stations to 1 km (HINT: remember your units!)

    2. +

  2. use the schools’ within attribute to check whether or not they’re within the buffers (HINT: don’t forget the unary_union!)

    3. +

  3. subset the Berkeley schools using the object returned by your spatial relationship query

    4. +

  4. as always, plot your results for a good visual check!

    5. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# load the BART stations from CSV
+bart_stations = pd.read_csv('notebook_data/transportation/bart.csv')
+# coerce to a GeoDataFrame
+bart_stations_gdf = gpd.GeoDataFrame(bart_stations, 
+                               geometry=gpd.points_from_xy(bart_stations.lon, bart_stations.lat))
+# define its unprojected (EPSG:4326) CRS
+bart_stations_gdf.crs = "epsg:4326"
+# transform to UTM Zone 10 N (EPSG:26910)
+bart_stations_gdf_utm10 = bart_stations_gdf.to_crs( "epsg:26910")
+# subset to Berkeley
+berkeley_bart = bart_stations_gdf_utm10[bart_stations_gdf_utm10.within(berkeley_utm10.unary_union)]
+
+
+
+
+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+
+
+

6.4 Recap

+

Leveraging what we’ve learned in our earlier lessons, we got to work with map overlays and start answering questions related to proximity. Key concepts include:

+
    +

  • Measuring area and length

    +
      +

    • .area,

      • +

    • .length

      • +
    +
    • +

  • Relationship Queries

    +
      +

    • .intersects()

      • +

    • .within()

      • +
    +
    • +

  • Buffer analysis

    +
      +

    • .buffer()

      • +

    • .distance()

      • +
    +
    • +
+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + Lesson 4. More Data, More Maps! + Lesson 7. Attribute and Spatial Joins + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/07_Joins_and_Aggregation.html b/_build/html/lessons/07_Joins_and_Aggregation.html new file mode 100644 index 0000000..ca21f9f --- /dev/null +++ b/_build/html/lessons/07_Joins_and_Aggregation.html @@ -0,0 +1,1290 @@ + + + + + + + Lesson 7. Attribute and Spatial Joins — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 7. Attribute and Spatial Joins

+

Now that we understand the logic of spatial relationship queries, +let’s take a look at another fundamental spatial operation that relies on them.

+

This operation, called a spatial join, is the process by which we can +leverage the spatial relationships between distinct datasets to merge +their information into a new, synthetic dataset.

+

This operation can be thought as the spatial equivalent of an +attribute join, in which multiple tabular datasets can be merged by +aligning matching values in a common column that they both contain. +Thus, we’ll start by developing an understanding of this operation first!

+
    +

  • 7.0 Data Input and Prep

    • +

  • 7.1 Attribute Joins

    • +

  • Exercise: Choropleth Map

    • +

  • 7.2 Spatial Joins

    • +

  • 7.3 Aggregation

    • +

  • Exercise: Aggregation

    • +

  • 7.4 Recap

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/census/ACS5yr/census_variables_CA.csv’

      • +

    • ‘notebook_data/census/Tracts/cb_2013_06_tract_500k.zip’

      • +

    • ‘notebook_data/alco_schools.csv’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 45 minutes

      • +

    • Exercises: 20 minutes +

      • +
    +
    • +
+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+

7.0 Data Input and Prep

+

Let’s read in a table of data from the US Census’ 5-year American Community Survey (ACS5).

+
+
+
# Read in the ACS5 data for CA into a pandas DataFrame.
+# Note: We force the FIPS_11_digit to be read in as a string to preserve any leading zeroes.
+acs5_df = pd.read_csv("notebook_data/census/ACS5yr/census_variables_CA.csv", dtype={'FIPS_11_digit':str})
+acs5_df.head()
+
+
+
+
+

Brief summary of the data:

+

Below is a table of the variables in this table. They were combined from +different ACS 5 year tables.

+

NOTE:

+
    +

  • variables that start with c_ are counts

    • +

  • variables that start with med_ are medians

    • +

  • variables that end in _moe are margin of error estimates

    • +

  • variables that start with _p are proportions calcuated from the counts divided by the table denominator (the total count for whom that variable was assessed)

    • +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Variable

Description

c_race

Total population

c_white

Total white non-Latinx

c_black

Total black and African American non-Latinx

c_asian

Total Asian non-Latinx

c_latinx

Total Latinx

state_fips

State level FIPS code

county_fips

County level FIPS code

tract_fips

Tracts level FIPS code

med_rent

Median rent

med_hhinc

Median household income

c_tenants

Total tenants

c_owners

Total owners

c_renters

Total renters

c_movers

Total number of people who moved

c_stay

Total number of people who stayed

c_movelocal

Number of people who moved locally

c_movecounty

Number of people who moved counties

c_movestate

Number of people who moved states

c_moveabroad

Number of people who moved abroad

c_commute

Total number of commuters

c_car

Number of commuters who use a car

c_carpool

Number of commuters who carpool

c_transit

Number of commuters who use public transit

c_bike

Number of commuters who bike

c_walk

Number of commuters who bike

year

ACS data year

FIPS_11_digit

11-digit FIPS code

+

We’re going to drop all of our moe columns by identifying all of those that end with _moe. We can do that in two steps, first by using filter to identify columns that contain the string _moe.

+
+
+
moe_cols = acs5_df.filter(like='_moe',axis=1).columns
+moe_cols
+
+
+
+
+
+
+
acs5_df.drop(moe_cols, axis=1, inplace=True)
+
+
+
+
+

And lastly, let’s grab only the rows for year 2018 and county FIPS code 1 (i.e. Alameda County)

+
+
+
acs5_df_ac = acs5_df[(acs5_df['year']==2018) & (acs5_df['county_fips']==1)]
+
+
+
+
+
+

Now let’s also read in our census tracts again!

+
+
+
tracts_gdf = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip")
+
+
+
+
+
+
+
tracts_gdf.head()
+
+
+
+
+
+
+
tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001']
+tracts_gdf_ac.plot()
+plt.show()
+
+
+
+
+
+
+

7.1 Attribute Joins

+

Attribute Joins between Geodataframes and Dataframes

+

We just mapped the census tracts. But what makes a map powerful is when you map the data associated with the locations.

+
    +

  • tracts_gdf_ac: These are polygon data in a GeoDataFrame. However, as we saw in the head of that dataset, they no attributes of interest!

    • +

  • acs5_df_ac: These are 2018 ACS data from a CSV file (‘census_variables_CA.csv’), imported and read in as a pandas DataFrame. However, they have no geometries!

    • +
+

In order to map the ACS data we need to associate it with the tracts. Let’s do that now, by joining the columns from acs5_df_ac to the columns of tracts_gdf_ac using a common column as the key for matching rows. This process is called an attribute join.

+
+ +
+ +
+
+
+

Question

+
+

The image above gives us a nice conceptual summary of the types of joins we could run.

+
    +

  1. In general, why might we choose one type of join over another?

    2. +

  2. In our case, do we want an inner, left, right, or outer (AKA ‘full’) join?

    3. +
+

(NOTE: You can read more about merging in geopandas here.)

+

Okay, here we go!

+

Let’s take a look at the common column in both our DataFrames.

+
+
+
tracts_gdf_ac['GEOID'].head()
+
+
+
+
+
+
+
acs5_df_ac['FIPS_11_digit'].head()
+
+
+
+
+

Note that they are not named the same thing.

+
    That's okay! We just need to know that they contain the same information.
+
+
+

Also note that they are not in the same order.

+
    That's not only okay... That's the point! (If they were in the same order already then we could just join them side by side, without having Python find and line up the matching rows from each!)
+
+
+
+

Let’s do a left join to keep all of the census tracts in Alameda County and only the ACS data for those tracts.

+

NOTE: To figure out how to do this we could always take a peek at the documentation by calling +?tracts_gdf_ac.merge, or help(tracts_gdf_ac).

+
+
+
# Left join keeps all tracts and the acs data for those tracts
+tracts_acs_gdf_ac = tracts_gdf_ac.merge(acs5_df_ac, left_on='GEOID',
+                                        right_on="FIPS_11_digit", how='left')
+tracts_acs_gdf_ac.head(2)
+
+
+
+
+

Let’s check that we have all the variables we have in our dataset now.

+
+
+
list(tracts_acs_gdf_ac.columns)
+
+
+
+
+
+ +
+
+
+
+

Question

+
+

It’s always important to run sanity checks on our results, at each step of the way!

+

In this case, how many rows and columns should we have?

+
+
+
print("Rows and columns in the Alameda County Census tract gdf:\n\t", tracts_gdf_ac.shape)
+print("Row and columns in the ACS5 2018 data:\n\t", acs5_df_ac.shape)
+print("Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\n\t", tracts_acs_gdf_ac.shape)
+
+
+
+
+

Let’s save out our merged data so we can use it in the final notebook.

+
+
+
tracts_acs_gdf_ac.to_file('outdata/tracts_acs_gdf_ac.json', driver='GeoJSON')
+
+
+
+
+
+
+
+

Exercise: Choropleth Map

+

We can now make choropleth maps using our attribute joined geodataframe. Go ahead and pick one variable to color the map, then map it. You can go back to lesson 5 if you need a refresher on how to make this!

+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE
+
+
+
+
+
+

Double-click to see solution!

+
+
+
+
+

7.2 Spatial Joins

+

Great! We’ve wrapped our heads around the concept of an attribute join.

+

Now let’s extend that concept to its spatially explicit equivalent: the spatial join!

+
+

To start, we’ll read in some other data: The Alameda County schools data.

+

Then we’ll work with that data and our tracts_acs_gdf_ac data together.

+
+
+
schools_df = pd.read_csv('notebook_data/alco_schools.csv')
+schools_gdf = gpd.GeoDataFrame(schools_df, 
+                               geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))
+schools_gdf.crs = "epsg:4326"
+
+
+
+
+

Let’s check if we have to transform the schools to match thetracts_acs_gdf_ac’s CRS.

+
+
+
print('schools_gdf CRS:', schools_gdf.crs)
+print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)
+
+
+
+
+

Yes we do! Let’s do that.

+

NOTE: Explicit syntax aiming at that dataset’s CRS leaves less room for human error!

+
+
+
schools_gdf = schools_gdf.to_crs(tracts_acs_gdf_ac.crs)
+
+print('schools_gdf CRS:', schools_gdf.crs)
+print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)
+
+
+
+
+

Now we’re ready to combine the datasets in an analysis.

+

In this case, we want to get data from the census tract within which each school is located.

+

But how can we do that? The two datasets don’t share a common column to use for a join.

+
+
+
tracts_acs_gdf_ac.columns
+
+
+
+
+
+
+
schools_gdf.columns
+
+
+
+
+

However, they do have a shared relationship by way of space!

+

So, we’ll use a spatial relationship query to figure out the census tract that +each school is in, then associate the tract’s data with that school (as additional data in the school’s row). +This is a spatial join!

+
+
+

Census Tract Data Associated with Each School

+

In this case, let’s say we’re interested in the relationship between the median household income +in a census tract (tracts_acs_gdf_ac['med_hhinc']) and a school’s Academic Performance Index +(schools_gdf['API']).

+

To start, let’s take a look at the distributions of our two variables of interest.

+
+
+
tracts_acs_gdf_ac.hist('med_hhinc')
+
+
+
+
+
+
+
schools_gdf.hist('API')
+
+
+
+
+

Oh, right! Those pesky schools with no reported APIs (i.e. API == 0)! Let’s drop those.

+
+
+
schools_gdf_api = schools_gdf.loc[schools_gdf['API'] > 0, ]
+
+
+
+
+
+
+
schools_gdf_api.hist('API')
+
+
+
+
+

Much better!

+

Now, maybe we think there ought to be some correlation between the two variables? +As a first pass at this possibility, let’s overlay the two datasets, coloring each one by +its variable of interest. This should give us a sense of whether or not similar values co-occur.

+
+
+
ax = tracts_acs_gdf_ac.plot(column='med_hhinc', cmap='cividis', figsize=[18,18],
+                            legend=True, legend_kwds={'label': "median household income ($)",
+                                                      'orientation': "horizontal"})
+schools_gdf_api.plot(column='API', cmap='cividis', edgecolor='black', alpha=1, ax=ax,
+                     legend=True, legend_kwds={'label': "API", 'orientation': "horizontal"})
+
+
+
+
+
+
+

Spatially Joining our Schools and Census Tracts

+

Though it’s hard to say for sure, it certainly looks possible. +It would be ideal to scatterplot the variables! But in order to do that, +we need to know the median household income in each school’s tract, which +means we definitely need our spatial join!

+

We’ll first take a look at the documentation for the spatial join function, gpd.sjoin.

+
+
+
help(gpd.sjoin)
+
+
+
+
+

Looks like the key arguments to consider are:

+
    +

  • the two GeoDataFrames (left_df and right_df)

    • +

  • the type of join to run (how), which can take the values left, right, or inner

    • +

  • the spatial relationship query to use (op)

    • +
+

NOTE:

+
    +

  • By default sjoin is an inner join. It keeps the data from both geodataframes only where the locations spatially intersect.

    • +

  • By default sjoin maintains the geometry of first geodataframe input to the operation.

    • +
+
+ +
+
+
+

Questions

+
+
    +

  1. Which GeoDataFrame are we joining onto which (i.e. which one is getting the other one’s data added to it)?

    2. +

  2. What happened to ‘outer’ as a join type?

    3. +

  3. Thus, in our operation, which GeoDataFrame should be the left_df, which should be the right_df, and how do we want our join to run?

    4. +
+

Alright! Let’s run our join!

+
+
+
schools_jointracts = gpd.sjoin(schools_gdf_api, tracts_acs_gdf_ac, how='left')
+
+
+
+
+
+
+
schools_jointracts.head()
+
+
+
+
+
+
+
+

Checking Our Output

+
+
+ +
+
+
+

Questions

+
+

As always, we want to sanity-check our intermediate result before we rush ahead.

+

One way to do that is to introspect the structure of the result object a bit.

+
    +

  1. What type of object should that have given us?

    2. +

  2. What should the dimensions of that object be, and why?

    3. +

  3. If we wanted a visual check of our results (i.e. a plot or map), what could we do?

    4. +
+
+
+
print(schools_jointracts.shape)
+print(schools_gdf.shape)
+print(tracts_acs_gdf_ac.shape)
+
+
+
+
+
+
+
schools_jointracts.head()
+
+
+
+
+

Confirmed! The output of the our sjoin operation is a GeoDataFrame (schools_jointracts) with:

+
    +

  • a row for each school that is located inside a census tract (all of them are)

    • +

  • the point geometry of that school

    • +

  • all of the attribute data columns (non-geometry columns) from both input GeoDataFrames

    • +
+
+

Let’s also take a look at an overlay map of the schools on the tracts. +If we color the schools categorically by their tracts IDs, then we should see +that all schools within a given tract polygon are the same color.

+
+
+
ax = tracts_acs_gdf_ac.plot(color='white', edgecolor='black', figsize=[18,18])
+schools_jointracts.plot(column='GEOID', ax=ax)
+
+
+
+
+
+
+
+

Assessing the Relationship between Median Household Income and API

+

Fantastic! That looks right!

+

Now we can create that scatterplot we were thinking about!

+
+
+
fig, ax = plt.subplots(figsize=(6,6))
+ax.scatter(schools_jointracts.med_hhinc, schools_jointracts.API)
+ax.set_xlabel('median household income ($)')
+ax.set_ylabel('API')
+
+
+
+
+

Wow! Just as we suspected based on our overlay map, +there’s a pretty obvious, strong, and positive correlation +between median household income in a school’s tract +and the school’s API.

+
+
+
+

7.3. Aggregation

+

We just saw that a spatial join in one way to leverage the spatial relationship +between two datasets in order to create a new, synthetic dataset.

+

An aggregation is another way we can generate new data from this relationship. +In this case, for each feature in one dataset we find all the features in another +dataset that satisfy our chosen spatial relationship query with it (e.g. within, intersects), +then aggregate them using some summary function (e.g. count, mean).

+
+
+

Getting the Aggregated School Counts

+

Let’s take this for a spin with our data. We’ll count all the schools within each census tract.

+

Note that we’ve already done the first step of spatially joining the data from the aggregating features +(the tracts) onto the data to be aggregated (our schools).

+

The next step is to group our GeoDataFrame by census tract, and then summarize our data by group. +We do this using the DataFrame method groupy.

+

To get the correct count, lets rejoin our schools on our tracts, this time keeping all schools +(not just those with APIs > 0, as before).

+
+
+
schools_jointracts = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='left')
+
+
+
+
+

Now for the groupy operation.

+

NOTE: We could really use any column, since we’re just taking a count. For now we’ll just use the school names (‘Site’).

+
+
+
schools_countsbytract = schools_jointracts[['GEOID','Site']].groupby('GEOID', as_index=False).count()
+print("Counts, rows and columns:", schools_countsbytract.shape)
+print("Tracts, rows and columns:", tracts_acs_gdf_ac.shape)
+
+# take a look at the data
+schools_countsbytract.head()
+
+
+
+
+
+
+

Getting Tract Polygons with School Counts

+

The above groupby and count operations give us the counts we wanted.

+
    +

  • We have the 263 (of 361) census tracts that have at least one school

    • +

  • We have the number of schools within each of those tracts

    • +
+

But the output of groupby is a plain DataFrame not a GeoDataFrame.

+

If we want a GeoDataFrame then we have two options:

+
    +

  1. We could join the groupby output to tracts_acs_gdf_ac by the attribute GEOID +or

    2. +

  2. We could start over, using the GeoDataFrame dissolve method, which we can think of as a spatial groupby.

    3. +
+
+

Since we already know how to do an attribute join, we’ll do the dissolve!

+

First, let’s run a new spatial join.

+
+
+
tracts_joinschools = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='right')
+
+
+
+
+
+
+
tracts_joinschools.geometry
+
+
+
+
+

Now, let’s run our dissolve!

+
+
+
tracts_schoolcounts = tracts_joinschools[['GEOID', 'Site', 'geometry']].dissolve(by='GEOID', aggfunc='count')
+print("Counts, rows and columns:", tracts_schoolcounts.shape)
+
+# take a look
+tracts_schoolcounts.head()
+
+
+
+
+

Nice! Let’s break that down.

+
    +

  • The dissolve operation requires a geometry column and a grouping column (in our case, ‘GEOID’). Any geometries within the same group will be dissolved if they have the same geometry or nested geometries.

    • +

  • The aggfunc, or aggregation function, of the dissolve operation will be applied to all numeric columns in the input geodataframe (unless the function is count in which case it will count rows.)

    • +
+

Check out the Geopandas documentation on dissolve for more information.

+
+ +
+
+
+

Questions

+
+
    +

  1. Above we selected three columns from the input GeoDataFrame to create a subset as input to the dissolve operation. Why?

    2. +

  2. Why did we run a new spatial join? What would have happened if we had used the schools_jointracts object instead?

    3. +

  3. What explains the dimensions of the new object (361, 2)?

    4. +
+
+
+
+

Mapping our Spatial Join Output

+

Also, because our sjoin plus dissolve pipeline outputs a GeoDataFrame, we can now easily map the school count by census tract!

+
+
+
fig, ax = plt.subplots(figsize = (14,8)) 
+
+# Display the output of our spatial join
+tracts_schoolcounts.plot(ax=ax,column='Site', 
+                         scheme="user_defined",
+                         classification_kwds={'bins':[*range(9)]},
+                         cmap="PuRd_r",
+                         edgecolor="grey",
+                         legend=True, 
+                         legend_kwds={'title':'Number of schools'})
+schools_gdf.plot(ax=ax, color='black', markersize=2)
+
+
+
+
+
+
+
+
+

Exercise: Aggregation

+
+

What is the mean API of each census tract?

+

As we mentioned, the spatial aggregation workflow that we just put together above +could have been used not to generate a new count variable, but also +to generate any other new variable the results from calling an aggregation function +on an attribute column.

+

In this case, we want to calculate and map the mean API of the schools in each census tract.

+

Copy and paste code from above where useful, then tweak and/or add to that code such that your new code:

+
    +

  1. joins the schools onto the tracts (HINT: make sure to decide whether or not you want to include schools with API = 0!)

    2. +

  2. dissolves that joined object by the tract IDs, giving you a new GeoDataFrame with each tract’s mean API (HINT: because this is now a different calculation, different problems may arise and need handling!)

    3. +

  3. plots the tracts, colored by API scores (HINT: overlay the schools points again, visualizing them in a way that will help you visually check your results!)

    4. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+
+
+

7.4 Recap

+

We discussed how we can combine datasets to enhance any geospatial data analyses you could do. Key concepts include:

+
    +

  • Attribute joins

    +
      +

    • .merge()

      • +
    +
    • +

  • Spatial joins (order matters!)

    +
      +

    • gpd.sjoin()

      • +
    +
    • +

  • Aggregation +-.groupby()

    +
      +

    • .dissolve() (preserves geometry)

      • +
    +
    • +
+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + Lesson 6. Spatial Queries + 08. Pulling it all Together + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/08_Pulling_It_All_Together.html b/_build/html/lessons/08_Pulling_It_All_Together.html new file mode 100644 index 0000000..41826c5 --- /dev/null +++ b/_build/html/lessons/08_Pulling_It_All_Together.html @@ -0,0 +1,763 @@ + + + + + + + 08. Pulling it all Together — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
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+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

08. Pulling it all Together

+

For this last lesson, we’ll practice going through a full workflow!! We’ll answer the question:

+
+

What is the total grocery-store sales volume of each census tract?

+
+

WORKFLOW:

+
+Here's a set of steps that we will implement in the labeled cells below: +

8.1 Read in and Prep Data

+
    +

  • read in tracts acs joined data

    • +

  • read our grocery-data CSV into a Pandas DataFrame (it lives at 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv)

    • +

  • coerce it to a GeoDataFrame

    • +

  • define its CRS (EPSG:4326)

    • +

  • transform it to match the CRS of tracts_acs_gdf_ac

    • +

  • take a peek

    • +
+

8.2 Spatial Join and Dissolve

+
    +

  • join the two datasets in such a way that you can then…

    • +

  • group by tract and calculate the total grocery-store sales volume

    • +

  • don’t forget to check the dimensions, contents, and any other relevant aspects of your results

    • +
+

8.3 Plot and Review

+
    +

  • plot the tracts, coloring them by total grocery-store sales volume

    • +

  • plot the grocery stores on top

    • +

  • bonus points for devising a nice visualization scheme that helps you heuristically check your results!

    • +
+
+
+

INSTRUCTIONS:

+

We’ve written out some of the code for you, but you’ll need to replace the ellipses with the correct +content.

+

You can check your answers by double-clicking on the Markdown cells where indicated.

+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘outdata/tracts_acs_gdf_ac.json’

      • +

    • ‘notebook_data/other/ca_grocery_stores_2019_wgs84.csv’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: N/A

      • +

    • Exercises: 30 minutes +

      • +
    +
    • +
+
+
+
+
+

Install Packages

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+
+
+

8.1 Read in the Prep Data

+

We first need to prepare our data by loading both our tracts/acs and grocery data, and conduct our usual steps to make there they have the same CRS.

+
    +

  • read in our tracts acs joined data

    • +

  • read our grocery-data CSV into a Pandas DataFrame (it lives at 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv)

    • +

  • coerce it to a GeoDataFrame

    • +

  • define its CRS (EPSG:4326)

    • +

  • transform it to match the CRS of tracts_acs_gdf_ac

    • +

  • take a peek

    • +
+
+
+
# read in tracts acs data
+
+tracts_acs_gdf_ac = gpd.read_file(..)
+
+
+
+
+
+
+
# read our grocery-data CSV into a Pandas DataFrame
+
+grocery_pts_df = pd.read_csv(...)
+
+
+
+
+
+
+
# coerce it to a GeoDataFrame
+
+grocery_pts_gdf = gpd.GeoDataFrame(grocery_pts_df, 
+                                   geometry=gpd.points_from_xy(...,...))
+
+
+
+
+
+
+
# define its CRS (NOTE: Use EPSG:4326)
+
+grocery_pts_gdf.crs = ...
+
+
+
+
+
+
+
# transform it to match the CRS of tracts_acs_gdf_ac
+
+grocery_pts_gdf.to_crs(..., inplace=...)
+
+
+
+
+
+
+
grocery_pts_gdf.crs
+
+
+
+
+
+
+
# take a peek
+
+print(grocery_pts_gdf.head())
+
+
+
+
+
+
+

Double-click here to see solution!

+ +
+
+
+

8.2 Spatial Join and Dissolve

+

Now that we have our data and they’re in the same projection, we’re going to conduct an attribute join to bring together the two datasets. From there we’ll be able to actually aggregate our data to count the total sales volume.

+
    +

  • join the two datasets in such a way that you can then…

    • +

  • group by tract and calculate the total grocery-store sales volume

    • +

  • don’t forget to check the dimensions, contents, and any other relevant aspects of your results

    • +
+
+
+
# join the two datasets in such a way that you can then...
+
+tracts_joingrocery = gpd.sjoin(..., ..., how= ...)
+
+
+
+
+
+
+
# group by tract and calculate the total grocery-store sales volume
+
+tracts_totsalesvol = tracts_joingrocery[['GEOID','geometry','SALESVOL']].dissolve(by= ...,
+                                                                                  aggfunc=..., as_index=False)
+
+
+
+
+
+
+
# don't forget to check the dimensions, contents, and any other relevant aspects of your results
+
+# check the dimensions
+print('Dimensions of result:', ...)
+print('Dimesions of census tracts:', ...)
+
+
+
+
+
+
+
# check the result
+print(tracts_totsalesvol.head())
+
+
+
+
+
+
+

Double-click here to see solution!

+ +
+
+
+

8.3 Plot and Review

+

With any time of geospatial analysis you do, it’s always nice to plot and visualize your results to check your work and start to understand the full story of your analysis.

+
    +

  • Plot the tracts, coloring them by total grocery-store sales volume

    • +

  • Plot the grocery stores on top

    • +

  • Bonus points for devising a nice visualization scheme that helps you heuristically check your results!

    • +
+
+
+
# create the figure and axes
+
+fig, ax = plt.subplots(figsize = (20,20)) 
+
+# plot the tracts, coloring by total SALESVOL
+
+tracts_totsalesvol.plot(ax=ax, column= ..., scheme="quantiles", cmap="autumn", edgecolor="grey",
+                        legend=True, legend_kwds={'title':...})
+
+
+
+
+
+
+
# subset the stores for only those within our tracts, to keep map within region of interest
+
+grocery_pts_gdf_ac = grocery_pts_gdf.loc[..., ]
+
+
+
+
+
+
+
# add the grocery stores, coloring by SALESVOL, for a visual check
+
+grocery_pts_gdf_ac.plot(ax=ax, column= ... , cmap= ..., linewidth= ..., markersize= ...,
+                        legend=True, legend_kwds={'label': ... , 'orientation': "horizontal"})
+
+
+
+
+
+
+

Double-click here to see solution!

+ +
+
+
+
+
+
+
+
+
+
+

Congrats!! Thanks for Joining Us for Geospatial Fundamentals!!

+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + Lesson 7. Attribute and Spatial Joins + Lesson 9. On Your Own: A Full Workflow + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/09_ON_YOUR_OWN_A_Full_Workflow.html b/_build/html/lessons/09_ON_YOUR_OWN_A_Full_Workflow.html new file mode 100644 index 0000000..a53fd4f --- /dev/null +++ b/_build/html/lessons/09_ON_YOUR_OWN_A_Full_Workflow.html @@ -0,0 +1,971 @@ + + + + + + + Lesson 9. On Your Own: A Full Workflow — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + +
+ +
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+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 9. On Your Own: A Full Workflow

+

Now is your chance to pull everything we’ve learned together and answer the questions:

+
    +

  • How many polling stations are in each census tract in Alameda County?

    • +

  • Which polling stations are within walking distance (100m) from a bus route in Berkeley?

    • +

  • How far are these polling stations from the bus routes in Berkeley?

    • +
+

All on your own!!

+
    +

  • 9.1 Polling Station Locations

    • +

  • 9.2 Tracts data

    • +

  • 9.3 Spatial Join

    • +

  • 9.4 Aggregate number of stations by census tracts

    • +

  • 9.5 Attribute join back to tracts data

    • +

  • 9.6 Berkeley outline

    • +

  • 9.7 Bus routes

    • +

  • 9.8 Polling station distance from bus routes

    • +
+

We’ve written out some of the code for you, and you can check your answers by clicking on the toggle solution button

+
+

Install Packages

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+

9.1 Polling Station Locations

+

We’ll be using the 2020 General Election voting locations for Alameda County for this analysis. Since the data is aspatial we’ll need to coerce it to be a geodataframe and define a CRS.

+
    +

  • read our grocery-data CSV into a Pandas DataFrame (it lives at 'notebook_data/ac_voting_locations.csv)

    • +

  • coerce it to a GeoDataFrame

    • +

  • define its CRS (EPSG:4326)

    • +

  • plot it

    • +
+
+
+
# Pull in polling location
+
+# polling_ac_df = pd.read_csv(...)
+# polling_ac_df.head()
+
+
+
+
+
+
+
# Make into geo data frame
+
+# polling_ac_gdf = gpd.GeoDataFrame(..., 
+#                                geometry=gpd.points_from_xy(...,...))
+# polling_ac_gdf.crs = ...
+
+# plot it 
+
+# polling_ac_gdf.plot(...)
+
+
+
+
+
+
+

Double-click here to see solution!

+
+
+

9.2 Tracts data

+

Since we want to answer the question How many polling stations are in each census tract?, we’ll pull in our tracts data.

+
    +

  • Bring in the census tracts data which lives at notebook_data/census/Tracts/cb_2013_06_tract_500k.zip

    • +

  • Narrow it down to Alameda County

    • +

  • Check CRS

    • +

  • Transform CRS to 26910 if needed

    • +
+
+
+
# Bring in census tracts
+# tracts_gdf = gpd.read_file(...)
+
+# Narrow it down to Alameda County
+# tracts_gdf_ac = tracts_gdf[...]
+# tracts_gdf_ac.plot()
+# plt.show()
+
+
+
+
+
+
+
# Check CRS
+print('polling_ac_gdf:', ...)
+print('tracts_gdf_ac CRS:', ...)
+
+
+
+
+
+
+
# Transform CRS
+polling_ac_gdf_utm10 = ...
+tracts_gdf_ac_utm10 = ...
+
+
+
+
+
+
+

Double-click here to see solution!

+
+
+

9.3 Spatial Join

+

Alright, now our data is all ready to go! We’re going to do a spatial join to answer our question about polling stations in each tract.

+
    +

  • Spatial join tracts/acs with the polling data (keep the tracts geometry!)

    • +

  • Plot it to make sure you have the right geometry

    • +

  • Check out your data and its dimensions

    • +
+
+
+
# Spatial join tracts/acs with the polling data (keep the tracts geometry!)
+
+# polls_jointracts = gpd.sjoin(..., ... , how=...)
+
+
+
+
+
+
+
# Plot it to make sure you have the right geometry
+
+# polls_jointracts.plot()
+
+
+
+
+
+
+
# Check out your data and its dimensions
+
+
+
+
+
+
+

Double-click here to see solution!

+
+
+

9.4 Aggregate number of stations by census tracts

+

Now that we have a GeoDataFrame with all our polling and tract data, we’ll need to aggregate to actually count the number of stations we have

+
    +

  • Use dissolve to count the number of polls we have

    • +

  • Create a choropleth map base don the number of stations there are

    • +
+
+
+
# Use `dissolve` to count the number of polls we have
+
+# polls_countsbytract = polls_jointracts[['TRACTCE', 'NAME_right', 
+#                                         'geometry']].dissolve(by=..., 
+#                                                               aggfunc=...).reset_index()
+# polls_countsbytract.head()
+
+
+
+
+
+
+
# rename the column to be for the number of polling stations (you dont have to change anything here)
+
+# polls_countsbytract.rename(columns={'NAME_right': 'Num_Polling'}, inplace=True)
+
+
+
+
+
+
+
# Create a choropleth map base don the number of stations there are
+fig, ax = plt.subplots(figsize = (14,8)) 
+
+# polls_countsbytract.plot(ax=ax,
+#                          column=..., 
+#                          cmap=...,
+#                          edgecolor="grey",
+#                          legend=True)
+
+# polling_ac_gdf_utm10.plot(ax=ax, color=..., edgecolor=..., markersize= ...)
+
+
+
+
+
+
+

Double-click here to see solution!

+
+
+

9.5 Attribute join back to tracts data

+

Amazing! Now that we have this information let’s do an attribute join to add this data into our tracts data

+
+
+
# merge onto census tract data
+
+# tracts_gdf_ac =  tracts_gdf_ac.merge(polls_countsbytract[['TRACTCE', 'Num_Polling']], left_on= ...,right_on= ... , how= ... ) 
+# tracts_gdf_ac.head()
+
+
+
+
+
+
+

Double-click here to see solution!

+
+
+

9.6 Berkeley outline

+

To answer our question Which polling stations are within walking distance (100m) from a bus route in Berkeley? we’ll need to know where Berkeley is! This is the perfect time to bring our Berkeley places data in.

+
    +

  • Read in outdata/berkeley_places.shp

    • +

  • Check the CRS

    • +

  • Transform CRS if necessary to EPSG:26910

    • +
+
+
+
# Read in outdata/berkeley_places.shp
+# berkeley_places = gpd.read_file(...)
+
+# Check the CRS
+
+
+# Transform CRS if necessary to EPSG:26910
+berkeley_places_utm10 = ...
+
+
+
+
+
+
+

Double-click here to see solution!

+ +
+
+

8.7 Bus routes

+
    +

  • Bring in bus routes (‘notebook_data/transportation/Fall20Routeshape.zip’), transform CRS to 26910

    • +

  • Intersect bus routes with Berkeley

    • +

  • Plot results of intersection

    • +

  • Clip bus routes to everything that is inside the berkley outline

    • +

  • Plot bus routes on top of Berkeley outline

    • +
+
+
+
# Bring in bus routes, transform CRS to 26910
+bus_routes = ...
+# bus_routes_utm10 = bus_routes.to_crs(...)
+# bus_routes_utm10.head()
+
+
+
+
+
+
+
# Look at intersection between bus routes and Berkeley
+# bus_routes_berkeley = .intersects(... .geometry.squeeze())
+
+# Create new geodataframe from these results
+# bus_berk = bus_routes_utm10.loc[bus_routes_berkeley].reset_index(drop=True)
+
+
+
+
+
+
+
# Plot results of intersection
+
+# fig, ax = plt.subplots(figsize=(10,10))
+# berkeley_places_utm10.plot(ax=ax)
+# bus_berk.plot(ax=ax, column ='PUB_RTE')
+
+
+
+
+
+
+
# BONUS: Look at route length
+# bus_berk.length
+
+
+
+
+
+
+
# Clip bus routes to everything that is inside the berkley outline
+# bus_berk_clip = gpd.clip(...,...)
+
+
+
+
+
+
+
# Plot bus routes on top of Berkeley outline
+# fig, ax = plt.subplots(figsize=(10,10))
+# berkeley_places_utm10.plot(ax=ax)
+# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')
+
+
+
+
+
+
+

Double-click here to see solution!

+
+
+

8.6 Polling stations within walking distance of bus routes

+

Now we can really answer the question Which polling stations are within walking distance (100m) from a bus route in Berkeley?

+
    +

  • Create buffer around bus route for 100m

    • +

  • Intersect polling locations in Alameda County with Berkeley outline

    • +

  • Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations

    • +

  • Calculate the distance from polling stations to the closest bus route

    • +
+
+
+
# Create buffer around bus route for 100m
+# bus_berk_buf =bus_berk_clip.buffer(distance= ...)
+
+
+
+
+
+
+
# Intersect polling locations in Alameda County with Berkeley outline
+# polling_berk =  ... .intersects(berkeley_places_utm10.geometry.squeeze())
+
+# polling_berk_gdf = polling_ac_gdf_utm10[polling_berk].reset_index(drop=True)
+
+
+
+
+
+
+
# Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations
+
+# fig, ax = plt.subplots(figsize=(10,10))
+# berkeley_places_utm10.plot(ax=ax)
+# bus_berk_buf.plot(color='pink', ax=ax, alpha=0.5)
+# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')
+# polling_berk_gdf.plot(ax=ax, color= 'yellow')
+
+
+
+
+
+
+
# Calculate the distance from polling stations to the closest bus route
+
+
+
+
+
+
+

Double-click here to see solution!

+
+
+

You’re done!!!!

+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + 08. Pulling it all Together + 10. Read in Data from Online Sources + CSV to Geodataframe + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/10_OPTIONAL_Fetching_Data.html b/_build/html/lessons/10_OPTIONAL_Fetching_Data.html new file mode 100644 index 0000000..144f900 --- /dev/null +++ b/_build/html/lessons/10_OPTIONAL_Fetching_Data.html @@ -0,0 +1,969 @@ + + + + + + + 10. Read in Data from Online Sources + CSV to Geodataframe — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

10. Read in Data from Online Sources + CSV to Geodataframe

+

In this optional notebook we’ll be going over how to read data into a notebook from online sources.

+ +
+ +

DEVELOPER NOTES:

+ +

+
+

10.1 Introduction

+

In the past examples, the data we have imported into our notebooks has come either from previously downloaded and saved files or from the census API. The goal of this notebook is to present other ways of accessing data, either from urls, other APIs or from predetermined Python libraries.

+
+

Set-Up

+

Let’s import the packages we need before we get started.

+
+
+
import pandas as pd
+import collections
+import requests 
+from urllib.request import urlopen, Request
+
+import json # for working with JSON data
+import geojson # ditto for GeoJSON data - an extension of JSON with support for geographic data
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+%matplotlib inline  
+import matplotlib.pyplot as plt # more plotting stuff
+
+
+
+
+

+
+
+
+

10.2 Read File from a url

+

The following link shows the different shapefile data available through the Census Bureau website. Clicking on any of the files will dowload the .zip file unto your computer.

+

This notebook will show a workaround to access the file directly from the notebook, without having to go through the process of previously downloading the shapefile.

+

For this example, we will download the cities for the state of California (cb_2018_06_tract_500k.zip). Remember that California’s State FIPS code is 06, which is how we recognize that this dataset is associated with the State of California.

+
+

Read the data from the url, read it using geopandas and create a subset of only Berkeley places

+

First, we’ll save the data from the url into a variable called ca_places

+
+
+
ca_places = "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_place_500k.zip"
+
+
+
+
+

Now, we’ll use geopandas to read the file and then we’ll visualize it to make sure we read it properly

+
+
+
places = gpd.read_file(ca_places)
+places.plot(); ### This takes a little bit, because the file is fairly large
+
+
+
+
+
+

CONFIRM THAT THIS IS TRUE

+

Notice that there are some spaces inside the boundaries of the state of California that are empty. These are unincorporated areas.

+

However, say we are only interested in the City of Berkeley. Let’s examine the file to see how we could select the polygon fob Berkeley. We’ll take a look at which columns are included in the dataset.

+
+
+
places.head()
+
+
+
+
+

Let’s try filtering by Name

+
+
+
berkeley = places[places['NAME']=='Berkeley']
+berkeley.plot();
+
+
+
+
+

Awesome! This worked! Now we have a polygon with the boundaries of the City of Berkeley.

+

+
+
+
+
+

10.3 Read in file from a API

+

In this section, we will be reading a file using an API, or Application Programming Interface. APIs are very useful, because they allow two different portals to talk to each other. For more information on APIs, take a look here.

+

In this case, we will be using the City of Berkeley Open Data Portal’s API to read in information on the bike network to out notebook.

+

Below you can find more information both on the City of Berkeley’s Open Data portal and on the bike network data.

+
+

Berkeley Open Data portal

+

https://data.cityofberkeley.info/

+
+
+

Berkeley Bike Network data

+

https://data.cityofberkeley.info/Transportation/Bicycle-Boulevards/fgw9-98ic

+

We will be reading the geospatial data for the bike network of the City of Berkeley.

+

As before, first we’ll save the data from the url into a variable called berkeley_bike_ways and then we’ll read it using geopandas.

+
+
+
berkeley_bike_ways = "https://data.cityofberkeley.info/api/geospatial/fgw9-98ic?method=export&format=GeoJSON"
+bikes = gpd.read_file(berkeley_bike_ways)
+
+
+
+
+

Now, we’ll plot the bikeways on top of the City of Berkeley polygon that we imported from the Census Bureau url

+
+
+
fig, ax = plt.subplots(figsize = (10,8)) 
+berkeley.plot(ax=ax)
+bikes.plot(ax=ax)
+plt.show()
+
+
+
+
+

Oops! Where did the bike lanes go? Well, python uses a default color for all plots, so the bike paths were plotted on top of the polygon in the exact same color. Let’s try to plot the bike lanes yellow.

+
+
+
fig, ax = plt.subplots(figsize = (10,8)) 
+berkeley.plot(ax=ax)
+bikes.plot(ax=ax, color="yellow")
+plt.show()
+
+
+
+
+

Now we have a map that shows where the bike network of the City of Berkeley is located.

+

+
+
+
+

10.4 Read in data via a Python library (OSMnx)

+

OSMnx is a Python library that lets you access Open Street Map’s street networks through an API.

+

You can explore more of Open Street Maps here

+

You can access the full documentation of OSMnx here

+
+
+
# Uncomment to install library
+# !pip install osmnx
+
+
+
+
+

If the below cell does not run, you need to install the library first, by uncommmenting and running the cell above

+
+

Note

+

If you get a numpy associated error you may need to uninstall and reinstall numpy as well as set up tools. Run the following lines of code in your terminal: + +pip uninstall -y numpy +pip uninstall -y setuptools +pip install setuptools +pip install numpy

+
+
+
+
import osmnx as ox
+
+
+
+
+

Now we can use the osmnx library to access data from Open Street Maps. Let’s try to load the Berkeley street map. +We are using the graph_from_place function. To see the full documentation for the function, go to this link: https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.graph.graph_from_place.

+

We need to define two arguments for the function: the query and the network type

+
    +

  • Query: For cities in the US, the query should follow the following format: “City Name, State Abbreviation, USA”

    • +

  • Network Type: This is where we define which network we are interested in. Some of the available options are:

    +
      +

    • all

      • +

    • drive

      • +

    • walk

      • +

    • bike

      • +
    +
    • +
+

Let’s try to read the data for the vehicular network for Berkeley

+
+
+
place = "Berkeley, CA,  USA"
+graph = ox.graph_from_place(place, network_type='drive')
+
+
+
+
+

This took a while to read. Let’s take a look at how many elements were loaded from OSM for Berkeley

+
+
+
len(graph)
+
+
+
+
+

Let’s check the data type

+
+
+
type(graph)
+
+
+
+
+

This is a new format. To get this into something that is familiar to us, we are going to extract the nodes and links by using the graph_to_gdfs function, which converts our data from a graph to two geodataframes. Because a street network is made up from nodes and links, and our geodatraframes can only have one geography type, the graph_to_gdfs returns 2 geodataframes: a node (point) and a street (line) geodataframe.

+
+
+
nodes, streets = ox.graph_to_gdfs(graph)
+streets.plot();
+
+
+
+
+

Now, let’s try to put everything together in the same map (the limits of the city, the bike lanes and the streets)

+
+
+
fig, ax = plt.subplots(figsize = (10,8)) 
+berkeley.plot(ax=ax)
+streets.plot(ax=ax, color="grey")
+bikes.plot(ax=ax, color="yellow")
+plt.show()
+
+
+
+
+

Another feature that we can extract form OSMnx is the bus stops. To do this, we use the pois_from_place function (see full documentation here)

+

This function requires two arguments: the query (same as above) and the tag:

+
    +

  • Query: For cities in the US, the query should follow the following format: “City Name, State Abbreviation, USA”

    • +

  • Tag: This is where we define which tags we are interested in. There are many options available. You can find a list of tag features here. These tags are coded as dictionaries. Bus stops are a value defined under the key highway, therefore, the format to call for bus stops looks like this: {‘highway’:’bus_stop’}

    • +
+

Let’s access the bus stops using the same query defined for Berkeley

+
+

Note

+

If you are using an older version of osmnx you would be able to use the function pois_from_place. This and other functions such as footprints_from_place are deprecated as of July 2020. geometries_from_place is meant to replace these functions.

+
+
+
+
### fetch and map POIs from osmnx
+busstops = ox.geometries_from_place(place, tags = {'highway':'bus_stop'})
+
+
+
+
+

Now, let’s check the data type busstops was read as

+
+
+
type(busstops)
+
+
+
+
+

As we can see, busstops is already a geodataframe. Therefore, we can plot it as it is unto out map.

+
+
+
fig, ax = plt.subplots(figsize = (10,8)) 
+berkeley.plot(ax=ax)
+streets.plot(ax=ax, color="grey")
+bikes.plot(ax=ax, color="yellow")
+busstops.plot(ax=ax, color="white")
+plt.show()
+
+
+
+
+

+
+
+

10.5 Exercise

+

Repeat above for SF. The link for accessing the bikeways for SF is already given to you below.

+
+

SF Open Data portal

+

https://datasf.org/opendata/

+
+

SF Bike Network data

+

https://data.sfgov.org/Transportation/SFMTA-Bikeway-Network/ygmz-vaxd

+
+
+
sf_bike_ways = "https://data.sfgov.org/api/geospatial/ygmz-vaxd?method=export&format=GeoJSON"
+
+
+
+
+
+
+
# Your code here
+
+
+
+
+
+
+
+
+

Double-click here to see solution!

+

+
+
+

10.6 Read in Data from a CSV and convert to geodataframe

+

In this example, we’ll learn how to read a csv file with latitude and longitude coordinates and convert it to a geodataframe for plotting.

+
+
+
# Read in CSV file
+stations = pd.read_csv("notebook_data/transportation/bart.csv")
+stations.head()
+
+
+
+
+

We now want to convert the csv file into a Point geodataframe, so we can produce maps and access the geospatial analysis tools.

+

We do this below with the geopandas GeoDataFrame function which takes as input

+
    +

  1. a pandas dataframe here stations, and

    2. +

  2. geometry for each row in the dataframe.

    3. +
+

We create the geometry using the geopandas points_from_xy function, using the data in the lon and lat columns of the pandas dataframe.

+
+
+
#Convert the DataFrame to a GeoDataFrame. 
+bart_gdf = gpd.GeoDataFrame(stations, geometry=gpd.points_from_xy(stations.lon, stations.lat)) 
+
+# and take a look
+bart_gdf.plot();
+
+
+
+
+

Now we have a map of BART stations! You can use this approach with any CSV file that has columns of x,y coordinates.

+
+

10.7 Exercises

+

Set the CRS for bart_gdf to WGS84

+

Below is the url for the 2018 census county geographic boundary file.

+
    +

  • Read in the county file

    • +

  • Subset on Marin County

    • +

  • Plot Marin County with the Bart stations you transformed

    • +

  • Question: what should do if the county name is not unique?

    • +
+
+
+
# Census Counties file for the USA
+county_file      = "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_500k.zip"
+
+
+
+
+
+
+
# Your code here
+
+
+
+
+
+
+
+

Double-click here to see solution!

+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + Lesson 9. On Your Own: A Full Workflow + 11. Adding Basemaps with Contextily + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/11_OPTIONAL_Basemap_with_Contextily.html b/_build/html/lessons/11_OPTIONAL_Basemap_with_Contextily.html new file mode 100644 index 0000000..6dcc0b0 --- /dev/null +++ b/_build/html/lessons/11_OPTIONAL_Basemap_with_Contextily.html @@ -0,0 +1,1078 @@ + + + + + + + 11. Adding Basemaps with Contextily — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ + + + + + +
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+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

11. Adding Basemaps with Contextily

+

If you work with geospatial data in Python, you most likely are familiar with the fantastic GeoPandas library. GeoPandas leverages the power of Matplotlib to enable users to make maps of their data. However, until recently, it has not been easy to add basemaps to these maps. Basemaps are the contextual map data, like Google Maps, on top of which geospatial data are often displayed.

+

The new Python library contextily, which stands for context map tiles, now makes it possible and relatively straight forward to add basemaps to Geopandas maps. Below we walk through a few common workflows for doing this.

+

First, let’s load are libraries. This assumes you have the following Python libraries installed in your environment:

+
    +

  • pandas

    • +

  • matplotlib

    • +

  • geopandas (and all dependancies)

    • +

  • contextily

    • +

  • descartes

    • +
+
+
+
%matplotlib inline
+
+import pandas as pd
+import geopandas as gpd
+import contextily as cx
+import matplotlib.pyplot as plt
+
+
+
+
+
/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.
+  warnings.warn(
+
+
+
+
+
+

Read data into a Geopandas GeoDataFrame

+

Fetch the census places data to map. Census places includes cities and other populated places. Here we fetch the 2019 cartographic boundary (cb_) file of California (06) places.

+
+
+
ca_places = "https://www2.census.gov/geo/tiger/GENZ2019/shp/cb_2019_06_place_500k.zip"
+places = gpd.read_file(ca_places)
+
+
+
+
+

Use the geodatarame plot method to make a quick map.

+
+
+
places.plot();
+
+
+
+
+../_images/11_OPTIONAL_Basemap_with_Contextily_5_0.png +
+
+

Now that we can see those cities, let’s take a look at the data in the geodataframe.

+
+
+
places.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
STATEFPPLACEFPPLACENSAFFGEOIDGEOIDNAMELSADALANDAWATERgeometry
00636490024101021600000US06364900636490Industry2530529397723181POLYGON ((-118.05750 34.01640, -118.05603 34.0...
10640130024116201600000US06401300640130Lancaster25244187339681671POLYGON ((-118.32517 34.75176, -118.32073 34.7...
20675000024119871600000US06750000675000Stockton251610256317985703POLYGON ((-121.41881 38.04418, -121.41801 38.0...
30643000024108661600000US06430000643000Long Beach2513130222275937543MULTIPOLYGON (((-118.12890 33.75801, -118.1273...
40678106024120421600000US06781060678106Tehama2520572100POLYGON ((-122.13364 40.02417, -122.13295 40.0...
+
+
+

We can subset the data by selecting a row or rows by place name. Let’s select the city of Berkeley, CA.

+
+
+
berkeley = places[places['NAME']=='Berkeley']
+
+
+
+
+
+
+
berkeley.plot();
+
+
+
+
+../_images/11_OPTIONAL_Basemap_with_Contextily_10_0.png +
+
+
+
+

Use Contextily to add a basemap

+

Above we can see the map of the boundary of the city of Berkeley, CA. The axis labels display the longitude and latitude coordinates for the bounding extent of the city.

+

Let’s use contextily in it’s most simple form to add a basemap to provide the geographic context for Berkeley.

+
+
+
ax = berkeley.to_crs('EPSG:3857').plot(figsize=(9, 9))
+cx.add_basemap(ax)
+
+
+
+
+../_images/11_OPTIONAL_Basemap_with_Contextily_12_0.png +
+
+

There are a few important things to note about the above code.

+
    +

  • We use matplotlib to define the plot canvas as ax.

    • +

  • We then add the contextily basemap to the map with the code cx.add_basemap(ax)

    • +
+

Additionally, we dynamically transform the coordinate reference system, or CRS, of the Berkeley geodataframe from geographic lat/lon coordinates to web mercator using the method to_crs(‘EPSG:3857’). Web mercator is the default CRS used by all web map tilesets. It is referenced by a the code EPSG:3857 where EPSG stands for the the initials of the organization that created these codes (the European Petroleum Survey Group).

+

Let’s clean up the map by adding some code to change the symbology of the Berkeley city boundary. This will highlight the value of adding a basemap.

+

First, let’s map the boundary with out a fill color.

+
+
+
berkeley.plot(edgecolor="red", facecolor="none")
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/11_OPTIONAL_Basemap_with_Contextily_14_1.png +
+
+

Now, let’s build on those symbology options and add the contextily basemap.

+
+
+
ax = berkeley.to_crs('EPSG:3857').plot(edgecolor="red", 
+                                       facecolor="none", # or a color 
+                                       alpha=0.95,       # opacity value for colors, 0-1
+                                       linewidth=2,      # line, or stroke, thickness
+                                       figsize=(9, 9)
+                                      )
+cx.add_basemap(ax)
+
+
+
+
+../_images/11_OPTIONAL_Basemap_with_Contextily_16_0.png +
+
+
+
+

Mapping Point Data

+

Let’s expand on this example by mapping a point dataset of BART station locations.

+

First we fetch these data from a D-Lab web mapping tutorial.

+
+
+
bart_url = 'https://raw.githubusercontent.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/master/notebook_data/transportation/bart.csv'
+
+
+
+
+
+
+
bart = pd.read_csv(bart_url)
+
+
+
+
+
+
+
bart.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
lonlatSTATIONOPERATORCOUNTY
0-122.28334837.874061NORTH BERKELEYBARTALA
1-122.26824937.869689DOWNTOWN BERKELEYBARTALA
2-122.27011937.853207ASHBYBARTALA
3-122.25177737.844510ROCKRIDGEBARTALA
4-122.26712037.828705MACARTHURBARTALA
+
+
+
+

Converting Point Data in a dataframe to Geospatial Data in a geodataframe

+

Because these data are in a CSV file we read them into a Pandas DataFrame.

+

In order to map these data we need to convert these data to a GeoPandas GeoDataFame. To do this, we need to specify:

+
    +

  • the data, here the geodataframe bart

    • +

  • the coordinate data, here bart['X'] and bart['Y']

    • +

  • the CRS of the bart coordinate data, here EPSG:4326

    • +
+

The CRS code ‘EPSG:4326’ stands for the World Geodectic System of 1984, or WGS84. This is the most commonly used CRS for geographic (lat/lon) coordinate data.

+
+
+
#Convert the DataFrame to a GeoDataFrame. 
+bart_gdf = gpd.GeoDataFrame(bart, geometry=gpd.points_from_xy(bart['lon'], 
+                                                              bart['lat']), 
+                            crs='EPSG:4326') 
+
+# and take a look
+bart_gdf.plot();
+
+
+
+
+../_images/11_OPTIONAL_Basemap_with_Contextily_22_0.png +
+
+

Now that we have the BART data in a geodataframe we can use the same commands as we did above to map it with a contextily basemap.

+
+
+
ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))
+cx.add_basemap(ax)
+
+
+
+
+../_images/11_OPTIONAL_Basemap_with_Contextily_24_0.png +
+
+

We have the full range of matplotlib style options to enhance the map, a few of which are shown in the example below.

+
+
+
ax = bart_gdf.to_crs('EPSG:3857').plot(
+                                    color="red",
+                                    edgecolor="black",
+                                    markersize=50, 
+                                    figsize=(9, 9))
+
+ax.set_title('Bay Area Bart Stations')
+cx.add_basemap(ax)
+
+
+
+
+../_images/11_OPTIONAL_Basemap_with_Contextily_26_0.png +
+
+
+
+
+

Changing the Basemap

+

By default contextiley returns maptiles from the OpenStreetmap Mapnik basemap. However, ther are other available tilesets from different providers. These tilesets are stored in the contextily cx.providers dictionary.

+

That’s a large dictionary and you can view it. Alternatively, and more simply, you can access the list of the providers in this dictionary using the command cs.providers.keys.

+
+
+
# change basemap - can be one of these
+# first see available provider names
+cx.providers.keys()
+
+
+
+
+
dict_keys(['OpenStreetMap', 'OpenSeaMap', 'OpenPtMap', 'OpenTopoMap', 'OpenRailwayMap', 'OpenFireMap', 'SafeCast', 'Thunderforest', 'OpenMapSurfer', 'Hydda', 'MapBox', 'Stamen', 'Esri', 'OpenWeatherMap', 'HERE', 'FreeMapSK', 'MtbMap', 'CartoDB', 'HikeBike', 'BasemapAT', 'nlmaps', 'NASAGIBS', 'NLS', 'JusticeMap', 'Wikimedia', 'GeoportailFrance', 'OneMapSG'])
+
+
+
+
+

Once you have the list of providers, you can find the names of their specific tilesets.

+

Below, we retrieve the list of the tilesets available from the provider CartoDB.

+
+
+
# Then find the names of the tile sets for a specific provider
+cx.providers.CartoDB.keys()
+
+
+
+
+
dict_keys(['Positron', 'PositronNoLabels', 'PositronOnlyLabels', 'DarkMatter', 'DarkMatterNoLabels', 'DarkMatterOnlyLabels', 'Voyager', 'VoyagerNoLabels', 'VoyagerOnlyLabels', 'VoyagerLabelsUnder'])
+
+
+
+
+

Now we can specify a different tileset using the source argument to the add_basemap method.

+
+
+
cx.providers.Esri.keys()
+
+
+
+
+
dict_keys(['WorldStreetMap', 'DeLorme', 'WorldTopoMap', 'WorldImagery', 'WorldTerrain', 'WorldShadedRelief', 'WorldPhysical', 'OceanBasemap', 'NatGeoWorldMap', 'WorldGrayCanvas'])
+
+
+
+
+
+
+
# Change the basemap provider and tileset
+ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))
+cx.add_basemap(ax,  source=cx.providers.NASAGIBS.ModisTerraTrueColorCR)
+
+
+
+
+
/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/contextily/tile.py:632: UserWarning: The inferred zoom level of 11 is not valid for the current tile provider (valid zooms: 1 - 9).
+  warnings.warn(msg)
+
+
+
---------------------------------------------------------------------------
+ConnectionResetError                      Traceback (most recent call last)
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
+    698             # Make the request on the httplib connection object.
+--> 699             httplib_response = self._make_request(
+    700                 conn,
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
+    444                     # Otherwise it looks like a bug in the code.
+--> 445                     six.raise_from(e, None)
+    446         except (SocketTimeout, BaseSSLError, SocketError) as e:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/packages/six.py in raise_from(value, from_value)
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
+    439                 try:
+--> 440                     httplib_response = conn.getresponse()
+    441                 except BaseException as e:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/http/client.py in getresponse(self)
+   1346             try:
+-> 1347                 response.begin()
+   1348             except ConnectionError:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/http/client.py in begin(self)
+    306         while True:
+--> 307             version, status, reason = self._read_status()
+    308             if status != CONTINUE:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/http/client.py in _read_status(self)
+    267     def _read_status(self):
+--> 268         line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+    269         if len(line) > _MAXLINE:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/socket.py in readinto(self, b)
+    703             try:
+--> 704                 return self._sock.recv_into(b)
+    705             except timeout:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/ssl.py in recv_into(self, buffer, nbytes, flags)
+   1240                   self.__class__)
+-> 1241             return self.read(nbytes, buffer)
+   1242         else:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/ssl.py in read(self, len, buffer)
+   1098             if buffer is not None:
+-> 1099                 return self._sslobj.read(len, buffer)
+   1100             else:
+
+ConnectionResetError: [Errno 54] Connection reset by peer
+
+During handling of the above exception, another exception occurred:
+
+ProtocolError                             Traceback (most recent call last)
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
+    438             if not chunked:
+--> 439                 resp = conn.urlopen(
+    440                     method=request.method,
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
+    754 
+--> 755             retries = retries.increment(
+    756                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
+    530             if read is False or not self._is_method_retryable(method):
+--> 531                 raise six.reraise(type(error), error, _stacktrace)
+    532             elif read is not None:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/packages/six.py in reraise(tp, value, tb)
+    733             if value.__traceback__ is not tb:
+--> 734                 raise value.with_traceback(tb)
+    735             raise value
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
+    698             # Make the request on the httplib connection object.
+--> 699             httplib_response = self._make_request(
+    700                 conn,
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
+    444                     # Otherwise it looks like a bug in the code.
+--> 445                     six.raise_from(e, None)
+    446         except (SocketTimeout, BaseSSLError, SocketError) as e:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/packages/six.py in raise_from(value, from_value)
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
+    439                 try:
+--> 440                     httplib_response = conn.getresponse()
+    441                 except BaseException as e:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/http/client.py in getresponse(self)
+   1346             try:
+-> 1347                 response.begin()
+   1348             except ConnectionError:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/http/client.py in begin(self)
+    306         while True:
+--> 307             version, status, reason = self._read_status()
+    308             if status != CONTINUE:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/http/client.py in _read_status(self)
+    267     def _read_status(self):
+--> 268         line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
+    269         if len(line) > _MAXLINE:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/socket.py in readinto(self, b)
+    703             try:
+--> 704                 return self._sock.recv_into(b)
+    705             except timeout:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/ssl.py in recv_into(self, buffer, nbytes, flags)
+   1240                   self.__class__)
+-> 1241             return self.read(nbytes, buffer)
+   1242         else:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/ssl.py in read(self, len, buffer)
+   1098             if buffer is not None:
+-> 1099                 return self._sslobj.read(len, buffer)
+   1100             else:
+
+ProtocolError: ('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))
+
+During handling of the above exception, another exception occurred:
+
+ConnectionError                           Traceback (most recent call last)
+<ipython-input-19-b75b516f4bbf> in <module>
+      1 # Change the basemap provider and tileset
+      2 ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))
+----> 3 cx.add_basemap(ax,  source=cx.providers.NASAGIBS.ModisTerraTrueColorCR)
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/contextily/plotting.py in add_basemap(ax, zoom, source, interpolation, attribution, attribution_size, reset_extent, crs, resampling, url, **extra_imshow_args)
+    141             )
+    142         # Download image
+--> 143         image, extent = bounds2img(
+    144             left, bottom, right, top, zoom=zoom, source=source, ll=False
+    145         )
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/contextily/tile.py in bounds2img(w, s, e, n, zoom, source, ll, wait, max_retries, url)
+    246         x, y, z = t.x, t.y, t.z
+    247         tile_url = _construct_tile_url(provider, x, y, z)
+--> 248         image = _fetch_tile(tile_url, wait, max_retries)
+    249         tiles.append(t)
+    250         arrays.append(image)
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/joblib/memory.py in __call__(self, *args, **kwargs)
+    589 
+    590     def __call__(self, *args, **kwargs):
+--> 591         return self._cached_call(args, kwargs)[0]
+    592 
+    593     def __getstate__(self):
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/joblib/memory.py in _cached_call(self, args, kwargs, shelving)
+    532 
+    533         if must_call:
+--> 534             out, metadata = self.call(*args, **kwargs)
+    535             if self.mmap_mode is not None:
+    536                 # Memmap the output at the first call to be consistent with
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/joblib/memory.py in call(self, *args, **kwargs)
+    759         if self._verbose > 0:
+    760             print(format_call(self.func, args, kwargs))
+--> 761         output = self.func(*args, **kwargs)
+    762         self.store_backend.dump_item(
+    763             [func_id, args_id], output, verbose=self._verbose)
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/contextily/tile.py in _fetch_tile(tile_url, wait, max_retries)
+    301 @memory.cache
+    302 def _fetch_tile(tile_url, wait, max_retries):
+--> 303     request = _retryer(tile_url, wait, max_retries)
+    304     with io.BytesIO(request.content) as image_stream:
+    305         image = Image.open(image_stream).convert("RGBA")
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/contextily/tile.py in _retryer(tile_url, wait, max_retries)
+    444     """
+    445     try:
+--> 446         request = requests.get(tile_url, headers={"user-agent": USER_AGENT})
+    447         request.raise_for_status()
+    448     except requests.HTTPError:
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/api.py in get(url, params, **kwargs)
+     74 
+     75     kwargs.setdefault('allow_redirects', True)
+---> 76     return request('get', url, params=params, **kwargs)
+     77 
+     78 
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/api.py in request(method, url, **kwargs)
+     59     # cases, and look like a memory leak in others.
+     60     with sessions.Session() as session:
+---> 61         return session.request(method=method, url=url, **kwargs)
+     62 
+     63 
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
+    540         }
+    541         send_kwargs.update(settings)
+--> 542         resp = self.send(prep, **send_kwargs)
+    543 
+    544         return resp
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/sessions.py in send(self, request, **kwargs)
+    653 
+    654         # Send the request
+--> 655         r = adapter.send(request, **kwargs)
+    656 
+    657         # Total elapsed time of the request (approximately)
+
+/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
+    496 
+    497         except (ProtocolError, socket.error) as err:
+--> 498             raise ConnectionError(err, request=request)
+    499 
+    500         except MaxRetryError as e:
+
+ConnectionError: ('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))
+
+
+../_images/11_OPTIONAL_Basemap_with_Contextily_33_2.png +
+
+
+
+

Learning More

+

Above, we prove a very short introduction to the excellent contextily library. You can find more detailed information on the contextily homepage, available at: https://github.com/geopandas/contextily. We especially encourage you to check out the notebook examples provided in that github repo.

+
+
+ + + + +
+ + +
+ + 10. Read in Data from Online Sources + CSV to Geodataframe + 12. Interactive Mapping with Folium + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.html b/_build/html/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.html new file mode 100644 index 0000000..a56b539 --- /dev/null +++ b/_build/html/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.html @@ -0,0 +1,2187 @@ + + + + + + + 12. Interactive Mapping with Folium — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

12. Interactive Mapping with Folium

+

In previous lessons we used Geopandas and matplotlib to create choropleth and point maps of our data. In this notebook we will take it to the next level by creating interactive maps with the folium library.

+
+
+
+

References

+

This notebook provides an introduction to folium. To see what else you can do, check out the references listed below.

+ +
+
+

Import Libraries

+
+
+
import pandas as pd
+import geopandas as gpd
+import numpy as np
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+import folium # popular python web mapping tool for creating Leaflet maps
+import folium.plugins
+
+# Supress minor warnings about the syntax of CRS definitions, 
+# ie "init=epsg:4269" vs "epsg:4269"
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+
+
+
+
/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.
+  warnings.warn(
+
+
+
+
+
+

Check your version of folium and geopandas.

+

Folium is a new and evolving Python library so make sure you have version 0.10.1 or later installed.

+
+
+
print(folium.__version__) # Make sure you have version 0.10.1 or later of folium!
+
+
+
+
+
unknown
+
+
+
+
+
+
+
print(gpd.__version__) # Make sure you have version 0.7.0 or later of GeoPandas!
+
+
+
+
+
0.9.0
+
+
+
+
+
+
+
+

12.1 Introduction

+

Interactive maps serve two very important purposes in geospatial analysis. First, they provde new tools for exploratory data analysis. With an interactive map you can:

+
    +

  • pan over the mapped data,

    • +

  • zoom into a smaller arear that is not easily visible when the full extent of the map is displayed, and

    • +

  • click on or hover over a feature to see more information about it.

    • +
+

Second, when saved and shared, interactive maps provide a new tool for communicating the results of your analysis and for inviting your online audience to actively explore your work.

+

For those of you who work with tools like ArcGIS or QGIS, interactive maps also make working in the jupyter notebook environment a bit more like working in a desktop GIS.

+

The goal of this notebook is to show you how to create an interactive map with your geospatial data so that you can better analyze your data and save your output to share with others.

+

After completing this lesson you will be able to create an interactive map like the one shown below.

+
+
+
%%html
+<iframe src="notebook_data/bartmap_example.html" width="1000" height="600"></iframe>
+
+
+
+
+
+
+
+

+
+
+

12.2 Interactive Mapping with Folium

+

Under the hood, folium is a Python package for creating interactive maps with Leaflet, a popular javascript web mapping library.

+

Let’s start by creating a interactive map with the folium.Map function and display it in the notebook.

+
+
+
# Create a new folium map and save it to the variable name map1
+map1 = folium.Map(location=[37.8721, -122.2578],   # lat, lon around which to center the map
+                 width="100%",                     # the width & height of the output map
+                 height=500,                       # in pixels (int) or in percent of available space (str)
+                 zoom_start=13)                    # the zoom level for the data to be displayed (3-20)
+
+map1  # display the map in the notebook
+
+
+
+
+
Make this Notebook Trusted to load map: File -> Trust Notebook
+
+

Let’s discuss the map above and the code we used to generate it.

+

At any time you can enter the following command to get help with folium.Map:

+
+
+
# uncomment to see help docs
+?folium.Map
+
+
+
+
+

Let’s make another folium map using the code below:

+
+
+
# Create a new folium map and save it to the variable name map1
+#
+map1 = folium.Map(location=[37.8721, -122.2578],   # lat, lon around which to center the map
+                 tiles='CartoDB Positron',
+                 #width=800,                        # the width & height of the output map
+                 #height=600,                       # in pixels or in percent of available space
+                 zoom_start=13)                    # the zoom level for the data to be displayed
+
+
+
+
+
+ +
+
+
+

Questions

+
+
    +

  • What’s new in the code?

    • +

  • How do you think that will change the map?

    • +
+

Let’s display the map and see what changes…

+
+
+
map1  # display map in notebook
+
+
+
+
+
Make this Notebook Trusted to load map: File -> Trust Notebook
+
+

Notice how the map changes when you change the underlying tileset from the default, which is OpenStreetMap, to CartoDB Positron.

+
+

OpenStreetMap is the largest free and open source dataset of geographic information about the world. So it is the default basemap for a lot of mapping tools and libraries.

+
+
    +

  • You can find a list of the available tilesets you can use in the help documentation (folium.Map?), a snippet of which is shown below:

    • +
+
+Generate a base map of given width and height with either default
+tilesets or a custom tileset URL. The following tilesets are built-in
+to Folium. Pass any of the following to the "tiles" keyword:
+
+    - "OpenStreetMap"
+    - "Mapbox Bright" (Limited levels of zoom for free tiles)
+    - "Mapbox Control Room" (Limited levels of zoom for free tiles)
+    - "Stamen" (Terrain, Toner, and Watercolor)
+    - "Cloudmade" (Must pass API key)
+    - "Mapbox" (Must pass API key)
+    - "CartoDB" (positron and dark_matter)
+
+
+
+

Exercise

+

Take a few minutes to try some of the different tilesets in the code below and see how they change the output map. Avoid the ones that don’t require an API key.

+
+
+
# Make changes to the code below to change the folium Map
+## Try changing the values for the zoom_start and tiles parameters.
+map1 = folium.Map(location=[37.8721, -122.2578],   # lat, lon around which to center the map
+                 tiles='Stamen Watercolor',         # basemap aka baselay or tile set
+                 width=800,                        # the width & height of the output map
+                 height=500,                       # in pixels or percent of available space
+                 zoom_start=13)                    # the zoom level for the data to be displayed
+
+#display the map
+map1
+
+
+
+
+

+
+
+
+

12.3 Adding a Map Layer

+

Now that we have created a folium map, let’s add our California County data to the map.

+

First, let’s read that data into a Geopandas geodataframe.

+
+
+
# Alameda county census tract data with the associated ACS 5yr variables.
+ca_counties_gdf = gpd.read_file("notebook_data/california_counties/CaliforniaCounties.shp")
+
+
+
+
+

Take another brief look at the geodataframe to recall the contents.

+
+
+
# take a look at first two rows
+ca_counties_gdf.head(2)
+
+
+
+
+
+
+
# take a look at all column names
+ca_counties_gdf.columns
+
+
+
+
+
+

Adding a layer with folium.GeoJson

+

Folium provides a number of ways to add vector data - points, lines, and polygons - to a map.

+

The data we are working with are in Geopandas geodataframes. The main folium function for adding these to the map is folium.GeoJson.

+

Let’s build on our last map and add the census tracts as a folium.GeoJson layer.

+
+
+
map1 = folium.Map(location=[37.8721, -122.2578],   # lat, lon around which to center the map
+                 tiles='CartoDB positron',         # basemap aka baselay or tile set
+                 width=800,                       # the width & height of the output map
+                 height=600,                      # in pixels or in percent of available space
+                 zoom_start=6)                    # the zoom level for the data to be displayed
+
+# Add the census tracts to the map
+folium.GeoJson(ca_counties_gdf).add_to(map1)
+
+#display the map
+map1
+
+
+
+
+

That was pretty straight-forward, but folium.GeoJSON provides a lot of arguments for customizing the display of the data in the map. We will review some of these soon. However, at any time you can get more information about folium.GeoJSON by taking a look at the function documentation.

+
+
+
# Uncomment to view documentation
+# folium.GeoJson?
+
+
+
+
+
+
+

Checking and Transforming the CRS

+

It’s always a good idea to check the CRS of your geodata before doing anything with that data. This is true when we use folium to make an interactive map.

+

Here is how folium deals with the CRS of a geodataframe before mapping it:

+
    +

  • Folium checks to see if the gdf has a defined CRS

    +
      +

    • If the CRS is not defined, it assumes the data to be in the WGS84 CRS (epsg=4326).

      • +

    • If the CRS is defined, it will be transformed dynamically to WGS84 before mapping.

      • +
    +
    • +
+

So, if your map data doesn’t show up where at all or where you think it should, check the CRS of your data!

+
    +

  • If it is not defined, define it.

    • +
+
+ +
+
+
+

Questions

+
+
    +

  • What is the CRS of the tract data?

    • +

  • How is folium dealing with the CRS of this gdf?

    • +
+
+
+
# Check the CRS of the data 
+print(...)
+
+
+
+
+

Click here for answers

+
+
+
+

Styling features with folium.GeoJson

+

Let’s dive deeper into the folium.GeoJson function. Below is an excerpt from the help documentation for the function that shows all the available function arguments that we can set.

+
+ +
+
+
+

Question

+
+What argument do we use to style the color for our polygons? +
+folium.GeoJson(
+    data,
+    style_function=None,
+    highlight_function=None,
+    name=None,
+    overlay=True,
+    control=True,
+    show=True,
+    smooth_factor=None,
+    tooltip=None,
+    embed=True,
+)
+

Let’s examine the options for the style_function in more detail since we will use these to change the style of our mapped data.

+

style_function = lambda x: { apply to all features being mapped (ie, all rows in the geodataframe)
+'weight': line_weight, set the thickness of a line or polyline where <1 is thin, >1 thick, 1 = default
+'opacity': line_opacity, set opacity where 1 is solid, 0.5 is semi-opaque and 0 is transparent
+'color': line_color set the color of the line, eg “red” or some hexidecimal color value +'fillOpacity': opacity, set opacity of the fill of a polygon
+'fillColor': color set color of the fill of a polygon
+'dashArray': '5, 5' set line pattern to a dash of 5 pixels on, off
+}

+

Ok! Let’s try setting the style of our census tract by defining a style function.

+
+
+
# Define the basemap
+map1 = folium.Map(location=[37.8721, -122.2578],           # lat, lon around which to center the map
+                 tiles='CartoDB Positron',
+                 width=1000,                       # the width & height of the output map
+                 height=600,                       # in pixels
+                 zoom_start=6)                    # the zoom level for the data to be displayed
+
+# Add  the census tracts gdf layer
+# setting the style of the data
+folium.GeoJson(ca_counties_gdf,
+               style_function = lambda x: {
+                   'weight':2,
+                   'color':"white",
+                   'opacity':1,
+                   'fillColor':"red",
+                   'fillOpacity':0.6
+               }
+              ).add_to(map1)
+
+
+map1
+
+
+
+
+
+
+

Exercise

+

Copy the code from our last map and paste it below. Take a few minutes edit the code to change the style of the census tract polygons.

+
+
+
# Your code here
+map1 = folium.Map(location=[37.8721, -122.2578],           # lat, lon around which to center the map
+                 tiles='Stamen Watercolor',
+                 width=1000,                       # the width & height of the output map
+                 height=600,                       # in pixels
+                 zoom_start=10)                    # the zoom level for the data to be displayed
+
+# Add  the census tracts gdf layer
+# setting the style of the data
+folium.GeoJson(ca_counties_gdf,
+               style_function = lambda x: {
+                   'weight':3,
+                   'color':"black",
+                   'opacity':1,
+                   'fillColor':"none",
+                   'fillOpacity':0.6
+               }
+              ).add_to(map1)
+
+
+map1
+
+
+
+
+
+
+
+

Adding a Tooltip

+

A tooltip can be added to a folium.GeoJson map layer to display data values when the mouse hovers over a feature.

+
+
+
# Double check what columns we have
+ca_counties_gdf.columns
+
+
+
+
+
+
+
?folium.GeoJsonTooltip
+
+
+
+
+
+
+
# Define the basemap
+map1 = folium.Map(location=[37.8721, -122.2578],           # lat, lon around which to center the map
+                 tiles='CartoDB Positron',
+                 width=1000,                       # the width & height of the output map
+                 height=600,                       # in pixels
+                 zoom_start=6)                    # the zoom level for the data to be displayed
+
+# Add  the census tracts gdf layer
+folium.GeoJson(ca_counties_gdf,
+               style_function = lambda x: {
+                   'weight':2,
+                   'color':"white",
+                   'opacity':1,
+                   'fillColor':"red",
+                   'fillOpacity':0.6
+               },
+               
+               tooltip=folium.GeoJsonTooltip(
+                   fields=['NAME','POP2012','POP12_SQMI' ], 
+                   aliases=['County', 'Population', 'Population Density (mi2)'],
+                   labels=True,
+                   localize=True
+               ),
+              ).add_to(map1)
+
+
+map1
+
+
+
+
+

As always, you can get more help by reading the documentation.

+
+
+
# Uncomment to view help
+#folium.GeoJsonTooltip?
+
+
+
+
+
+

Exercise

+

Edit the code in the cell below to add the median age(MED_AGE) to the tooltip.

+
+
+
# Define the basemap
+map1 = folium.Map(location=[37.8721, -122.2578],           # lat, lon around which to center the map
+                 tiles='CartoDB Positron',
+                 width=1000,                       # the width & height of the output map
+                 height=600,                       # in pixels
+                 zoom_start=6)                    # the zoom level for the data to be displayed
+
+# Add  the census tracts gdf layer
+folium.GeoJson(ca_counties_gdf,
+               style_function = lambda x: {
+                   'weight':2,
+                   'color':"white",
+                   'opacity':1,
+                   'fillColor':"red",
+                   'fillOpacity':0.6
+               },
+               
+               tooltip=folium.GeoJsonTooltip(
+                   fields=['NAME','POP2012','POP12_SQMI','MED_AGE' ], 
+                   aliases=['County', 'Population', 'Population Density (mi2)', 'Median Age'],
+                   labels=True,
+                   localize=True
+               ),
+              ).add_to(map1)
+
+
+map1
+
+
+
+
+

Click here for answers

+

+
+
+
+
+

12.4 Data Mapping

+

Above, we set the style for all of the census tracts to the same fill and outline colors and opacity values.

+

Let’s take a look at how we would use the data values to set the color values for the polygons. This is called a choropleth map or, more generally, a thematic map.

+

The folium.Choropleth function can be used for this.

+
+
+
# Uncomment to view help docs
+## folium.Choropleth?
+
+
+
+
+

With folium.Choropleth, we will use some of the same style parameters that we used with folium.GeoJson.

+

We will also use some new parameters, as shown below.

+

First, let’s take a look at the data we will map to refresh our knowledge.

+
+
+
print(ca_counties_gdf.columns)
+ca_counties_gdf.head(2)
+
+
+
+
+

Now let’s create a choropleth map of total population, which is in the c_race column.

+
+
+
ca_counties_gdf.head()
+
+
+
+
+
+
+
# Define the basemap
+map2 = folium.Map(location=[37.8721, -122.2578],           # lat, lon around which to center the map
+                 tiles='CartoDB Positron',
+                 width=1000,                       # the width & height of the output map
+                 height=600,                       # in pixels
+                 zoom_start=6)                    # the zoom level for the data to be displayed
+
+
+# Add the Choropleth layer
+folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),   # The object with the geospatial data
+           data=ca_counties_gdf,                                 # The object with the attribute data (can be same)
+           columns=['NAME','POP2012'],                      # the ID and data columns in the data objects
+           key_on="feature.id",                             # the ID in the geo_data object (don't change)
+           fill_color="Reds",                               # The color palette (or color map) - see help
+           fill_opacity=0.65,
+           line_color="grey",
+           legend=True,
+           legend_name="Population",
+          ).add_to(map2)
+
+# Display the map
+map2  
+
+
+
+
+
+

Choropleth Mapping with Folium - discussion

+

Let’s discuss the following lines from the code above in more detail.

+
+# Add the Choropleth layer
+folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),
+           data=ca_counties_gdf, 
+           columns=['NAME','POP2012'],
+           key_on="feature.id",
+           fill_color="Reds",                               
+           ...)
+
+
+
+

geo_data and the data: we need to identify the objects that contains both because they could be different objects. In our example they are in the same object.

+

ca_counties_gdf.set_index('NAME'): We need to set_index(‘NAME’) in order to identify the column in geo_data that will be used to join the geometries in the geo_data to the data values in data.

+

columns=['NAME','POP2012']: we identify in data (1) the column that will join these data to geo_data and (2) the second column is the column with the values that will determine the color.

+

fill_color="Reds": Here we identify the name of the color palette that we will use to style the polygons. These will be the same as the matplotlib colormaps.

+
+

Question

+

Recall our discussion about best practices for choropleth maps. Is population count an appropriate variable to plot as a choropleth?

+
+
+
# Write your thoughts here
+
+
+
+
+
+
+

Exercise

+

Copy and paste the code from above into the cell below to create a choropleth map of population density (POP12_SQMI).

+

Feel free to experiment with any of the folium.Choropleth style parameters, especially the fill_color which needs to be one of the color brewer palettes listed below:

+
+fill_color: string, default 'blue'
+    Area fill color. Can pass a hex code, color name, or if you are
+    binding data, one of the following color brewer palettes:
+    'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
+    'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
+
+
+
# Your code here
+# Define the basemap
+map2 = folium.Map(location=[37.7749, -122.4194],           # lat, lon around which to center the map
+                 tiles='Stamen Toner',
+                 width=1000,                       # the width & height of the output map
+                 height=600,                       # in pixels
+                 zoom_start=10)                    # the zoom level for the data to be displayed
+
+
+# Add the Choropleth layer 
+folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),   # The object with the geospatial data
+           data=ca_counties_gdf,                                 # The object with the attribute data (can be same)
+           columns=['NAME','POP12_SQMI'],                      # the ID and data columns in the data objects
+           key_on="feature.id",                             # the ID in the geo_data object (don't change)
+           fill_color="RdPu",                               # The color palette (or color map) - see help
+           fill_opacity=0.8).add_to(map2)
+
+map2
+
+
+
+
+

Click here for answers

+
+
+
+

Choropleth Maps with Tooltips

+

You can add a tooltip to a folium.Choropleth map but the process is not straigthforward. The folium.Choropleth function does not have a tooltip argument the way folium.GeoJson does.

+

The workaround is to add the layer as both a folium.Choropleth layer and as a folium.GeoJson layer and bind the tooltip to the GeoJson layer.

+

Let’s check it out below.

+
+
+
# Define the basemap
+map3 = folium.Map(location=[37.8721, -122.2578],           # lat, lon around which to center the map
+                 tiles='CartoDB Positron',
+                 width=1000,                       # the width & height of the output map
+                 height=600,                       # in pixels
+                 zoom_start=6)                    # the zoom level for the data to be displayed
+
+
+# Add the Choropleth layer
+folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),   # The object with the geospatial data
+           data=ca_counties_gdf,                                 # The object with the attribute data (can be same)
+           columns=['NAME','POP2012'],                      # the ID and data columns in the data objects
+           key_on="feature.id",                             # the ID in the geo_data object (don't change)
+           fill_color="Reds",                               # The color palette (or color map) - see help
+           fill_opacity=0.65,
+           line_color="grey",
+           legend=True,
+           legend_name="Population",
+          ).add_to(map3)
+
+# ADD the same geodataframe to the map to display a tooltip
+layer2 = folium.GeoJson(ca_counties_gdf,
+    style_function=lambda x: {'color':'transparent','fillColor':'transparent'},
+    tooltip=folium.GeoJsonTooltip(
+        fields=['NAME','POP2012'], 
+        aliases=['County', 'Population'],
+        labels=True,
+        localize=True
+    ),
+    highlight_function=lambda x: {'weight':3,'color':'white'}
+).add_to(map3)
+
+
+
+map3  # show map
+
+
+
+
+
+

Question

+

Do you notice anything different about the style_function for layer2 above?

+
+
+

Exercise

+

Redo the above choropleth map code to map population density. Add both population and population density to the tooltip. Don’t forget to update the legend name.

+
+
+
# Your code here
+
+
+
+
+

+
+
+
+
+

12.5 Overlays

+

We can overlay other geospatial data on our folium maps.

+

Let’s say we want to focus the previous choropleth map with tooltips (map3) on the City of Berkeley. We can fetch the border of the city from our census Places dataset. These data can be downloaded from the Census website. We use the cartographic boundary files not the TIGER line files as these look better on a map (clipped to shoreline).

+

Specifically, we will fetch the city boundaries from the following census cartographic boundary file:

+ +

Then we can overlay the border of the city on the map and set the initial zoom to the center of the Berkeley boundary.

+

Let’s try that.

+

First we need to read in the census places data and create a subset geodataframe for our city of interest, here Berkeley.

+
+
+
places = gpd.read_file("zip://notebook_data/census/Places/cb_2018_06_place_500k.zip")
+
+
+
+
+
+
+
places.head(2)
+
+
+
+
+
+
+
berkeley = places[places.NAME=='Berkeley'].copy()
+berkeley.head(2)
+
+
+
+
+

Plot the Berkeley geodataframe to make sure it looks ok.

+
+
+
berkeley.plot()
+
+
+
+
+
+
+
# Create a new map centered on Berkeley
+berkeley_map = folium.Map(location=[berkeley.centroid.y.mean(), 
+                                    berkeley.centroid.x.mean()], 
+                  tiles='CartoDB Positron',
+                  width=800,height=600,
+                  zoom_start=13)
+
+
+# Add the census tract polygons as a choropleth map
+layer1=folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),
+           data=ca_counties_gdf,
+           columns=['NAME','POP2012'],
+           fill_color="Reds",
+           fill_opacity=0.65,
+           line_color="grey", #"white",
+           line_weight=1,
+           line_opacity=1,
+           key_on="feature.id",
+           legend=True,
+           legend_name="Population",
+           highlight=True
+          ).add_to(berkeley_map)
+
+# Add the berkeley boundary - note the fill color
+layer2 = folium.GeoJson(data=berkeley,
+               name='Berkeley',smooth_factor=2,
+               style_function=lambda x: {'color':'black',
+                                         'opacity':1,
+                                         'fillColor':
+                                         'transparent',
+                                         'weight':3},
+               ).add_to(berkeley_map)
+
+# Add the tooltip for the census tracts as its own layer
+layer3 = folium.GeoJson(ca_counties_gdf,
+    style_function=lambda x: {'color':'transparent','fillColor':'transparent'},
+    tooltip=folium.features.GeoJsonTooltip(
+        fields=['NAME','POP2012'], 
+        aliases=['County', 'Population'],
+        labels=True,
+        localize=True
+    ),
+    highlight_function=lambda x: {'weight':3,'color':'white'}
+).add_to(berkeley_map)
+
+berkeley_map  # show map
+
+
+
+
+
+ +
+
+
+

Questions

+
+

Any questions about the above map?

+

Does the code for the Berkeley map above differ from our previous choropleth map code?

+

Does the order of layer2 & layer3 matter (can they be switched?)

+
+
+

Exercise

+

Redo the above map with population density. Create and display the Oakland city boundary on the map instead of Berkeley and center the map on Oakland.

+
+
+
# Your code here
+
+
+
+
+

Click here for solution

+

+
+
+
+

12.6 Mapping Points and Lines

+

We can also add points and lines to a folium map.

+

Let’s overlay BART stations as points and BART lines as lines to the interactive map. For the Bay Area these are data are available from the Metropoliton Transportation Commission (MTC) Open Data portal.

+

We’re going to try pulling in BART station data that we downloaded from the website and subsetted from the passenger-rail-stations. You can learn more about the dataset through here: http://opendata.mtc.ca.gov/datasets/passenger-rail-stations-2019

+

As usual, let’s try pulling in the data and inspect the first couple of rows.

+
+
+
# Load light rail stop data
+railstops = gpd.read_file("zip://notebook_data/transportation/Passenger_Rail_Stations_2019.zip")  
+railstops.tail()
+
+
+
+
+
+
+
# Subset to keep just bart stations
+bart_stations = railstops[railstops['agencyname']=='BART'].sort_values(by="station_na")
+bart_stations.head()
+
+
+
+
+
+
+
# Repeat for the rail lines
+rail_lines = gpd.read_file("zip://notebook_data/transportation/Passenger_Railways_2019.zip")  
+rail_lines.head()
+
+
+
+
+
+
+
rail_lines.operator.value_counts()
+
+
+
+
+
+
+
# subset by operator to get the bart lines
+bart_lines = rail_lines[rail_lines['operator']=='BART']
+
+
+
+
+
+
+
# Check the CRS of the geodataframes
+print(bart_stations.crs)
+print(bart_lines.crs)
+
+
+
+
+
+
+
# Quick plot
+bart_stations.plot()
+bart_lines.plot()
+
+
+
+
+

Now that we have fetched and checked the Bart data, let’s do a quick folium map with it.

+

We will use folium.GeoJson to add these data to the map, just as we used it previously for the census tract polygons.

+
+
+
# Bart Map
+map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], 
+                  tiles='CartoDB Positron',
+                  width=800,height=600,
+                  zoom_start=10)
+
+
+folium.GeoJson(bart_lines).add_to(map4)
+
+folium.GeoJson(bart_stations).add_to(map4)
+
+
+map4  # show map
+
+
+
+
+

We can also add tooltips, just as we did previously.

+
+
+
# Bart Map
+map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], 
+                  tiles='CartoDB Positron',
+                  #width=800,height=600,
+                  zoom_start=10)
+
+# Add Bart lines
+folium.GeoJson(bart_lines,
+               tooltip=folium.GeoJsonTooltip(
+                   fields=['operator' ],
+                   aliases=['Line operator'],
+                   labels=True,
+                   localize=True
+               ),
+              ).add_to(map4)
+
+# Add Bart stations
+folium.GeoJson(bart_stations,
+              tooltip=folium.GeoJsonTooltip(fields=['ts_locatio'], 
+                   aliases=['Stop Name'],
+                   labels=True,
+                   localize=True
+               ),
+              ).add_to(map4)
+
+
+map4  # show map
+
+
+
+
+

That’s pretty cool, but don’t you just want to click on those marker points to get a popup rather than hovering over for a tooltip?

+
+

Mapping Points

+

So far we have used folium.GeoJson to map our BART points. By default this uses the push-pin marker symbology made popular by Google Maps.

+

Under the hood, folium.GeoJson uses the default object type folium.Marker when the input data are points.

+

This is helpful to know because folium.Marker has a few options that allow further customization of our points.

+
+
+
# Uncomment to view help docs
+folium.Marker?
+
+
+
+
+

Let’s explicitly add the Bart Stations as points so we can change the tooltips to popups.

+
+
+
# Bart Map
+map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], 
+                  tiles='CartoDB Positron',
+                  #width=800,height=800,
+                  zoom_start=10)
+
+# Add Bart lines
+folium.GeoJson(bart_lines,
+               tooltip=folium.GeoJsonTooltip(
+                   fields=['operator' ],
+                   aliases=['Line operator'],
+                   labels=True,
+                   localize=True
+               ),
+              ).add_to(map4)
+
+# Add Bart stations
+bart_stations.apply(lambda row:
+                        folium.Marker(
+                                  location=[row['geometry'].y, row['geometry'].x],
+                                  popup=row['ts_locatio'],
+                                 ).add_to(map4), axis=1)
+
+map4  # show map
+
+
+
+
+

That folium.Marker code is a bit more complex than folium.GeoJson and may not be worth it unless you really want that popup behavior.

+

But let’s see what else we can do with a folium.Marker by viewing the next map.

+
+
+
# Bart Map
+map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], 
+                  tiles='CartoDB Positron',
+                  #width=800,height=600,
+                  zoom_start=10)
+
+# Add BART lines
+folium.GeoJson(bart_lines,
+               tooltip=folium.GeoJsonTooltip(
+                   fields=['operator' ],
+                   aliases=['Line operator'],
+                   labels=True,
+                   localize=True
+               ),
+              ).add_to(map4)
+
+# Add BART Stations
+icon_url = "https://gomentumstation.net/wp-content/uploads/2018/08/Bay-area-rapid-transit-1000.png"
+bart_stations.apply(lambda row:
+                        folium.Marker(
+                                  location=[row['geometry'].y,row['geometry'].x],
+                                  popup=row['ts_locatio'],
+                                  icon=folium.features.CustomIcon(icon_url,icon_size=(20, 20)),
+                                 ).add_to(map4), axis=1)
+
+map4  # show map
+
+
+
+
+
+

Exercise

+

Copy and paste the code for the previous cell into the next cell and

+
    +

  1. change the bart icon to “https://ya-webdesign.com/transparent450_/train-emoji-png-14.png”

    2. +

  2. change the popup back to a tooltip.

    3. +
+
+
+
# Your code here
+
+
+
+
+

Click here for solution

+
+
+
+

folium.CircleMarkers

+

You may prefer to customize points as CircleMarkers instead of the icon or pushpin Marker style. This allows you to set size and color of a marker, either manually or as a function of a data variable.

+

Let’s look at some code for doing this.

+
+
+
# Define the basemap
+map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()],   # lat, lon around which to center the map
+                 tiles='CartoDB Positron',
+                 #width=1000,                        # the width & height of the output map
+                 #height=600,                       # in pixels
+                 zoom_start=10)                    # the zoom level for the data to be displayed
+
+# Add BART Lines
+folium.GeoJson(bart_lines).add_to(map5)
+
+
+# Add BART Stations
+bart_stations.apply(lambda row:
+                        folium.CircleMarker(
+                                  location=[row['geometry'].y, row['geometry'].x],
+                                  radius=10,
+                                  color='purple',
+                                  fill=True,
+                                  fill_color='purple',
+                                  popup=row['ts_locatio'],
+                                 ).add_to(map5), 
+                         axis=1)
+
+
+map5
+
+
+
+
+
+
+

folium.Circle

+

You can also set the size of your circles to a fixed radius, in meters, using folium.Circle. This is great for exploratory data analysis. For example, you can see what the census tract values are within 500 meters of a BART station.

+
+
+
# Uncomment to view
+#?folium.Circle
+
+
+
+
+
+
+
# Define the basemap
+map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()],   # lat, lon around which to center the map
+                 tiles='CartoDB Positron',
+                 #width=1000,                        # the width & height of the output map
+                 #height=600,                       # in pixels
+                 zoom_start=10)                    # the zoom level for the data to be displayed
+
+# Add BART Lines
+folium.GeoJson(bart_lines).add_to(map5)
+
+
+# Add BART Stations
+bart_stations.apply(lambda row:
+                        folium.Circle(
+                                  location=[row['geometry'].y, row['geometry'].x],
+                                  radius=500,
+                                  color='purple',
+                                  fill=True,
+                                  fill_color='purple',
+                                  popup=row['ts_locatio'],
+                                 ).add_to(map5), 
+                         axis=1)
+
+
+map5
+
+
+
+
+
+ +
+
+
+

Question

+
+

What do you notice about the size of the circles as you zoom in/out when you compare folium.Circles and folium.CircleMarkers?

+
+
+
+

Proportional Symbol Maps

+

One of the advantages of the folium.CircleMarker is that we can set the size of the map to vary based on a data value.

+

To give this a try, let’s add a fake column to the bart_stations gdf called millions_served and set it to a value between 1 and 10.

+
+
+
# add a column to the bart stations gdf
+bart_stations['millions_served'] = np.random.randint(1,10, size=len(bart_stations))
+bart_stations.head()
+
+
+
+
+
+
+
# Define the basemap
+map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()],
+                 tiles='CartoDB Positron',
+                 #width=1000,                        # the width & height of the output map
+                 #height=600,                       # in pixels
+                 zoom_start=10)                    # the zoom level for the data to be displayed
+
+folium.GeoJson(bart_lines).add_to(map5)
+
+# Add BART Stations as CircleMarkers
+# Here, some knowlege of Python string formatting is useful
+bart_stations.apply(lambda row:
+                        folium.CircleMarker(
+                                  location=[row['geometry'].y, row['geometry'].x],
+                                  radius=row['millions_served'],
+                                  color='purple',
+                                  fill=True,
+                                  fill_color='purple',
+                                  tooltip = "Bart Station: %s<br>Millions served: %s" % (row['ts_locatio'], row['millions_served'])
+                    
+                                 ).add_to(map5), axis=1)
+map5
+
+
+
+
+

So if you hover over our BART stations, you see that we’ve formatted it nicely! Using some HTML and Python string formatting we can make our tooltip easier to read.

+

If you want to learn more about customizing these, you can go check this out to learn HTML basics. You can then go here to learn about Python string formatting.

+

+
+
+
+

12.7 Creating and Saving a folium Interactive Map

+

Now that you have seen most of the ways you can add a geodataframe to a folium map, let’s create one big map that includes several of our geodataframes.

+

To control the display of the data layers, we will add a folium.LayerControl

+
    +

  • A folium.LayerControl will allow you to toggle on/off a map’s visible layers.

    • +

  • In order to add a layer to the LayerControl, the layer must have value set for its name.

    • +
+

Let’s take a look.

+
+
+
# Create a new map centered on the census tract data
+map6 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], 
+                  tiles='CartoDB Positron',
+                  #width=800,height=600,
+                  zoom_start=10)
+
+# Add the counties polygons as a choropleth map
+layer1=folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),
+           data=ca_counties_gdf,
+           columns=['NAME','POP2012'],
+           fill_color="Reds",
+           fill_opacity=0.65,
+           line_color="grey", #"white",
+           line_weight=1,
+           line_opacity=1,
+           key_on="feature.id",
+           legend=True,
+           legend_name="Population",
+           highlight=True,
+           name="Counties"
+          ).add_to(map6)
+
+# Add the tooltip for the counties as its own layer
+# Don't display in the Layer control!
+layer2 = folium.GeoJson(ca_counties_gdf,
+    style_function=lambda x: {'color':'transparent','fillColor':'transparent'},
+    tooltip=folium.features.GeoJsonTooltip(
+        fields=['NAME','POP2012'], 
+        aliases=['Name', 'Population'],
+        labels=True,
+        localize=True
+    ),
+    highlight_function=lambda x: {'weight':3,'color':'white'}
+).add_to(layer1.geojson)
+
+# Add Bart lines
+folium.GeoJson(bart_lines,
+               name="Bart Lines",
+               tooltip=folium.GeoJsonTooltip(
+                   fields=['operator' ],
+                   aliases=['Line operator'],
+                   labels=True,
+                   localize=True
+               ),
+              ).add_to(map6)
+
+
+# Add Bart stations
+folium.GeoJson(bart_stations,
+               name="Bart stations",
+              tooltip=folium.GeoJsonTooltip(fields=['ts_locatio' ], 
+                   aliases=['Stop Name'],
+                   labels=True,
+                   localize=True
+               ),
+              ).add_to(map6)
+
+# ADD LAYER CONTROL
+folium.LayerControl(collapsed=False).add_to(map6)
+
+map6  # show map
+
+
+
+
+
+ +
+
+
+

Questions

+
+
    +

  1. Take a look at the help docs folium.LayerControl?. What parameter would move the location of the LayerControl? What parameter would allow it to be closed by default?

    2. +

  2. Take a look at the way we added layer2 above (this has the census tract tooltips). How has the code we use to add the layer to the map changed? Why do you think we made this change?

    3. +
+
+
+
# Uncomment to view
+#folium.LayerControl?
+
+
+
+
+
+
+

Saving to an html file

+

By saving our map to a html we can use it later as something to add to a website or email to a colleague.

+

You can save any of the maps you have in the notebook using this syntax:

+
+

map_name.save(“file_name.html”)

+
+

Let’s try that.

+
+
+
map6.save('outdata/bartmap.html')
+
+
+
+
+

Find your html file on your computer and double-click on it to open it in a browser.

+
+

Extra Challenge

+

Check out the notebook examples and find one to try with the data we have used in this notebook. I recommend the following.

+ +

+
+
+
+
+

12.8 Recap

+

Here we learned about the wonderful world of Folium! We created interactive maps– whether it be choropleth, points, lines, symbols… we mapped it all.

+

Below you’ll find a list of key functionalities we learned:

+
    +

  • Interactive mapping

    +
      +

    • folium.Map()

      • +
    +
    • +

  • Adding a map layer

    +
      +

    • .add_to()

      • +

    • folium.Choropleth()

      +
        +

      • geo_data

        • +

      • columns

        • +

      • fill_color

        • +
      +
      • +

    • folium.GeoJson()

      +
        +

      • style_function

        • +
      +
      • +

    • folium.Marker()

      +
        +

      • icon

        • +
      +
      • +

    • folium.CircleMarker()

      +
        +

      • radius

        • +
      +
      • +
    +
    • +

  • Adding a Tooltip

    +
      +

    • folium.GeoJsonTooltip

      • +

    • folium.features.GeoJsonTooltip

      • +
    +
    • +

  • Adding layer control

    +
      +

    • folium.LayerControl()

      • +
    +
    • +
+
+
+

Important note

+

The folium library changes often so I recommend you update your package frequently. This will give you increased functionality and may make future code easier to write. However, it might cause your existing code to break.

+
+

References

+

This notebook provides an introduction to folium. To see what else you can do, check out the references listed below.

+ +
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+
+ + + + +
+ + +
+ + 11. Adding Basemaps with Contextily + Geocoding Addresses in Python + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/13_OPTIONAL_geocoding.html b/_build/html/lessons/13_OPTIONAL_geocoding.html new file mode 100644 index 0000000..db65d5f --- /dev/null +++ b/_build/html/lessons/13_OPTIONAL_geocoding.html @@ -0,0 +1,768 @@ + + + + + + + Geocoding Addresses in Python — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Geocoding Addresses in Python

+

This notebook demonstrates how to geocode a dataframe of addresses

+
+
+
# import our packages
+import numpy as np
+import pandas as pd
+import geopandas as gpd
+import contextily as cx
+import matplotlib.pyplot as plt
+import folium
+
+# FOR geocoding
+import geopy
+
+
+
+
+
+

Sample Data

+

Let’s use as our sample data a CSV file of Alameda County Schools.

+
+
+
df0 = pd.read_csv("./notebook_data/alco_schools.csv")
+df0.head()
+
+
+
+
+

We can see that this datafile already has coordinates, but we will ignore those columns and subset it to Berkeley schools only for our geocoding example. We will also only keep public schools to limit the number of addresses to be geocoded.

+
+
+
df = df0[(df0['City']=='Berkeley' )& (df0['Org']== 'Public')][['Site','Address','City','State']].reset_index(drop=True)
+df.head()
+
+
+
+
+
+
+
df.shape  # SEE HOW MANY SCHOOLS WILL BE GEOCODED
+
+
+
+
+

Next we create a column that has all address components as this format is favored by many geocoders.

+
+
+
df['full_address'] =  df['Address'] +' '+ df['City']+ ' '+ df['State']
+df.head()
+
+
+
+
+
+
+

Create a GeoDataFrame

+

We will create a Geopandas Geodataframe that has no geometry so that we can use GeoPandas functionality for geocoding.

+
+
+
gdf =  gpd.GeoDataFrame(data=df, 
+                        geometry=None)
+
+
+
+
+
+
+
gdf.head()
+
+
+
+
+
+
+
gdf.info()
+
+
+
+
+
+
+

Define Geocoders and associated parameters

+
+
+
##################################################################
+## Geocoder to use 
+## see https://geopy.readthedocs.io/en/latest/
+## and https://geopandas.org/geocoding.html
+##################################################################
+
+# By default, the geocode function uses the GeoCode.Farm geocoding API with a rate limitation applied. 
+# But a different geocoding service can be specified (we really like the google geocoder!)
+# Set your Google geocoding API Key if you want to geocode using that API
+geocoder_name =  'Nominatim'   # or "GoogleV3" or None to skip geocoding step
+geocoder_apikey = None           # None if not required or google api key, or other api key
+geopy.geocoders.options.default_user_agent = 'D-Lab GeoFUN Workshop at UC Berkeley'
+
+
+
+
+
+
+

Test the geocoder

+
+
+
# test the geocoder
+if geocoder_name is not None: 
+    print("Geocoding is enabled with this geocoder:", geocoder_name)
+    
+    if geocoder_apikey is None:      
+        x= gpd.tools.geocode('1600 pennsylvania ave. washington, dc', provider=geocoder_name)['geometry'].squeeze()
+    
+    else:
+        x=gpd.tools.geocode('1600 pennsylvania ave. washington, dc', provider=geocoder_name, api_key=geocoder_apikey)['geometry'].squeeze()
+else:
+    print("Geocoding is NOT enabled.")
+    
+print(x)
+
+
+
+
+
+
+

Make a Geocoding Function

+

We can apply a geocoding function to a pandas dataframe to geocode all rows

+
+
+
def geocode_one_address(addr, geocoder_name=geocoder_name, geocoder_apikey=geocoder_apikey):
+    '''
+    Function to geocode an input address IFF geom is None
+    Use geopy with google geocoder to geocode addresses.
+    Requires the api_key value to be set prior to running this function
+    
+    Parameters:
+        addr (str): address to geocode, eg "1 Main St, Oakland, CA"
+        geocoder_name (str): name of geocoder ("nominatim" or "GoogleV3")
+        geocoder_apikey (str): api_key if needed by geocoder
+    Returns: 
+        geom (POINT): a point geometry or None if unsuccessful
+        
+    '''   
+    
+    if addr != None:
+        tempaddr = addr
+    
+        print("...geocoding this address: [%s]" % tempaddr)
+        
+        try:
+            if geocoder_apikey == None:
+                return gpd.tools.geocode(tempaddr, provider=geocoder_name)['geometry'].squeeze()
+            else:
+                return gpd.tools.geocode(tempaddr, provider=geocoder_name, api_key=geocoder_apikey)['geometry'].squeeze()
+        except:
+            print("...Problem with address: ", tempaddr)
+            return None
+
+    else: 
+        print("No address to geocode")
+        return None
+
+
+
+
+
+
+
# test geocoding function on one address
+x = geocode_one_address('1600 pennsylvania ave. washington, dc')
+print(x)
+
+
+
+
+
+
+
#batch geocode addresses in a data frame
+if geocoder_name is None:
+    print("Geocoding is NOT enabled.")
+    print("Will NOT geocode addresses")
+else:
+    print("Geocoding is enabled with this geocoder:", geocoder_name)
+    print("Ready to Geocode addresses")
+        
+    if geocoder_apikey is None:  
+        gdf['geometry'] = gdf.apply(lambda x: geocode_one_address(x['full_address']), axis=1)
+    else:
+        gdf['geometry'] = gdf.apply(lambda x: geocode_one_address(x['full_address']), axis=1)
+
+
+
+
+
+
+
gdf.head()
+
+
+
+
+
+
+

Set the CRS

+

Since we now have geographic coordinates we need to set the Coordinate Reference System of the data (WGS84)

+
+
+
gdf = gdf.set_crs(epsg=4326)
+
+
+
+
+
+
+

Map the geocoded Addresses

+
+
+
gdf.plot();
+
+
+
+
+
+
+

Add basemap with Contextily

+

We can map the schools that were successfully geocoded, i.e. where the geometry is not equal to None.

+
+
+
ax = gdf[gdf.geometry!=None].to_crs('EPSG:3857').plot(figsize=(9, 9), color="red")
+cx.add_basemap(ax)
+
+
+
+
+
+
+

Interactive Map with Folium

+

We can create an interactive map of the schools that were successfully geocoded.

+
+
+
map1 = folium.Map(location=[gdf.geometry.y.mean(), gdf.geometry.x.mean()], 
+                  tiles='CartoDB Positron',
+                  zoom_start=12)
+
+folium.GeoJson(gdf[gdf.geometry!=None],
+               tooltip=folium.GeoJsonTooltip(
+                   fields=['Site'], 
+                   aliases=[""],
+                   #labels=True,
+                   localize=True)
+              ).add_to(map1)
+
+map1  # show map
+
+
+
+
+
+
+

Save output to GeoJson File

+
+
+
# Save Geodataframe to file
+#gdf.to_file("my_geocoded_schools.geojson", driver='GeoJSON')
+
+
+
+
+
+
+ + + + +
+ + +
+ + 12. Interactive Mapping with Folium + 14. Making Plots and Maps with Altair + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.html b/_build/html/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.html new file mode 100644 index 0000000..807de9f --- /dev/null +++ b/_build/html/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.html @@ -0,0 +1,1441 @@ + + + + + + + 14. Making Plots and Maps with Altair — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

14. Making Plots and Maps with Altair

+

The Python Altair library is great because it works with both pandas dataframes and geopandas geodataframes. It allows you to create all kinds of plots and also to make makes. Moreover the plots can be linked to the maps (but not vice versa) so that selecting data on the plot in turn highlights the geographies for related areas. We demonstrate this below with census data.

+

This is powerful because you can do all this with just one Python library - instead of learning one for plotting and one for mapping. You can do this with matplotlib as well but the Altair syntax is a bit less complex.

+

For more information see the Altair website: https://altair-viz.github.io/

+
+
+
#Import libraries including altair
+import numpy as np
+import pandas as pd
+import altair as alt
+
+
+
+
+
+
+
# Uncomment & Install or Upgrade geopandas if necessary
+#!pip install GeoPandas==0.8.2
+
+
+
+
+
+
+
import geopandas as gpd
+
+
+
+
+
/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.
+  warnings.warn(
+
+
+
+
+
+
+
!ls notebook_data/census/ACS5yr/
+
+
+
+
+
census_income_CA_2018.csv        census_variables_CA_2013.zip
+census_mhhinc_CA_county_2018.csv census_variables_CA_2018.csv
+census_tracts_CA_2018.zip        census_variables_CA_2018.zip
+census_variables_CA.csv          s4_cenvars_CA.csv
+census_variables_CA_2013.csv     s4_cenvars_CA_2018.csv
+
+
+
+
+
+

Load ACS 5 year (2014 - 2018) data

+
+
+
df = pd.read_csv("notebook_data/census/ACS5yr/census_variables_CA_2018.csv", dtype={'FIPS_11_digit':str})
+
+
+
+
+
+
+
# Take a look at the data
+df.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NAMEc_racec_whitec_blackc_asianc_latinxc_race_moec_white_moec_black_moec_asian_moe...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0Census Tract 8.02, Merced County, California3996160950231208232324936103...0.8498610.1465890.0035510.0000000.0000000.8243080.1221420.0126350.0000000.000000
1Census Tract 9.01, Merced County, California38361402973422204951864625...0.8284430.1490880.0195610.0015860.0013220.7879250.0671700.0000000.0000000.096604
2Census Tract 15.02, Merced County, California24931581812421542271052257...0.8537870.1049010.0182260.0097210.0133660.6448150.0941600.0083430.0119190.057211
3Census Tract 9.02, Merced County, California98113752871358417279686383621...0.8912110.0956770.0043020.0000000.0088100.9085480.0439620.0000000.0000000.007598
4Census Tract 12, Merced County, California543121871373582388450266104140...0.9201410.0588240.0053980.0107970.0048400.8387240.0642450.0004430.0000000.012406
+

5 rows × 66 columns

+
+
+
+
+
# See what columns we have complete data for (no nulls) and what the datatypes are
+df.info()
+
+
+
+
+
<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 8057 entries, 0 to 8056
+Data columns (total 66 columns):
+ #   Column            Non-Null Count  Dtype  
+---  ------            --------------  -----  
+ 0   NAME              8057 non-null   object 
+ 1   c_race            8057 non-null   int64  
+ 2   c_white           8057 non-null   int64  
+ 3   c_black           8057 non-null   int64  
+ 4   c_asian           8057 non-null   int64  
+ 5   c_latinx          8057 non-null   int64  
+ 6   c_race_moe        8057 non-null   int64  
+ 7   c_white_moe       8057 non-null   int64  
+ 8   c_black_moe       8057 non-null   int64  
+ 9   c_asian_moe       8057 non-null   int64  
+ 10  c_latinx_moe      8057 non-null   int64  
+ 11  state_fips        8057 non-null   int64  
+ 12  county_fips       8057 non-null   int64  
+ 13  tract_fips        8057 non-null   int64  
+ 14  med_rent          7906 non-null   float64
+ 15  med_hhinc         7965 non-null   float64
+ 16  c_tenants         8057 non-null   int64  
+ 17  c_owners          8057 non-null   int64  
+ 18  c_renters         8057 non-null   int64  
+ 19  med_rent_moe      7846 non-null   float64
+ 20  med_hhinc_moe     7945 non-null   float64
+ 21  c_tenants_moe     8057 non-null   int64  
+ 22  c_owners_moe      8057 non-null   int64  
+ 23  c_renters_moe     8057 non-null   int64  
+ 24  c_movers          8057 non-null   int64  
+ 25  c_stay            8057 non-null   int64  
+ 26  c_movelocal       8057 non-null   int64  
+ 27  c_movecounty      8057 non-null   int64  
+ 28  c_movestate       8057 non-null   int64  
+ 29  c_moveabroad      8057 non-null   int64  
+ 30  c_movers_moe      8057 non-null   int64  
+ 31  c_stay_moe        8057 non-null   int64  
+ 32  c_movelocal_moe   8057 non-null   int64  
+ 33  c_movecounty_moe  8057 non-null   int64  
+ 34  c_movestate_moe   8057 non-null   int64  
+ 35  c_moveabroad_moe  8057 non-null   int64  
+ 36  c_commute         8057 non-null   int64  
+ 37  c_car             8057 non-null   int64  
+ 38  c_carpool         8057 non-null   int64  
+ 39  c_transit         8057 non-null   int64  
+ 40  c_bike            8057 non-null   int64  
+ 41  c_walk            8057 non-null   int64  
+ 42  c_commute_moe     8057 non-null   int64  
+ 43  c_car_moe         8057 non-null   int64  
+ 44  c_carpool_moe     8057 non-null   int64  
+ 45  c_transit_moe     8057 non-null   int64  
+ 46  c_bike_moe        8057 non-null   int64  
+ 47  c_walk_moe        8057 non-null   int64  
+ 48  year              8057 non-null   int64  
+ 49  FIPS_11_digit     8057 non-null   object 
+ 50  p_white           8012 non-null   float64
+ 51  p_black           8012 non-null   float64
+ 52  p_asian           8012 non-null   float64
+ 53  p_latinx          8012 non-null   float64
+ 54  p_owners          7981 non-null   float64
+ 55  p_renters         7981 non-null   float64
+ 56  p_stay            8012 non-null   float64
+ 57  p_movelocal       8012 non-null   float64
+ 58  p_movecounty      8012 non-null   float64
+ 59  p_movestate       8012 non-null   float64
+ 60  p_moveabroad      8012 non-null   float64
+ 61  p_car             7992 non-null   float64
+ 62  p_carpool         7992 non-null   float64
+ 63  p_transit         7992 non-null   float64
+ 64  p_bike            7992 non-null   float64
+ 65  p_walk            7992 non-null   float64
+dtypes: float64(20), int64(44), object(2)
+memory usage: 4.1+ MB
+
+
+
+
+
+
+

Subset the data so we are only looking at Alameda County (fips code == 1)

+
+
+
df2 = df[df.county_fips==1]
+
+
+
+
+
+
+
df2.head(2)
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NAMEc_racec_whitec_blackc_asianc_latinxc_race_moec_white_moec_black_moec_asian_moe...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
266Census Tract 4415.01, Alameda County, California6570677111474057036314883389...0.9258970.0395930.0104760.0198740.0041600.7617610.1139400.0548120.0120850.003453
267Census Tract 4047, Alameda County, California207915151341991751331376289...0.8918260.0283900.0376900.0318160.0102790.5320930.1776740.1581400.0065120.005581
+

2 rows × 66 columns

+
+
+
+
+

Make an Altair scatter plot

+

that visualizes the relationship between median household income and the percent of households that are owner-occupied.

+
+
+
alt.Chart(df2).mark_circle(size=50).encode(
+   x='med_hhinc',
+   y='p_owners'
+).properties(
+   height=350, width=500
+)
+
+
+
+
+
+
+
+
+
+
+
df2.shape
+
+
+
+
+
(361, 66)
+
+
+
+
+
+
+
!ls notebook_data/census/Tracts
+
+
+
+
+
cb_2013_06_tract_500k.zip            cb_2018_06_tract_500k.shp.ea.iso.xml
+cb_2017_06_tract_500k.zip            cb_2018_06_tract_500k.shp.iso.xml
+cb_2018_06_tract_500k.cpg            cb_2018_06_tract_500k.shx
+cb_2018_06_tract_500k.dbf            cb_2018_06_tract_500k.zip
+cb_2018_06_tract_500k.prj            oakland_tracts_2018.zip
+cb_2018_06_tract_500k.shp
+
+
+
+
+
+
+

Read in the Census Tract geographic data

+

into a GeoPandas GeoDataFrame

+
+
+
tracts = gpd.read_file('zip://./notebook_data/census/Tracts/cb_2018_06_tract_500k.zip')
+
+
+
+
+
+
+
tracts.head(2)
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
STATEFPCOUNTYFPTRACTCEAFFGEOIDGEOIDNAMELSADALANDAWATERgeometry
0060090003001400000US06009000300060090003003CT457009794394122POLYGON ((-120.76399 38.21389, -120.76197 38.2...
1060110003001400000US06011000300060110003003CT952744514195376POLYGON ((-122.50006 39.12232, -122.50022 39.1...
+
+
+
+
+
tracts.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/14_OPTIONAL_Plotting_and_Mapping_with_Altair_19_1.png +
+
+
+
+

Subset to keep only the tracts for Alameda County

+
+
+
tracts=tracts[tracts.COUNTYFP=='001']
+
+
+
+
+
+
+
tracts.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/14_OPTIONAL_Plotting_and_Mapping_with_Altair_22_1.png +
+
+
+
+

Merge the ACS dataframe into the census tracts geodataframe

+
+
+
tracts2 = tracts.merge(df2, how='left', left_on="GEOID", right_on="FIPS_11_digit")
+
+
+
+
+
+
+
tracts2.head(2)
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
STATEFPCOUNTYFPTRACTCEAFFGEOIDGEOIDNAME_xLSADALANDAWATERgeometry...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0060014251011400000US06001425101060014251014251.01CT5908702045459POLYGON ((-122.31419 37.84231, -122.29923 37.8......0.8652390.0365240.0358940.0371540.0251890.5509980.1075390.1696230.0155210.062084
1060014286001400000US06001428600060014286004286CT8989671080420POLYGON ((-122.27993 37.76818, -122.27849 37.7......0.7674690.0678460.1104670.0365320.0176860.5501400.0190480.2705880.0347340.035294
+

2 rows × 76 columns

+
+
+
+
+

Create a Thematic Map

+

Use the Geopandas Plot method to create a map of tracts colored by median household income values.

+
+
+
tracts2.plot(column='med_hhinc', legend=True)
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/14_OPTIONAL_Plotting_and_Mapping_with_Altair_27_1.png +
+
+
+
+

Make the same map with Altair

+
+
+
alt.Chart(tracts2).mark_geoshape().encode(
+    color='med_hhinc'
+).properties(
+    width=500,
+    height=300
+)
+
+
+
+
+
+
+
+
+
+ +
+

Try dragging a box around a subset of the points on the scatterplot and see what happens to the map.

+
+
+ + + + +
+ + +
+ + Geocoding Addresses in Python + 15. Voronoi Tessellation + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/15_OPTIONAL_Voronoi_Tessellation.html b/_build/html/lessons/15_OPTIONAL_Voronoi_Tessellation.html new file mode 100644 index 0000000..19163aa --- /dev/null +++ b/_build/html/lessons/15_OPTIONAL_Voronoi_Tessellation.html @@ -0,0 +1,679 @@ + + + + + + + 15. Voronoi Tessellation — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

15. Voronoi Tessellation

+

In some of the earlier lessons we dicussed how to conduct proximity analyses using buffer polygons. We looked at how accessible schools were via bike paths in Berkeley. Instead of using a buffers drawn at differnt radii around our locations or objects of interest, we could also use something called a Voronoi diagram.

+ +

As seen above, we have a bunch of Voronoi cells that are delineated by encompassing all locations that are closest to our point of interest than any other points.

+

In this notebook, we’ll experiment with making these type of diagrams in Python.

+
+
+
import pandas as pd
+import geopandas as gpd
+import random
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+

We’ll be using a Python package called geovoronoi

+
+
+
from geovoronoi.plotting import subplot_for_map, plot_voronoi_polys_with_points_in_area
+from geovoronoi import voronoi_regions_from_coords, points_to_coords
+
+
+
+
+
+

15.1 Polling locations

+

We’ll be using the 2020 General Election voting locations for Alameda County for this analysis. Since the data is aspatial we’ll need to coerce it to be a geodataframe and define a CRS.

+
+
+
# Pull in polling location
+polling_ac_df = pd.read_csv('notebook_data/ac_voting_locations.csv')
+polling_ac_df.head()
+
+# Make into geo data frame
+polling_ac_gdf = gpd.GeoDataFrame(polling_ac_df, 
+                               geometry=gpd.points_from_xy(polling_ac_df.X, polling_ac_df.Y))
+polling_ac_gdf.crs = "epsg:4326"
+
+polling_ac_gdf.plot()
+
+
+
+
+
+
+

15.2 Census tracts

+

We’ll also bring in our census tracts data for Alameda county.

+
+
+
# Bring in census tracts
+tracts_gdf = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip")
+
+# Narrow it down to Alameda County
+tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001']
+tracts_gdf_ac.plot()
+plt.show()
+
+
+
+
+

To make sure we can use it with our polling locations data, we’ll check the Coordinate Reference System (CRS).

+
+
+
# Check CRS
+print('polling_ac_gdf:', polling_ac_gdf.crs)
+print('tracts_gdf_ac CRS:', tracts_gdf_ac.crs)
+
+
+
+
+

Uh oh! It looks like they have different CRS. We’ll transform them both

+
+

Note: If you need a refresher on CRS check out Lesson 3, Coordinate Reference Systems (CRS) & Map Projections

+
+
+
+
# Transform CRS
+polling_ac_gdf_utm10 = polling_ac_gdf.to_crs("epsg:26910")
+tracts_gdf_ac_utm10 = tracts_gdf_ac.to_crs("epsg:26910")
+
+
+
+
+

And now let’s plot them together to see how the polling locations are spread across the county.

+
+
+
fig, ax = plt.subplots(figsize = (14,8)) 
+
+tracts_gdf_ac_utm10.plot(ax=ax,color='lightgrey',
+                         legend=True)
+polling_ac_gdf_utm10.plot(ax=ax, color='seagreen', markersize=9)
+
+
+
+
+
+
+

15.3 Voronoi Tessellation

+

To make our Voronoi geometries, we’ll be using the voronoi_regions_from_coords from the geovoronai package. Let’s check the helper function.

+
+
+
?voronoi_regions_from_coords
+
+
+
+
+

You’ll see that the helper function says enerate Voronoi regions from NumPy array of 2D coordinates or list of Shapely Point objects in coord. That means instead of GeoDataframe as an input, we’ll need to first convert all our geometries to numpy arrays.

+

We can easily do this by using points_to_coords

+
+
+
polling_array = points_to_coords(polling_ac_gdf_utm10.geometry)
+
+
+
+
+

And now we’re ready to run our voronoi region creation! We put in two inputs: our polling locations as a numpy array and our tracts boundary (which we created using unary_union).

+
+
+
region_polys, region_pts = voronoi_regions_from_coords(polling_array, tracts_gdf_ac_utm10.unary_union)
+
+
+
+
+

You’ll also notice we get two outputs from our line of code. The first object, in our case region_polys gives us the shape of the Voronoi geometry, while region_pts gives us the list of points.

+

To easily plot our points, we can use the plot_voronoi_polys_with_points_in_area which takes the following arguments:

+
    +

  • ax: Matplotlib axes object on which you want to plot

    • +

  • area_shape: the boundary shape that encompasses our Voronoi regions. In our case this is the shape of Alameda County.

    • +

  • region_polys: The dictionary that we got from above that gives the IDs and the polygons of our Voronoi geoemtries.

    • +

  • points: The numpy array of our shapely point objects, which we got above as region_pts

    • +
+

There are more arguments than this that you can use to customize your plot. Uncomment the code below to see the helper file.

+
+
+
# ?plot_voronoi_polys_with_points_in_area
+
+
+
+
+
+
+
fig, ax =  subplot_for_map(figsize=(10,10))
+plot_voronoi_polys_with_points_in_area(ax, tracts_gdf_ac_utm10.unary_union, 
+                                       region_polys, 
+                                       polling_array, 
+                                       region_pts,
+                                       points_markersize=10)
+
+
+
+
+

Ta-da!!!!

+
+
+

15.4 Voronoi colored by an attribute

+

Now we can go a step beyond this by changing the colors of each of our Voronoi regions based on a certain attribute.

+

To do that, let’s first get all of our region geometries as a list.

+
+
+
list_polys = list(region_polys.values())
+list_polys[0:5]
+
+
+
+
+

And we’ll replace our point geometries in our original polling locations geodataframe.

+
+
+
polling_v = gpd.GeoDataFrame(polling_ac_gdf_utm10.drop('geometry',axis=1),
+                                  geometry=list_polys)
+polling_v.plot()
+
+
+
+
+

Say we had a number of votes cast count for every polling location. We’ll randomly generate it here…

+
+
+
polling_v['votes_cast'] = random.sample(range(10000,50000), polling_v.shape[0])
+
+
+
+
+

And we can now color our polygons based on the number of votes cast there.

+
+
+
fig, ax= plt.subplots(figsize=(10,6))
+polling_v.plot(column='votes_cast', cmap='Purples', legend=True, ax=ax)
+plt.show()
+
+
+
+
+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + 14. Making Plots and Maps with Altair + 16. Introduction to Raster Data + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/16_OPTIONAL_Introduction_to_Raster_Data.html b/_build/html/lessons/16_OPTIONAL_Introduction_to_Raster_Data.html new file mode 100644 index 0000000..803a521 --- /dev/null +++ b/_build/html/lessons/16_OPTIONAL_Introduction_to_Raster_Data.html @@ -0,0 +1,1003 @@ + + + + + + + 16. Introduction to Raster Data — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

16. Introduction to Raster Data

+

This is a very brief introduction to reading raster data and basic manipulations in Python. We’ll walk through one of the most commonly used raster python packages, rasterio. We’ll be using the National Land Cover Database (NLCD) from 2011 that was downloaded from here.

+ +
+

Note: They also have a cool online viewer that is free and open access.

+
+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+from matplotlib.patches import Patch
+
+import json
+import numpy as np
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+

To use raster data we’ll be using the rasterio package, which is a popular package that helps you read, write, and manipulate raster data. We’ll also be using rasterstats.

+
+
+
import rasterio
+from rasterio.plot import show, plotting_extent
+from rasterio.mask import mask
+
+from rasterstats import zonal_stats
+
+
+
+
+
+

16.1 Import data and plot

+

To open our NLCD subset data, we’ll use the rasterio.open function

+
+
+
nlcd_2011 = rasterio.open('notebook_data/raster/nlcd2011_sf.tif')
+
+
+
+
+

Let’s check out what we get.

+
+
+
nlcd_2011
+
+
+
+
+

Let’s dissect this output here. We can look at the helper documentation for clues.

+
+
+
?rasterio.open
+
+
+
+
+

Which reads that the function returns a DatasetReader or DatasetWriter object. Unlike in GeoPandas which we’ve been utilizing a lot of, we don’t have a directly editable object here. However, rasterio does have functions in place where we can still use this returned object directly.

+

For example, we can easily plot our NLCD data using rasterio.plot.show.

+
+
+
rasterio.plot.show(nlcd_2011)
+
+
+
+
+

And just like how we formatted our matplotlib plots when we were using GeoDataFrames, we can still do that with this raster plotting function.

+
+
+
?rasterio.plot.show
+
+
+
+
+
+
+
fig, ax = plt.subplots(figsize=(8,8))
+plt_nlcd = rasterio.plot.show(nlcd_2011, cmap='Pastel2', ax=ax)
+
+
+
+
+

(Take note of what you think could be improved here… we’ll come back to this)

+

We can also plot a histogram of our data in a very similar way.

+
+
+
rasterio.plot.show_hist(nlcd_2011, bins=30)
+
+
+
+
+

We can see that we have more values on the lower end than on the higher end. To really understand the values that we see here let’s take a look at the legend.

+
+
+

16.2 Raster data structure

+
+

Note: If you need a refresher on what raster data is and relevant terminology. Check out the first lesson that covers geospatial topics

+
+

Now that we have a basic grasp on how to pull in and plot raster data, we can dig a little deeper to see what information we have.

+

First let’s check the number of bands there are in our dataset.

+
+
+
nlcd_2011.count
+
+
+
+
+

In this case we only have 1 band. If you’re pulling in aerial image, you might have 3 bands (red, green, blue). In the case you’re bringing in remote sensing data like Landsat or MODIS you might have more!

+

Not let’s check out what meta data we have.

+
+
+
nlcd_2011.meta
+
+
+
+
+

So we have a lot of good information here. Let’s unpack it:

+
    +

  • driver: the file type (simialr to what we see in open and Geopandas open)

    • +

  • dtype: the data type of each of your pixels

    • +

  • nodata: the value that is set for no data pixels

    • +

  • width: the number of pixels wide your dataset is

    • +

  • height: the number of pixels high your dataset is

    • +

  • count: the number of bands in your dataset

    • +

  • crs: the coordiante reference system (CRS) of your data

    • +

  • transform: the affine transform matrix that tell us which pixel locations in each row and column align with spatial locations (longitude, latitude).

    • +
+

We can also get similar information by calling profile.

+
+
+
nlcd_2011.profile
+
+
+
+
+
+
+
nlcd_2011.crs
+
+
+
+
+

Okay, but now we want to actually access our data. We can read in our data as a Numpy ndarray.

+
+
+
nlcd_2011_array = nlcd_2011.read()
+nlcd_2011_array
+
+
+
+
+

And we can call shape and see we have a 3D array.

+
+
+
nlcd_2011_array.shape
+
+
+
+
+

Much like other Numpy arrays, we can look at the min, mean, and max of our data

+
+
+
print("Minimum: ", np.nanmin(nlcd_2011_array))
+print("Max: ", np.nanmean(nlcd_2011_array))
+print("Mean: ", np.nanmax(nlcd_2011_array))
+
+
+
+
+

And since we have our data in an array form now, we can plot it using not a rasterio function, but simply plt.imshow.

+
+
+
plt.imshow(nlcd_2011_array[0,:,:])
+
+
+
+
+

Notice that we specified this plotting by making our array 2D. This gives us more flexibility about how we want to create our plots. You can do something like this:

+
+

This definitely looks more scary than it actually is. Essentially we are:

+
    +

  1. constructing a full color spectrum with all the colors we want

    2. +

  2. If values are outside of this range, we set the color tot white

    3. +

  3. we set the boudnaries for each of these colors so we know which color to assign to what value

    4. +

  4. we create legend labels for our legend

    5. +
+

This process is only really needed if we want to have a color map for specific values outside of a specific named matplotlib named color map.

+
+
+
+
# Define the colors you want
+cmap = matplotlib.colors.ListedColormap(['royalblue', #11
+                                        'white', #12
+                                        'beige', #21
+                                        'salmon', #22
+                                        'red', #23
+                                        'darkred', #24
+                                        'grey', #31
+                                        'yellowgreen', #41
+                                        'darkgreen', #42
+                                        'lightgreen', # 43
+                                        'darkgoldenrod', #51
+                                        'tan', # 52
+                                        'wheat', # 71
+                                        'darkkhaki', #72
+                                        'darkseagreen', #73
+                                         'mediumseagreen', #74
+                                         'gold', #81
+                                         'chocolate', #82
+                                         'lightsteelblue', #90
+                                         'steelblue', #95
+                                        ])
+cmap.set_under('#FFFFFF')
+cmap.set_over('#FFFFFF')
+# Define a normalization from values -> colors
+norm = matplotlib.colors.BoundaryNorm([10.5,
+                                       11.5,
+                                       12.5,
+                                       21.5,
+                                       22.5,
+                                       23.5,
+                                       24.5,
+                                       31.5,
+                                       41.5, 
+                                       42.5,
+                                       43.5,
+                                       51.5,
+                                       52.5,
+                                       71.5,
+                                       72.5,
+                                       73.5,
+                                       74.5,
+                                       81.5,
+                                       82.5,
+                                       90.5,
+                                       95.5,
+                                      ],20)
+
+
+legend_labels = { 'royalblue':'Open Water', 
+                  'white':'Perennial Ice/Snow',
+                  'beige':'Developed, Open Space',
+                  'salmon':'Developed, Low Intensity',
+                  'red':'Developed, Medium Intensity',
+                  'darkred':'Developed High Intensity',
+                  'grey':'Barren Land (Rock/Sand/Clay)',
+                  'yellowgreen':'Deciduous Forest',
+                  'darkgreen':'Evergreen Forest',
+                  'lightgreen':'Mixed Forest',
+                  'darkgoldenrod':'Dwarf Scrub',
+                  'tan':'Shrub/Scrub',
+                  'wheat':'Grassland/Herbaceous',
+                  'darkkhaki':'Sedge/Herbaceous',
+                  'darkseagreen':'Lichens',
+                  'mediumseagreen':'Moss',
+                  'gold':'Pasture/Hay',
+                  'chocolate':'Cultivated Crops',
+                  'lightsteelblue':'Woody Wetlands',
+                  'steelblue':'Emergent Herbaceous Wetlands'}
+
+
+
+
+
+
+
fig, ax = plt.subplots(figsize=(8, 8))
+plt_nlcd = ax.imshow(nlcd_2011_array[0,:,:], cmap=cmap, norm=norm)
+ax.set_title('NLCD 2011', fontsize=30)
+
+# Remove axes
+ax.set_frame_on(False)
+plt.setp(ax.get_xticklabels(), visible=False)
+plt.setp(ax.get_yticklabels(), visible=False)
+ax.set_xticks([])
+ax.set_yticks([])
+
+# Add color bar
+patches = [Patch(color=color, label=label)
+           for color, label in legend_labels.items()]
+
+fig.legend(handles=patches, facecolor="white",bbox_to_anchor=(1.1, 1.05))
+
+
+
+
+
+
+

16.2 Mask raster data

+

Masking is a common action that is done with raster data where you “mask” everything outside of a certain geometry.

+

To do this let’s first bring in the san francisco county data.

+
+
+
# Bring in census tracts
+tracts_gdf = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip").to_crs('epsg:4326')
+
+# Narrow it down to San Francisco County
+tracts_gdf_sf = tracts_gdf[tracts_gdf['COUNTYFP']=='075']
+
+tracts_gdf_sf.plot()
+plt.show()
+
+
+
+
+

We forgot about the Farollon islands! Let’s crop those out.

+
+
+
# Crop out Farallon
+tracts_gdf_sf = tracts_gdf_sf.cx[-122.8:-122.35, 37.65:37.85].copy().reset_index(drop=True)
+
+tracts_gdf_sf.plot()
+plt.show()
+
+
+
+
+

We’ll want to check the crs of our GeoDataFrame

+
+
+
tracts_gdf_sf.crs
+
+
+
+
+

Now we will call the mask function from rasterio. Let’s look at the documentation first.

+
+
+
?mask
+
+
+
+
+

We actually recommend using the rioxarray method instesd. So we’ll import a new package.

+
+
+
import rioxarray as rxr
+
+
+
+
+

Open our same NLCD data…

+
+
+
nlcd_2011 = rxr.open_rasterio('notebook_data/raster/nlcd2011_sf.tif',
+                              masked=True).squeeze()
+
+
+
+
+

Reproject our NLCD to be in the same coordinate reference system as the san francisco data

+
+
+
from rasterio.crs import CRS
+
+
+
+
+
+
+
!rio --version
+
+
+
+
+
+
+
# Currently doesn't work
+# Issue: https://github.com/mapbox/rasterio/issues/2103
+test = nlcd_2011.rio.reproject(tracts_gdf_sf.crs)
+
+
+
+
+

And clip our data to the san francisco geometry

+
+
+
clipped = test.rio.clip(tracts_gdf_sf.geometry, tracts_gdf_sf.crs, drop=False, invert=False)
+
+
+
+
+

We can easily plot this using .plot()

+
+
+
clipped.plot()
+
+
+
+
+

And we can also make a pretty map like we did before.

+
+
+
fig, ax = plt.subplots(figsize=(8, 8))
+clipped.plot(cmap=cmap, norm=norm, ax=ax,  add_colorbar=False)
+ax.set_title('NLCD 2011 (Cropped)', fontsize=30)
+
+# Add color bar
+patches = [Patch(color=color, label=label)
+           for color, label in legend_labels.items()]
+
+fig.legend(handles=patches, facecolor="white",bbox_to_anchor=(1.1, 1.05))
+
+# Remove axes
+ax.set_frame_on(False)
+plt.setp(ax.get_xticklabels(), visible=False)
+plt.setp(ax.get_yticklabels(), visible=False)
+ax.set_xticks([])
+ax.set_yticks([])
+
+
+
+
+

and you can save your work out to a new file!

+
+
+
clipped.rio.to_raster("outdata/nlcd2011_sf_cropped.tif", tiled=True)
+
+
+
+
+
+
+

16.3 Aggregate raster to vector

+

Another common step we see in a lot of raster work flows is questions that go along the lines of “How do I find the average of my raster within my vector data shapes”?

+

We can do this by aggregating to our vector data. For this example we’ll ask the question, “What is the majority class I have in each of the census tracts in San Francisco?”

+

For this we’ll turn to the rasterstas pacakge which has a handy function called zonal_stats. By default, the function will give us the minimum, maximum, mean, and count. But there also a lot more statistics that the function can return beyond this:

+
    +

  • sum

    • +

  • std

    • +

  • median

    • +

  • majority

    • +

  • minority

    • +

  • unique

    • +

  • range

    • +

  • nodata

    • +

  • percentile

    • +
+

So we’ll first bring back our clipped census tracts shapefile we have for san francisco.

+
+
+
tracts_gdf_sf.plot()
+plt.show()
+
+
+
+
+

And we’ll check out the zonal_stats documentation to get a better sense of how we can customize the arguments to better fit our needs.

+
+
+
?zonal_stats
+
+
+
+
+

Which doesn’t tell us a ton. Since we don’t have gen_zonal_stas loaded, we can go look at the documentation online: https://pythonhosted.org/rasterstats/rasterstats.html

+

After we check that out, let’s get on rolling and actually get our zonal stats by census tract.

+
+
+
with rasterio.open('notebook_data/raster/nlcd2011_sf.tif') as src:
+    affine = src.transform
+    array = src.read(1)
+    df_zonal_stats = pd.DataFrame(zonal_stats(tracts_gdf_sf, array, affine=affine, stats=['majority', 'unique']))
+
+
+
+
+

There’s a lot going on in the cell above, let’s break it down:

+
    +

  • affine object grabbed the transform of our raster data

    • +

  • array object read the first band we have in our raster dataset

    • +

  • df_zonal_stats has the results of our zonal_stats and then coerced it to be a dataframe.

    • +
+

So from that caell, we get df_zonal_stats which looks like:

+
+
+
df_zonal_stats
+
+
+
+
+

So now, we can merge this back onto our geodataframe so we can add the majority classes and unique number of classes as attributes.

+
+
+
tracts_gdf_sf_zs = pd.concat([tracts_gdf_sf, df_zonal_stats[['majority','unique']]], axis=1) 
+
+
+
+
+

And we can make a map that shows, for example, the majority class we have in each census tract.

+
+
+
fig, ax = plt.subplots(figsize=(8,8))
+tracts_gdf_sf_zs.plot(column='majority', cmap=cmap, norm=norm, ax=ax)
+
+# Add color bar
+patches = [Patch(color=color, label=label)
+           for color, label in legend_labels.items()]
+
+fig.legend(handles=patches, facecolor="white",bbox_to_anchor=(1.1, 1.05))
+
+plt.show()
+
+
+
+
+
+
+

16.4 Other resources

+

We really only grazed the surface here. We’ve linked a couple of resources that dive into raster data.

+ +
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + 15. Voronoi Tessellation + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/99_Questions_Answers.html b/_build/html/lessons/99_Questions_Answers.html new file mode 100644 index 0000000..0de06c4 --- /dev/null +++ b/_build/html/lessons/99_Questions_Answers.html @@ -0,0 +1,576 @@ + + + + + + + Common questions and answers — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ + +
+ +
+
+
+
+
+ +
+ +
+

Common questions and answers

+

This document lists comment questions and their respective answers pointing to specific parts of the workshop files.

+

I’m having trouble installing GeoPandas on a Windows computer.

+
    +

  • I’m having trouble installing GeoPandas on a Mac and I usually use pip install.

    • +

  • When using pip to install GeoPandas, you need to make sure that all dependencies are installed correctly. Fiona provides binary wheels with the dependencies included for Mac and Linux. The easiest way to attempt to fix this, first order, is to uninstall geopandas and it’s dependencies and reinstall.

    • +
+

I’m having trouble with packages versions not working with each other.

+
    +

  • You can try creating a virtual environment, see the bottom of the README

    • +
+

What’s the difference between GeoPandas and Pandas?

+ +

How do I read in geospatial data vector file formats?

+
    +

  • gpd.read_file is a great function that reads in multiple vector data file formats.

    • +

  • Lesson 2.2 and 2.6

    • +
+

How do I save geospatial data file formats?

+ +

What are Coordinate Reference Systems

+ +

I’m trying to plot two shapefile together but they’re not showing up

+
    +

  • This is the #1 folks run into! It’s most likely that the CRS for your two datasets are different.

    • +

  • Lesson 3.1-3.3

    • +
+

How do I get the CRS of my data and transform it?

+ +

How do I set the CRS of my data if it’s missing?

+ +

I have a CSV that has latitude and longitude values, how do I coerce it to be a GeoDataFrame?

+ +

How do I find the geospatial extent of my data?

+ +

How do I create a choropleth map?

+ +

What kinds of color maps are there?

+ +

What types of data is best for choropleth mapping?

+ +

What is a classification scheme and how do I use different ones in Python?

+ +

Can I define my own classification scheme?

+ +

How do I create a point map?

+ +

How does mapping categorical data different from mapping quantitative data?

+
    +

  • It’s basically the same except you’ll have to specify that it’s categorical.

    • +

  • Lesson 5.5

    • +
+

How do I calculate the area or length of my GeoDataFrame?

+ +

What is a relationship query?

+ +

How do I do a proximity analysis?

+ +

How do I know what units my buffer size is in?

+
    +

  • The units are what your CRS says they are.

    • +

  • Lesson 6.3

    • +
+

Can I do a merge like I do in Pandas for GeoDataFrames?

+ +

What is a spatial join and how do I do it?

+ +

What’s the best way to aggregate my geospatial data (for example, after doing a join)?

+
    +

  • Using .dissolve is better than a groupby since it’ll preserve your geometries.

    • +

  • Lesson 7.3

    • +
+

Do you have any full workflows we can work through and ask questions about?

+ +

How do I fetch and use geospatial data without downloading it as a file?

+ +

How do I create maps with basemaps?

+ +

How do I create interactive maps?

+ +

How do I geocode address in Python?

+ +

Is there a package to do both panda and geopandas plots with some interactive functionality?

+ +

How do I do a Voronoi Tessellation?

+ +

I want to start using raster data. Where’s a good place ot start?

+ +
+
+ + +
+
+    
 D-Lab @ University of California - Berkeley
+    
 Team Geo
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
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+ + +
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+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/intro.html b/_build/html/lessons/intro.html new file mode 100644 index 0000000..99b4ff4 --- /dev/null +++ b/_build/html/lessons/intro.html @@ -0,0 +1,561 @@ + + + + + + + Welcome to Geospatial Fundamentals in Python: From A to Z to Fancy — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Welcome to Geospatial Fundamentals in Python: From A to Z to Fancy

+
+

Overview

+

Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The Python programming language is a great platform for exploring these data and integrating them into your research. This JupyterBook explores everything from A to Z to get started to work with Geospatial data in Python. We then take you all the way to fancy to work with online data sources, basemaps, interactive maps, geocoding, tessellation, and raster data.

+
+

1. Getting Started with Spatial Dataframes

+

Part one will introduce basic methods for working with geospatial data in Python using the GeoPandas library. You will learn how to import and export spatial data and store them as GeoPandas GeoDataFrames (or spatial dataframes). We will explore and compare several methods for mapping the data including the GeoPandas plot function and the matplotlib library. We will review coordinate reference systems and methods for reading, defining and transforming these.

+
+
+

2. Geoprocessing and Analysis

+

Part two dives deeper into data driven mapping in Python, using color palettes and data classification to communicate information with maps. We will also introduce basic methods for processing spatial data, which are the building blocks of common spatial analysis workflows.

+
+
+

3. Exercises

+

Part 3 provides two full workflows for you to try to work through on your own. These exercises uses techniques and concepts from both the first and second parts.

+
+
+

4. Get Fancy

+

Part 4 dives builds off of the foundational work from the earlier sections. The topics included involve:

+
    +

  • Reading in online sources data

    • +

  • Adding basemaps

    • +

  • Creating interactive maps

    • +

  • Geocoding addresses

    • +

  • Using Altair for plotting

    • +

  • Creating voronoi tessellations

    • +

  • Starting out with raster data

    • +
+
+
+

Pre-requisites

+
+

Knowledge Requirements

+

You’ll probably get the most out of this workshop if you have a basic foundation in Python and Pandas, similar to what you would have from taking the D-Lab Python Fundamentals workshop series. Here are a couple of suggestions for materials to check-out prior to the workshop.

+

D-Lab Workshops:

+ +

Other:

+ +
+
+

Technology Requirements:

+

Bring a laptop with Python and the following packages installed: pandas, geopandas, matplotlib, descartes and dependencies. More details are provided on the workshop github page https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python).

+
+
+
+
+

1.0 Python and Jupyter Notebook installation

+

There are many ways to install python and python libraries, distributed as packages, on your computer. Here is one way that we recommend.

+
    +

  • Anaconda installs IDEs and several important packages like NumPy, Pandas, and so on, and this is a really convenient package which can be downloaded and installed.

    • +
+

Anaconda is a free and open-source distribution of Python. Anaconda installs IDEs (integrated development environments, aka where you can write and run code) and several important packages like NumPy and Pandas, making it a really convenient package to use.

+
+

1.1 Download Anaconda:

+

Follow this link to download Anaconda: https://www.anaconda.com/distribution. The same link can be used for Mac, Windows, and Linux.

+

We recommend downloading the latest version, which will be Python 3. +downloadinstruc

+

Open the .exe file that was downloaded and follow the instructions in the installation wizard prompt.

+
+
+

1.2 Launch Anaconda and open a Jupyter Notebook

+

Once installation is complete open Anaconda Navigator and launch Jupyter Notebook. +launchnav

+

Jupyter Notebook will open in your web browser (it does not require internet to work). In Jupyter, navigate to the folder where you saved the code file you plan to use and open the .ipynb file (the extension for Jupyter Notebook files written in Python) to view it in the Notebook.

+
+
+
+

2.0 Installing Geopandas

+
    +

  • From within Anaconda Navigator click on the Environments selection in the left sidebar menu

    • +
+
+

anacondanav

+
+
    +

  • Click on the arrow to the right of your base (root) environment and select Open Terminal

    • +
+
+

anacondanav

+
+
    +

  • This will give you access to the command line interface (CLI) on your computer in a window that looks like this:

    • +
+
+

openterminal

+
+
    +

  • Install some needed software by entering the following commands, one at a time:

    • +
+
conda install python=3 geopandas
+conda install juypter
+conda install matplotlib
+conda install descartes
+conda install mapclassify
+conda install contextily
+
+
+

Once you have those libraries all installed you will be able to go to Anaconda Navigator, launch a Jupyter Notebook, navigate to the workshop files and run all of the notebooks.

+

Optionally you can create a virtual environment In the terminal window, type the conda commands shown on the GeoPandas website for installing Geopandas in a virtual environment. These are:

+
conda create -n geo_env
+conda activate geo_env
+conda config --env --add channels conda-forge
+conda config --env --set channel_priority strict
+conda install python=3 geopandas
+
+
+

After creating your virtual environment, you can process and install the rest of your packages listed above. You will be able to select your geo_env in Anaconda Navigator.

+
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+ + +
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+
 D-Lab @ University of California - Berkeley
+
 Team Geo
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+ + Lesson 1. Overview of Geospatial Data + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
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+ + + + + + + + \ No newline at end of file diff --git a/_build/html/lessons/notebook_data/README.html b/_build/html/lessons/notebook_data/README.html new file mode 100644 index 0000000..5d566a3 --- /dev/null +++ b/_build/html/lessons/notebook_data/README.html @@ -0,0 +1,422 @@ + + + + + + + Data Folder — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Data Folder

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This is a holding place for the notebook data during development.

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+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

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+ + +
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+ + + + + + + + \ No newline at end of file diff --git a/_build/html/objects.inv b/_build/html/objects.inv new file mode 100644 index 0000000..5653115 Binary files /dev/null and b/_build/html/objects.inv differ diff --git a/_build/html/ran/02_Introduction_to_GeoPandas-Copy1.html b/_build/html/ran/02_Introduction_to_GeoPandas-Copy1.html new file mode 100644 index 0000000..ff91d65 --- /dev/null +++ b/_build/html/ran/02_Introduction_to_GeoPandas-Copy1.html @@ -0,0 +1,1249 @@ + + + + + + + Lesson 2. Introduction to Geopandas — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Lesson 2. Introduction to Geopandas

+

In this lesson we’ll learn about a package that is core to using geospatial data in Python. We’ll go through the structure of the data (it’s not too different from regular DataFrames!), geometries, shapefiles, and how to save your hard work.

+
    +

  • 2.1 What is GeoPandas?

    • +

  • 2.2 Read in a shapefile

    • +

  • 2.3 Explore the GeoDataFrame

    • +

  • 2.4 Plot the GeoDataFrame

    • +

  • 2.5 Subset the GeoDataFrame

    • +

  • 2.6 Save your data

    • +

  • 2.7 Recap

    • +

  • Exercise: IO, Manipulation, and Mapping

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/california_counties/CaliforniaCounties.shp’

      • +

    • ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 30 minutes

      • +

    • Exercises: 5 minutes +

      • +
    +
    • +
+
+

2.1 What is GeoPandas?

+ +
+

GeoPandas = pandas + geo

+

GeoPandas gives you access to all of the functionality of pandas, which is the primary data analysis tool for working with tabular data in Python. GeoPandas extends pandas with attributes and methods for working with geospatial data.

+
+
+

Import Libraries

+

Let’s start by importing the libraries that we will use.

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+
+

2.2 Read in a shapefile

+

As we discussed in the initial geospatial overview, a shapefile is one type of geospatial data that holds vector data.

+
+

To learn more about ESRI Shapefiles, this is a good place to start: ESRI Shapefile Wiki Page

+
+

The tricky thing to remember about shapefiles is that they’re actually a collection of 3 to 9+ files together. Here’s a list of all the files that can make up a shapefile:

+
+

shp: The main file that stores the feature geometry

+

shx: The index file that stores the index of the feature geometry

+

dbf: The dBASE table that stores the attribute information of features

+

prj: The file that stores the coordinate system information. (should be required!)

+

xml: Metadata —Stores information about the shapefile.

+

cpg: Specifies the code page for identifying the character set to be used.

+
+

But it remains the most commonly used file format for vector spatial data, and it’s really easy to visualize in one go!

+

Let’s try it out with California counties, and use geopandas for the first time. gpd.read_file is a flexible function that let’s you read in many different types of geospatial data.

+
+
+
# Read in the counties shapefile
+counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')
+
+
+
+
+
+
+
# Plot out California counties
+counties.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/02_Introduction_to_GeoPandas-Copy1_6_1.png +
+
+

Bam! Amazing! We’re off to a running start.

+
+
+

2.3 Explore the GeoDataFrame

+

Before we get in too deep, let’s discuss what a GeoDataFrame is and how it’s different from pandas DataFrames.

+
+

The GeoPandas GeoDataFrame

+

A GeoPandas GeoDataFrame, or gdf for short, is just like a pandas dataframe (df) but with an extra geometry column and methods & attributes that work on that column. I repeat because it’s important:

+
+

A GeoPandas GeoDataFrame is a pandas DataFrame with a geometry column and methods & attributes that work on that column.

+
+
+

This means all the methods and attributes of a pandas DataFrame also work on a Geopandas GeoDataFrame!!

+
+

With that in mind, let’s start exploring out dataframe just like we would do in pandas.

+
+
+
# Find the number of rows and columnds in counties
+counties.shape
+
+
+
+
+
(58, 59)
+
+
+
+
+
+
+
# Look at the first couple of rows in our geodataframe
+counties.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
00KernCalifornia839631102.9851089104.2828704997664892112676...1513.538161.3506103San Bernardino,Tulare,Inyo2495935NoneNoneNoneNonePOLYGON ((193446.035 -244342.585, 194033.795 -...
10KingsCalifornia152982109.9155039111.42742183027110142562...1203.201391.3906089Fresno,Kern,Tulare2212260NoneNoneNoneNonePOLYGON ((12524.028 -179431.328, 12358.142 -17...
20LakeCalifornia6466548.66525349.0823345203312322049...72.311329.4606106None0NoneNoneNoneNoneMULTIPOLYGON (((-240632.150 93056.104, -240669...
30LassenCalifornia348957.4350397.4228562553228341234...120.924720.4206086None0NoneNoneNoneNonePOLYGON ((-45364.032 352060.633, -45248.844 35...
40Los AngelesCalifornia98186052402.399043412423.264150493659985687472828...187.944087.1906073San Bernardino,Kern2874841NoneNoneNoneNoneMULTIPOLYGON (((173874.519 -471855.293, 173852...
+

5 rows × 59 columns

+
+
+
+
+
# Look at all the variables included in our data
+counties.columns
+
+
+
+
+
Index(['FID_', 'NAME', 'STATE_NAME', 'POP2010', 'POP10_SQMI', 'POP2012',
+       'POP12_SQMI', 'WHITE', 'BLACK', 'AMERI_ES', 'ASIAN', 'HAWN_PI',
+       'HISPANIC', 'OTHER', 'MULT_RACE', 'MALES', 'FEMALES', 'AGE_UNDER5',
+       'AGE_5_9', 'AGE_10_14', 'AGE_15_19', 'AGE_20_24', 'AGE_25_34',
+       'AGE_35_44', 'AGE_45_54', 'AGE_55_64', 'AGE_65_74', 'AGE_75_84',
+       'AGE_85_UP', 'MED_AGE', 'MED_AGE_M', 'MED_AGE_F', 'HOUSEHOLDS',
+       'AVE_HH_SZ', 'HSEHLD_1_M', 'HSEHLD_1_F', 'MARHH_CHD', 'MARHH_NO_C',
+       'MHH_CHILD', 'FHH_CHILD', 'FAMILIES', 'AVE_FAM_SZ', 'HSE_UNITS',
+       'VACANT', 'OWNER_OCC', 'RENTER_OCC', 'NO_FARMS07', 'AVG_SIZE07',
+       'CROP_ACR07', 'AVG_SALE07', 'SQMI', 'CountyFIPS', 'NEIGHBORS',
+       'PopNeigh', 'NEIGHBOR_1', 'PopNeigh_1', 'NEIGHBOR_2', 'PopNeigh_2',
+       'geometry'],
+      dtype='object')
+
+
+
+
+

It looks like we have a good amount of information about the total population for different years and the densities, as well as race, age, and occupancy info.

+
+
+
+

2.4 Plot the GeoDataFrame

+

We’re able to plot our GeoDataFrame because of the extra geometry column.

+
+

Geopandas Geometries

+

There are three main types of geometries that can be associated with your geodataframe: points, lines and polygons:

+

+

In the geodataframe these geometries are encoded in a format known as Well-Known Text (WKT). For example:

+
+
    +

  • POINT (30 10)

    • +

  • LINESTRING (30 10, 10 30, 40 40)

    • +

  • POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))

    • +
+

where coordinates are separated by a space and coordinate pairs by a comma

+
+

Your geodataframe may also include the variants multipoints, multilines, and multipolgyons if the row-level feature of interest is comprised of multiple parts. For example, a geodataframe of states, where one row represents one state, would have a POLYGON geometry for Utah but MULTIPOLYGON for Hawaii, which includes many islands.

+
+

It’s ok to mix and match geometries of the same family, e.g., POLYGON and MULTIPOLYGON, in the same geodatafame.

+
+

Question What kind of geometry would a roads geodataframe have? What about one that includes landmarks in the San Francisco Bay Area?

+

You can check the types of geometries in a geodataframe or a subset of the geodataframe by combining the type and unique methods.

+
+
+
# Let's check what geometries we have in our counties geodataframe
+counties['geometry'].head()
+
+
+
+
+
0    POLYGON ((193446.035 -244342.585, 194033.795 -...
+1    POLYGON ((12524.028 -179431.328, 12358.142 -17...
+2    MULTIPOLYGON (((-240632.150 93056.104, -240669...
+3    POLYGON ((-45364.032 352060.633, -45248.844 35...
+4    MULTIPOLYGON (((173874.519 -471855.293, 173852...
+Name: geometry, dtype: geometry
+
+
+
+
+
+
+
# Let's check to make sure that we only have polygons and multipolygons 
+counties['geometry'].type.unique()
+
+
+
+
+
array(['Polygon', 'MultiPolygon'], dtype=object)
+
+
+
+
+
+
+
counties.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/02_Introduction_to_GeoPandas-Copy1_19_1.png +
+
+

Just like with other plots you can make in Python, we can start customizing our map with colors, size, etc.

+
+
+
# We can run the following line of code to get more info about the parameters we can specify:
+
+# ?counties.plot
+
+
+
+
+
+
+
# Make the figure size bigger
+counties.plot(figsize=(6,9))
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/02_Introduction_to_GeoPandas-Copy1_22_1.png +
+
+
+
+
counties.plot(figsize=(6,9), 
+              edgecolor='grey',  # grey colored border lines
+              facecolor='pink' , # fill in our counties as pink
+              linewidth=2)       # make the linedwith a width of 2
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/02_Introduction_to_GeoPandas-Copy1_23_1.png +
+
+
+
+
+

2.5 Subset the GeoDataframe

+

Since we’ll be focusing on Berkeley later in the workshop, let’s subset our GeoDataFrame to just be for Alameda County.

+
+
+
# See all county names included in our dataset
+counties['NAME'].values
+
+
+
+
+
array(['Kern', 'Kings', 'Lake', 'Lassen', 'Los Angeles', 'Madera',
+       'Marin', 'Mariposa', 'Mendocino', 'Merced', 'Modoc', 'Mono',
+       'Monterey', 'Napa', 'Nevada', 'Orange', 'Placer', 'Plumas',
+       'Riverside', 'Sacramento', 'San Benito', 'San Bernardino',
+       'San Diego', 'San Francisco', 'San Joaquin', 'San Luis Obispo',
+       'San Mateo', 'Santa Barbara', 'Santa Clara', 'Santa Cruz',
+       'Shasta', 'Sierra', 'Siskiyou', 'Solano', 'Alameda', 'Alpine',
+       'Sonoma', 'Amador', 'Stanislaus', 'Sutter', 'Butte', 'Calaveras',
+       'Tehama', 'Colusa', 'Trinity', 'Tulare', 'Contra Costa',
+       'Del Norte', 'Tuolumne', 'Ventura', 'El Dorado', 'Yolo', 'Fresno',
+       'Glenn', 'Yuba', 'Humboldt', 'Imperial', 'Inyo'], dtype=object)
+
+
+
+
+

It looks like Alameda county is specified as “Alameda” in this dataset.

+
+
+
counties.loc[counties['NAME'] == 'Alameda']
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
340AlamedaCalifornia15102712029.815345512062.4022266491221904519799...95.92744.0606068None0NoneNoneNoneNoneMULTIPOLYGON (((-197580.800 -24065.060, -19763...
+

1 rows × 59 columns

+
+
+

Now we can create a new geodataframe called alameda_county that is a subset of our counties geodataframe.

+
+
+
alameda_county = counties.loc[counties['NAME'] == 'Alameda'].copy().reset_index(drop=True)
+
+
+
+
+
+
+
# Plot our newly subsetted geodataframe
+alameda_county.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/02_Introduction_to_GeoPandas-Copy1_30_1.png +
+
+

Nice! Looks like we have what we were looking for.

+

FYI: You can also make dynamic plots of one or more county without saving to a new gdf.

+
+
+
bay_area_counties = ['Alameda', 'Contra Costa', 'Marin', 'Napa', 'San Francisco', 
+                        'San Mateo', 'Santa Clara', 'Santa Cruz', 'Solano', 'Sonoma']
+counties.loc[counties['NAME'].isin(bay_area_counties)].plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/02_Introduction_to_GeoPandas-Copy1_32_1.png +
+
+
+
+

2.6 Save your Data

+

Let’s not forget to save out our Alameda County geodataframe alameda_county. This way we won’t need to repeat the processing steps and attribute join we did above.

+

We can save it as a shapefile.

+
+
+
alameda_county.to_file("outdata/alameda_county.shp")
+
+
+
+
+

One of the problems of saving to a shapefile is that our column names get truncated to 10 characters (a shapefile limitation.)

+

Instead of renaming all columns with obscure names that are less than 10 characters, we can save our GeoDataFrame to a spatial data file format that does not have this limation - GeoJSON or GPKG (geopackage) file.

+
    +

  • These formats have the added benefit of outputting only one file in contrast tothe multi-file shapefile format.

    • +
+
+
+
alameda_county.to_file("outdata/alameda_county.json", driver="GeoJSON")
+
+
+
+
+
+
+
alameda_county.to_file("outdata/alameda_county.gpkg", driver="GPKG")
+
+
+
+
+

You can read these in, just as you would a shapefile with gpd.read_file

+
+
+
alameda_county_test = gpd.read_file("outdata/alameda_county.gpkg")
+alameda_county_test.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/02_Introduction_to_GeoPandas-Copy1_40_1.png +
+
+
+
+
alameda_county_test2 = gpd.read_file("outdata/alameda_county.json")
+alameda_county_test2.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/02_Introduction_to_GeoPandas-Copy1_41_1.png +
+
+

There are also many other formats we could use for data output.

+

NOTE: If you’re working with point data (i.e. a single latitude and longitude value per feature), +then CSV might be a good option!

+
+
+

2.7 Recap

+

In this lesson we learned about…

+
    +

  • The geopandas package

    • +

  • Reading in shapefiles

    +
      +

    • gpd.read_file

      • +
    +
    • +

  • GeoDataFrame structures

    +
      +

    • shape, head, columns

      • +
    +
    • +

  • Plotting GeoDataFrames

    +
      +

    • plot

      • +
    +
    • +

  • Subsetting GeoDatFrames

    +
      +

    • loc

      • +
    +
    • +

  • Saving out GeoDataFrames

    +
      +

    • to_file

      • +
    +
    • +
+
+
+

Exercise: IO, Manipulation, and Mapping

+

Now you’ll get a chance to practice the operations we learned above.

+

In the following cell, compose code to:

+
    +

  1. Read in the California places data (notebook_data/census/Places/cb_2018_06_place_500k.zip)

    2. +

  2. Subset the data to Berkeley

    3. +

  3. Plot, and customize as desired

    4. +

  4. Save out as a shapefile (outdata/berkeley_places.shp)

    5. +
+

Note: pulling in a zipped shapefile has the same syntax as just pulling in a shapefile. The only difference is that insead of just putting in the filepath you’ll want to write zip://notebook_data/census/Places/cb_2018_06_place_500k.zip

+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE
+
+
+
+
+
+
+

Double-click to see solution!

+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/ran/03_CRS_Map_Projections-Copy1.html b/_build/html/ran/03_CRS_Map_Projections-Copy1.html new file mode 100644 index 0000000..f6206e1 --- /dev/null +++ b/_build/html/ran/03_CRS_Map_Projections-Copy1.html @@ -0,0 +1,2288 @@ + + + + + + + Lesson 3. Coordinate Reference Systems (CRS) & Map Projections — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 3. Coordinate Reference Systems (CRS) & Map Projections

+

Building off of what we learned in the previous notebook, we’ll get to understand an integral aspect of geospatial data: Coordinate Reference Systems.

+
    +

  • 3.1 California County Shapefile

    • +

  • 3.2 USA State Shapefile

    • +

  • 3.3 Plot the Two Together

    • +

  • 3.4 Coordinate Reference System (CRS)

    • +

  • 3.5 Getting the CRS

    • +

  • 3.6 Setting the CRS

    • +

  • 3.7 Transforming or Reprojecting the CRS

    • +

  • 3.8 Plotting States and Counties Togther

    • +

  • 3.9 Recap

    • +

  • Exercise: CRS Management

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/california_counties/CaliforniaCounties.shp’

      • +

    • ‘notebook_data/us_states/us_states.shp’

      • +

    • ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 45 minutes

      • +

    • Exercises: 10 minutes +

      • +
    +
    • +
+
+

Import Libraries

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+

3.1 California County shapefile

+

Let’s go ahead and bring back in our California County shapefile. As before, we can read the file in using gpd.read_file and plot it straight away.

+
+
+
counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')
+counties.plot(color='darkgreen')
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/03_CRS_Map_Projections-Copy1_4_1.png +
+
+

Even if we have an awesome map like this, sometimes we want to have more geographical context, or we just want additional information. We’re going to try overlaying our counties GeoDataFrame on our USA states shapefile.

+
+
+

3.2 USA State shapefile

+

We’re going to bring in our states geodataframe, and let’s do the usual operations to start exploring our data.

+
+
+
# Read in states shapefile
+states = gpd.read_file('notebook_data/us_states/us_states.shp')
+
+
+
+
+
+
+
# Look at the first few rows
+states.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
STATEGEOIDABBREVgeometry
0Alabama01ALMULTIPOLYGON (((-88.05338 30.50699, -88.05109 ...
1Alaska02AKMULTIPOLYGON (((-134.73726 58.26135, -134.7344...
2Arizona04AZPOLYGON ((-114.81629 32.50804, -114.81432 32.5...
3Arkansas05ARPOLYGON ((-94.61783 36.49941, -94.61765 36.499...
4California06CAMULTIPOLYGON (((-118.60442 33.47855, -118.5987...
+
+
+
+
+
# Count how many rows and columns we have
+states.shape
+
+
+
+
+
(56, 4)
+
+
+
+
+
+
+
# Plot our states data
+states.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/03_CRS_Map_Projections-Copy1_10_1.png +
+
+

You might have noticed that our plot extends beyond the 50 states (which we also saw when we executed the shape method). Let’s double check what states we have included in our data.

+
+
+
states['STATE'].values
+
+
+
+
+
array(['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California',
+       'Colorado', 'Connecticut', 'Delaware', 'District of Columbia',
+       'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa',
+       'Kansas', 'Maryland', 'Minnesota', 'Mississippi', 'Montana',
+       'Nevada', 'New Jersey', 'New Mexico', 'North Dakota', 'Oklahoma',
+       'Pennsylvania', 'South Carolina', 'South Dakota', 'Utah',
+       'Vermont', 'West Virginia', 'Wyoming', 'American Samoa',
+       'Puerto Rico', 'Florida', 'Kentucky', 'Louisiana', 'Maine',
+       'Massachusetts', 'Michigan', 'Missouri', 'Nebraska',
+       'New Hampshire', 'New York', 'North Carolina', 'Ohio', 'Oregon',
+       'Rhode Island', 'Tennessee', 'Texas', 'Virginia', 'Washington',
+       'Wisconsin', 'Guam',
+       'Commonwealth of the Northern Mariana Islands',
+       'United States Virgin Islands'], dtype=object)
+
+
+
+
+

Beyond the 50 states we seem to have American Samoa, Puerto Rico, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands included in this geodataframe. To make our map cleaner, let’s limit the states to the contiguous states (so we’ll also exclude Alaska and Hawaii).

+
+
+
# Define list of non-contiguous states
+non_contiguous_us = [ 'American Samoa','Puerto Rico','Guam',
+                      'Commonwealth of the Northern Mariana Islands',
+                      'United States Virgin Islands', 'Alaska','Hawaii']
+# Limit data according to above list
+states_limited = states.loc[~states['STATE'].isin(non_contiguous_us)]
+
+
+
+
+
+
+
# Plot it
+states_limited.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/03_CRS_Map_Projections-Copy1_15_1.png +
+
+

To prepare for our mapping overlay, let’s make our states a nice, light grey color.

+
+
+
states_limited.plot(color='lightgrey', figsize=(10,10))
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/03_CRS_Map_Projections-Copy1_17_1.png +
+
+
+
+

3.3 Plot the two together

+

Now that we have both geodataframes in our environment, we can plot both in the same figure.

+

NOTE: To do this, note that we’re getting a Matplotlib Axes object (ax), then explicitly adding each our layers to it +by providing the ax=ax argument to the plot method.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+counties.plot(color='darkgreen',ax=ax)
+states_limited.plot(color='lightgrey', ax=ax)
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/03_CRS_Map_Projections-Copy1_19_1.png +
+
+

Oh no, what happened here?

+

Question Without looking ahead, what do you think happened?

+
+
+If you look at the numbers we have on the x and y axes in our two plots, you'll see that the county data has much larger numbers than our states data. It's represented in some different type of unit other than decimal degrees! +

In fcat, that means if we zoom in really close into our plot we’ll probably see the states data plotted. We can explore this in two ways:

+
    +

  • Set our matplotlib preferences to %matplotlib notebook to zoom in and out of our plot

    • +

  • Limit the extent of our plot using set_xlim and set_ylim

    • +
+
+
+
%matplotlib notebook
+
+fig, ax = plt.subplots(figsize=(10,10))
+counties.plot(color='darkgreen',ax=ax)
+states_limited.plot(color='lightgrey', ax=ax)
+
+
+
+
+
<AxesSubplot:>
+
+
+
+
+
+
+
%matplotlib inline
+fig, ax = plt.subplots(figsize=(10,10))
+counties.plot(color='darkgreen',ax=ax)
+states_limited.plot(color='lightgrey', ax=ax)
+ax.set_xlim(-140,-50)
+ax.set_ylim(20,50)
+
+
+
+
+
(20.0, 50.0)
+
+
+../_images/03_CRS_Map_Projections-Copy1_24_1.png +
+
+

This is a key issue that you’ll have to resolve time and time again when working with geospatial data!

+

It all revolves around coordinate reference systems and projections.

+
+
+
+

3.4 Coordinate Reference Systems (CRS)

+

Question Do you have experience with Coordinate Reference Systems?

+



As a refresher, a CRS describes how the coordinates in a geospatial dataset relate to locations on the surface of the earth.

+

A geographic CRS consists of:

+
    +

  • a 3D model of the shape of the earth (a datum), approximated as a sphere or spheroid (aka ellipsoid)

    • +

  • the units of the coordinate system (e.g, decimal degrees, meters, feet) and

    • +

  • the origin (i.e. the 0,0 location), specified as the meeting of the equator and the prime meridian(

    • +
+

A projected CRS consists of

+
    +

  • a geographic CRS

    • +

  • a map projection and related parameters used to transform the geographic coordinates to 2D space.

    +
      +

    • a map projection is a mathematical model used to transform coordinate data

      • +
    +
    • +
+
+

A Geographic vs Projected CRS

+
+

There are many, many CRSs

+

Theoretically the number of CRSs is unlimited!

+

Why? Primariy, because there are many different definitions of the shape of the earth, multiplied by many different ways to cast its surface into 2 dimensions. Our understanding of the earth’s shape and our ability to measure it has changed greatly over time.

+
+
+

Why are CRSs Important?

+
    +

  • You need to know the data about your data (or metadata) to use it appropriately.

    • +

  • All projected CRSs introduce distortion in shape, area, and/or distance. So understanding what CRS best maintains the characteristics you need for your area of interest and your analysis is important.

    • +

  • Some analysis methods expect geospatial data to be in a projected CRS

    +
      +

    • For example, geopandas expects a geodataframe to be in a projected CRS for area or distance based analyses.

      • +
    +
    • +

  • Some Python libraries, but not all, implement dynamic reprojection from the input CRS to the required CRS and assume a specific CRS (WGS84) when a CRS is not explicitly defined.

    • +

  • Most Python spatial libraries, including Geopandas, require geospatial data to be in the same CRS if they are being analysed together.

    • +
+
+
+

What you need to know when working with CRSs

+
    +

  • What CRSs used in your study area and their main characteristics

    • +

  • How to identify, or get, the CRS of a geodataframe

    • +

  • How to set the CRS of geodataframe (i.e. define the projection)

    • +

  • Hot to transform the CRS of a geodataframe (i.e. reproject the data)

    • +
+
+
+
+

Codes for CRSs commonly used with CA data

+

CRSs are typically referenced by an EPSG code.

+

It’s important to know the commonly used CRSs and their EPSG codes for your geographic area of interest.

+

For example, below is a list of commonly used CRSs for California geospatial data along with their EPSG codes.

+
+

Geographic CRSs

+

-4326: WGS84 (units decimal degrees) - the most commonly used geographic CRS

+

-4269: NAD83 (units decimal degrees) - the geographic CRS customized to best fit the USA. This is used by all Census geographic data.

+
+

NAD83 (epsg:4269) are approximately the same as WGS84(epsg:4326) although locations can differ by up to 1 meter in the continental USA and elsewhere up to 3m. That is not a big issue with census tract data as these data are only accurate within +/-7meters.

+
+
+
+

Projected CRSs

+

-5070: CONUS NAD83 (units meters) projected CRS for mapping the entire contiguous USA (CONUS)

+

-3857: Web Mercator (units meters) conformal (shape preserving) CRS used as the default in web mapping

+

-3310: CA Albers Equal Area, NAD83 (units meters) projected CRS for CA statewide mapping and spatial analysis

+

-26910: UTM Zone 10N, NAD83 (units meters) projected CRS for northern CA mapping & analysis

+

-26911: UTM Zone 11N, NAD83 (units meters) projected CRS for Southern CA mapping & analysis

+

-102641 to 102646: CA State Plane zones 1-6, NAD83 (units feet) projected CRS used for local analysis.

+

You can find the full CRS details on the website https://www.spatialreference.org

+
+
+
+
+

3.5 Getting the CRS

+
+

Getting the CRS of a gdf

+

GeoPandas GeoDataFrames have a crs attribute that returns the CRS of the data.

+
+
+
counties.crs
+
+
+
+
+
<Projected CRS: EPSG:3310>
+Name: NAD83 / California Albers
+Axis Info [cartesian]:
+- X[east]: Easting (metre)
+- Y[north]: Northing (metre)
+Area of Use:
+- name: USA - California
+- bounds: (-124.45, 32.53, -114.12, 42.01)
+Coordinate Operation:
+- name: California Albers
+- method: Albers Equal Area
+Datum: North American Datum 1983
+- Ellipsoid: GRS 1980
+- Prime Meridian: Greenwich
+
+
+
+
+
+
+
states_limited.crs
+
+
+
+
+
<Geographic 2D CRS: EPSG:4326>
+Name: WGS 84
+Axis Info [ellipsoidal]:
+- Lat[north]: Geodetic latitude (degree)
+- Lon[east]: Geodetic longitude (degree)
+Area of Use:
+- name: World
+- bounds: (-180.0, -90.0, 180.0, 90.0)
+Datum: World Geodetic System 1984
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
+
+
+
+
+

As we can clearly see from those two printouts (even if we don’t understand all the content!), +the CRSs of our two datasets are different! This explains why we couldn’t overlay them correctly!

+
+

The above CRS definition specifies

+
    +

  • the name of the CRS (WGS84),

    • +

  • the axis units (degree)

    • +

  • the shape (datum),

    • +

  • and the origin (Prime Meridian, and the equator)

    • +

  • and the area for which it is best suited (World)

    • +
+
+

Notes:

+
    +

  • geocentric latitude and longitude assume a spherical (round) model of the shape of the earth

    • +

  • geodetic latitude and longitude assume a spheriodal (ellipsoidal) model, which is closer to the true shape.

    • +

  • geodesy is the study of the shape of the earth.

    • +
+
+

NOTE: If you print a crs call, Python will just display the EPSG code used to initiate the CRS object. Depending on your versions of Geopandas and its dependencies, this may or may not look different from what we just saw above.

+
+
+
print(states_limited.crs)
+
+
+
+
+
epsg:4326
+
+
+
+
+
+
+
+

3.6 Setting the CRS

+

You can also set the CRS of a gdf using the crs attribute. You would set the CRS if is not defined or if you think it is incorrectly defined.

+
+

In desktop GIS terminology setting the CRS is called defining the CRS

+
+

As an example, let’s set the CRS of our data to None

+
+
+
# first set the CRS to None
+states_limited.crs = None
+
+
+
+
+
+
+
# Check it again
+states_limited.crs
+
+
+
+
+

…hummm…

+

If a variable has a null value (None) then displaying it without printing it won’t display anything!

+
+
+
# Check it again
+print(states_limited.crs)
+
+
+
+
+
None
+
+
+
+
+

Now we’ll set it back to its correct CRS.

+
+
+
# Set it to 4326
+states_limited.crs = "epsg:4326"
+
+
+
+
+
+
+
# Show it
+states_limited.crs
+
+
+
+
+
<Geographic 2D CRS: EPSG:4326>
+Name: WGS 84
+Axis Info [ellipsoidal]:
+- Lat[north]: Geodetic latitude (degree)
+- Lon[east]: Geodetic longitude (degree)
+Area of Use:
+- name: World
+- bounds: (-180.0, -90.0, 180.0, 90.0)
+Datum: World Geodetic System 1984
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
+
+
+
+
+

NOTE: You can set the CRS to anything you like, but that doesn’t make it correct! This is because setting the CRS does not change the coordinate data; it just tells the software how to interpret it.

+
+
+

3.7 Transforming or Reprojecting the CRS

+

You can transform the CRS of a geodataframe with the to_crs method.

+
+

In desktop GIS terminology transforming the CRS is called projecting the data (or reprojecting the data)

+
+

When you do this you want to save the output to a new GeoDataFrame.

+
+
+
states_limited_utm10 = states_limited.to_crs( "epsg:26910")
+
+
+
+
+

Now take a look at the CRS.

+
+
+
states_limited_utm10.crs
+
+
+
+
+
<Projected CRS: EPSG:26910>
+Name: NAD83 / UTM zone 10N
+Axis Info [cartesian]:
+- E[east]: Easting (metre)
+- N[north]: Northing (metre)
+Area of Use:
+- name: North America - 126°W to 120°W and NAD83 by country
+- bounds: (-126.0, 30.54, -119.99, 81.8)
+Coordinate Operation:
+- name: UTM zone 10N
+- method: Transverse Mercator
+Datum: North American Datum 1983
+- Ellipsoid: GRS 1980
+- Prime Meridian: Greenwich
+
+
+
+
+

You can see the result immediately by plotting the data.

+
+
+
# plot geographic gdf
+states_limited.plot();
+plt.axis('square');
+
+# plot utm gdf
+states_limited_utm10.plot();
+plt.axis('square')
+
+
+
+
+
(134312.9521453322, 5295973.096958174, 2936443.847710154, 8098103.992522996)
+
+
+../_images/03_CRS_Map_Projections-Copy1_53_1.png +../_images/03_CRS_Map_Projections-Copy1_53_2.png +
+
+
+
+
# Your thoughts here
+
+
+
+
+
+ +
+
+
+

Questions

+
+
    +

  1. What two key differences do you see between the two plots above?

    2. +

  2. Do either of these plotted USA maps look good?

    3. +

  3. Try looking at the common CRS EPSG codes above and see if any of them look better for the whole country than what we have now. Then try transforming the states data to the CRS that you think would be best and plotting it. (Use the code cell two cells below.)

    4. +
+
+
+
# YOUR CODE HERE
+
+
+
+
+

Double-click to see solution!

+
+
+
+

3.8 Plotting states and counties together

+

Now that we know what a CRS is and how we can set them, let’s convert our counties GeoDataFrame to match up with out states’ crs.

+
+
+
# Convert counties data to NAD83 
+counties_utm10 = counties.to_crs("epsg:26910")
+
+
+
+
+
+
+
counties_utm10.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/03_CRS_Map_Projections-Copy1_61_1.png +
+
+
+
+
# Plot it together!
+fig, ax = plt.subplots(figsize=(10,10))
+states_limited_utm10.plot(color='lightgrey', ax=ax)
+counties_utm10.plot(color='darkgreen',ax=ax)
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/03_CRS_Map_Projections-Copy1_62_1.png +
+
+

Since we know that the best CRS to plot the contiguous US from the above question is 5070, let’s also transform and plot everything in that CRS.

+
+
+
counties_conus = counties.to_crs("epsg:5070")
+
+
+
+
+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+states_limited_conus.plot(color='lightgrey', ax=ax)
+counties_conus.plot(color='darkgreen',ax=ax)
+
+
+
+
+
---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+<ipython-input-31-d0b87fee21a9> in <module>
+      1 fig, ax = plt.subplots(figsize=(10,10))
+----> 2 states_limited_conus.plot(color='lightgrey', ax=ax)
+      3 counties_conus.plot(color='darkgreen',ax=ax)
+
+NameError: name 'states_limited_conus' is not defined
+
+
+../_images/03_CRS_Map_Projections-Copy1_65_1.png +
+
+
+
+

3.9 Recap

+

In this lesson we learned about…

+
    +

  • Coordinate Reference Systems

    • +

  • Getting the CRS of a geodataframe

    +
      +

    • crs

      • +
    +
    • +

  • Transforming/repojecting CRS

    +
      +

    • to_crs

      • +
    +
    • +

  • Overlaying maps

    • +
+
+
+

Exercise: CRS Management

+

Now it’s time to take a crack and managing the CRS of a new dataset. In the code cell below, write code to:

+
    +

  1. Bring in the CA places data (notebook_data/census/Places/cb_2018_06_place_500k.zip)

    2. +

  2. Check if the CRS is EPSG code 26910. If not, transform the CRS

    3. +

  3. Plot the California counties and places together.

    4. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE
+
+
+
+
+
+
+

Double-click to see solution!

+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/ran/04_More_Data_More_Maps-Copy1.html b/_build/html/ran/04_More_Data_More_Maps-Copy1.html new file mode 100644 index 0000000..a34e6b3 --- /dev/null +++ b/_build/html/ran/04_More_Data_More_Maps-Copy1.html @@ -0,0 +1,1367 @@ + + + + + + + Lesson 4. More Data, More Maps! — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 4. More Data, More Maps!

+

Now that we know how to pull in data, check and transform Coordinate Reference Systems (CRS), and plot GeoDataFrames together - let’s practice doing the same thing with other geometry types. In this notebook we’ll be bringing in bike boulevards and schools, which will get us primed to think about spatial relationship questions.

+
    +

  • 4.1 Berkeley Bike Boulevards

    • +

  • 4.2 Alameda County Schools

    • +

  • Exercise: Even More Data!

    • +

  • 4.3 Map Overlays with Matplotlib

    • +

  • 4.4 Recap

    • +

  • Exercise: Overlay Mapping

    • +

  • 4.5 Teaser for Day 2

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/transportation/BerkeleyBikeBlvds.geojson’

      • +

    • ‘notebook_data/alco_schools.csv’

      • +

    • ‘notebook_data/parcels/parcel_pts_rand30pct.geojson’

      • +

    • ‘notebook_data/berkeley/BerkeleyCityLimits.shp’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 30 minutes

      • +

    • Exercises: 20 minutes +

      • +
    +
    • +
+
+

Import Libraries

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+

4.1 Berkeley Bike Boulevards

+

We’re going to bring in data bike boulevards in Berkeley. Note two things that are different from our previous data:

+
    +

  • We’re bringing in a GeoJSON this time and not a shapefile

    • +

  • We have a line geometry GeoDataFrame (our county and states data had polygon geometries)

    • +
+
+
+
bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')
+bike_blvds.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/04_More_Data_More_Maps-Copy1_4_1.png +
+
+

As usual, we’ll want to do our usual data exploration…

+
+
+
bike_blvds.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BB_STRNAMBB_STRIDBB_FROBB_TOBB_SECIDDIR_StatusALT_bikeCAShape_lenlen_kmgeometry
0Heinz/RussellRUS7th8thRUS01E/WExistingNo101.1281660.101MULTILINESTRING ((562293.786 4189795.092, 5623...
1Heinz/RussellRUS8th9thRUS02E/WEzistingNo100.8140720.101MULTILINESTRING ((562391.553 4189820.949, 5624...
2Heinz/RussellRUS9th10thRUS03E/WExistingNo100.0373960.100MULTILINESTRING ((562489.017 4189846.721, 5625...
3Heinz/RussellRUS10thSan PabloRUS04E/WExistingNo106.5928780.107MULTILINESTRING ((562585.723 4189872.321, 5626...
4San PabloRUSHeinzRussellRUS05N/SExistingNo89.5634780.090MULTILINESTRING ((562688.854 4189899.267, 5627...
+
+
+
+
+
bike_blvds.shape
+
+
+
+
+
(211, 11)
+
+
+
+
+
+
+
bike_blvds.columns
+
+
+
+
+
Index(['BB_STRNAM', 'BB_STRID', 'BB_FRO', 'BB_TO', 'BB_SECID', 'DIR_',
+       'Status', 'ALT_bikeCA', 'Shape_len', 'len_km', 'geometry'],
+      dtype='object')
+
+
+
+
+

Our bike boulevard data includes the following information:

+
    +

  • BB_STRNAM - bike boulevard Streetname

    • +

  • BB_STRID - bike boulevard Street ID

    • +

  • BB_FRO - bike boulevard origin street

    • +

  • BB_TO - bike boulevard end street

    • +

  • BB_SECID- bike boulevard section id

    • +

  • DIR_ - cardinal directions the bike boulevard runs

    • +

  • Status - status on whether the bike boulevard exists

    • +

  • ALT_bikeCA - ?

    • +

  • Shape_len - length of the boulevard in meters

    • +

  • len_km - length of the boulevard in kilometers

    • +

  • geometry

    • +
+
+ +
+
+
+

Question

+
+

Why are there 211 features when we only have 8 bike boulevards?

+

And now take a look at our CRS…

+
+
+
bike_blvds.crs
+
+
+
+
+
<Projected CRS: EPSG:32610>
+Name: WGS 84 / UTM zone 10N
+Axis Info [cartesian]:
+- E[east]: Easting (metre)
+- N[north]: Northing (metre)
+Area of Use:
+- name: World - N hemisphere - 126°W to 120°W - by country
+- bounds: (-126.0, 0.0, -120.0, 84.0)
+Coordinate Operation:
+- name: UTM zone 10N
+- method: Transverse Mercator
+Datum: World Geodetic System 1984
+- Ellipsoid: WGS 84
+- Prime Meridian: Greenwich
+
+
+
+
+

Let’s tranform our CRS to UTM Zone 10N, NAD83 that we used in the last lesson.

+
+
+
bike_blvds_utm10 = bike_blvds.to_crs( "epsg:26910")
+
+
+
+
+
+
+
bike_blvds_utm10.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BB_STRNAMBB_STRIDBB_FROBB_TOBB_SECIDDIR_StatusALT_bikeCAShape_lenlen_kmgeometry
0Heinz/RussellRUS7th8thRUS01E/WExistingNo101.1281660.101MULTILINESTRING ((562293.837 4189794.938, 5623...
1Heinz/RussellRUS8th9thRUS02E/WEzistingNo100.8140720.101MULTILINESTRING ((562391.603 4189820.796, 5624...
2Heinz/RussellRUS9th10thRUS03E/WExistingNo100.0373960.100MULTILINESTRING ((562489.067 4189846.568, 5625...
3Heinz/RussellRUS10thSan PabloRUS04E/WExistingNo106.5928780.107MULTILINESTRING ((562585.773 4189872.168, 5626...
4San PabloRUSHeinzRussellRUS05N/SExistingNo89.5634780.090MULTILINESTRING ((562688.904 4189899.113, 5627...
+
+
+
+
+
+

4.2 Alameda County Schools

+

Alright! Now that we have our bike boulevard data squared away, we’re going to bring in our Alameda County school data.

+
+
+
schools_df = pd.read_csv('notebook_data/alco_schools.csv')
+schools_df.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
XYSiteAddressCityStateTypeAPIOrg
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933Public
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932Public
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853Public
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927Public
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894Public
+
+
+
+
+
schools_df.shape
+
+
+
+
+
(550, 9)
+
+
+
+
+

Questions

+

Without looking ahead:

+
    +

  1. Is this a geodataframe?

    2. +

  2. How do you know?

    3. +
+
+
+This is not a GeoDataFrame! A couple of clues to figure that out are.. +
    +

  1. We’re pulling in a Comma Separated Value (CSV) file, which is not a geospatial data format

    2. +

  2. There is no geometry column (although we do have latitude and longitude values)

    3. +
+
+

Although our school data is not starting off as a GeoDataFrame, we actually have the tools and information to make it one. Using the gpd.GeoDataFrame constructor, we can transform our plain DataFrame into a GeoDataFrame (specifying the geometry information and then the CRS).

+
+
+
schools_gdf = gpd.GeoDataFrame(schools_df, 
+                               geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))
+schools_gdf.crs = "epsg:4326"
+schools_gdf.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
XYSiteAddressCityStateTypeAPIOrggeometry
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933PublicPOINT (-122.23876 37.74476)
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932PublicPOINT (-122.25186 37.73900)
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853PublicPOINT (-122.25892 37.76206)
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927PublicPOINT (-122.23484 37.76525)
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894PublicPOINT (-122.23808 37.75396)
+
+
+

You’ll notice that the shape is the same from what we had as a dataframe, just with the added geometry column.

+
+
+
schools_gdf.shape
+
+
+
+
+
(550, 10)
+
+
+
+
+

And with it being a GeoDataFrame, we can plot it as we did for our other data sets. +Notice that we have our first point geometry GeoDataFrame.

+
+
+
schools_gdf.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/04_More_Data_More_Maps-Copy1_27_1.png +
+
+

But of course we’ll want to transform the CRS, so that we can later plot it with our bike boulevard data.

+
+
+
schools_gdf_utm10 = schools_gdf.to_crs( "epsg:26910")
+schools_gdf_utm10.plot()
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/04_More_Data_More_Maps-Copy1_29_1.png +
+
+

In Lesson 2 we discussed that you can save out GeoDataFrames in multiple file formats. You could opt for a GeoJSON, a shapefile, etc… for point data sets it is also an option to save it out as a CSV since the geometry isn’t complicated

+
+
+

Exercise: Even More Data!

+

Let’s play around with another point GeoDataFrame.

+

In the code cell provided below, compose code to:

+
    +

  1. Read in the parcel points data (notebook_data/parcels/parcel_pts_rand30pct.geojson)

    2. +

  2. Set the CRS to be 4326

    3. +

  3. Transform the CRS to 26910

    4. +

  4. Plot and customize as desired!

    5. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+
+

4.3 Map Overlays with Matplotlib

+

No matter the geometry type we have for our GeoDataFrame, we can create overlay plots.

+

Since we’ve already done the legwork of transforming our CRS, we can go ahead and plot them together.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+bike_blvds_utm10.plot(ax=ax, color='red')
+schools_gdf_utm10 .plot(ax=ax)
+
+
+
+
+
<AxesSubplot:>
+
+
+../_images/04_More_Data_More_Maps-Copy1_35_1.png +
+
+

If we want to answer questions like “What schools are close to bike boulevards in Berkeley?”, the above plot isn’t super helpful, since the extent covers all of Alameda county.

+

Luckily, GeoDataFrames have an easy method to extract the minimium and maximum values for both x and y, so we can use that information to set the bounds for our plot.

+
+
+
minx, miny, maxx, maxy = bike_blvds.total_bounds
+print(minx, miny, maxx, maxy)
+
+
+
+
+
561541.1531499997 4189007.11635 566451.5549499998 4193483.09445
+
+
+
+
+

Using xlim and ylim we can zoom in to see if there are schools proximal to the bike boulevards.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+bike_blvds_utm10.plot(ax=ax, color='red')
+schools_gdf_utm10 .plot(ax=ax)
+plt.xlim(minx, maxx)
+plt.ylim(miny, maxy)
+
+
+
+
+
(4189007.11635, 4193483.09445)
+
+
+../_images/04_More_Data_More_Maps-Copy1_39_1.png +
+
+
+
+

4.4 Recap

+

In this lesson we learned a several new skills:

+
    +

  • Transformed an a-spatial dataframe into a geospatial one

    +
      +

    • gpd.GeoDataFrame

      • +
    +
    • +

  • Worked with point and line GeoDataFrames

    • +

  • Overlayed point and line GeoDataFrames

    • +

  • Limited the extent of a map

    +
      +

    • total_bounds

      • +
    +
    • +
+
+
+

Exercise: Overlay Mapping

+

Let’s take some time to practice reading in and reconciling new datasets, then mapping them together.

+

In the code cell provided below, write code to:

+
    +

  1. Bring in your Berkeley places shapefile (and don’t forget to check/transform the crs!) (notebook_data/berkeley/BerkeleyCityLimits.shp)

    2. +

  2. Overlay the parcel points on top of the bike boulevards

    3. +

  3. Create the same plot but limit it to the extent of Berkeley city limits

    4. +
+

BONUS: Add the Berkeley outline to your last plot!

+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+
+

Double-click the see the solution!

+ +
+
+
+

4.5 Teaser for Day 2…

+

You may be wondering if and how we could make our maps more interesting and informative than this.

+

To give you a tantalizing taste of Day 2, the answer is: Yes, we can! And here’s how!

+
+
+
ax = schools_gdf_utm10.plot(column='Org', cmap='winter', 
+                               markersize=35, edgecolor='black',
+                               linewidth=0.5, alpha=1, figsize=[9, 9],
+                               legend=True)
+ax.set_title('Public and Private Schools, Alameda County')
+
+
+
+
+
Text(0.5, 1.0, 'Public and Private Schools, Alameda County')
+
+
+../_images/04_More_Data_More_Maps-Copy1_45_1.png +
+
+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/ran/05_Data-Driven_Mapping-Copy1.html b/_build/html/ran/05_Data-Driven_Mapping-Copy1.html new file mode 100644 index 0000000..c775db7 --- /dev/null +++ b/_build/html/ran/05_Data-Driven_Mapping-Copy1.html @@ -0,0 +1,1808 @@ + + + + + + + Lesson 5. Data-driven Mapping — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 5. Data-driven Mapping

+

Data-driven mapping refers to the process of using data values to determine the symbology of mapped features. Color, shape, and size are the three most common symbology types used in data-driven mapping. +Data-driven maps are often refered to as thematic maps.

+
    +

  • 5.1 Choropleth Maps

    • +

  • 5.2 Issues with Visualization

    • +

  • 5.3 Classification Schemes

    • +

  • 5.4 Point Maps

    • +

  • 5.5 Mapping Categorical Data

    • +

  • 5.6 Recap

    • +

  • Exercise: Data-Driven Mapping

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/california_counties/CaliforniaCounties.shp’

      • +

    • ‘notebook_data/alco_schools.csv’

      • +

    • ‘notebook_data/transportation/BerkeleyBikeBlvds.geojson’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 30 minutes

      • +

    • Exercises: 15 minutes +

      • +
    +
    • +
+
+

Types of Thematic Maps

+

There are two primary types of maps used to convey data values:

+
    +

  • Choropleth maps: set the color of areas (polygons) by data value

    • +

  • Point symbol maps: set the color or size of points by data value

    • +
+

We will discuss both of these types of maps in more detail in this lesson. But let’s take a quick look at choropleth maps.

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+
+

5.1 Choropleth Maps

+

Choropleth maps are the most common type of thematic map.

+

Let’s take a look at how we can use a geodataframe to make a choropleth map.

+

We’ll start by reloading our counties dataset from Day 1.

+
+
+
counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')
+
+
+
+
+
+
+
counties.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
00KernCalifornia839631102.9851089104.2828704997664892112676...1513.538161.3506103San Bernardino,Tulare,Inyo2495935NoneNoneNoneNonePOLYGON ((193446.035 -244342.585, 194033.795 -...
10KingsCalifornia152982109.9155039111.42742183027110142562...1203.201391.3906089Fresno,Kern,Tulare2212260NoneNoneNoneNonePOLYGON ((12524.028 -179431.328, 12358.142 -17...
20LakeCalifornia6466548.66525349.0823345203312322049...72.311329.4606106None0NoneNoneNoneNoneMULTIPOLYGON (((-240632.150 93056.104, -240669...
30LassenCalifornia348957.4350397.4228562553228341234...120.924720.4206086None0NoneNoneNoneNonePOLYGON ((-45364.032 352060.633, -45248.844 35...
40Los AngelesCalifornia98186052402.399043412423.264150493659985687472828...187.944087.1906073San Bernardino,Kern2874841NoneNoneNoneNoneMULTIPOLYGON (((173874.519 -471855.293, 173852...
+

5 rows × 59 columns

+
+
+
+
+
counties.columns
+
+
+
+
+
Index(['FID_', 'NAME', 'STATE_NAME', 'POP2010', 'POP10_SQMI', 'POP2012',
+       'POP12_SQMI', 'WHITE', 'BLACK', 'AMERI_ES', 'ASIAN', 'HAWN_PI',
+       'HISPANIC', 'OTHER', 'MULT_RACE', 'MALES', 'FEMALES', 'AGE_UNDER5',
+       'AGE_5_9', 'AGE_10_14', 'AGE_15_19', 'AGE_20_24', 'AGE_25_34',
+       'AGE_35_44', 'AGE_45_54', 'AGE_55_64', 'AGE_65_74', 'AGE_75_84',
+       'AGE_85_UP', 'MED_AGE', 'MED_AGE_M', 'MED_AGE_F', 'HOUSEHOLDS',
+       'AVE_HH_SZ', 'HSEHLD_1_M', 'HSEHLD_1_F', 'MARHH_CHD', 'MARHH_NO_C',
+       'MHH_CHILD', 'FHH_CHILD', 'FAMILIES', 'AVE_FAM_SZ', 'HSE_UNITS',
+       'VACANT', 'OWNER_OCC', 'RENTER_OCC', 'NO_FARMS07', 'AVG_SIZE07',
+       'CROP_ACR07', 'AVG_SALE07', 'SQMI', 'CountyFIPS', 'NEIGHBORS',
+       'PopNeigh', 'NEIGHBOR_1', 'PopNeigh_1', 'NEIGHBOR_2', 'PopNeigh_2',
+       'geometry'],
+      dtype='object')
+
+
+
+
+

Here’s a plain map of our polygons.

+
+
+
counties.plot()
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fecd2099580>
+
+
+../_images/05_Data-Driven_Mapping-Copy1_7_1.png +
+
+

Now, for comparison, let’s create a choropleth map by setting the color of the county based on the values in the population per square mile (POP12_SQMI) column.

+
+
+
counties.plot(column='POP12_SQMI', figsize=(10,10))
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fecd3cb95e0>
+
+
+../_images/05_Data-Driven_Mapping-Copy1_9_1.png +
+
+

That’s really the heart of it. To set the color of the features based on the values in a column, set the column argument to the column name in the gdf.

+
+

Protip:

+
+
    +

  • You can quickly right-click on the plot and save to a file or open in a new browser window.

    • +
+

By default map colors are linearly scaled to data values. This is called a proportional color map.

+
    +

  • The great thing about proportional color maps is that you can visualize the full range of data values.

    • +
+

We can also add a legend, and even tweak its display.

+
+
+
counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True)
+plt.show()
+
+
+
+
+../_images/05_Data-Driven_Mapping-Copy1_13_0.png +
+
+
+
+
counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True,
+                    legend_kwds={'label': "Population Density per m$^2$",
+                                 'orientation': "horizontal"},)
+plt.show()
+
+
+
+
+../_images/05_Data-Driven_Mapping-Copy1_14_0.png +
+
+
+ +
+
+
+

Question

+
+

Why are we plotting POP12_SQMI instead of POP2012?

+
+
+

Note: Types of Color Maps

+

There are a few different types of color maps (or color palettes), each of which has a different purpose:

+
    +

  • diverging - a “diverging” set of colors are used so emphasize mid-range values as well as extremes.

    • +

  • sequential - usually with a single color hue to emphasize changes in magnitude, where darker colors typically mean higher values

    • +

  • qualitative - a diverse set of colors to identify categories and avoid implying quantitative significance.

    • +
+

+
+

Pro-tip: You can actually see all your color map options if you misspell what you put in cmap and try to run-in. Try it out!

+
+
+

Pro-tip: Sites like ColorBrewer let’s you play around with different types of color maps. If you want to create your own, The Python Graph Gallery is a way to see what your Python color options are.

+
+
+
+
+

5.2 Issues with Visualization

+
+

Types of choropleth data

+

There are several types of quantitative data variables that can be used to create a choropleth map. Let’s consider these in terms of our ACS data.

+
    +

  • Count

    +
      +

    • counts, aggregated by feature

      +
        +

      • e.g. population within a census tract

        • +
      +
      • +
    +
    • +

  • Density

    +
      +

    • count, aggregated by feature, normalized by feature area

      +
        +

      • e.g. population per square mile within a census tract

        • +
      +
      • +
    +
    • +

  • Proportions / Percentages

    +
      +

    • value in a specific category divided by total value across in all categories

      +
        +

      • e.g. proportion of the tract population that is white compared to the total tract population

        • +
      +
      • +
    +
    • +

  • Rates / Ratios

    +
      +

    • value in one category divided by value in another category

      +
        +

      • e.g. homeowner-to-renter ratio would be calculated as the number of homeowners (c_owners/ c_renters)

        • +
      +
      • +
    +
    • +
+
+
+

Interpretability of plotted data

+

The goal of a choropleth map is to use color to visualize the spatial distribution of a quantitative variable.

+

Brighter or richer colors are typically used to signify higher values.

+

A big problem with choropleth maps is that our eyes are drawn to the color of larger areas, even if the values being mapped in one or more smaller areas are more important.

+

We see just this sort of problem in our population-density map.

+

Why does our map not look that interesting? Take a look at the histogram below, then consider the following question.

+
+
+
plt.hist(counties['POP12_SQMI'],bins=40)
+plt.title('Population Density per m$^2$')
+plt.show()
+
+
+
+
+../_images/05_Data-Driven_Mapping-Copy1_21_0.png +
+
+
+ +
+
+
+

Question

+
+

What county does that outlier represent? What problem does that pose?

+
+
+
+
+

5.3 Classification schemes

+

Let’s try to make our map more interpretable!

+

The common alternative to a proportionial color map is to use a classification scheme to create a graduated color map. This is the standard way to create a choropleth map.

+

A classification scheme is a method for binning continuous data values into 4-7 classes (the default is 5) and map those classes to a color palette.

+
+

The commonly used classifications schemes:

+
    +

  • Equal intervals

    +
      +

    • equal-size data ranges (e.g., values within 0-10, 10-20, 20-30, etc.)

      • +

    • pros:

      +
        +

      • best for data spread across entire range of values

        • +

      • easily understood by map readers

        • +
      +
      • +

    • cons:

      +
        +

      • but avoid if you have highly skewed data or a few big outliers

        • +
      +
      • +
    +
    • +

  • Quantiles

    +
      +

    • equal number of observations in each bin

      • +

    • pros:

      +
        +

      • looks nice, becuase it best spreads colors across full set of data values

        • +

      • thus, it’s often the default scheme for mapping software

        • +
      +
      • +

    • cons:

      +
        +

      • bin ranges based on the number of observations, not on the data values

        • +

      • thus, different classes can have very similar or very different values.

        • +
      +
      • +
    +
    • +

  • Natural breaks

    +
      +

    • minimize within-class variance and maximize between-class differences

      • +

    • e.g. ‘fisher-jenks’

      • +

    • pros:

      +
        +

      • great for exploratory data analysis, because it can identify natural groupings

        • +
      +
      • +

    • cons:

      +
        +

      • class breaks are best fit to one dataset, so the same bins can’t always be used for multiple years

        • +
      +
      • +
    +
    • +

  • Manual

    +
      +

    • classifications are user-defined

      • +

    • pros:

      +
        +

      • especially useful if you want to slightly change the breaks produced by another scheme

        • +

      • can be used as a fixed set of breaks to compare data over time

        • +
      +
      • +

    • cons:

      +
        +

      • more work involved

        • +
      +
      • +
    +
    • +
+
+
+

Classification schemes and GeoDataFrames

+

Classification schemes can be implemented using the geodataframe plot method by setting a value for the scheme argument. This requires the pysal and mapclassify libraries to be installed in your Python environment.

+

Here is a list of the classification schemes names that we will use:

+
    +

  • equalinterval, quantiles,fisherjenks,naturalbreaks, and userdefined.

    • +
+

For more information about these classification schemes see the pysal mapclassifiers web page or check out the help docs.

+
+
+
+

Classification schemes in action

+

Let’s redo the last map using the quantile classification scheme.

+
    +

  • What is different about the code? About the output map?

    • +
+
+
+
# Plot population density - mile^2
+fig, ax = plt.subplots(figsize = (10,5)) 
+counties.plot(column='POP12_SQMI', 
+                   scheme="quantiles",
+                   legend=True,
+                   ax=ax
+                   )
+ax.set_title("Population Density per Sq Mile")
+
+
+
+
+
Text(0.5, 1.0, 'Population Density per Sq Mile')
+
+
+../_images/05_Data-Driven_Mapping-Copy1_27_1.png +
+
+
+
+

User Defined Classification Schemes

+

You may get pretty close to your final map without being completely satisfied. In this case you can manually define a classification scheme.

+

Let’s customize our map with a user-defined classification scheme where we manually set the breaks for the bins using the classification_kwds argument.

+
+
+
fig, ax = plt.subplots(figsize = (14,8)) 
+counties.plot(column='POP12_SQMI',
+                    legend=True, 
+                    cmap="RdYlGn", 
+                    scheme='user_defined', 
+                    classification_kwds={'bins':[50,100,200,300,400]},
+                    ax=ax)
+ax.set_title("Population Density per Sq Mile")
+
+
+
+
+
Text(0.5, 1.0, 'Population Density per Sq Mile')
+
+
+../_images/05_Data-Driven_Mapping-Copy1_29_1.png +
+
+

Since we are customizing our plot, we can also edit our legend to specify and format the text so that it’s easier to read.

+
    +

  • We’ll use legend_labels_list to customize the labels for group in the legend.

    • +
+
+
+
fig, ax = plt.subplots(figsize = (14,8)) 
+counties.plot(column='POP12_SQMI',
+                    legend=True, 
+                    cmap="RdYlGn", 
+                    scheme='user_defined', 
+                    classification_kwds={'bins':[50,100,200,300,400]},
+                    ax=ax)
+
+# Create the labels for the legend
+legend_labels_list = ['<50','50 to 100','100 to 200','200 to 300','300 to 400','>400']
+
+# Apply the labels to the plot
+for j in range(0,len(ax.get_legend().get_texts())):
+        ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])
+
+ax.set_title("Population Density per Sq Mile")
+
+
+
+
+
Text(0.5, 1.0, 'Population Density per Sq Mile')
+
+
+../_images/05_Data-Driven_Mapping-Copy1_31_1.png +
+
+
+
+

Let’s plot a ratio

+

If we look at the columns in our dataset, we see we have a number of variables +from which we can calculate proportions, rates, and the like.

+

Let’s try that out:

+
+
+
counties.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
00KernCalifornia839631102.9851089104.2828704997664892112676...1513.538161.3506103San Bernardino,Tulare,Inyo2495935NoneNoneNoneNonePOLYGON ((193446.035 -244342.585, 194033.795 -...
10KingsCalifornia152982109.9155039111.42742183027110142562...1203.201391.3906089Fresno,Kern,Tulare2212260NoneNoneNoneNonePOLYGON ((12524.028 -179431.328, 12358.142 -17...
20LakeCalifornia6466548.66525349.0823345203312322049...72.311329.4606106None0NoneNoneNoneNoneMULTIPOLYGON (((-240632.150 93056.104, -240669...
30LassenCalifornia348957.4350397.4228562553228341234...120.924720.4206086None0NoneNoneNoneNonePOLYGON ((-45364.032 352060.633, -45248.844 35...
40Los AngelesCalifornia98186052402.399043412423.264150493659985687472828...187.944087.1906073San Bernardino,Kern2874841NoneNoneNoneNoneMULTIPOLYGON (((173874.519 -471855.293, 173852...
+

5 rows × 59 columns

+
+
+
+
+
fig, ax = plt.subplots(figsize = (15,6)) 
+
+# Plot percent hispanic as choropleth
+counties.plot(column=(counties['HISPANIC']/counties['POP2012'] * 100), 
+                        legend=True, 
+                        cmap="Blues", 
+                        scheme='user_defined', 
+                        classification_kwds={'bins':[20,40,60,80]},
+                        edgecolor="grey",
+                        linewidth=0.5,
+                        ax=ax)
+
+legend_labels_list = ['<20%','20% - 40%','40% - 60%','60% - 80%','80% - 100%']
+for j in range(0,len(ax.get_legend().get_texts())):
+        ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])
+
+ax.set_title("Percent Hispanic Population")
+plt.tight_layout()
+
+
+
+
+../_images/05_Data-Driven_Mapping-Copy1_34_0.png +
+
+
+ +
+
+
+

Questions

+
+
    +

  1. What new options and operations have we added to our code?

    2. +

  2. Based on our code, what title would you give this plot to describe what it displays?

    3. +

  3. How many bins do we specify in the legend_labels_list object, and how many bins are in the map legend? Why?

    4. +
+
+
+
+
+

5.4 Point maps

+

Choropleth maps are great, but mapping using point symbols enables us to visualize our spatial data in another way.

+

If you know both mapping methods you can expand how much information you can show in one map.

+

For example, point maps are a great way to map counts because the varying sizes of areas are deemphasized.

+
+

Let’s read in some point data on Alameda County schools.

+
+
+
schools_df = pd.read_csv('notebook_data/alco_schools.csv')
+schools_df.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
XYSiteAddressCityStateTypeAPIOrg
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933Public
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932Public
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853Public
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927Public
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894Public
+
+
+

We got it from a plain CSV file, let’s coerce it to a GeoDataFrame.

+
+
+
schools_gdf = gpd.GeoDataFrame(schools_df, 
+                               geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))
+schools_gdf.crs = "epsg:4326"
+
+
+
+
+

Then we can map it.

+
+
+
schools_gdf.plot()
+plt.title('Alameda County Schools')
+
+
+
+
+
Text(0.5, 1.0, 'Alameda County Schools')
+
+
+../_images/05_Data-Driven_Mapping-Copy1_44_1.png +
+
+
+

Proportional Color Maps

+

Proportional color maps linearly scale the color of a point symbol by the data values.

+

Let’s try this by creating a map of API. API stands for Academic Performance Index, which is a measurement system that looks at the performance of an individual school.

+
+
+
schools_gdf.plot(column="API", cmap="gist_heat", edgecolor="grey", figsize=(10,8), legend=True)
+plt.title("Alameda County, School API scores")
+
+
+
+
+
Text(0.5, 1.0, 'Alameda County, School API scores')
+
+
+../_images/05_Data-Driven_Mapping-Copy1_46_1.png +
+
+

When you see that continuous color bar in the legend you know that the mapping of data values to colors is not classified.

+
+
+

Graduated Color Maps

+

We can also create graduated color maps by binning data values before associating them with colors. These are just like choropleth maps, except that the term “choropleth” is only used with polygon data.

+

Graduated color maps use the same syntax as the choropleth maps above - you create them by setting a value for scheme.

+

Below, we copy the code we used above to create a choropleth, but we change the name of the geodataframe to use the point gdf.

+
+
+
fig, ax = plt.subplots(figsize = (15,6)) 
+
+# Plot percent non-white with graduated colors
+schools_gdf.plot(column='API', 
+                        legend=True, 
+                        cmap="Blues",
+                        scheme='user_defined', 
+                        classification_kwds={'bins':[0,200,400,600,800]},
+                        edgecolor="grey",
+                        linewidth=0.5,
+                        #markersize=60,
+                        ax=ax)
+
+# Create a custom legend
+legend_labels_list = ['0','0 - 200','200 - 400','400 - 600','600 - 800','>800']
+
+# Apply the legend to the map
+for j in range(0,len(ax.get_legend().get_texts())):
+        ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])
+
+# Create the plot
+plt.tight_layout()
+plt.title("Alameda County, School API scores")
+
+
+
+
+
Text(0.5, 1.0, 'Alameda County, School API scores')
+
+
+../_images/05_Data-Driven_Mapping-Copy1_48_1.png +
+
+

As you can see, the syntax for a choropleth and graduated color map is the same, +although some options only apply to one or the other.

+

For example, uncomment the markersize parameter above to see how you can further customize a graduated color map.

+
+
+

Graduated symbol maps

+

Graduated symbol maps are also a great method for mapping points. These are just like graduated color maps but instead of associating symbol color with data values they associate point size. Similarly,graduated symbol maps use classification schemes to set the size of point symbols.

+
+

We demonstrate how to make graduated symbol maps along with some other mapping techniques in the Optional Mapping notebook which we encourage you to explore on your own. (Coming Soon)

+
+
+
+

5.5 Mapping Categorical Data

+

Mapping categorical data, also called qualitative data, is a bit more straightforward. There is no need to scale or classify data values. The goal of the color map is to provide a contrasting set of colors so as to clearly delineate different categories. Here’s a point-based example:

+
+
+
schools_gdf.plot(column='Org', cmap='bwr',categorical=True, legend=True)
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fecba48b5b0>
+
+
+../_images/05_Data-Driven_Mapping-Copy1_53_1.png +
+
+
+
+

5.6 Recap

+

We learned about important data driven mapping strategies and mapping concepts and can leverage what many of us know about matplotlib

+
    +

  • Choropleth Maps

    • +

  • Point maps

    • +

  • Color schemes

    • +

  • Classifications

    • +
+
+
+
+

Exercise: Data-Driven Mapping

+

Point and polygons are not the only geometry-types that we can use in data-driven mapping!

+

Run the next cell to load a dataset containing Berkeley’s bicycle boulevards (which we’ll be using more in the following notebook).

+

Then in the following cell, write your own code to:

+
    +

  1. plot the bike boulevards;

    2. +

  2. color them by status (find the correct column in the head of the dataframe, displayed below);

    3. +

  3. color them using a fitting, good-looking qualitative colormap that you choose from The Matplotlib Colormap Reference;

    4. +

  4. set the line width to 5 (check the plot method’s documentation to find the right argument for this!);

    5. +

  5. add the argument figsize=[20,20], to make your map nice and big and visible!

    6. +
+

Then answer the questions posed in the last cell.

+
+

To see the solution, double-click the Markdown cell below.

+
+
+
bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')
+bike_blvds.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BB_STRNAMBB_STRIDBB_FROBB_TOBB_SECIDDIR_StatusALT_bikeCAShape_lenlen_kmgeometry
0Heinz/RussellRUS7th8thRUS01E/WExistingNo101.1281660.101MULTILINESTRING ((562293.786 4189795.092, 5623...
1Heinz/RussellRUS8th9thRUS02E/WEzistingNo100.8140720.101MULTILINESTRING ((562391.553 4189820.949, 5624...
2Heinz/RussellRUS9th10thRUS03E/WExistingNo100.0373960.100MULTILINESTRING ((562489.017 4189846.721, 5625...
3Heinz/RussellRUS10thSan PabloRUS04E/WExistingNo106.5928780.107MULTILINESTRING ((562585.723 4189872.321, 5626...
4San PabloRUSHeinzRussellRUS05N/SExistingNo89.5634780.090MULTILINESTRING ((562688.854 4189899.267, 5627...
+
+
+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+ +
+
+
+

Questions

+
+
    +

  1. What does that map indicate about the status of the Berkeley bike boulevards?

    2. +

  2. What does that map indicate about the status of your Berkeley bike-boulevard dataset?

    3. +
+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/ran/06_Spatial_Queries-Copy1.html b/_build/html/ran/06_Spatial_Queries-Copy1.html new file mode 100644 index 0000000..6d44562 --- /dev/null +++ b/_build/html/ran/06_Spatial_Queries-Copy1.html @@ -0,0 +1,1697 @@ + + + + + + + Lesson 6. Spatial Queries — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 6. Spatial Queries

+

In spatial analysis, our goal is not just to make nice maps, +but to actually run analyses that leverage the explicitly spatial +nature of our data. The process of doing this is known as +spatial analysis.

+

To construct spatial analyses, we string together series of spatial +operations in such a way that the end result answers our question of interest. +There are many such spatial operations. These are known as spatial queries.

+
    +

  • 6.0 Load and prep some data

    • +

  • 6.1 Measurement Queries

    • +

  • 6.2 Relationship Queries

    • +

  • Exercise: Spatial Relationship Query

    • +

  • 6.3 Proximity Analysis

    • +

  • Exercise: Proximity Analysis

    • +

  • 6.4 Recap

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/census/Tracts/cb_2013_06_tract_500k.zip’

      • +

    • ‘notebook_data/protected_areas/CPAD_2020a_Units.shp’

      • +

    • ‘notebook_data/berkeley/BerkeleyCityLimits.shp’

      • +

    • ‘notebook_data/alco_schools.csv’

      • +

    • ‘notebook_data/transportation/BerkeleyBikeBlvds.geojson’

      • +

    • ‘notebook_data/transportation/bart.csv’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 45 minutes

      • +

    • Exercises: 20 minutes +

      • +
    +
    • +
+
+

We will start by reviewing the most +fundamental set, which we’ll refer to as spatial queries. +These can be divided into:

+
    +

  • Measurement queries

    +
      +

    • What is feature A’s length?

      • +

    • What is feature A’s area?

      • +

    • What is feature A’s perimeter?

      • +

    • What is feature A’s distance from feature B?

      • +

    • etc.

      • +
    +
    • +

  • Relationship queries

    +
      +

    • Is feature A within feature B?

      • +

    • Does feature A intersect with feature B?

      • +

    • Does feature A cross feature B?

      • +

    • etc.

      • +
    +
    • +
+

We’ll work through examples of each of those types of queries.

+

Then we’ll see an example of a very common spatial analysis that +is a conceptual amalgam of those two types: proximity analysis.

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+

6.0 Load and prep some data

+

Let’s read in our census tracts data again.

+
+
+
census_tracts = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip")
+census_tracts.plot()
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fac5d9dc7f0>
+
+
+../_images/06_Spatial_Queries-Copy1_5_1.png +
+
+
+
+
census_tracts.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
STATEFPCOUNTYFPTRACTCEAFFGEOIDGEOIDNAMELSADALANDAWATERgeometry
0060014003001400000US06001400300060014003004003CT11053290POLYGON ((-122.26416 37.84000, -122.26186 37.8...
1060014009001400000US06001400900060014009004009CT4208770POLYGON ((-122.28558 37.83978, -122.28319 37.8...
2060014022001400000US06001402200060014022004022CT7120820POLYGON ((-122.30403 37.80739, -122.30239 37.8...
3060014028001400000US06001402800060014028004028CT3983110POLYGON ((-122.27598 37.80622, -122.27335 37.8...
4060014048001400000US06001404800060014048004048CT6284050POLYGON ((-122.21825 37.80086, -122.21582 37.8...
+
+
+

Then we’ll grab just the Alameda Country tracts.

+
+
+
census_tracts_ac = census_tracts.loc[census_tracts['COUNTYFP']=='001'].reset_index(drop=True)
+census_tracts_ac.plot()
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fac5d71bbe0>
+
+
+../_images/06_Spatial_Queries-Copy1_8_1.png +
+
+
+
+

6.1 Measurement Queries

+

We’ll start off with some simple measurement queries.

+

For example, here’s how we can get the areas of each of our census tracts.

+
+
+
census_tracts_ac.area
+
+
+
+
+
<ipython-input-5-e8c4d6d3ea03>:1: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.
+
+  census_tracts_ac.area
+
+
+
0      0.000113
+1      0.000045
+2      0.000071
+3      0.000041
+4      0.000063
+         ...   
+356    0.000098
+357    0.002275
+358    0.000033
+359    0.000139
+360    0.000316
+Length: 361, dtype: float64
+
+
+
+
+

Okay!

+

We got…

+

numbers!

+

…?

+
+ +
+
+
+

Questions

+
+
    +

  1. What do those numbers mean?

    2. +

  2. What are our units?

    3. +

  3. And if we’re not sure, how might be find out?

    4. +
+

Let’s take a look at our CRS.

+
+
+
census_tracts_ac.crs
+
+
+
+
+
<Geographic 2D CRS: EPSG:4269>
+Name: NAD83
+Axis Info [ellipsoidal]:
+- Lat[north]: Geodetic latitude (degree)
+- Lon[east]: Geodetic longitude (degree)
+Area of Use:
+- name: North America - NAD83
+- bounds: (167.65, 14.92, -47.74, 86.46)
+Datum: North American Datum 1983
+- Ellipsoid: GRS 1980
+- Prime Meridian: Greenwich
+
+
+
+
+

Ah-hah! We’re working in an unprojected CRS, with units of decimal degrees.

+

When doing spatial analysis, we will almost always want to work in a projected CRS +that has natural distance units, such as meters!

+

Time to project!

+

(As previously, we’ll use UTM Zone 10N with a NAD83 data. +This is a good choice for our region of interest.)

+
+
+
census_tracts_ac_utm10 = census_tracts_ac.to_crs( "epsg:26910")
+
+
+
+
+
+
+
census_tracts_ac_utm10.crs
+
+
+
+
+
<Projected CRS: EPSG:26910>
+Name: NAD83 / UTM zone 10N
+Axis Info [cartesian]:
+- E[east]: Easting (metre)
+- N[north]: Northing (metre)
+Area of Use:
+- name: North America - 126°W to 120°W and NAD83 by country
+- bounds: (-126.0, 30.54, -119.99, 81.8)
+Coordinate Operation:
+- name: UTM zone 10N
+- method: Transverse Mercator
+Datum: North American Datum 1983
+- Ellipsoid: GRS 1980
+- Prime Meridian: Greenwich
+
+
+
+
+

Now let’s try our area calculation again.

+
+
+
census_tracts_ac_utm10.area
+
+
+
+
+
0      1.105797e+06
+1      4.355184e+05
+2      6.930523e+05
+3      4.003615e+05
+4      6.183936e+05
+           ...     
+356    9.653980e+05
+357    2.230584e+07
+358    3.197167e+05
+359    1.355161e+06
+360    3.087534e+06
+Length: 361, dtype: float64
+
+
+
+
+

That looks much more reasonable!

+
+ +
+
+
+
+

Question

+
+

What are our units now?

+

You may have noticed that our census tracts already have an area column in them.

+

Let’s do a sanity check on our results.

+
+
+
# calculate the area for the 0th feature
+census_tracts_ac_utm10.area[0]
+
+
+
+
+
1105796.6056938928
+
+
+
+
+
+
+
# get the area for the 0th feature according to its 'ALAND' attribute
+census_tracts['ALAND'][0]
+
+
+
+
+
1105329
+
+
+
+
+
+
+
# check equivalence of the calculated areas and the 'ALAND' column
+census_tracts_ac_utm10['ALAND'].values == census_tracts_ac_utm10.area
+
+
+
+
+
0      False
+1      False
+2      False
+3      False
+4      False
+       ...  
+356    False
+357    False
+358    False
+359    False
+360    False
+Length: 361, dtype: bool
+
+
+
+
+
+ +
+
+
+
+

Question

+
+

What explains this disagreement? Are the calculated areas incorrect?

+

We can also sum the area for Alameda county by adding .sum() to the end of our area calculation.

+
+
+
census_tracts_ac_utm10.area.sum()
+
+
+
+
+
1948917581.1122904
+
+
+
+
+

We can actually look up how large Alameda County is to check our work.The county is 739 miles2, which is around 1,914,001,213 meters2. I’d say we’re pretty close!

+

As it turns out, we can similarly use another attribute +to get the features’ lengths.

+

NOTE: In this case, given we’re +dealing with polygons, this is equivalent to getting the features’ perimeters.

+
+
+
census_tracts_ac_utm10.length
+
+
+
+
+
0       5357.060239
+1       2756.937555
+2       5395.895162
+3       2681.974829
+4       3710.388859
+           ...     
+356     4331.600289
+357    32004.773556
+358     2353.624225
+359     4718.701537
+360     8176.643793
+Length: 361, dtype: float64
+
+
+
+
+
+
+
+

6.2 Relationship Queries

+

Spatial relationship queries consider how two geometries or sets of geometries relate to one another in space.

+

+

Here is a list of the most commonly used GeoPandas methods to test spatial relationships.

+ +
+There several other GeoPandas spatial relationship predicates but they are more complex to properly employ. For example the following two operations only work with geometries that are completely aligned. + +

All of these methods takes the form:

+
Geoseries.<predicate>(geometry)
+
+
+

For example:

+
Geoseries.contains(geometry)
+
+
+
+

Let’s load a new dataset to demonstrate these queries.

+

This is a dataset containing all the protected areas (parks and the like) in California.

+
+
+
pas = gpd.read_file('./notebook_data/protected_areas/CPAD_2020a_Units.shp')
+
+
+
+
+

Does this need to be reprojected too?

+
+
+
pas.crs
+
+
+
+
+
<Projected CRS: EPSG:3310>
+Name: NAD83 / California Albers
+Axis Info [cartesian]:
+- X[east]: Easting (metre)
+- Y[north]: Northing (metre)
+Area of Use:
+- name: USA - California
+- bounds: (-124.45, 32.53, -114.12, 42.01)
+Coordinate Operation:
+- name: California Albers
+- method: Albers Equal Area
+Datum: North American Datum 1983
+- Ellipsoid: GRS 1980
+- Prime Meridian: Greenwich
+
+
+
+
+

Yes it does!

+

Let’s reproject it.

+
+
+
pas_utm10 = pas.to_crs("epsg:26910")
+
+
+
+
+

One common use for spatial queries is for spatial subsetting of data.

+

In our case, lets use intersects to +find all of the parks that have land in Alameda County.

+
+
+
census_tracts_ac_utm10.geometry.squeeze()
+
+
+
+
+
0      POLYGON ((564744.993 4188317.651, 564946.532 4...
+1      POLYGON ((562861.148 4188278.725, 563070.421 4...
+2      POLYGON ((561264.509 4184672.770, 561409.095 4...
+3      POLYGON ((563734.437 4184562.158, 563961.943 4...
+4      POLYGON ((568821.460 4184008.066, 569030.992 4...
+                             ...                        
+356    POLYGON ((591097.402 4154398.989, 591400.070 4...
+357    POLYGON ((578528.935 4151915.982, 578732.686 4...
+358    POLYGON ((563141.438 4184274.978, 563293.747 4...
+359    POLYGON ((572695.844 4175004.761, 572801.274 4...
+360    POLYGON ((581072.943 4169465.752, 581136.259 4...
+Name: geometry, Length: 361, dtype: geometry
+
+
+
+
+
+
+
pas_in_ac = pas_utm10.intersects(census_tracts_ac_utm10.geometry.unary_union)
+
+
+
+
+

If we scroll the resulting GeoDataFrame to the right we’ll see that +the COUNTY column of our resulting subset gives us a good sanity check on our results.

+
+
+
pas_utm10[pas_in_ac].head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ACCESS_TYPUNIT_IDUNIT_NAMESUID_NMAAGNCY_IDAGNCY_NAMEAGNCY_LEVAGNCY_TYPAGNCY_WEBLAYER...MNG_AG_LEVMNG_AG_TYPPARK_URLCOUNTYACRESLABEL_NAMEYR_ESTDES_TPGAP_STSgeometry
63Open Access185Augustin Bernal Park87321257Pleasanton, City ofCityCity Agencyhttp://www.cityofpleasantonca.gov/City...CityCity Agencyhttp://www.cityofpleasantonca.gov/services/rec...Alameda217.388Augustin Bernal Park0.0Local Park4POLYGON ((595746.574 4165882.573, 595740.013 4...
145Open Access366San Antonio Park248321228Oakland, City ofCityCity Agencyhttp://www2.oaklandnet.com/Government/o/opr/in...City...CityCity AgencyNoneAlameda10.619San Antonio Park0.0Local Park4POLYGON ((566704.422 4182789.292, 566827.750 4...
217Open Access586Quarry Lakes Regional Recreation Area305942032East Bay Regional Park DistrictSpecial DistrictRecreation/Parks Districthttp://www.ebparks.org/Special District...Special DistrictRecreation/Parks DistrictNoneAlameda254.616Quarry Lakes Reg. Rec. Area2001.0Local Recreation Area4MULTIPOLYGON (((588060.979 4158338.499, 587843...
393Open Access1438Tennis & Community Park262431257Pleasanton, City ofCityCity Agencyhttp://www.cityofpleasantonca.gov/City...CityCity AgencyNoneAlameda15.595Tennis & Community Park0.0Local Park4POLYGON ((596761.389 4170334.335, 597109.868 4...
408Open Access48353Sean Diamond Park329171090Dublin, City ofCityCity Agencyhttp://www.ci.dublin.ca.us/index.aspx?nid=1458City...CityCity Agencyhttps://www.dublin.ca.gov/Facilities/Facility/...Alameda4.986Sean Diamond Park2018.0Local Park4POLYGON ((601693.284 4175288.100, 601695.836 4...
+

5 rows × 22 columns

+
+
+

So does this overlay plot!

+
+
+
ax = census_tracts_ac_utm10.plot(color='gray', figsize=[12,16])
+pas_utm10[pas_in_ac].plot(ax=ax, column='ACRES', cmap='summer', legend=True,
+                          edgecolor='black', linewidth=0.4, alpha=0.8,
+                          legend_kwds={'label': "acres",
+                                       'orientation': "horizontal"})
+ax.set_title('Protected areas in Alameda County, colored by area', size=18);
+
+
+
+
+../_images/06_Spatial_Queries-Copy1_51_0.png +
+
+
+
+
# color by county?
+
+
+
+
+
+
+

Exercise: Spatial Relationship Query

+

Let’s use a spatial relationship query to create a new dataset containing Berkeley schools!

+

Run the next two cells to load datasets containing Berkeley’s city boundary and Alameda County’s +schools and to reproject them to EPSG: 26910.

+

Then in the following cell, write your own code to:

+
    +

  1. subset the schools for only those within Berkeley

    2. +

  2. plot the Berkeley boundary and then the schools as an overlay map

    3. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# load the Berkeley boundary
+berkeley = gpd.read_file("notebook_data/berkeley/BerkeleyCityLimits.shp")
+
+# transform to EPSG:26910
+berkeley_utm10 = berkeley.to_crs("epsg:26910")
+
+# display
+berkeley_utm10.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + +
CNTY_FIPSgeometry
0001POLYGON ((564127.982 4195462.653, 564144.101 4...
+
+
+
+
+
# load the Alameda County schools CSV
+schools_df = pd.read_csv('notebook_data/alco_schools.csv')
+
+# coerce it to a GeoDataFrame
+schools_gdf = gpd.GeoDataFrame(schools_df, 
+                               geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))
+# define its unprojected (EPSG:4326) CRS
+schools_gdf.crs = "epsg:4326"
+
+# transform to EPSG:26910
+schools_gdf_utm10 = schools_gdf.to_crs( "epsg:26910")
+
+# display
+schools_df.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
XYSiteAddressCityStateTypeAPIOrggeometry
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933PublicPOINT (-122.23876 37.74476)
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932PublicPOINT (-122.25186 37.73900)
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853PublicPOINT (-122.25892 37.76206)
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927PublicPOINT (-122.23484 37.76525)
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894PublicPOINT (-122.23808 37.75396)
+
+
+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+
+
+

6.3 Proximity Analysis

+

Now that we’ve seen the basic idea of spatial measurement and relationship queries, +let’s take a look at a common analysis that combines those concepts: promximity analysis.

+

Proximity analysis seeks to identify all features in a focal feature set +that are within some maximum distance of features in a reference feature set.

+

A common workflow for this analysis is:

+
    +

  1. Buffer (i.e. add a margin around) the reference dataset, out to the maximum distance.

    2. +

  2. Run a spatial relationship query to find all focal features that intersect (or are within) the buffer.

    3. +
+
+

Let’s read in our bike boulevard data again.

+

Then we’ll find out which of our Berkeley schools are within a block’s distance (200 m) of the boulevards.

+
+
+
bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')
+bike_blvds.plot()
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fac4a5e2820>
+
+
+../_images/06_Spatial_Queries-Copy1_59_1.png +
+
+

Of course, we need to reproject the boulevards to our projected CRS.

+
+
+
bike_blvds_utm10 = bike_blvds.to_crs( "epsg:26910")
+
+
+
+
+

Now we can create our 200 meter bike boulevard buffers.

+
+
+
bike_blvds_buf = bike_blvds_utm10.buffer(distance=200)
+
+
+
+
+

Now let’s overlay everything.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+berkeley_utm10.plot(color='lightgrey', ax=ax)
+bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)
+bike_blvds_utm10.plot(ax=ax)
+schools_gdf_utm10.plot(color='purple',ax=ax)
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fac4a59dfd0>
+
+
+../_images/06_Spatial_Queries-Copy1_65_1.png +
+
+

Great! Looks like we’re all ready to run our intersection to complete the proximity analysis.

+

NOTE: In order to subset with our buffers we need to call the unary_union attribute of the buffer object. +This gives us a single unified polygon, rather than a series of multipolygons representing buffers around each of the points in our multilines.

+
+
+
schools_near_blvds = berkeley_schools.within(bike_blvds_buf.unary_union)
+blvd_schools = berkeley_schools[schools_near_blvds]
+
+
+
+
+
---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+<ipython-input-30-e03b12e3d093> in <module>
+----> 1 schools_near_blvds = berkeley_schools.within(bike_blvds_buf.unary_union)
+      2 blvd_schools = berkeley_schools[schools_near_blvds]
+
+NameError: name 'berkeley_schools' is not defined
+
+
+
+
+

Now let’s overlay again, to see if the schools we subsetted make sense.

+
+
+
fig, ax = plt.subplots(figsize=(10,10))
+berkeley_utm10.plot(color='lightgrey', ax=ax)
+bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)
+bike_blvds_utm10.plot(ax=ax)
+berkeley_schools.plot(color='purple',ax=ax)
+blvd_schools.plot(color='yellow', markersize=50, ax=ax)
+
+
+
+
+

If we want to find the shortest distance from one school to the bike boulevards, we can use the distance function.

+
+
+
berkeley_schools.distance(bike_blvds_utm10.unary_union)
+
+
+
+
+
+
+

Exercise: Proximity Analysis

+

Now it’s your turn to try out a proximity analysis!

+

Run the next cell to load our BART-system data, reproject it to EPSG: 26910, and subset it to Berkeley.

+

Then in the following cell, write your own code to find all schools within walking distance (1 km) of a BART station.

+

As a reminder, let’s break this into steps:

+
    +

  1. buffer your Berkeley BART stations to 1 km (HINT: remember your units!)

    2. +

  2. use the schools’ within attribute to check whether or not they’re within the buffers (HINT: don’t forget the unary_union!)

    3. +

  3. subset the Berkeley schools using the object returned by your spatial relationship query

    4. +

  4. as always, plot your results for a good visual check!

    5. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# load the BART stations from CSV
+bart_stations = pd.read_csv('notebook_data/transportation/bart.csv')
+# coerce to a GeoDataFrame
+bart_stations_gdf = gpd.GeoDataFrame(bart_stations, 
+                               geometry=gpd.points_from_xy(bart_stations.lon, bart_stations.lat))
+# define its unprojected (EPSG:4326) CRS
+bart_stations_gdf.crs = "epsg:4326"
+# transform to UTM Zone 10 N (EPSG:26910)
+bart_stations_gdf_utm10 = bart_stations_gdf.to_crs( "epsg:26910")
+# subset to Berkeley
+berkeley_bart = bart_stations_gdf_utm10[bart_stations_gdf_utm10.within(berkeley_utm10.unary_union)]
+
+
+
+
+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+
+

6.4 Recap

+

Leveraging what we’ve learned in our earlier lessons, we got to work with map overlays and start answering questions related to proximity. Key concepts include:

+
    +

  • Measuring area and length

    +
      +

    • .area,

      • +

    • .length

      • +
    +
    • +

  • Relationship Queries

    +
      +

    • .intersects()

      • +

    • .within()

      • +
    +
    • +

  • Buffer analysis

    +
      +

    • .buffer()

      • +

    • .distance()

      • +
    +
    • +
+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/ran/07_Joins_and_Aggregation-Copy1.html b/_build/html/ran/07_Joins_and_Aggregation-Copy1.html new file mode 100644 index 0000000..efb5122 --- /dev/null +++ b/_build/html/ran/07_Joins_and_Aggregation-Copy1.html @@ -0,0 +1,2206 @@ + + + + + + + Lesson 7. Attribute and Spatial Joins — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

Lesson 7. Attribute and Spatial Joins

+

Now that we understand the logic of spatial relationship queries, +let’s take a look at another fundamental spatial operation that relies on them.

+

This operation, called a spatial join, is the process by which we can +leverage the spatial relationships between distinct datasets to merge +their information into a new, synthetic dataset.

+

This operation can be thought as the spatial equivalent of an +attribute join, in which multiple tabular datasets can be merged by +aligning matching values in a common column that they both contain. +Thus, we’ll start by developing an understanding of this operation first!

+
    +

  • 7.0 Data Input and Prep

    • +

  • 7.1 Attribute Joins

    • +

  • Exercise: Choropleth Map

    • +

  • 7.2 Spatial Joins

    • +

  • 7.3 Aggregation

    • +

  • Exercise: Aggregation

    • +

  • 7.4 Recap

    • +
+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘notebook_data/census/ACS5yr/census_variables_CA.csv’

      • +

    • ‘notebook_data/census/Tracts/cb_2013_06_tract_500k.zip’

      • +

    • ‘notebook_data/alco_schools.csv’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: 45 minutes

      • +

    • Exercises: 20 minutes +

      • +
    +
    • +
+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+

7.0 Data Input and Prep

+

Let’s read in a table of data from the US Census’ 5-year American Community Survey (ACS5).

+
+
+
# Read in the ACS5 data for CA into a pandas DataFrame.
+# Note: We force the FIPS_11_digit to be read in as a string to preserve any leading zeroes.
+acs5_df = pd.read_csv("notebook_data/census/ACS5yr/census_variables_CA.csv", dtype={'FIPS_11_digit':str})
+acs5_df.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NAMEc_racec_whitec_blackc_asianc_latinxc_race_moec_white_moec_black_moec_asian_moe...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0Census Tract 4012, Alameda County, California24561287476259283213191116124...0.8149510.1033500.0584150.0102120.0130720.5513700.0643840.1890410.0835620.058219
1Census Tract 4013, Alameda County, California39838451348827796680186411283...0.6118650.2800400.0633480.0226240.0221220.3411530.1089930.3914960.0180840.104594
2Census Tract 4014, Alameda County, California43407131902593981644314440198...0.8076830.1637390.0178030.0063250.0044510.4708460.0213170.2557990.1166140.102194
3Census Tract 4015, Alameda County, California20805631064215190369222283116...0.8413460.1014420.0538460.0033650.0000000.5020370.0906310.2301430.0478620.017312
4Census Tract 4016, Alameda County, California1889324960247274400135376164...0.8306450.0795700.0822580.0021510.0053760.5704810.1227200.1774460.0630180.000000
+

5 rows × 66 columns

+
+
+

Brief summary of the data:

+

Below is a table of the variables in this table. They were combined from +different ACS 5 year tables.

+

NOTE:

+
    +

  • variables that start with c_ are counts

    • +

  • variables that start with med_ are medians

    • +

  • variables that end in _moe are margin of error estimates

    • +

  • variables that start with _p are proportions calcuated from the counts divided by the table denominator (the total count for whom that variable was assessed)

    • +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Variable

Description

c_race

Total population

c_white

Total white non-Latinx

c_black

Total black and African American non-Latinx

c_asian

Total Asian non-Latinx

c_latinx

Total Latinx

state_fips

State level FIPS code

county_fips

County level FIPS code

tract_fips

Tracts level FIPS code

med_rent

Median rent

med_hhinc

Median household income

c_tenants

Total tenants

c_owners

Total owners

c_renters

Total renters

c_movers

Total number of people who moved

c_stay

Total number of people who stayed

c_movelocal

Number of people who moved locally

c_movecounty

Number of people who moved counties

c_movestate

Number of people who moved states

c_moveabroad

Number of people who moved abroad

c_commute

Total number of commuters

c_car

Number of commuters who use a car

c_carpool

Number of commuters who carpool

c_transit

Number of commuters who use public transit

c_bike

Number of commuters who bike

c_walk

Number of commuters who bike

year

ACS data year

FIPS_11_digit

11-digit FIPS code

+

We’re going to drop all of our moe columns by identifying all of those that end with _moe. We can do that in two steps, first by using filter to identify columns that contain the string _moe.

+
+
+
moe_cols = acs5_df.filter(like='_moe',axis=1).columns
+moe_cols
+
+
+
+
+
Index(['c_race_moe', 'c_white_moe', 'c_black_moe', 'c_asian_moe',
+       'c_latinx_moe', 'med_rent_moe', 'med_hhinc_moe', 'c_tenants_moe',
+       'c_owners_moe', 'c_renters_moe', 'c_movers_moe', 'c_stay_moe',
+       'c_movelocal_moe', 'c_movecounty_moe', 'c_movestate_moe',
+       'c_moveabroad_moe', 'c_commute_moe', 'c_car_moe', 'c_carpool_moe',
+       'c_transit_moe', 'c_bike_moe', 'c_walk_moe'],
+      dtype='object')
+
+
+
+
+
+
+
acs5_df.drop(moe_cols, axis=1, inplace=True)
+
+
+
+
+

And lastly, let’s grab only the rows for year 2018 and county FIPS code 1 (i.e. Alameda County)

+
+
+
acs5_df_ac = acs5_df[(acs5_df['year']==2018) & (acs5_df['county_fips']==1)]
+
+
+
+
+
+

Now let’s also read in our census tracts again!

+
+
+
tracts_gdf = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip")
+
+
+
+
+
+
+
tracts_gdf.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
STATEFPCOUNTYFPTRACTCEAFFGEOIDGEOIDNAMELSADALANDAWATERgeometry
0060014003001400000US06001400300060014003004003CT11053290POLYGON ((-122.26416 37.84000, -122.26186 37.8...
1060014009001400000US06001400900060014009004009CT4208770POLYGON ((-122.28558 37.83978, -122.28319 37.8...
2060014022001400000US06001402200060014022004022CT7120820POLYGON ((-122.30403 37.80739, -122.30239 37.8...
3060014028001400000US06001402800060014028004028CT3983110POLYGON ((-122.27598 37.80622, -122.27335 37.8...
4060014048001400000US06001404800060014048004048CT6284050POLYGON ((-122.21825 37.80086, -122.21582 37.8...
+
+
+
+
+
tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001']
+tracts_gdf_ac.plot()
+plt.show()
+
+
+
+
+../_images/07_Joins_and_Aggregation-Copy1_14_0.png +
+
+
+
+

7.1 Attribute Joins

+

Attribute Joins between Geodataframes and Dataframes

+

We just mapped the census tracts. But what makes a map powerful is when you map the data associated with the locations.

+
    +

  • tracts_gdf_ac: These are polygon data in a GeoDataFrame. However, as we saw in the head of that dataset, they no attributes of interest!

    • +

  • acs5_df_ac: These are 2018 ACS data from a CSV file (‘census_variables_CA.csv’), imported and read in as a pandas DataFrame. However, they have no geometries!

    • +
+

In order to map the ACS data we need to associate it with the tracts. Let’s do that now, by joining the columns from acs5_df_ac to the columns of tracts_gdf_ac using a common column as the key for matching rows. This process is called an attribute join.

+
+ +
+ +
+
+
+

Question

+
+

The image above gives us a nice conceptual summary of the types of joins we could run.

+
    +

  1. In general, why might we choose one type of join over another?

    2. +

  2. In our case, do we want an inner, left, right, or outer (AKA ‘full’) join?

    3. +
+

(NOTE: You can read more about merging in geopandas here.)

+

Okay, here we go!

+

Let’s take a look at the common column in both our DataFrames.

+
+
+
tracts_gdf_ac['GEOID'].head()
+
+
+
+
+
0    06001400300
+1    06001400900
+2    06001402200
+3    06001402800
+4    06001404800
+Name: GEOID, dtype: object
+
+
+
+
+
+
+
acs5_df_ac['FIPS_11_digit'].head()
+
+
+
+
+
8323    06001441501
+8324    06001404700
+8325    06001442500
+8326    06001450300
+8327    06001450607
+Name: FIPS_11_digit, dtype: object
+
+
+
+
+

Note that they are not named the same thing.

+
    That's okay! We just need to know that they contain the same information.
+
+
+

Also note that they are not in the same order.

+
    That's not only okay... That's the point! (If they were in the same order already then we could just join them side by side, without having Python find and line up the matching rows from each!)
+
+
+
+

Let’s do a left join to keep all of the census tracts in Alameda County and only the ACS data for those tracts.

+

NOTE: To figure out how to do this we could always take a peek at the documentation by calling +?tracts_gdf_ac.merge, or help(tracts_gdf_ac).

+
+
+
# Left join keeps all tracts and the acs data for those tracts
+tracts_acs_gdf_ac = tracts_gdf_ac.merge(acs5_df_ac, left_on='GEOID',right_on="FIPS_11_digit", how='left')
+tracts_acs_gdf_ac.head(2)
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
STATEFPCOUNTYFPTRACTCEAFFGEOIDGEOIDNAME_xLSADALANDAWATERgeometry...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0060014003001400000US06001400300060014003004003CT11053290POLYGON ((-122.26416 37.84000, -122.26186 37.8......0.8405420.0450690.0584070.0315280.0244540.4208400.0594960.2806720.0678990.057479
1060014009001400000US06001400900060014009004009CT4208770POLYGON ((-122.28558 37.83978, -122.28319 37.8......0.9061610.0656870.0057120.0224400.0000000.5557180.0689150.2133430.0608500.044721
+

2 rows × 54 columns

+
+
+

Let’s check that we have all the variables we have in our dataset now.

+
+
+
list(tracts_acs_gdf_ac.columns)
+
+
+
+
+
['STATEFP',
+ 'COUNTYFP',
+ 'TRACTCE',
+ 'AFFGEOID',
+ 'GEOID',
+ 'NAME_x',
+ 'LSAD',
+ 'ALAND',
+ 'AWATER',
+ 'geometry',
+ 'NAME_y',
+ 'c_race',
+ 'c_white',
+ 'c_black',
+ 'c_asian',
+ 'c_latinx',
+ 'state_fips',
+ 'county_fips',
+ 'tract_fips',
+ 'med_rent',
+ 'med_hhinc',
+ 'c_tenants',
+ 'c_owners',
+ 'c_renters',
+ 'c_movers',
+ 'c_stay',
+ 'c_movelocal',
+ 'c_movecounty',
+ 'c_movestate',
+ 'c_moveabroad',
+ 'c_commute',
+ 'c_car',
+ 'c_carpool',
+ 'c_transit',
+ 'c_bike',
+ 'c_walk',
+ 'year',
+ 'FIPS_11_digit',
+ 'p_white',
+ 'p_black',
+ 'p_asian',
+ 'p_latinx',
+ 'p_owners',
+ 'p_renters',
+ 'p_stay',
+ 'p_movelocal',
+ 'p_movecounty',
+ 'p_movestate',
+ 'p_moveabroad',
+ 'p_car',
+ 'p_carpool',
+ 'p_transit',
+ 'p_bike',
+ 'p_walk']
+
+
+
+
+
+ +
+
+
+
+

Question

+
+

It’s always important to run sanity checks on our results, at each step of the way!

+

In this case, how many rows and columns should we have?

+
+
+
print("Rows and columns in the Alameda County Census tract gdf:\n\t", tracts_gdf_ac.shape)
+print("Row and columns in the ACS5 2018 data:\n\t", acs5_df_ac.shape)
+print("Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\n\t", tracts_acs_gdf_ac.shape)
+
+
+
+
+
Rows and columns in the Alameda County Census tract gdf:
+	 (361, 10)
+Row and columns in the ACS5 2018 data:
+	 (361, 44)
+Rows and columns in the Alameda County Census tract gdf joined to the ACS data:
+	 (361, 54)
+
+
+
+
+

Let’s save out our merged data so we can use it in the final notebook.

+
+
+
tracts_acs_gdf_ac.to_file('outdata/tracts_acs_gdf_ac.json', driver='GeoJSON')
+
+
+
+
+
+
+

Exercise: Choropleth Map

+

We can now make choropleth maps using our attribute joined geodataframe. Go ahead and pick one variable to color the map, then map it. You can go back to lesson 5 if you need a refresher on how to make this!

+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE
+
+
+
+
+
+
+

Double-click to see solution!

+
+
+
+
+

7.2 Spatial Joins

+

Great! We’ve wrapped our heads around the concept of an attribute join.

+

Now let’s extend that concept to its spatially explicit equivalent: the spatial join!

+
+

To start, we’ll read in some other data: The Alameda County schools data.

+

Then we’ll work with that data and our tracts_acs_gdf_ac data together.

+
+
+
schools_df = pd.read_csv('notebook_data/alco_schools.csv')
+schools_gdf = gpd.GeoDataFrame(schools_df, 
+                               geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))
+schools_gdf.crs = "epsg:4326"
+
+
+
+
+

Let’s check if we have to transform the schools to match thetracts_acs_gdf_ac’s CRS.

+
+
+
print('schools_gdf CRS:', schools_gdf.crs)
+print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)
+
+
+
+
+
schools_gdf CRS: epsg:4326
+tracts_acs_gdf_ac CRS: epsg:4269
+
+
+
+
+

Yes we do! Let’s do that.

+

NOTE: Explicit syntax aiming at that dataset’s CRS leaves less room for human error!

+
+
+
schools_gdf = schools_gdf.to_crs(tracts_acs_gdf_ac.crs)
+
+print('schools_gdf CRS:', schools_gdf.crs)
+print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)
+
+
+
+
+
schools_gdf CRS: epsg:4269
+tracts_acs_gdf_ac CRS: epsg:4269
+
+
+
+
+

Now we’re ready to combine the datasets in an analysis.

+

In this case, we want to get data from the census tract within which each school is located.

+

But how can we do that? The two datasets don’t share a common column to use for a join.

+
+
+
tracts_acs_gdf_ac.columns
+
+
+
+
+
Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'AFFGEOID', 'GEOID', 'NAME_x', 'LSAD',
+       'ALAND', 'AWATER', 'geometry', 'NAME_y', 'c_race', 'c_white', 'c_black',
+       'c_asian', 'c_latinx', 'state_fips', 'county_fips', 'tract_fips',
+       'med_rent', 'med_hhinc', 'c_tenants', 'c_owners', 'c_renters',
+       'c_movers', 'c_stay', 'c_movelocal', 'c_movecounty', 'c_movestate',
+       'c_moveabroad', 'c_commute', 'c_car', 'c_carpool', 'c_transit',
+       'c_bike', 'c_walk', 'year', 'FIPS_11_digit', 'p_white', 'p_black',
+       'p_asian', 'p_latinx', 'p_owners', 'p_renters', 'p_stay', 'p_movelocal',
+       'p_movecounty', 'p_movestate', 'p_moveabroad', 'p_car', 'p_carpool',
+       'p_transit', 'p_bike', 'p_walk'],
+      dtype='object')
+
+
+
+
+
+
+
schools_gdf.columns
+
+
+
+
+
Index(['X', 'Y', 'Site', 'Address', 'City', 'State', 'Type', 'API', 'Org',
+       'geometry'],
+      dtype='object')
+
+
+
+
+

However, they do have a shared relationship by way of space!

+

So, we’ll use a spatial relationship query to figure out the census tract that +each school is in, then associate the tract’s data with that school (as additional data in the school’s row). +This is a spatial join!

+
+
+

Census Tract Data Associated with Each School

+

In this case, let’s say we’re interested in the relationship between the median household income +in a census tract (tracts_acs_gdf_ac['med_hhinc']) and a school’s Academic Performance Index +(schools_gdf['API']).

+

To start, let’s take a look at the distributions of our two variables of interest.

+
+
+
tracts_acs_gdf_ac.hist('med_hhinc')
+
+
+
+
+
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fa6ca1bda00>]],
+      dtype=object)
+
+
+../_images/07_Joins_and_Aggregation-Copy1_45_1.png +
+
+
+
+
schools_gdf.hist('API')
+
+
+
+
+
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fa6ca5d8730>]],
+      dtype=object)
+
+
+../_images/07_Joins_and_Aggregation-Copy1_46_1.png +
+
+

Oh, right! Those pesky schools with no reported APIs (i.e. API == 0)! Let’s drop those.

+
+
+
schools_gdf_api = schools_gdf.loc[schools_gdf['API'] > 0, ]
+
+
+
+
+
+
+
schools_gdf_api.hist('API')
+
+
+
+
+
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fa6c9ce9e20>]],
+      dtype=object)
+
+
+../_images/07_Joins_and_Aggregation-Copy1_49_1.png +
+
+

Much better!

+

Now, maybe we think there ought to be some correlation between the two variables? +As a first pass at this possibility, let’s overlay the two datasets, coloring each one by +its variable of interest. This should give us a sense of whether or not similar values co-occur.

+
+
+
ax = tracts_acs_gdf_ac.plot(column='med_hhinc', cmap='cividis', figsize=[18,18],
+                            legend=True, legend_kwds={'label': "median household income ($)",
+                                                      'orientation': "horizontal"})
+schools_gdf_api.plot(column='API', cmap='cividis', edgecolor='black', alpha=1, ax=ax,
+                     legend=True, legend_kwds={'label': "API", 'orientation': "horizontal"})
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fa6ca74d6d0>
+
+
+../_images/07_Joins_and_Aggregation-Copy1_51_1.png +
+
+
+
+

Spatially Joining our Schools and Census Tracts

+

Though it’s hard to say for sure, it certainly looks possible. +It would be ideal to scatterplot the variables! But in order to do that, +we need to know the median household income in each school’s tract, which +means we definitely need our spatial join!

+

We’ll first take a look at the documentation for the spatial join function, gpd.sjoin.

+
+
+
help(gpd.sjoin)
+
+
+
+
+
Help on function sjoin in module geopandas.tools.sjoin:
+
+sjoin(left_df, right_df, how='inner', op='intersects', lsuffix='left', rsuffix='right')
+    Spatial join of two GeoDataFrames.
+    
+    Parameters
+    ----------
+    left_df, right_df : GeoDataFrames
+    how : string, default 'inner'
+        The type of join:
+    
+        * 'left': use keys from left_df; retain only left_df geometry column
+        * 'right': use keys from right_df; retain only right_df geometry column
+        * 'inner': use intersection of keys from both dfs; retain only
+          left_df geometry column
+    op : string, default 'intersects'
+        Binary predicate, one of {'intersects', 'contains', 'within'}.
+        See http://shapely.readthedocs.io/en/latest/manual.html#binary-predicates.
+    lsuffix : string, default 'left'
+        Suffix to apply to overlapping column names (left GeoDataFrame).
+    rsuffix : string, default 'right'
+        Suffix to apply to overlapping column names (right GeoDataFrame).
+
+
+
+
+

Looks like the key arguments to consider are:

+
    +

  • the two GeoDataFrames (left_df and right_df)

    • +

  • the type of join to run (how), which can take the values left, right, or inner

    • +

  • the spatial relationship query to use (op)

    • +
+

NOTE:

+
    +

  • By default sjoin is an inner join. It keeps the data from both geodataframes only where the locations spatially intersect.

    • +

  • By default sjoin maintains the geometry of first geodataframe input to the operation.

    • +
+
+ +
+
+
+

Questions

+
+
    +

  1. Which GeoDataFrame are we joining onto which (i.e. which one is getting the other one’s data added to it)?

    2. +

  2. What happened to ‘outer’ as a join type?

    3. +

  3. Thus, in our operation, which GeoDataFrame should be the left_df, which should be the right_df, and how do we want our join to run?

    4. +
+

Alright! Let’s run our join!

+
+
+
schools_jointracts = gpd.sjoin(schools_gdf_api, tracts_acs_gdf_ac, how='left')
+
+
+
+
+
+
+
+

Checking Our Output

+
+
+ +
+
+
+

Questions

+
+

As always, we want to sanity-check our intermediate result before we rush ahead.

+

One way to do that is to introspect the structure of the result object a bit.

+
    +

  1. What type of object should that have given us?

    2. +

  2. What should the dimensions of that object be, and why?

    3. +

  3. If we wanted a visual check of our results (i.e. a plot or map), what could we do?

    4. +
+
+
+
print(schools_jointracts.shape)
+print(schools_gdf.shape)
+print(tracts_acs_gdf_ac.shape)
+
+
+
+
+
(325, 64)
+(550, 10)
+(361, 54)
+
+
+
+
+
+
+
schools_jointracts.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
XYSiteAddressCityStateTypeAPIOrggeometry...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933PublicPOINT (-122.23876 37.74476)...0.9016940.0531200.0133140.0235340.0083380.6807450.0776500.1072930.0047220.019150
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932PublicPOINT (-122.25186 37.73900)...0.9016940.0531200.0133140.0235340.0083380.6807450.0776500.1072930.0047220.019150
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853PublicPOINT (-122.25892 37.76206)...0.8451200.0902400.0326400.0320000.0000000.6010570.0429330.2470280.0330250.011889
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927PublicPOINT (-122.23484 37.76525)...0.9393130.0324920.0230930.0000000.0051020.5618230.0774930.1726500.0188030.036467
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894PublicPOINT (-122.23808 37.75396)...0.9344160.0311220.0107790.0214060.0022770.6455320.0675320.1503980.0150400.031849
+

5 rows × 64 columns

+
+
+

Confirmed! The output of the our sjoin operation is a GeoDataFrame (schools_jointracts) with:

+
    +

  • a row for each school that is located inside a census tract (all of them are)

    • +

  • the point geometry of that school

    • +

  • all of the attribute data columns (non-geometry columns) from both input GeoDataFrames

    • +
+
+

Let’s also take a look at an overlay map of the schools on the tracts. +If we color the schools categorically by their tracts IDs, then we should see +that all schools within a given tract polygon are the same color.

+
+
+
ax = tracts_acs_gdf_ac.plot(color='white', edgecolor='black', figsize=[18,18])
+schools_jointracts.plot(column='GEOID', ax=ax)
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fa6ca74d5e0>
+
+
+../_images/07_Joins_and_Aggregation-Copy1_64_1.png +
+
+
+
+
+

Assessing the Relationship between Median Household Income and API

+

Fantastic! That looks right!

+

Now we can create that scatterplot we were thinking about!

+
+
+
fig, ax = plt.subplots(figsize=(6,6))
+ax.scatter(schools_jointracts.med_hhinc, schools_jointracts.API)
+ax.set_xlabel('median household income ($)')
+ax.set_ylabel('API')
+
+
+
+
+
Text(0, 0.5, 'API')
+
+
+../_images/07_Joins_and_Aggregation-Copy1_66_1.png +
+
+

Wow! Just as we suspected based on our overlay map, +there’s a pretty obvious, strong, and positive correlation +between median household income in a school’s tract +and the school’s API.

+
+
+
+

7.3: Aggregation

+

We just saw that a spatial join in one way to leverage the spatial relationship +between two datasets in order to create a new, synthetic dataset.

+

An aggregation is another way we can generate new data from this relationship. +In this case, for each feature in one dataset we find all the features in another +dataset that satisfy our chosen spatial relationship query with it (e.g. within, intersects), +then aggregate them using some summary function (e.g. count, mean).

+
+
+

Getting the Aggregated School Counts

+

Let’s take this for a spin with our data. We’ll count all the schools within each census tract.

+

Note that we’ve already done the first step of spatially joining the data from the aggregating features +(the tracts) onto the data to be aggregated (our schools).

+

The next step is to group our GeoDataFrame by census tract, and then summarize our data by group. +We do this using the DataFrame method groupy.

+

To get the correct count, lets rejoin our schools on our tracts, this time keeping all schools +(not just those with APIs > 0, as before).

+
+
+
schools_jointracts = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='left')
+
+
+
+
+

Now for the groupy operation.

+

NOTE: We could really use any column, since we’re just taking a count. For now we’ll just use the school names (‘Site’).

+
+
+
schools_countsbytract = schools_jointracts[['GEOID','Site']].groupby('GEOID', as_index=False).count()
+print("Counts, rows and columns:", schools_countsbytract.shape)
+print("Tracts, rows and columns:", tracts_acs_gdf_ac.shape)
+
+# take a look at the data
+schools_countsbytract.head()
+
+
+
+
+
Counts, rows and columns: (263, 2)
+Tracts, rows and columns: (361, 54)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
GEOIDSite
0060014001001
1060014002001
2060014004002
3060014005001
4060014007002
+
+
+
+
+

Getting Tract Polygons with School Counts

+

The above groupby and count operations give us the counts we wanted.

+
    +

  • We have the 263 (of 361) census tracts that have at least one school

    • +

  • We have the number of schools within each of those tracts

    • +
+

But the output of groupby is a plain DataFrame not a GeoDataFrame.

+

If we want a GeoDataFrame then we have two options:

+
    +

  1. We could join the groupby output to tracts_acs_gdf_ac by the attribute GEOID +or

    2. +

  2. We could start over, using the GeoDataFrame dissolve method, which we can think of as a spatial groupby.

    3. +
+
+

Since we already know how to do an attribute join, we’ll do the dissolve!

+

First, let’s run a new spatial join.

+
+
+
tracts_joinschools = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='right')
+
+
+
+
+

Now, let’s run our dissolve!

+
+
+
tracts_schoolcounts = tracts_joinschools[['GEOID', 'Site', 'geometry']].dissolve(by='GEOID', aggfunc='count')
+print("Counts, rows and columns:", tracts_schoolcounts.shape)
+
+# take a look
+tracts_schoolcounts.head()
+
+
+
+
+
Counts, rows and columns: (361, 2)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
geometrySite
GEOID
06001400100POLYGON ((-122.24692 37.88544, -122.24197 37.8...1
06001400200POLYGON ((-122.25742 37.84310, -122.25620 37.8...1
06001400300POLYGON ((-122.26416 37.84000, -122.26186 37.8...0
06001400400POLYGON ((-122.26180 37.84179, -122.26130 37.8...2
06001400500POLYGON ((-122.26941 37.84811, -122.26891 37.8...1
+
+
+

Nice! Let’s break that down.

+
    +

  • The dissolve operation requires a geometry column and a grouping column (in our case, ‘GEOID’). Any geometries within the same group will be dissolved if they have the same geometry or nested geometries.

    • +

  • The aggfunc, or aggregation function, of the dissolve operation will be applied to all numeric columns in the input geodataframe (unless the function is count in which case it will count rows.)

    • +
+

Check out the Geopandas documentation on dissolve for more information.

+
+ +
+
+
+

Questions

+
+
    +

  1. Above we selected three columns from the input GeoDataFrame to create a subset as input to the dissolve operation. Why?

    2. +

  2. Why did we run a new spatial join? What would have happened if we had used the schools_jointracts object instead?

    3. +

  3. What explains the dimensions of the new object (361, 2)?

    4. +
+
+
+
+

Mapping our Spatial Join Output

+

Also, because our sjoin plus dissolve pipeline outputs a GeoDataFrame, we can now easily map the school count by census tract!

+
+
+
fig, ax = plt.subplots(figsize = (14,8)) 
+
+# Display the output of our spatial join
+tracts_schoolcounts.plot(ax=ax,column='Site', 
+                         scheme="user_defined",
+                         classification_kwds={'bins':[*range(9)]},
+                         cmap="PuRd_r",
+                         edgecolor="grey",
+                         legend=True, 
+                         legend_kwds={'title':'Number of schools'})
+schools_gdf.plot(ax=ax, color='black', markersize=2)
+
+
+
+
+
<matplotlib.axes._subplots.AxesSubplot at 0x7fa6cae870d0>
+
+
+../_images/07_Joins_and_Aggregation-Copy1_82_1.png +
+
+
+
+
+
+

Exercise: Aggregation

+
+

What is the mean API of each census tract?

+

As we mentioned, the spatial aggregation workflow that we just put together above +could have been used not to generate a new count variable, but also +to generate any other new variable the results from calling an aggregation function +on an attribute column.

+

In this case, we want to calculate and map the mean API of the schools in each census tract.

+

Copy and paste code from above where useful, then tweak and/or add to that code such that your new code:

+
    +

  1. joins the schools onto the tracts (HINT: make sure to decide whether or not you want to include schools with API = 0!)

    2. +

  2. dissolves that joined object by the tract IDs, giving you a new GeoDataFrame with each tract’s mean API (HINT: because this is now a different calculation, different problems may arise and need handling!)

    3. +

  3. plots the tracts, colored by API scores (HINT: overlay the schools points again, visualizing them in a way that will help you visually check your results!)

    4. +
+

To see the solution, double-click the Markdown cell below.

+
+
+
# YOUR CODE HERE:
+
+
+
+
+
+
+

Double-click to see solution!

+ +
+
+
+

7.4 Recap

+

We discussed how we can combine datasets to enhance any geospatial data analyses you could do. Key concepts include:

+
    +

  • Attribute joins

    +
      +

    • .merge()

      • +
    +
    • +

  • Spatial joins (order matters!)

    +
      +

    • gpd.sjoin()

      • +
    +
    • +

  • Aggregation +-.groupby()

    +
      +

    • .dissolve() (preserves geometry)

      • +
    +
    • +
+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/ran/08_Pulling_It_All_Together-Copy1.html b/_build/html/ran/08_Pulling_It_All_Together-Copy1.html new file mode 100644 index 0000000..f360679 --- /dev/null +++ b/_build/html/ran/08_Pulling_It_All_Together-Copy1.html @@ -0,0 +1,840 @@ + + + + + + + 08. Pulling it all Together — Geospatial Fundamentals in Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + + +
+ +
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+ + + +
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+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ + Contents +
+ + +
+
+
+
+
+ +
+ +
+

08. Pulling it all Together

+

For this last lesson, we’ll practice going through a full workflow!! We’ll answer the question:

+
+

What is the total grocery-store sales volume of each census tract?

+
+

WORKFLOW:

+
+Here's a set of steps that we will implement in the labeled cells below: +

8.1 Read in and Prep Data

+
    +

  • read in tracts acs joined data

    • +

  • read our grocery-data CSV into a Pandas DataFrame (it lives at 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv)

    • +

  • coerce it to a GeoDataFrame

    • +

  • define its CRS (EPSG:4326)

    • +

  • transform it to match the CRS of tracts_acs_gdf_ac

    • +

  • take a peek

    • +
+

8.2 Spatial Join and Dissolve

+
    +

  • join the two datasets in such a way that you can then…

    • +

  • group by tract and calculate the total grocery-store sales volume

    • +

  • don’t forget to check the dimensions, contents, and any other relevant aspects of your results

    • +
+

8.3 Plot and Review

+
    +

  • plot the tracts, coloring them by total grocery-store sales volume

    • +

  • plot the grocery stores on top

    • +

  • bonus points for devising a nice visualization scheme that helps you heuristically check your results!

    • +
+
+
+

INSTRUCTIONS:

+

We’ve written out some of the code for you, but you’ll need to replace the ellipses with the correct +content.

+

You can check your answers by double-clicking on the Markdown cells where indicated.

+
+ + Instructor Notes +
    +

  • Datasets used

    +
      +

    • ‘outdata/tracts_acs_gdf_ac.json’

      • +

    • ‘notebook_data/other/ca_grocery_stores_2019_wgs84.csv’

      • +
    +
    • +

  • Expected time to complete

    +
      +

    • Lecture + Questions: N/A

      • +

    • Exercises: 30 minutes +

      • +
    +
    • +
+
+
+
+
+

Install Packages

+
+
+
import pandas as pd
+import geopandas as gpd
+
+import matplotlib # base python plotting library
+import matplotlib.pyplot as plt # submodule of matplotlib
+
+# To display plots, maps, charts etc in the notebook
+%matplotlib inline  
+
+
+
+
+
+
+
+
+

8.1 Read in the Prep Data

+

We first need to prepare our data by loading both our tracts/acs and grocery data, and conduct our usual steps to make there they have the same CRS.

+
    +

  • read in our tracts acs joined data

    • +

  • read our grocery-data CSV into a Pandas DataFrame (it lives at 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv)

    • +

  • coerce it to a GeoDataFrame

    • +

  • define its CRS (EPSG:4326)

    • +

  • transform it to match the CRS of tracts_acs_gdf_ac

    • +

  • take a peek

    • +
+
+
+
# read in tracts acs data
+
+tracts_acs_gdf_ac = gpd.read_file(..)
+
+
+
+
+
  File "<ipython-input-2-668fd88e49fb>", line 3
+    tracts_acs_gdf_ac = gpd.read_file(..)
+                                      ^
+SyntaxError: invalid syntax
+
+
+
+
+
+
+
# read our grocery-data CSV into a Pandas DataFrame
+
+grocery_pts_df = pd.read_csv(...)
+
+
+
+
+
+
+
# coerce it to a GeoDataFrame
+
+grocery_pts_gdf = gpd.GeoDataFrame(grocery_pts_df, 
+                                   geometry=gpd.points_from_xy(...,...))
+
+
+
+
+
+
+
# define its CRS (NOTE: Use EPSG:4326)
+
+grocery_pts_gdf.crs = ...
+
+
+
+
+
+
+
# transform it to match the CRS of tracts_acs_gdf_ac
+
+grocery_pts_gdf.to_crs(..., inplace=...)
+
+
+
+
+
+
+
# take a peek
+
+print(grocery_pts_gdf.head())
+
+
+
+
+
+
+

Double-click here to see solution!

+ +
+
+
+

8.2 Spatial Join and Dissolve

+

Now that we have our data and they’re in the same projection, we’re going to conduct an attribute join to bring together the two datasets. From there we’ll be able to actually aggregate our data to count the total sales volume.

+
    +

  • join the two datasets in such a way that you can then…

    • +

  • group by tract and calculate the total grocery-store sales volume

    • +

  • don’t forget to check the dimensions, contents, and any other relevant aspects of your results

    • +
+
+
+
# join the two datasets in such a way that you can then...
+
+tracts_joingrocery = gpd.sjoin(..., ..., how= ...)
+
+
+
+
+
+
+
# group by tract and calculate the total grocery-store sales volume
+
+tracts_totsalesvol = tracts_joingrocery[['GEOID','geometry','SALESVOL']].dissolve(by= ...,
+                                                                                  aggfunc=..., as_index=False)
+
+
+
+
+
+
+
# don't forget to check the dimensions, contents, and any other relevant aspects of your results
+
+# check the dimensions
+print('Dimensions of result:', ...)
+print('Dimesions of census tracts:', ...)
+
+
+
+
+
+
+
# check the result
+print(tracts_totsalesvol.head())
+
+
+
+
+
+
+

Double-click here to see solution!

+ +
+
+
+

8.3 Plot and Review

+

With any time of geospatial analysis you do, it’s always nice to plot and visualize your results to check your work and start to understand the full story of your analysis.

+
    +

  • Plot the tracts, coloring them by total grocery-store sales volume

    • +

  • Plot the grocery stores on top

    • +

  • Bonus points for devising a nice visualization scheme that helps you heuristically check your results!

    • +
+
+
+
# create the figure and axes
+
+fig, ax = plt.subplots(figsize = (20,20)) 
+
+# plot the tracts, coloring by total SALESVOL
+
+tracts_totsalesvol.plot(ax=ax, column= ..., scheme="quantiles", cmap="autumn", edgecolor="grey",
+                        legend=True, legend_kwds={'title':...})
+
+
+
+
+
+
+
# subset the stores for only those within our tracts, to keep map within region of interest
+
+grocery_pts_gdf_ac = grocery_pts_gdf.loc[..., ]
+
+
+
+
+
+
+
# add the grocery stores, coloring by SALESVOL, for a visual check
+
+grocery_pts_gdf_ac.plot(ax=ax, column= ... , cmap= ..., linewidth= ..., markersize= ...,
+                        legend=True, legend_kwds={'label': ... , 'orientation': "horizontal"})
+
+
+
+
+
+
+

Double-click here to see solution!

+ +
+
+
+
+
+
+
+
+
+
+
+

Congrats!! Thanks for Joining Us for Geospatial Fundamentals!!

+
+
+ + +
+
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+
+ + + + +
+ + +
+ + +
+ +
+
+
+
+

+ + By Hikari Murayama, Drew Hart, Patty Frontiera
+ + © Copyright 2020.
+

+
+
+
+ + +
+
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+ + +
+
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Overview of Geospatial Data","Lesson 2. Introduction to Geopandas","Lesson 3. Coordinate Reference Systems (CRS) & Map Projections","Lesson 4. More Data, More Maps!","Lesson 5. Data-driven Mapping","Lesson 6. Spatial Queries","Lesson 7. Attribute and Spatial Joins","08. Pulling it all Together","Lesson 9. On Your Own: A Full Workflow","10. Read in Data from Online Sources + CSV to Geodataframe","11. Adding Basemaps with Contextily","12. Interactive Mapping with Folium","Geocoding Addresses in Python","14. Making Plots and Maps with Altair","15. Voronoi Tessellation","16. 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Overview of Geospatial Data\n", + "\n", + "Before diving into any coding, let's first go over some core concepts.\n", + "\n", + "- 1.1 Geospatial Data\n", + "- 1.2 Coordinate Reference Systems\n", + "- 1.3 Types of Spatial Data\n", + "- 1.4 Other Resources\n", + "\n", + "Note that this Jupyterbook covers *a lot*! There's so much to learn and understand about the world of doing geospatial work. But we want you to keep in mind that this really only the start of your journey. All the authors who contributed to this are still learning too :)\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.1 Geospatial Data\n", + "\n", + "So there are a couple of terms that get confused when we're trying to talk about work in this area:\n", + "- *Geographic Information Systems (GIS)*\n", + "- *Geographic Data*\n", + "- *Geospatial Data*\n", + "We'll walk through each of these term-by-term.\n", + "\n", + "**Geographic Information Systems (GIS)** is probably a term that you've heard of before and it integrates many types of data, which includes spatial location. You can think of it as a framework to analyze spatial and geographic data.\n", + "> **Note**: GIS can also be an acronym for Geographic Information Science, which is the study of the study of geographic systems.\n", + "\n", + "**Geographic data** can answer the questions \"where\" and \"what\". To make this a little bit more concrete, let's use this sign in Anatone, WA, USA as an example.\n", + "\n", + "\n", + "\n", + "
Image Credit: Dsdugan at English Wikipedia
\n", + "\n", + "\n", + "Dsdugan at English Wikipedia\n", + "\n", + "Here, our answer to the question to \"where\" is Anatone, WA. The \"what\" question is answered by all the details on the sign, for example we know that the number of dogs in Anatone is 22. These types of details are also called *attributes*.\n", + "\n", + "Another component of geographic data is *metadata*. This component includes things such as when the data was taken, by whom, how, the quality, as wel as other information about the geographic data itself. \n", + "\n", + "**Geospatial Data** is a location that is given by a set of coordinates. For example, the location for Anatone could be specified with a specific latitude and longitude ($46.135570$, $-117.132659$). \n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 Coordinate Reference Systems\n", + "\n", + "A **Coordinate Reference System** or **CRS** is a system for associating specific numerical coordinates with a position on earth. So depending on the CRS that is used the numbers for the latitude and longitude could differ.\n", + "\n", + "\n", + "\n", + "
Image Credit: Wikimedia Commons
\n", + "\n", + "\n", + "There are many CRSs because our understanding and ability to measure the surface of the earth has evolved over time. We can think of these different reasonings as an orange peel or a lamp.\n", + "\n", + "Think if we take a regular orange as our earth:\n", + "\n", + "\n", + "\n", + "
Image Credit: ESRI project package by j_nelson
\n", + "\n", + "\n", + "And the first assumption we make is that it is spherical: \n", + "\n", + "\n", + "\n", + "
Image Credit: ESRI project package by j_nelson
\n", + "\n", + "\n", + "Assuming that it's spherical will introduce some distortion, as well as how I choose to draw all of my continents on it. Plus when I decide to peel it, depending on how I do that, It'll look like different maps on a flat surface:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Image Credit: ESRI project package by j_nelson
\n", + "\n", + "\n", + "Another way to think about this is by thinking about our planet earth as a lamp in a dark room.\n", + "\n", + "\n", + "\n", + "
Image Credit: Brando
\n", + "\n", + "\n", + "\n", + "Depending on factors such as how we tilt the lamp and if our walls our flat the image that we project onto the wall will be different.\n", + "\n", + "*In short, since our earth isn't flat, our earth is distorted to make it feasible to show it on a flat surface*.\n", + "\n", + "\n", + "There are two types of coordinate reference systems.\n", + "- *Geographic CRS*\n", + "- *Projected CRS*\n", + "\n", + "*Geographic CRS* are great for storing data and has units of degrees and are widely used. WGS84 is the most commonly used CRS and is basd on satellites and used by cellphones and GPS. It has the best overall fir for most places on earth. Another common one is NAD83 which is based on both satellite and survey data. It's a great fit for USA based work and is utilized in a lot of federal data products such as the census data. Both of these CRS have *EPSG codes*, which a 4+ digit number used to reference a CRs. For WGS84 the code is 4326, while for NAD83 its 4269. You'll be using these codes when you're using CRS in Python.\n", + "\n", + "*Projected CRS* are good for mapping and spatial analysis. They transform the geographic coordinates (latitude, longitude) to be 2D (X, Y) with units such as meters. All map projections include some type of distortion, whether that be in area, shape, distance or direction. Depending on the CRS it'll probably be minimizing distortion for one of these characteristics. For example, the Mercator projection places importance on shape and direction, but in turn has distorted area as you move away from the equator.\n", + "\n", + "\n", + "\n", + "
Image Credit: QGIS Documentation
\n", + "\n", + "\n", + "\n", + "Of course some projections are worse than others. This joke projection has somehow made all continents look like South America! This story of distortion tells us that some projections are better than others.\n", + "\n", + "\n", + "\n", + "
Image Credit: xkcd comics
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "> **Note**: Here are some videos related to the concept of CRS. \n", + "> - Drawing projections on fruits: [Link](https://www.youtube.com/watch?v=wkK_HsY7S_4&t=399s)\n", + "> - West Wing discussion on using specific projections: [Link](https://www.youtube.com/watch?v=vVX-PrBRtTY&t=55s)\n", + "> - Vox on why world maps are wrong: [Link](https://www.youtube.com/watch?v=kIID5FDi2JQ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 Types of Spatial Data\n", + "\n", + "As you start to gather geospatial data, you'll encounter two types: **vector** and **raster** data.\n", + "\n", + "**Vector data** can be thought of as that that you can connect the dots with. This type of data includes points, lines, and polygons.\n", + "\n", + "\n", + "\n", + "As an example, we can look at these different types of vector data by looking at different data in San Francisco.\n", + "\n", + "\n", + "\n", + "Each of these geometry types can be used for different types of information. Point geometries are great for showing where crimes have occurred historically. Lines can show us the location and length of the freeways in the city. Polygons could help us show information such as population per square mile in different neighborhoods.\n", + "\n", + "Now let's think about what this vector data could look like when you open it up.\n", + "\n", + "\n", + "\n", + "You might get something like this. Each row represents one geospatial feature. So for our second attribute we have the ID number 2, the plot size 20, vegetation type, and a vegetation class of deciduous. Those additional information like the plot size, are **attributes**. These help describe our features. \n", + "\n", + "Furthermore, each of these features have an associated geometry or geometry collection. So in our first table our geometry is a point,\n", + "\n", + "One last thing about vector data-- each group of features is called a layer. So you could have all three of these data, and each dataset would be its own layer. \n", + "\n", + "\n", + "**Raster data** on the other hand is continous. Each location is represented by a grid cell, which are usually all the same size. There a fixed number of rows and columns, and each cell has a value that represents the attribute of interest. \n", + "\n", + "\n", + "\n", + "
Image Credit: Humboldt GSP
\n", + "\n", + "\n", + "\n", + "Raster data should feel familiar to you since images are basically raster data! \n", + "\n", + "Now that we know we have these two types of datasets, we can talk about when to use each. Vector data are better for when you have discreetly bounded data. This could be for counties, rivers, etc. On the other hand, raster data is better for continuous data (like the image we just looked at), or maybe something like temperature, elevation or rainfall.\n", + "\n", + "Now these two datasets come in different file formats, so you’ll know what it is before you pull it in for whatever GIS software you’re using. Some common ones I use are shapefile and geojsons for vector data, and geotiffs for raster data. \n", + "\n", + "| Vector | Raster |\n", + "| ----------- | ----------- |\n", + "| Shapefile (.shp…) | GeoTIFF |\n", + "| GeoJSON, JSON | netCDF |\n", + "| KML | DEM |\n", + "| GeoPackage | |\n", + "\n", + "Although these two types of data look different, and come in different formats, you can still use a combination of raster and vector data to answers questions that you’re probably aiming to answer through your own work.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.4 Other Resources\n", + "\n", + "This is really only a brief introduction to geospatial concepts! If you want to dive a little deeper, here are a couple of resources you can check out:\n", + "\n", + "- [Kaggle Learn: Geospatial Analysis in Python](https://www.kaggle.com/learn/geospatial-analysis), an online interactive tutorial\n", + "\n", + "- [Campbell & Shin, Geographic Information System Basics, v1.0](https://2012books.lardbucket.org/books/geographic-information-system-basics/index.html)\n", + "\n", + "- [ESRI Introduction to Map Design](https://www.esri.com/industries/k-12/education/~/media/Files/Pdfs/industries/k-12/pdfs/intrcart.pdf)\n", + "\n", + "- [AxisMaps Cartography Guide](https://www.axismaps.com/guide/)\n", + "\n", + "- [Gentle Introduction to GIS (QGIS)](https://docs.qgis.org/3.16/en/docs/gentle_gis_introduction/index.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/01_Overview_Geospatial_Data.py b/_build/jupyter_execute/lessons/01_Overview_Geospatial_Data.py new file mode 100644 index 0000000..e4b733e --- /dev/null +++ b/_build/jupyter_execute/lessons/01_Overview_Geospatial_Data.py @@ -0,0 +1,191 @@ +# Lesson 1. Overview of Geospatial Data + +Before diving into any coding, let's first go over some core concepts. + +- 1.1 Geospatial Data +- 1.2 Coordinate Reference Systems +- 1.3 Types of Spatial Data +- 1.4 Other Resources + +Note that this Jupyterbook covers *a lot*! There's so much to learn and understand about the world of doing geospatial work. But we want you to keep in mind that this really only the start of your journey. All the authors who contributed to this are still learning too :) + + + +## 1.1 Geospatial Data + +So there are a couple of terms that get confused when we're trying to talk about work in this area: +- *Geographic Information Systems (GIS)* +- *Geographic Data* +- *Geospatial Data* +We'll walk through each of these term-by-term. + +**Geographic Information Systems (GIS)** is probably a term that you've heard of before and it integrates many types of data, which includes spatial location. You can think of it as a framework to analyze spatial and geographic data. +> **Note**: GIS can also be an acronym for Geographic Information Science, which is the study of the study of geographic systems. + +**Geographic data** can answer the questions "where" and "what". To make this a little bit more concrete, let's use this sign in Anatone, WA, USA as an example. + + + +
Image Credit: Dsdugan at English Wikipedia
+ + +Dsdugan at English Wikipedia + +Here, our answer to the question to "where" is Anatone, WA. The "what" question is answered by all the details on the sign, for example we know that the number of dogs in Anatone is 22. These types of details are also called *attributes*. + +Another component of geographic data is *metadata*. This component includes things such as when the data was taken, by whom, how, the quality, as wel as other information about the geographic data itself. + +**Geospatial Data** is a location that is given by a set of coordinates. For example, the location for Anatone could be specified with a specific latitude and longitude ($46.135570$, $-117.132659$). + + + +## 1.2 Coordinate Reference Systems + +A **Coordinate Reference System** or **CRS** is a system for associating specific numerical coordinates with a position on earth. So depending on the CRS that is used the numbers for the latitude and longitude could differ. + + + +
Image Credit: Wikimedia Commons
+ + +There are many CRSs because our understanding and ability to measure the surface of the earth has evolved over time. We can think of these different reasonings as an orange peel or a lamp. + +Think if we take a regular orange as our earth: + + + +
Image Credit: ESRI project package by j_nelson
+ + +And the first assumption we make is that it is spherical: + + + +
Image Credit: ESRI project package by j_nelson
+ + +Assuming that it's spherical will introduce some distortion, as well as how I choose to draw all of my continents on it. Plus when I decide to peel it, depending on how I do that, It'll look like different maps on a flat surface: + + + + + + +
Image Credit: ESRI project package by j_nelson
+ + +Another way to think about this is by thinking about our planet earth as a lamp in a dark room. + + + +
Image Credit: Brando
+ + + +Depending on factors such as how we tilt the lamp and if our walls our flat the image that we project onto the wall will be different. + +*In short, since our earth isn't flat, our earth is distorted to make it feasible to show it on a flat surface*. + + +There are two types of coordinate reference systems. +- *Geographic CRS* +- *Projected CRS* + +*Geographic CRS* are great for storing data and has units of degrees and are widely used. WGS84 is the most commonly used CRS and is basd on satellites and used by cellphones and GPS. It has the best overall fir for most places on earth. Another common one is NAD83 which is based on both satellite and survey data. It's a great fit for USA based work and is utilized in a lot of federal data products such as the census data. Both of these CRS have *EPSG codes*, which a 4+ digit number used to reference a CRs. For WGS84 the code is 4326, while for NAD83 its 4269. You'll be using these codes when you're using CRS in Python. + +*Projected CRS* are good for mapping and spatial analysis. They transform the geographic coordinates (latitude, longitude) to be 2D (X, Y) with units such as meters. All map projections include some type of distortion, whether that be in area, shape, distance or direction. Depending on the CRS it'll probably be minimizing distortion for one of these characteristics. For example, the Mercator projection places importance on shape and direction, but in turn has distorted area as you move away from the equator. + + + +
Image Credit: QGIS Documentation
+ + + +Of course some projections are worse than others. This joke projection has somehow made all continents look like South America! This story of distortion tells us that some projections are better than others. + + + +
Image Credit: xkcd comics
+ + + + + +> **Note**: Here are some videos related to the concept of CRS. +> - Drawing projections on fruits: [Link](https://www.youtube.com/watch?v=wkK_HsY7S_4&t=399s) +> - West Wing discussion on using specific projections: [Link](https://www.youtube.com/watch?v=vVX-PrBRtTY&t=55s) +> - Vox on why world maps are wrong: [Link](https://www.youtube.com/watch?v=kIID5FDi2JQ) + +## 1.3 Types of Spatial Data + +As you start to gather geospatial data, you'll encounter two types: **vector** and **raster** data. + +**Vector data** can be thought of as that that you can connect the dots with. This type of data includes points, lines, and polygons. + + + +As an example, we can look at these different types of vector data by looking at different data in San Francisco. + + + +Each of these geometry types can be used for different types of information. Point geometries are great for showing where crimes have occurred historically. Lines can show us the location and length of the freeways in the city. Polygons could help us show information such as population per square mile in different neighborhoods. + +Now let's think about what this vector data could look like when you open it up. + + + +You might get something like this. Each row represents one geospatial feature. So for our second attribute we have the ID number 2, the plot size 20, vegetation type, and a vegetation class of deciduous. Those additional information like the plot size, are **attributes**. These help describe our features. + +Furthermore, each of these features have an associated geometry or geometry collection. So in our first table our geometry is a point, + +One last thing about vector data-- each group of features is called a layer. So you could have all three of these data, and each dataset would be its own layer. + + +**Raster data** on the other hand is continous. Each location is represented by a grid cell, which are usually all the same size. There a fixed number of rows and columns, and each cell has a value that represents the attribute of interest. + + + +
Image Credit: Humboldt GSP
+ + + +Raster data should feel familiar to you since images are basically raster data! + +Now that we know we have these two types of datasets, we can talk about when to use each. Vector data are better for when you have discreetly bounded data. This could be for counties, rivers, etc. On the other hand, raster data is better for continuous data (like the image we just looked at), or maybe something like temperature, elevation or rainfall. + +Now these two datasets come in different file formats, so you’ll know what it is before you pull it in for whatever GIS software you’re using. Some common ones I use are shapefile and geojsons for vector data, and geotiffs for raster data. + +| Vector | Raster | +| ----------- | ----------- | +| Shapefile (.shp…) | GeoTIFF | +| GeoJSON, JSON | netCDF | +| KML | DEM | +| GeoPackage | | + +Although these two types of data look different, and come in different formats, you can still use a combination of raster and vector data to answers questions that you’re probably aiming to answer through your own work. + + +## 1.4 Other Resources + +This is really only a brief introduction to geospatial concepts! If you want to dive a little deeper, here are a couple of resources you can check out: + +- [Kaggle Learn: Geospatial Analysis in Python](https://www.kaggle.com/learn/geospatial-analysis), an online interactive tutorial + +- [Campbell & Shin, Geographic Information System Basics, v1.0](https://2012books.lardbucket.org/books/geographic-information-system-basics/index.html) + +- [ESRI Introduction to Map Design](https://www.esri.com/industries/k-12/education/~/media/Files/Pdfs/industries/k-12/pdfs/intrcart.pdf) + +- [AxisMaps Cartography Guide](https://www.axismaps.com/guide/) + +- [Gentle Introduction to GIS (QGIS)](https://docs.qgis.org/3.16/en/docs/gentle_gis_introduction/index.html) + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
\ No newline at end of file diff --git a/_build/jupyter_execute/lessons/02_Introduction_to_GeoPandas.ipynb b/_build/jupyter_execute/lessons/02_Introduction_to_GeoPandas.ipynb new file mode 100644 index 0000000..9f968a0 --- /dev/null +++ b/_build/jupyter_execute/lessons/02_Introduction_to_GeoPandas.ipynb @@ -0,0 +1,602 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 2. Introduction to Geopandas\n", + "\n", + "In this lesson we'll learn about a package that is core to using geospatial data in Python. We'll go through the structure of the data (it's not too different from regular DataFrames!), geometries, shapefiles, and how to save your hard work.\n", + "\n", + "- 2.1 What is GeoPandas?\n", + "- 2.2 Read in a shapefile\n", + "- 2.3 Explore the GeoDataFrame\n", + "- 2.4 Plot the GeoDataFrame\n", + "- 2.5 Subset the GeoDataFrame\n", + "- 2.6 Save your data\n", + "- 2.7 Recap\n", + "- **Exercise**: IO, Manipulation, and Mapping\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/california_counties/CaliforniaCounties.shp'\n", + " - 'notebook_data/census/Places/cb_2018_06_place_500k.zip'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 5 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 What is GeoPandas?\n", + "\n", + "### GeoPandas and related Geospatial Packages\n", + "\n", + "[GeoPandas](http://geopandas.org/) is a relatively new package that makes it easier to work with geospatial data in Python. In the last few years it has grown more powerful and stable. This is really great because previously it was quite complex to work with geospatial data in Python. GeoPandas is now the go-to package for working with `vector` geospatial data in Python. \n", + "\n", + "> **Protip**: If you work with `raster` data you will want to checkout the [rasterio](https://rasterio.readthedocs.io/en/latest/) package. We will not cover raster data in this tutorial.\n", + "\n", + "### GeoPandas = pandas + geo\n", + "GeoPandas gives you access to all of the functionality of [pandas](https://pandas.pydata.org/), which is the primary data analysis tool for working with tabular data in Python. GeoPandas extends pandas with attributes and methods for working with geospatial data.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries\n", + "\n", + "Let's start by importing the libraries that we will use." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Read in a shapefile\n", + "\n", + "As we discussed in the initial geospatial overview, a *shapefile* is one type of geospatial data that holds vector data. \n", + "\n", + "> To learn more about ESRI Shapefiles, this is a good place to start: [ESRI Shapefile Wiki Page](https://en.wikipedia.org/wiki/Shapefile) \n", + "\n", + "The tricky thing to remember about shapefiles is that they're actually a collection of 3 to 9+ files together. Here's a list of all the files that can make up a shapefile:\n", + " \n", + ">`shp`: The main file that stores the feature geometry\n", + ">\n", + ">`shx`: The index file that stores the index of the feature geometry \n", + ">\n", + ">`dbf`: The dBASE table that stores the attribute information of features \n", + ">\n", + ">`prj`: The file that stores the coordinate system information. (should be required!)\n", + ">\n", + ">`xml`: Metadata —Stores information about the shapefile.\n", + ">\n", + ">`cpg`: Specifies the code page for identifying the character set to be used.\n", + "\n", + "But it remains the most commonly used file format for vector spatial data, and it's really easy to visualize in one go!\n", + "\n", + "Let's try it out with California counties, and use `geopandas` for the first time. `gpd.read_file` is a flexible function that let's you read in many different types of geospatial data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the counties shapefile\n", + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot out California counties\n", + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bam! Amazing! We're off to a running start." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Explore the GeoDataFrame\n", + "\n", + "Before we get in too deep, let's discuss what a *GeoDataFrame* is and how it's different from `pandas` *DataFrames*.\n", + "\n", + "### The GeoPandas GeoDataFrame\n", + "\n", + "A [GeoPandas GeoDataFrame](https://geopandas.org/data_structures.html#geodataframe), or `gdf` for short, is just like a pandas dataframe (`df`) but with an extra geometry column and methods & attributes that work on that column. I repeat because it's important:\n", + "\n", + "> `A GeoPandas GeoDataFrame is a pandas DataFrame with a geometry column and methods & attributes that work on that column.`\n", + "\n", + "> This means all the methods and attributes of a pandas DataFrame also work on a Geopandas GeoDataFrame!!\n", + "\n", + "With that in mind, let's start exploring out dataframe just like we would do in `pandas`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Find the number of rows and columns in counties\n", + "counties.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at the first couple of rows in our geodataframe\n", + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at all the variables included in our data\n", + "counties.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like we have a good amount of information about the total population for different years and the densities, as well as race, age, and occupancy info." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.4 Plot the GeoDataFrame\n", + "\n", + "We're able to plot our GeoDataFrame because of the extra `geometry` column.\n", + "\n", + "### Geopandas Geometries\n", + "There are three main types of geometries that can be associated with your geodataframe: points, lines and polygons:\n", + "\n", + "\n", + "\n", + "In the geodataframe these geometries are encoded in a format known as [Well-Known Text (WKT)](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry). For example:\n", + "\n", + "> - POINT (30 10)\n", + "> - LINESTRING (30 10, 10 30, 40 40)\n", + "> - POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))\n", + ">\n", + "> *where coordinates are separated by a space and coordinate pairs by a comma*\n", + "\n", + "Your geodataframe may also include the variants **multipoints, multilines, and multipolgyons** if the row-level feature of interest is comprised of multiple parts. For example, a geodataframe of states, where one row represents one state, would have a POLYGON geometry for Utah but MULTIPOLYGON for Hawaii, which includes many islands.\n", + "\n", + "> It's ok to mix and match geometries of the same family, e.g., POLYGON and MULTIPOLYGON, in the same geodatafame.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " **Question** What kind of geometry would a roads geodataframe have? What about one that includes landmarks in the San Francisco Bay Area?\n", + "\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the types of geometries in a geodataframe or a subset of the geodataframe by combining the `type` and `unique` methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's check what geometries we have in our counties geodataframe\n", + "counties['geometry'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's check to make sure that we only have polygons and multipolygons \n", + "counties['geometry'].type.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like with other plots you can make in Python, we can start customizing our map with colors, size, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We can run the following line of code to get more info about the parameters we can specify:\n", + "\n", + "?counties.plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make the figure size bigger\n", + "counties.plot(figsize=(6,9))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot(figsize=(6,9), \n", + " edgecolor='grey', # grey colored border lines\n", + " facecolor='pink' , # fill in our counties as pink\n", + " linewidth=2) # make the linedwith a width of 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.5 Subset the GeoDataframe\n", + "\n", + "Since we'll be focusing on Berkeley later in the workshop, let's subset our GeoDataFrame to just be for Alameda County." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# See all county names included in our dataset\n", + "counties['NAME'].values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like Alameda county is specified as \"Alameda\" in this dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create a new geodataframe called `alameda_county` that is a subset of our counties geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county = counties.loc[counties['NAME'] == 'Alameda'].copy().reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot our newly subsetted geodataframe\n", + "alameda_county.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice! Looks like we have what we were looking for.\n", + "\n", + "*FYI*: You can also make dynamic plots of one or more county without saving to a new gdf." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bay_area_counties = ['Alameda', 'Contra Costa', 'Marin', 'Napa', 'San Francisco', \n", + " 'San Mateo', 'Santa Clara', 'Santa Cruz', 'Solano', 'Sonoma']\n", + "counties.loc[counties['NAME'].isin(bay_area_counties)].plot()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.6 Save your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's not forget to save out our Alameda County geodataframe `alameda_county`. This way we won't need to repeat the processing steps and attribute join we did above.\n", + "\n", + "We can save it as a shapefile." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.shp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the problems of saving to a shapefile is that our column names get truncated to 10 characters (a shapefile limitation.) \n", + "\n", + "Instead of renaming all columns with obscure names that are less than 10 characters, we can save our GeoDataFrame to a spatial data file format that does not have this limation - [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) or [GPKG](https://en.wikipedia.org/wiki/GeoPackage) (geopackage) file.\n", + "- These formats have the added benefit of outputting only one file in contrast tothe multi-file shapefile format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.json\", driver=\"GeoJSON\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.gpkg\", driver=\"GPKG\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can read these in, just as you would a shapefile with `gpd.read_file`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county_test = gpd.read_file(\"outdata/alameda_county.gpkg\")\n", + "alameda_county_test.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county_test2 = gpd.read_file(\"outdata/alameda_county.json\")\n", + "alameda_county_test2.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are also many other formats we could use for data output.\n", + "\n", + "**NOTE**: If you're working with point data (i.e. a single latitude and longitude value per feature),\n", + "then CSV might be a good option!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.7 Recap\n", + "\n", + "In this lesson we learned about...\n", + "- The `geopandas` package \n", + "- Reading in shapefiles \n", + " - `gpd.read_file`\n", + "- GeoDataFrame structures\n", + " - `shape`, `head`, `columns`\n", + "- Plotting GeoDataFrames\n", + " - `plot`\n", + "- Subsetting GeoDatFrames\n", + " - `loc`\n", + "- Saving out GeoDataFrames\n", + " - `to_file`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: IO, Manipulation, and Mapping\n", + "\n", + "Now you'll get a chance to practice the operations we learned above.\n", + "\n", + "In the following cell, compose code to:\n", + "\n", + "1. Read in the California places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`)\n", + "2. Subset the data to Berkeley\n", + "3. Plot, and customize as desired\n", + "4. Save out as a shapefile (`outdata/berkeley_places.shp`)\n", + "\n", + "\n", + "*Note: pulling in a zipped shapefile has the same syntax as just pulling in a shapefile. The only difference is that insead of just putting in the filepath you'll want to write `zip://notebook_data/census/Places/cb_2018_06_place_500k.zip`*\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/02_Introduction_to_GeoPandas.py b/_build/jupyter_execute/lessons/02_Introduction_to_GeoPandas.py new file mode 100644 index 0000000..e8a3f51 --- /dev/null +++ b/_build/jupyter_execute/lessons/02_Introduction_to_GeoPandas.py @@ -0,0 +1,294 @@ +# Lesson 2. Introduction to Geopandas + +In this lesson we'll learn about a package that is core to using geospatial data in Python. We'll go through the structure of the data (it's not too different from regular DataFrames!), geometries, shapefiles, and how to save your hard work. + +- 2.1 What is GeoPandas? +- 2.2 Read in a shapefile +- 2.3 Explore the GeoDataFrame +- 2.4 Plot the GeoDataFrame +- 2.5 Subset the GeoDataFrame +- 2.6 Save your data +- 2.7 Recap +- **Exercise**: IO, Manipulation, and Mapping + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/california_counties/CaliforniaCounties.shp' + - 'notebook_data/census/Places/cb_2018_06_place_500k.zip' + +- Expected time to complete + - Lecture + Questions: 30 minutes + - Exercises: 5 minutes + + +## 2.1 What is GeoPandas? + +### GeoPandas and related Geospatial Packages + +[GeoPandas](http://geopandas.org/) is a relatively new package that makes it easier to work with geospatial data in Python. In the last few years it has grown more powerful and stable. This is really great because previously it was quite complex to work with geospatial data in Python. GeoPandas is now the go-to package for working with `vector` geospatial data in Python. + +> **Protip**: If you work with `raster` data you will want to checkout the [rasterio](https://rasterio.readthedocs.io/en/latest/) package. We will not cover raster data in this tutorial. + +### GeoPandas = pandas + geo +GeoPandas gives you access to all of the functionality of [pandas](https://pandas.pydata.org/), which is the primary data analysis tool for working with tabular data in Python. GeoPandas extends pandas with attributes and methods for working with geospatial data. + + + + +### Import Libraries + +Let's start by importing the libraries that we will use. + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +## 2.2 Read in a shapefile + +As we discussed in the initial geospatial overview, a *shapefile* is one type of geospatial data that holds vector data. + +> To learn more about ESRI Shapefiles, this is a good place to start: [ESRI Shapefile Wiki Page](https://en.wikipedia.org/wiki/Shapefile) + +The tricky thing to remember about shapefiles is that they're actually a collection of 3 to 9+ files together. Here's a list of all the files that can make up a shapefile: + +>`shp`: The main file that stores the feature geometry +> +>`shx`: The index file that stores the index of the feature geometry +> +>`dbf`: The dBASE table that stores the attribute information of features +> +>`prj`: The file that stores the coordinate system information. (should be required!) +> +>`xml`: Metadata —Stores information about the shapefile. +> +>`cpg`: Specifies the code page for identifying the character set to be used. + +But it remains the most commonly used file format for vector spatial data, and it's really easy to visualize in one go! + +Let's try it out with California counties, and use `geopandas` for the first time. `gpd.read_file` is a flexible function that let's you read in many different types of geospatial data. + +# Read in the counties shapefile +counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp') + +# Plot out California counties +counties.plot() + +Bam! Amazing! We're off to a running start. + +## 2.3 Explore the GeoDataFrame + +Before we get in too deep, let's discuss what a *GeoDataFrame* is and how it's different from `pandas` *DataFrames*. + +### The GeoPandas GeoDataFrame + +A [GeoPandas GeoDataFrame](https://geopandas.org/data_structures.html#geodataframe), or `gdf` for short, is just like a pandas dataframe (`df`) but with an extra geometry column and methods & attributes that work on that column. I repeat because it's important: + +> `A GeoPandas GeoDataFrame is a pandas DataFrame with a geometry column and methods & attributes that work on that column.` + +> This means all the methods and attributes of a pandas DataFrame also work on a Geopandas GeoDataFrame!! + +With that in mind, let's start exploring out dataframe just like we would do in `pandas`. + +# Find the number of rows and columns in counties +counties.shape + +# Look at the first couple of rows in our geodataframe +counties.head() + +# Look at all the variables included in our data +counties.columns + +It looks like we have a good amount of information about the total population for different years and the densities, as well as race, age, and occupancy info. + +## 2.4 Plot the GeoDataFrame + +We're able to plot our GeoDataFrame because of the extra `geometry` column. + +### Geopandas Geometries +There are three main types of geometries that can be associated with your geodataframe: points, lines and polygons: + + + +In the geodataframe these geometries are encoded in a format known as [Well-Known Text (WKT)](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry). For example: + +> - POINT (30 10) +> - LINESTRING (30 10, 10 30, 40 40) +> - POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10)) +> +> *where coordinates are separated by a space and coordinate pairs by a comma* + +Your geodataframe may also include the variants **multipoints, multilines, and multipolgyons** if the row-level feature of interest is comprised of multiple parts. For example, a geodataframe of states, where one row represents one state, would have a POLYGON geometry for Utah but MULTIPOLYGON for Hawaii, which includes many islands. + +> It's ok to mix and match geometries of the same family, e.g., POLYGON and MULTIPOLYGON, in the same geodatafame. + + + + + **Question** What kind of geometry would a roads geodataframe have? What about one that includes landmarks in the San Francisco Bay Area? + + + + +Your response here: + + + + + + +You can check the types of geometries in a geodataframe or a subset of the geodataframe by combining the `type` and `unique` methods. + +# Let's check what geometries we have in our counties geodataframe +counties['geometry'].head() + +# Let's check to make sure that we only have polygons and multipolygons +counties['geometry'].type.unique() + +counties.plot() + +Just like with other plots you can make in Python, we can start customizing our map with colors, size, etc. + +# We can run the following line of code to get more info about the parameters we can specify: + +?counties.plot + +# Make the figure size bigger +counties.plot(figsize=(6,9)) + +counties.plot(figsize=(6,9), + edgecolor='grey', # grey colored border lines + facecolor='pink' , # fill in our counties as pink + linewidth=2) # make the linedwith a width of 2 + +## 2.5 Subset the GeoDataframe + +Since we'll be focusing on Berkeley later in the workshop, let's subset our GeoDataFrame to just be for Alameda County. + +# See all county names included in our dataset +counties['NAME'].values + +It looks like Alameda county is specified as "Alameda" in this dataset. + +counties + +Now we can create a new geodataframe called `alameda_county` that is a subset of our counties geodataframe. + +alameda_county = counties.loc[counties['NAME'] == 'Alameda'].copy().reset_index(drop=True) + +# Plot our newly subsetted geodataframe +alameda_county.plot() + +Nice! Looks like we have what we were looking for. + +*FYI*: You can also make dynamic plots of one or more county without saving to a new gdf. + +bay_area_counties = ['Alameda', 'Contra Costa', 'Marin', 'Napa', 'San Francisco', + 'San Mateo', 'Santa Clara', 'Santa Cruz', 'Solano', 'Sonoma'] +counties.loc[counties['NAME'].isin(bay_area_counties)].plot() + + +## 2.6 Save your Data + +Let's not forget to save out our Alameda County geodataframe `alameda_county`. This way we won't need to repeat the processing steps and attribute join we did above. + +We can save it as a shapefile. + +alameda_county.to_file("outdata/alameda_county.shp") + +One of the problems of saving to a shapefile is that our column names get truncated to 10 characters (a shapefile limitation.) + +Instead of renaming all columns with obscure names that are less than 10 characters, we can save our GeoDataFrame to a spatial data file format that does not have this limation - [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) or [GPKG](https://en.wikipedia.org/wiki/GeoPackage) (geopackage) file. +- These formats have the added benefit of outputting only one file in contrast tothe multi-file shapefile format. + +alameda_county.to_file("outdata/alameda_county.json", driver="GeoJSON") + +alameda_county.to_file("outdata/alameda_county.gpkg", driver="GPKG") + +You can read these in, just as you would a shapefile with `gpd.read_file` + +alameda_county_test = gpd.read_file("outdata/alameda_county.gpkg") +alameda_county_test.plot() + +alameda_county_test2 = gpd.read_file("outdata/alameda_county.json") +alameda_county_test2.plot() + +There are also many other formats we could use for data output. + +**NOTE**: If you're working with point data (i.e. a single latitude and longitude value per feature), +then CSV might be a good option! + +## 2.7 Recap + +In this lesson we learned about... +- The `geopandas` package +- Reading in shapefiles + - `gpd.read_file` +- GeoDataFrame structures + - `shape`, `head`, `columns` +- Plotting GeoDataFrames + - `plot` +- Subsetting GeoDatFrames + - `loc` +- Saving out GeoDataFrames + - `to_file` + +## Exercise: IO, Manipulation, and Mapping + +Now you'll get a chance to practice the operations we learned above. + +In the following cell, compose code to: + +1. Read in the California places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`) +2. Subset the data to Berkeley +3. Plot, and customize as desired +4. Save out as a shapefile (`outdata/berkeley_places.shp`) + + +*Note: pulling in a zipped shapefile has the same syntax as just pulling in a shapefile. The only difference is that insead of just putting in the filepath you'll want to write `zip://notebook_data/census/Places/cb_2018_06_place_500k.zip`* + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE + + + +## Double-click to see solution! + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + diff --git a/_build/jupyter_execute/lessons/03_CRS_Map_Projections.ipynb b/_build/jupyter_execute/lessons/03_CRS_Map_Projections.ipynb new file mode 100644 index 0000000..561cbdb --- /dev/null +++ b/_build/jupyter_execute/lessons/03_CRS_Map_Projections.ipynb @@ -0,0 +1,853 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 3. Coordinate Reference Systems (CRS) & Map Projections\n", + "\n", + "Building off of what we learned in the previous notebook, we'll get to understand an integral aspect of geospatial data: Coordinate Reference Systems.\n", + "\n", + "- 3.1 California County Shapefile\n", + "- 3.2 USA State Shapefile\n", + "- 3.3 Plot the Two Together\n", + "- 3.4 Coordinate Reference System (CRS)\n", + "- 3.5 Getting the CRS\n", + "- 3.6 Setting the CRS\n", + "- 3.7 Transforming or Reprojecting the CRS\n", + "- 3.8 Plotting States and Counties Togther\n", + "- 3.9 Recap\n", + "- **Exercise**: CRS Management\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - ‘notebook_data/california_counties/CaliforniaCounties.shp’\n", + " - ‘notebook_data/us_states/us_states.shp’\n", + " - ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 10 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 California County shapefile\n", + "Let's go ahead and bring back in our California County shapefile. As before, we can read the file in using `gpd.read_file` and plot it straight away." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')\n", + "counties.plot(color='darkgreen')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even if we have an awesome map like this, sometimes we want to have more geographical context, or we just want additional information. We're going to try **overlaying** our counties GeoDataFrame on our USA states shapefile." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 USA State shapefile\n", + "\n", + "We're going to bring in our states geodataframe, and let's do the usual operations to start exploring our data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in states shapefile\n", + "states = gpd.read_file('notebook_data/us_states/us_states.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at the first few rows\n", + "states.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Count how many rows and columns we have\n", + "states.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot our states data\n", + "states.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might have noticed that our plot extends beyond the 50 states (which we also saw when we executed the `shape` method). Let's double check what states we have included in our data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states['STATE'].values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beyond the 50 states we seem to have American Samoa, Puerto Rico, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands included in this geodataframe. To make our map cleaner, let's limit the states to the contiguous states (so we'll also exclude Alaska and Hawaii)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define list of non-contiguous states\n", + "non_contiguous_us = [ 'American Samoa','Puerto Rico','Guam',\n", + " 'Commonwealth of the Northern Mariana Islands',\n", + " 'United States Virgin Islands', 'Alaska','Hawaii']\n", + "# Limit data according to above list\n", + "states_limited = states.loc[~states['STATE'].isin(non_contiguous_us)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot it\n", + "states_limited.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To prepare for our mapping overlay, let's make our states a nice, light grey color." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited.plot(color='lightgrey', figsize=(10,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Plot the two together\n", + "\n", + "Now that we have both geodataframes in our environment, we can plot both in the same figure.\n", + "\n", + "**NOTE**: To do this, note that we're getting a Matplotlib Axes object (`ax`), then explicitly adding each our layers to it\n", + "by providing the `ax=ax` argument to the `plot` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh no, what happened here?\n", + "\n", + " **Question** Without looking ahead, what do you think happened?\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "If you look at the numbers we have on the x and y axes in our two plots, you'll see that the county data has much larger numbers than our states data. It's represented in some different type of unit other than decimal degrees! \n", + "\n", + "In fact, that means if we zoom in really close into our plot we'll probably see the states data plotted. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)\n", + "ax.set_xlim(-140,-50)\n", + "ax.set_ylim(20,50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a key issue that you'll have to resolve time and time again when working with geospatial data!\n", + "\n", + "It all revolves around **coordinate reference systems** and **projections**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----------------------------\n", + "\n", + "## 3.4 Coordinate Reference Systems (CRS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **Question** Do you have experience with Coordinate Reference Systems?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

As a refresher, a CRS describes how the coordinates in a geospatial dataset relate to locations on the surface of the earth. \n", + "\n", + "A `geographic CRS` consists of: \n", + "- a 3D model of the shape of the earth (a **datum**), approximated as a sphere or spheroid (aka ellipsoid)\n", + "- the **units** of the coordinate system (e.g, decimal degrees, meters, feet) and \n", + "- the **origin** (i.e. the 0,0 location), specified as the meeting of the **equator** and the **prime meridian**( \n", + "\n", + "A `projected CRS` consists of\n", + "- a geographic CRS\n", + "- a **map projection** and related parameters used to transform the geographic coordinates to `2D` space.\n", + " - a map projection is a mathematical model used to transform coordinate data\n", + "\n", + "### A Geographic vs Projected CRS\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### There are many, many CRSs\n", + "\n", + "Theoretically the number of CRSs is unlimited!\n", + "\n", + "Why? Primariy, because there are many different definitions of the shape of the earth, multiplied by many different ways to cast its surface into 2 dimensions. Our understanding of the earth's shape and our ability to measure it has changed greatly over time.\n", + "\n", + "#### Why are CRSs Important?\n", + "\n", + "- You need to know the data about your data (or `metadata`) to use it appropriately.\n", + "\n", + "\n", + "- All projected CRSs introduce distortion in shape, area, and/or distance. So understanding what CRS best maintains the characteristics you need for your area of interest and your analysis is important.\n", + "\n", + "\n", + "- Some analysis methods expect geospatial data to be in a projected CRS\n", + " - For example, `geopandas` expects a geodataframe to be in a projected CRS for area or distance based analyses.\n", + "\n", + "\n", + "- Some Python libraries, but not all, implement dynamic reprojection from the input CRS to the required CRS and assume a specific CRS (WGS84) when a CRS is not explicitly defined.\n", + "\n", + "\n", + "- Most Python spatial libraries, including Geopandas, require geospatial data to be in the same CRS if they are being analysed together.\n", + "\n", + "#### What you need to know when working with CRSs\n", + "\n", + "- What CRSs used in your study area and their main characteristics\n", + "- How to identify, or `get`, the CRS of a geodataframe\n", + "- How to `set` the CRS of geodataframe (i.e. define the projection)\n", + "- Hot to `transform` the CRS of a geodataframe (i.e. reproject the data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Codes for CRSs commonly used with CA data\n", + "\n", + "CRSs are typically referenced by an [EPSG code](http://wiki.gis.com/wiki/index.php/European_Petroleum_Survey_Group). \n", + "\n", + "It's important to know the commonly used CRSs and their EPSG codes for your geographic area of interest. \n", + "\n", + "For example, below is a list of commonly used CRSs for California geospatial data along with their EPSG codes.\n", + "\n", + "##### Geographic CRSs\n", + "-`4326: WGS84` (units decimal degrees) - the most commonly used geographic CRS\n", + "\n", + "-`4269: NAD83` (units decimal degrees) - the geographic CRS customized to best fit the USA. This is used by all Census geographic data.\n", + "\n", + "> `NAD83 (epsg:4269)` are approximately the same as `WGS84(epsg:4326)` although locations can differ by up to 1 meter in the continental USA and elsewhere up to 3m. That is not a big issue with census tract data as these data are only accurate within +/-7meters.\n", + "##### Projected CRSs\n", + "\n", + "-`5070: CONUS NAD83` (units meters) projected CRS for mapping the entire contiguous USA (CONUS)\n", + "\n", + "-`3857: Web Mercator` (units meters) conformal (shape preserving) CRS used as the default in web mapping\n", + "\n", + "-`3310: CA Albers Equal Area, NAD83` (units meters) projected CRS for CA statewide mapping and spatial analysis\n", + "\n", + "-`26910: UTM Zone 10N, NAD83` (units meters) projected CRS for northern CA mapping & analysis\n", + "\n", + "-`26911: UTM Zone 11N, NAD83` (units meters) projected CRS for Southern CA mapping & analysis\n", + "\n", + "-`102641 to 102646: CA State Plane zones 1-6, NAD83` (units feet) projected CRS used for local analysis.\n", + "\n", + "You can find the full CRS details on the website https://www.spatialreference.org" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.5 Getting the CRS\n", + "\n", + "### Getting the CRS of a gdf\n", + "\n", + "GeoPandas GeoDataFrames have a `crs` attribute that returns the CRS of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.crs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can clearly see from those two printouts (even if we don't understand all the content!),\n", + "the CRSs of our two datasets are different! **This explains why we couldn't overlay them correctly!**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------------------------\n", + "The above CRS definition specifies \n", + "- the name of the CRS (`WGS84`), \n", + "- the axis units (`degree`)\n", + "- the shape (`datum`),\n", + "- and the origin (`Prime Meridian`, and the equator)\n", + "- and the area for which it is best suited (`World`)\n", + "\n", + "> Notes:\n", + "> - `geocentric` latitude and longitude assume a spherical (round) model of the shape of the earth\n", + "> - `geodetic` latitude and longitude assume a spheriodal (ellipsoidal) model, which is closer to the true shape.\n", + "> - `geodesy` is the study of the shape of the earth." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE**: If you print a `crs` call, Python will just display the EPSG code used to initiate the CRS object. Depending on your versions of Geopandas and its dependencies, this may or may not look different from what we just saw above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(states_limited.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.6 Setting the CRS\n", + "\n", + "You can also set the CRS of a gdf using the `crs` attribute. You would set the CRS if is not defined or if you think it is incorrectly defined.\n", + "\n", + "> In desktop GIS terminology setting the CRS is called **defining the CRS**\n", + "\n", + "As an example, let's set the CRS of our data to `None`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# first set the CRS to None\n", + "states_limited.crs = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check it again\n", + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "...hummm...\n", + "\n", + "If a variable has a null value (None) then displaying it without printing it won't display anything!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check it again\n", + "print(states_limited.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll set it back to its correct CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set it to 4326\n", + "states_limited.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show it\n", + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE**: You can set the CRS to anything you like, but **that doesn't make it correct**! This is because setting the CRS does not change the coordinate data; it just tells the software how to interpret it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.7 Transforming or Reprojecting the CRS\n", + "You can transform the CRS of a geodataframe with the `to_crs` method.\n", + "\n", + "\n", + "> In desktop GIS terminology transforming the CRS is called **projecting the data** (or **reprojecting the data**)\n", + "\n", + "When you do this you want to save the output to a new GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited_utm10 = states_limited.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now take a look at the CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see the result immediately by plotting the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# plot geographic gdf\n", + "states_limited.plot();\n", + "plt.axis('square');\n", + "\n", + "# plot utm gdf\n", + "states_limited_utm10.plot();\n", + "plt.axis('square')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your thoughts here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What two key differences do you see between the two plots above?\n", + "1. Do either of these plotted USA maps look good?\n", + "1. Try looking at the common CRS EPSG codes above and see if any of them look better for the whole country than what we have now. Then try transforming the states data to the CRS that you think would be best and plotting it. (Use the code cell two cells below.)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Double-click to see solution!**\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.8 Plotting states and counties together\n", + "\n", + "Now that we know what a CRS is and how we can set them, let's convert our counties GeoDataFrame to match up with out states' crs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert counties data to NAD83 \n", + "counties_utm10 = counties.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties_utm10.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot it together!\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "states_limited_utm10.plot(color='lightgrey', ax=ax)\n", + "counties_utm10.plot(color='darkgreen',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we know that the best CRS to plot the contiguous US from the above question is 5070, let's also transform and plot everything in that CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties_conus = counties.to_crs(\"epsg:5070\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "states_limited_conus.plot(color='lightgrey', ax=ax)\n", + "counties_conus.plot(color='darkgreen',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.9 Recap\n", + "\n", + "In this lesson we learned about...\n", + "- Coordinate Reference Systems \n", + "- Getting the CRS of a geodataframe\n", + " - `crs`\n", + "- Transforming/repojecting CRS\n", + " - `to_crs`\n", + "- Overlaying maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: CRS Management\n", + "\n", + "Now it's time to take a crack and managing the CRS of a new dataset. In the code cell below, write code to:\n", + "\n", + "1. Bring in the CA places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`)\n", + "2. Check if the CRS is EPSG code 26910. If not, transform the CRS\n", + "3. Plot the California counties and places together.\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/03_CRS_Map_Projections.py b/_build/jupyter_execute/lessons/03_CRS_Map_Projections.py new file mode 100644 index 0000000..7f3a0d2 --- /dev/null +++ b/_build/jupyter_execute/lessons/03_CRS_Map_Projections.py @@ -0,0 +1,415 @@ +# Lesson 3. Coordinate Reference Systems (CRS) & Map Projections + +Building off of what we learned in the previous notebook, we'll get to understand an integral aspect of geospatial data: Coordinate Reference Systems. + +- 3.1 California County Shapefile +- 3.2 USA State Shapefile +- 3.3 Plot the Two Together +- 3.4 Coordinate Reference System (CRS) +- 3.5 Getting the CRS +- 3.6 Setting the CRS +- 3.7 Transforming or Reprojecting the CRS +- 3.8 Plotting States and Counties Togther +- 3.9 Recap +- **Exercise**: CRS Management + +
+ + Instructor Notes + +- Datasets used + - ‘notebook_data/california_counties/CaliforniaCounties.shp’ + - ‘notebook_data/us_states/us_states.shp’ + - ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’ + +- Expected time to complete + - Lecture + Questions: 45 minutes + - Exercises: 10 minutes + + +### Import Libraries + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +## 3.1 California County shapefile +Let's go ahead and bring back in our California County shapefile. As before, we can read the file in using `gpd.read_file` and plot it straight away. + +counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp') +counties.plot(color='darkgreen') + +Even if we have an awesome map like this, sometimes we want to have more geographical context, or we just want additional information. We're going to try **overlaying** our counties GeoDataFrame on our USA states shapefile. + +## 3.2 USA State shapefile + +We're going to bring in our states geodataframe, and let's do the usual operations to start exploring our data. + +# Read in states shapefile +states = gpd.read_file('notebook_data/us_states/us_states.shp') + +# Look at the first few rows +states.head() + +# Count how many rows and columns we have +states.shape + +# Plot our states data +states.plot() + +You might have noticed that our plot extends beyond the 50 states (which we also saw when we executed the `shape` method). Let's double check what states we have included in our data. + +states['STATE'].values + +Beyond the 50 states we seem to have American Samoa, Puerto Rico, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands included in this geodataframe. To make our map cleaner, let's limit the states to the contiguous states (so we'll also exclude Alaska and Hawaii). + +# Define list of non-contiguous states +non_contiguous_us = [ 'American Samoa','Puerto Rico','Guam', + 'Commonwealth of the Northern Mariana Islands', + 'United States Virgin Islands', 'Alaska','Hawaii'] +# Limit data according to above list +states_limited = states.loc[~states['STATE'].isin(non_contiguous_us)] + +# Plot it +states_limited.plot() + +To prepare for our mapping overlay, let's make our states a nice, light grey color. + +states_limited.plot(color='lightgrey', figsize=(10,10)) + +## 3.3 Plot the two together + +Now that we have both geodataframes in our environment, we can plot both in the same figure. + +**NOTE**: To do this, note that we're getting a Matplotlib Axes object (`ax`), then explicitly adding each our layers to it +by providing the `ax=ax` argument to the `plot` method. + +fig, ax = plt.subplots(figsize=(10,10)) +counties.plot(color='darkgreen',ax=ax) +states_limited.plot(color='lightgrey', ax=ax) + +Oh no, what happened here? + + **Question** Without looking ahead, what do you think happened? + + + +Your response here: + + + + + + + +
+
+If you look at the numbers we have on the x and y axes in our two plots, you'll see that the county data has much larger numbers than our states data. It's represented in some different type of unit other than decimal degrees! + +In fact, that means if we zoom in really close into our plot we'll probably see the states data plotted. + +%matplotlib inline +fig, ax = plt.subplots(figsize=(10,10)) +counties.plot(color='darkgreen',ax=ax) +states_limited.plot(color='lightgrey', ax=ax) +ax.set_xlim(-140,-50) +ax.set_ylim(20,50) + +This is a key issue that you'll have to resolve time and time again when working with geospatial data! + +It all revolves around **coordinate reference systems** and **projections**. + +---------------------------- + +## 3.4 Coordinate Reference Systems (CRS) + + **Question** Do you have experience with Coordinate Reference Systems? + +Your response here: + + + + + + + +

As a refresher, a CRS describes how the coordinates in a geospatial dataset relate to locations on the surface of the earth. + +A `geographic CRS` consists of: +- a 3D model of the shape of the earth (a **datum**), approximated as a sphere or spheroid (aka ellipsoid) +- the **units** of the coordinate system (e.g, decimal degrees, meters, feet) and +- the **origin** (i.e. the 0,0 location), specified as the meeting of the **equator** and the **prime meridian**( + +A `projected CRS` consists of +- a geographic CRS +- a **map projection** and related parameters used to transform the geographic coordinates to `2D` space. + - a map projection is a mathematical model used to transform coordinate data + +### A Geographic vs Projected CRS + + +#### There are many, many CRSs + +Theoretically the number of CRSs is unlimited! + +Why? Primariy, because there are many different definitions of the shape of the earth, multiplied by many different ways to cast its surface into 2 dimensions. Our understanding of the earth's shape and our ability to measure it has changed greatly over time. + +#### Why are CRSs Important? + +- You need to know the data about your data (or `metadata`) to use it appropriately. + + +- All projected CRSs introduce distortion in shape, area, and/or distance. So understanding what CRS best maintains the characteristics you need for your area of interest and your analysis is important. + + +- Some analysis methods expect geospatial data to be in a projected CRS + - For example, `geopandas` expects a geodataframe to be in a projected CRS for area or distance based analyses. + + +- Some Python libraries, but not all, implement dynamic reprojection from the input CRS to the required CRS and assume a specific CRS (WGS84) when a CRS is not explicitly defined. + + +- Most Python spatial libraries, including Geopandas, require geospatial data to be in the same CRS if they are being analysed together. + +#### What you need to know when working with CRSs + +- What CRSs used in your study area and their main characteristics +- How to identify, or `get`, the CRS of a geodataframe +- How to `set` the CRS of geodataframe (i.e. define the projection) +- Hot to `transform` the CRS of a geodataframe (i.e. reproject the data) + +### Codes for CRSs commonly used with CA data + +CRSs are typically referenced by an [EPSG code](http://wiki.gis.com/wiki/index.php/European_Petroleum_Survey_Group). + +It's important to know the commonly used CRSs and their EPSG codes for your geographic area of interest. + +For example, below is a list of commonly used CRSs for California geospatial data along with their EPSG codes. + +##### Geographic CRSs +-`4326: WGS84` (units decimal degrees) - the most commonly used geographic CRS + +-`4269: NAD83` (units decimal degrees) - the geographic CRS customized to best fit the USA. This is used by all Census geographic data. + +> `NAD83 (epsg:4269)` are approximately the same as `WGS84(epsg:4326)` although locations can differ by up to 1 meter in the continental USA and elsewhere up to 3m. That is not a big issue with census tract data as these data are only accurate within +/-7meters. +##### Projected CRSs + +-`5070: CONUS NAD83` (units meters) projected CRS for mapping the entire contiguous USA (CONUS) + +-`3857: Web Mercator` (units meters) conformal (shape preserving) CRS used as the default in web mapping + +-`3310: CA Albers Equal Area, NAD83` (units meters) projected CRS for CA statewide mapping and spatial analysis + +-`26910: UTM Zone 10N, NAD83` (units meters) projected CRS for northern CA mapping & analysis + +-`26911: UTM Zone 11N, NAD83` (units meters) projected CRS for Southern CA mapping & analysis + +-`102641 to 102646: CA State Plane zones 1-6, NAD83` (units feet) projected CRS used for local analysis. + +You can find the full CRS details on the website https://www.spatialreference.org + +## 3.5 Getting the CRS + +### Getting the CRS of a gdf + +GeoPandas GeoDataFrames have a `crs` attribute that returns the CRS of the data. + +counties.crs + +states_limited.crs + +As we can clearly see from those two printouts (even if we don't understand all the content!), +the CRSs of our two datasets are different! **This explains why we couldn't overlay them correctly!** + +----------------------------------------- +The above CRS definition specifies +- the name of the CRS (`WGS84`), +- the axis units (`degree`) +- the shape (`datum`), +- and the origin (`Prime Meridian`, and the equator) +- and the area for which it is best suited (`World`) + +> Notes: +> - `geocentric` latitude and longitude assume a spherical (round) model of the shape of the earth +> - `geodetic` latitude and longitude assume a spheriodal (ellipsoidal) model, which is closer to the true shape. +> - `geodesy` is the study of the shape of the earth. + +**NOTE**: If you print a `crs` call, Python will just display the EPSG code used to initiate the CRS object. Depending on your versions of Geopandas and its dependencies, this may or may not look different from what we just saw above. + +print(states_limited.crs) + +## 3.6 Setting the CRS + +You can also set the CRS of a gdf using the `crs` attribute. You would set the CRS if is not defined or if you think it is incorrectly defined. + +> In desktop GIS terminology setting the CRS is called **defining the CRS** + +As an example, let's set the CRS of our data to `None` + +# first set the CRS to None +states_limited.crs = None + +# Check it again +states_limited.crs + +...hummm... + +If a variable has a null value (None) then displaying it without printing it won't display anything! + +# Check it again +print(states_limited.crs) + +Now we'll set it back to its correct CRS. + +# Set it to 4326 +states_limited.crs = "epsg:4326" + +# Show it +states_limited.crs + +**NOTE**: You can set the CRS to anything you like, but **that doesn't make it correct**! This is because setting the CRS does not change the coordinate data; it just tells the software how to interpret it. + +## 3.7 Transforming or Reprojecting the CRS +You can transform the CRS of a geodataframe with the `to_crs` method. + + +> In desktop GIS terminology transforming the CRS is called **projecting the data** (or **reprojecting the data**) + +When you do this you want to save the output to a new GeoDataFrame. + +states_limited_utm10 = states_limited.to_crs( "epsg:26910") + +Now take a look at the CRS. + +states_limited_utm10.crs + +You can see the result immediately by plotting the data. + +# plot geographic gdf +states_limited.plot(); +plt.axis('square'); + +# plot utm gdf +states_limited_utm10.plot(); +plt.axis('square') + +# Your thoughts here + +
+ +
+
+ +#### Questions +
+ +1. What two key differences do you see between the two plots above? +1. Do either of these plotted USA maps look good? +1. Try looking at the common CRS EPSG codes above and see if any of them look better for the whole country than what we have now. Then try transforming the states data to the CRS that you think would be best and plotting it. (Use the code cell two cells below.) + +Your responses here: + + + + + + + +# YOUR CODE HERE + + + + + +**Double-click to see solution!** + + + +## 3.8 Plotting states and counties together + +Now that we know what a CRS is and how we can set them, let's convert our counties GeoDataFrame to match up with out states' crs. + +# Convert counties data to NAD83 +counties_utm10 = counties.to_crs("epsg:26910") + +counties_utm10.plot() + +# Plot it together! +fig, ax = plt.subplots(figsize=(10,10)) +states_limited_utm10.plot(color='lightgrey', ax=ax) +counties_utm10.plot(color='darkgreen',ax=ax) + +Since we know that the best CRS to plot the contiguous US from the above question is 5070, let's also transform and plot everything in that CRS. + +counties_conus = counties.to_crs("epsg:5070") + +fig, ax = plt.subplots(figsize=(10,10)) +states_limited_conus.plot(color='lightgrey', ax=ax) +counties_conus.plot(color='darkgreen',ax=ax) + +## 3.9 Recap + +In this lesson we learned about... +- Coordinate Reference Systems +- Getting the CRS of a geodataframe + - `crs` +- Transforming/repojecting CRS + - `to_crs` +- Overlaying maps + +## Exercise: CRS Management + +Now it's time to take a crack and managing the CRS of a new dataset. In the code cell below, write code to: + +1. Bring in the CA places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`) +2. Check if the CRS is EPSG code 26910. If not, transform the CRS +3. Plot the California counties and places together. + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE + + + +## Double-click to see solution! + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ diff --git a/_build/jupyter_execute/lessons/04_More_Data_More_Maps.ipynb b/_build/jupyter_execute/lessons/04_More_Data_More_Maps.ipynb new file mode 100644 index 0000000..defb7ff --- /dev/null +++ b/_build/jupyter_execute/lessons/04_More_Data_More_Maps.ipynb @@ -0,0 +1,650 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 4. More Data, More Maps!\n", + "\n", + "Now that we know how to pull in data, check and transform Coordinate Reference Systems (CRS), and plot GeoDataFrames together - let's practice doing the same thing with other geometry types. In this notebook we'll be bringing in bike boulevards and schools, which will get us primed to think about spatial relationship questions.\n", + "\n", + "- 4.1 Berkeley Bike Boulevards\n", + "- 4.2 Alameda County Schools\n", + "- **Exercise**: Even More Data!\n", + "- 4.3 Map Overlays with Matplotlib\n", + "- 4.4 Recap\n", + "- **Exercise**: Overlay Mapping\n", + "- 4.5 Teaser for Day 2\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/parcels/parcel_pts_rand30pct.geojson'\n", + " - ‘notebook_data/berkeley/BerkeleyCityLimits.shp’\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Berkeley Bike Boulevards\n", + "\n", + "We're going to bring in data bike boulevards in Berkeley. Note two things that are different from our previous data:\n", + "- We're bringing in a [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) this time and not a shapefile\n", + "- We have a **line** geometry GeoDataFrame (our county and states data had **polygon** geometries)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual, we'll want to do our usual data exploration..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our bike boulevard data includes the following information:\n", + " - `BB_STRNAM` - bike boulevard Streetname\n", + " - `BB_STRID` - bike boulevard Street ID\n", + " - `BB_FRO` - bike boulevard origin street\n", + " - `BB_TO` - bike boulevard end street\n", + " - `BB_SECID`- bike boulevard section id\n", + " - `DIR_` - cardinal directions the bike boulevard runs\n", + " - `Status` - status on whether the bike boulevard exists\n", + " - `ALT_bikeCA` - ? \n", + " - `Shape_len` - length of the boulevard in meters \n", + " - `len_km` - length of the boulevard in kilometers\n", + " - `geometry`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "Why are there 211 features when we only have 8 bike boulevards?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your reponse here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig,ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds.plot(ax=ax)\n", + "bike_blvds.head(1).plot(color='orange',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now take a look at our CRS..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's tranform our CRS to UTM Zone 10N, NAD83 that we used in the last lesson." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10 = bike_blvds.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Alameda County Schools\n", + "\n", + "Alright! Now that we have our bike boulevard data squared away, we're going to bring in our Alameda County school data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **Questions** \n", + "\n", + "Without looking ahead:\n", + "\n", + "1. Is this a geodataframe? \n", + "2. How do you know?\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your reponse here:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "This is not a GeoDataFrame! A couple of clues to figure that out are..\n", + "\n", + "1. We're pulling in a Comma Separated Value (CSV) file, which is not a geospatial data format\n", + "2. There is no geometry column (although we do have latitude and longitude values)\n", + "\n", + "\n", + "-------------------------------\n", + "\n", + "Although our school data is not starting off as a GeoDataFrame, we actually have the tools and information to make it one. Using the `gpd.GeoDataFrame` constructor, we can transform our plain DataFrame into a GeoDataFrame (specifying the geometry information and then the CRS)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(schools_gdf.crs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.crs = \"epsg:4326\"\n", + "schools_gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll notice that the shape is the same from what we had as a dataframe, just with the added `geometry` column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "schools_gdf.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And with it being a GeoDataFrame, we can plot it as we did for our other data sets.\n", + "Notice that we have our first **point** geometry GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But of course we'll want to transform the CRS, so that we can later plot it with our bike boulevard data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf_utm10 = schools_gdf.to_crs( \"epsg:26910\")\n", + "schools_gdf_utm10.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*In Lesson 2 we discussed that you can save out GeoDataFrames in multiple file formats. You could opt for a GeoJSON, a shapefile, etc... for point data sets it is also an option to save it out as a CSV since the geometry isn't complicated*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Even More Data!\n", + "Let's play around with another point GeoDataFrame.\n", + "\n", + "In the code cell provided below, compose code to:\n", + "\n", + "1. Read in the parcel points data (`notebook_data/parcels/parcel_pts_rand30pct.geojson`)\n", + "2. Transform the CRS to 26910\n", + "3. Plot and customize as desired!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.3 Map Overlays with Matplotlib\n", + "\n", + "No matter the geometry type we have for our GeoDataFrame, we can create overlay plots.\n", + "\n", + "Since we've already done the legwork of transforming our CRS, we can go ahead and plot them together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds_utm10.plot(ax=ax, color='red')\n", + "schools_gdf_utm10.plot(ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to answer questions like *\"What schools are close to bike boulevards in Berkeley?\"*, the above plot isn't super helpful, since the extent covers all of Alameda county.\n", + "\n", + "Luckily, GeoDataFrames have an easy method to extract the minimium and maximum values for both x and y, so we can use that information to set the bounds for our plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "minx, miny, maxx, maxy = bike_blvds.total_bounds\n", + "print(minx, miny, maxx, maxy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `xlim` and `ylim` we can zoom in to see if there are schools proximal to the bike boulevards." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds_utm10.plot(ax=ax, color='red')\n", + "schools_gdf_utm10 .plot(ax=ax)\n", + "plt.xlim(minx, maxx)\n", + "plt.ylim(miny, maxy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.4 Recap\n", + "\n", + "In this lesson we learned a several new skills:\n", + "- Transformed an a-spatial dataframe into a geospatial one\n", + " - `gpd.GeoDataFrame`\n", + "- Worked with point and line GeoDataFrames\n", + "- Overlayed point and line GeoDataFrames\n", + "- Limited the extent of a map\n", + " - `total_bounds`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Overlay Mapping\n", + "\n", + "Let's take some time to practice reading in and reconciling new datasets, then mapping them together.\n", + "\n", + "In the code cell provided below, write code to:\n", + "\n", + "1. Bring in your Berkeley places shapefile (and don't forget to check/transform the crs!) (`notebook_data/berkeley/BerkeleyCityLimits.shp`)\n", + "1. Overlay the parcel points on top of the bike boulevards\n", + "1. Create the same plot but limit it to the extent of Berkeley city limits\n", + "\n", + "***BONUS***: *Add the Berkeley outline to your last plot!*\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click the see the solution!\n", + "\n", + "\n", + "\n", + "-----------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.5 Teaser for Day 2...\n", + "\n", + "You may be wondering if and how we could make our maps more interesting and informative than this.\n", + "\n", + "To give you a tantalizing taste of Day 2, the answer is: Yes, we can! And here's how!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = schools_gdf_utm10.plot(column='Org', cmap='winter', \n", + " markersize=35, edgecolor='black',\n", + " linewidth=0.5, alpha=1, figsize=[9, 9],\n", + " legend=True)\n", + "ax.set_title('Public and Private Schools, Alameda County')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/04_More_Data_More_Maps.py b/_build/jupyter_execute/lessons/04_More_Data_More_Maps.py new file mode 100644 index 0000000..76eb8a3 --- /dev/null +++ b/_build/jupyter_execute/lessons/04_More_Data_More_Maps.py @@ -0,0 +1,320 @@ +# Lesson 4. More Data, More Maps! + +Now that we know how to pull in data, check and transform Coordinate Reference Systems (CRS), and plot GeoDataFrames together - let's practice doing the same thing with other geometry types. In this notebook we'll be bringing in bike boulevards and schools, which will get us primed to think about spatial relationship questions. + +- 4.1 Berkeley Bike Boulevards +- 4.2 Alameda County Schools +- **Exercise**: Even More Data! +- 4.3 Map Overlays with Matplotlib +- 4.4 Recap +- **Exercise**: Overlay Mapping +- 4.5 Teaser for Day 2 + + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson' + - 'notebook_data/alco_schools.csv' + - 'notebook_data/parcels/parcel_pts_rand30pct.geojson' + - ‘notebook_data/berkeley/BerkeleyCityLimits.shp’ + +- Expected time to complete + - Lecture + Questions: 30 minutes + - Exercises: 20 minutes + + +### Import Libraries + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +## 4.1 Berkeley Bike Boulevards + +We're going to bring in data bike boulevards in Berkeley. Note two things that are different from our previous data: +- We're bringing in a [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) this time and not a shapefile +- We have a **line** geometry GeoDataFrame (our county and states data had **polygon** geometries) + +bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson') +bike_blvds.plot() + +As usual, we'll want to do our usual data exploration... + +bike_blvds.head() + +bike_blvds.shape + +bike_blvds.columns + +Our bike boulevard data includes the following information: + - `BB_STRNAM` - bike boulevard Streetname + - `BB_STRID` - bike boulevard Street ID + - `BB_FRO` - bike boulevard origin street + - `BB_TO` - bike boulevard end street + - `BB_SECID`- bike boulevard section id + - `DIR_` - cardinal directions the bike boulevard runs + - `Status` - status on whether the bike boulevard exists + - `ALT_bikeCA` - ? + - `Shape_len` - length of the boulevard in meters + - `len_km` - length of the boulevard in kilometers + - `geometry` + + +
+ +
+
+ +#### Question +
+ +Why are there 211 features when we only have 8 bike boulevards? + +Your reponse here: + + + + + + + +fig,ax = plt.subplots(figsize=(10,10)) +bike_blvds.plot(ax=ax) +bike_blvds.head(1).plot(color='orange',ax=ax) + +And now take a look at our CRS... + +bike_blvds.crs + +Let's tranform our CRS to UTM Zone 10N, NAD83 that we used in the last lesson. + +bike_blvds_utm10 = bike_blvds.to_crs( "epsg:26910") + +bike_blvds_utm10.head() + +bike_blvds_utm10.crs + +## 4.2 Alameda County Schools + +Alright! Now that we have our bike boulevard data squared away, we're going to bring in our Alameda County school data. + +schools_df = pd.read_csv('notebook_data/alco_schools.csv') +schools_df.head() + +schools_df.shape + + **Questions** + +Without looking ahead: + +1. Is this a geodataframe? +2. How do you know? + + + +Your reponse here: + + + + + + + +
+
+This is not a GeoDataFrame! A couple of clues to figure that out are.. + +1. We're pulling in a Comma Separated Value (CSV) file, which is not a geospatial data format +2. There is no geometry column (although we do have latitude and longitude values) + + +------------------------------- + +Although our school data is not starting off as a GeoDataFrame, we actually have the tools and information to make it one. Using the `gpd.GeoDataFrame` constructor, we can transform our plain DataFrame into a GeoDataFrame (specifying the geometry information and then the CRS). + +schools_gdf = gpd.GeoDataFrame(schools_df, + geometry=gpd.points_from_xy(schools_df.X, schools_df.Y)) + +print(schools_gdf.crs) + +schools_gdf.crs = "epsg:4326" +schools_gdf.head() + +You'll notice that the shape is the same from what we had as a dataframe, just with the added `geometry` column. + +schools_gdf.shape + +And with it being a GeoDataFrame, we can plot it as we did for our other data sets. +Notice that we have our first **point** geometry GeoDataFrame. + +schools_gdf.plot() + +But of course we'll want to transform the CRS, so that we can later plot it with our bike boulevard data. + +schools_gdf_utm10 = schools_gdf.to_crs( "epsg:26910") +schools_gdf_utm10.plot() + +*In Lesson 2 we discussed that you can save out GeoDataFrames in multiple file formats. You could opt for a GeoJSON, a shapefile, etc... for point data sets it is also an option to save it out as a CSV since the geometry isn't complicated* + +## Exercise: Even More Data! +Let's play around with another point GeoDataFrame. + +In the code cell provided below, compose code to: + +1. Read in the parcel points data (`notebook_data/parcels/parcel_pts_rand30pct.geojson`) +2. Transform the CRS to 26910 +3. Plot and customize as desired! + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE: + + + + +## Double-click to see solution! + + + +------------------------- + +## 4.3 Map Overlays with Matplotlib + +No matter the geometry type we have for our GeoDataFrame, we can create overlay plots. + +Since we've already done the legwork of transforming our CRS, we can go ahead and plot them together. + +fig, ax = plt.subplots(figsize=(10,10)) +bike_blvds_utm10.plot(ax=ax, color='red') +schools_gdf_utm10.plot(ax=ax) + +If we want to answer questions like *"What schools are close to bike boulevards in Berkeley?"*, the above plot isn't super helpful, since the extent covers all of Alameda county. + +Luckily, GeoDataFrames have an easy method to extract the minimium and maximum values for both x and y, so we can use that information to set the bounds for our plot. + +minx, miny, maxx, maxy = bike_blvds.total_bounds +print(minx, miny, maxx, maxy) + +Using `xlim` and `ylim` we can zoom in to see if there are schools proximal to the bike boulevards. + +fig, ax = plt.subplots(figsize=(10,10)) +bike_blvds_utm10.plot(ax=ax, color='red') +schools_gdf_utm10 .plot(ax=ax) +plt.xlim(minx, maxx) +plt.ylim(miny, maxy) + +## 4.4 Recap + +In this lesson we learned a several new skills: +- Transformed an a-spatial dataframe into a geospatial one + - `gpd.GeoDataFrame` +- Worked with point and line GeoDataFrames +- Overlayed point and line GeoDataFrames +- Limited the extent of a map + - `total_bounds` + + +## Exercise: Overlay Mapping + +Let's take some time to practice reading in and reconciling new datasets, then mapping them together. + +In the code cell provided below, write code to: + +1. Bring in your Berkeley places shapefile (and don't forget to check/transform the crs!) (`notebook_data/berkeley/BerkeleyCityLimits.shp`) +1. Overlay the parcel points on top of the bike boulevards +1. Create the same plot but limit it to the extent of Berkeley city limits + +***BONUS***: *Add the Berkeley outline to your last plot!* + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE: + + +## Double-click the see the solution! + + + +----------------------------------- + +## 4.5 Teaser for Day 2... + +You may be wondering if and how we could make our maps more interesting and informative than this. + +To give you a tantalizing taste of Day 2, the answer is: Yes, we can! And here's how! + +ax = schools_gdf_utm10.plot(column='Org', cmap='winter', + markersize=35, edgecolor='black', + linewidth=0.5, alpha=1, figsize=[9, 9], + legend=True) +ax.set_title('Public and Private Schools, Alameda County') + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + diff --git a/_build/jupyter_execute/lessons/05_Data-Driven_Mapping.ipynb b/_build/jupyter_execute/lessons/05_Data-Driven_Mapping.ipynb new file mode 100644 index 0000000..0294b41 --- /dev/null +++ b/_build/jupyter_execute/lessons/05_Data-Driven_Mapping.ipynb @@ -0,0 +1,889 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 5. Data-driven Mapping\n", + "\n", + "*Data-driven mapping* refers to the process of using data values to determine the symbology of mapped features. Color, shape, and size are the three most common symbology types used in data-driven mapping.\n", + "Data-driven maps are often refered to as thematic maps.\n", + "\n", + "\n", + "- 5.1 Choropleth Maps\n", + "- 5.2 Issues with Visualization\n", + "- 5.3 Classification Schemes\n", + "- 5.4 Point Maps\n", + "- 5.5 Mapping Categorical Data\n", + "- 5.6 Recap\n", + "- **Exercise**: Data-Driven Mapping\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/california_counties/CaliforniaCounties.shp'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 15 minutes\n", + "\n", + "\n", + "\n", + "### Types of Thematic Maps\n", + "\n", + "There are two primary types of maps used to convey data values:\n", + "\n", + "- `Choropleth maps`: set the color of areas (polygons) by data value\n", + "- `Point symbol maps`: set the color or size of points by data value\n", + "\n", + "We will discuss both of these types of maps in more detail in this lesson. But let's take a quick look at choropleth maps. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.1 Choropleth Maps\n", + "Choropleth maps are the most common type of thematic map.\n", + "\n", + "Let's take a look at how we can use a geodataframe to make a choropleth map.\n", + "\n", + "We'll start by reloading our counties dataset from Day 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a plain map of our polygons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for comparison, let's create a choropleth map by setting the color of the county based on the values in the population per square mile (`POP12_SQMI`) column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot(column='POP12_SQMI', figsize=(10,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's really the heart of it. To set the color of the features based on the values in a column, set the `column` argument to the column name in the gdf.\n", + "> **Protip:** \n", + "- You can quickly right-click on the plot and save to a file or open in a new browser window." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default map colors are linearly scaled to data values. This is called a `proportional color map`.\n", + "\n", + "- The great thing about `proportional color maps` is that you can visualize the full range of data values.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also add a legend, and even tweak its display." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True,\n", + " legend_kwds={'label': \"Population Density per mile$^2$\",\n", + " 'orientation': \"horizontal\"},)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "Why are we plotting `POP12_SQMI` instead of `POP2012`?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note: Types of Color Maps\n", + "\n", + "There are a few different types of color maps (or color palettes), each of which has a different purpose:\n", + "- *diverging* - a \"diverging\" set of colors are used so emphasize mid-range values as well as extremes.\n", + "- *sequential* - usually with a single color hue to emphasize changes in magnitude, where darker colors typically mean higher values\n", + "- *qualitative* - a diverse set of colors to identify categories and avoid implying quantitative significance.\n", + "\n", + "\n", + "\n", + "
Image Credit: Dsdugan at English Wikipedia
\n", + "\n", + "> **Pro-tip**: You can actually see all your color map options if you misspell what you put in `cmap` and try to run-in. Try it out!\n", + "\n", + "> **Pro-tip**: Sites like [ColorBrewer](https://colorbrewer2.org/#type=sequential&scheme=Blues&n=3) let's you play around with different types of color maps. If you want to create your own, [The Python Graph Gallery](https://python-graph-gallery.com/python-colors/) is a way to see what your Python color options are.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.2 Issues with Visualization\n", + "\n", + "### Types of choropleth data\n", + "\n", + "There are several types of quantitative data variables that can be used to create a choropleth map. Let's consider these in terms of our ACS data.\n", + "\n", + "- **Count**\n", + " - counts, aggregated by feature\n", + " - *e.g. population within a census tract*\n", + "\n", + "- **Density**\n", + " - count, aggregated by feature, normalized by feature area\n", + " - *e.g. population per square mile within a census tract*\n", + "\n", + "- **Proportions / Percentages**\n", + " - value in a specific category divided by total value across in all categories\n", + " - *e.g. proportion of the tract population that is white compared to the total tract population*\n", + "\n", + "- **Rates / Ratios**\n", + " - value in one category divided by value in another category\n", + " - *e.g. homeowner-to-renter ratio would be calculated as the number of homeowners (c_owners/ c_renters)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretability of plotted data\n", + "The goal of a choropleth map is to use color to visualize the spatial distribution of a quantitative variable.\n", + "\n", + "Brighter or richer colors are typically used to signify higher values.\n", + "\n", + "A big problem with choropleth maps is that our eyes are drawn to the color of larger areas, even if the values being mapped in one or more smaller areas are more important.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see just this sort of problem in our population-density map. \n", + "\n", + "***Why does our map not look that interesting?*** Take a look at the histogram below, then consider the following question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.hist(counties['POP12_SQMI'],bins=40)\n", + "plt.title('Population Density per mile$^2$')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What county does that outlier represent? What problem does that pose?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.3 Classification schemes\n", + "\n", + "Let's try to make our map more interpretable!\n", + "\n", + "The common alternative to a proportionial color map is to use a **classification scheme** to create a **graduated color map**. This is the standard way to create a **choropleth map**.\n", + "\n", + "A **classification scheme** is a method for binning continuous data values into 4-7 classes (the default is 5) and map those classes to a color palette. \n", + "\n", + "### The commonly used classifications schemes:\n", + "\n", + "- **Equal intervals**\n", + " - equal-size data ranges (e.g., values within 0-10, 10-20, 20-30, etc.)\n", + " - pros:\n", + " - best for data spread across entire range of values\n", + " - easily understood by map readers\n", + " - cons:\n", + " - but avoid if you have highly skewed data or a few big outliers\n", + " \n", + " \n", + "- **Quantiles**\n", + " - equal number of observations in each bin\n", + " - pros:\n", + " - looks nice, becuase it best spreads colors across full set of data values\n", + " - thus, it's often the default scheme for mapping software\n", + " - cons:\n", + " - bin ranges based on the number of observations, not on the data values\n", + " - thus, different classes can have very similar or very different values.\n", + " \n", + " \n", + "- **Natural breaks**\n", + " - minimize within-class variance and maximize between-class differences\n", + " - e.g. 'fisher-jenks'\n", + " - pros:\n", + " - great for exploratory data analysis, because it can identify natural groupings\n", + " - cons:\n", + " - class breaks are best fit to one dataset, so the same bins can't always be used for multiple years\n", + " \n", + " \n", + "- **Manual** \n", + " - classifications are user-defined\n", + " - pros: \n", + " - especially useful if you want to slightly change the breaks produced by another scheme\n", + " - can be used as a fixed set of breaks to compare data over time\n", + " - cons:\n", + " - more work involved" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classification schemes and GeoDataFrames\n", + "\n", + "Classification schemes can be implemented using the geodataframe `plot` method by setting a value for the **scheme** argument. This requires the [pysal](https://pysal.org/) and [mapclassify](https://pysal.org/mapclassify) libraries to be installed in your Python environment. \n", + "\n", + "Here is a list of the `classification schemes` names that we will use:\n", + "- `equalinterval`, `quantiles`,`fisherjenks`,`naturalbreaks`, and `userdefined`.\n", + "\n", + "For more information about these classification schemes see the [pysal mapclassifiers web page](https://pysal.org/mapclassify/api.html) or check out the help docs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------\n", + "\n", + "### Classification schemes in action\n", + "\n", + "Let's redo the last map using the `quantile` classification scheme.\n", + "\n", + "- What is different about the code? About the output map?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Plot population density - mile^2\n", + "fig, ax = plt.subplots(figsize = (10,5)) \n", + "counties.plot(column='POP12_SQMI', \n", + " scheme=\"quantiles\",\n", + " legend=True,\n", + " ax=ax\n", + " )\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: For interval notation\n", + "- A square bracket is *inclusive*\n", + "- A parentheses is *exclusive*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### User Defined Classification Schemes\n", + "\n", + "You may get pretty close to your final map without being completely satisfied. In this case you can manually define a classification scheme.\n", + "\n", + "Let's customize our map with a `user-defined` classification scheme where we manually set the breaks for the bins using the `classification_kwds` argument." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "counties.plot(column='POP12_SQMI',\n", + " legend=True, \n", + " cmap=\"RdYlGn\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[50,100,200,300,400]},\n", + " ax=ax)\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we are customizing our plot, we can also edit our legend to specify and format the text so that it's easier to read.\n", + "\n", + "- We'll use `legend_labels_list` to customize the labels for group in the legend." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "counties.plot(column='POP12_SQMI',\n", + " legend=True, \n", + " cmap=\"RdYlGn\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[50,100,200,300,400]},\n", + " ax=ax)\n", + "\n", + "# Create the labels for the legend\n", + "legend_labels_list = ['<50','50 to 100','100 to 200','200 to 300','300 to 400','>400']\n", + "\n", + "# Apply the labels to the plot\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's plot a ratio\n", + "\n", + "If we look at the columns in our dataset, we see we have a number of variables\n", + "from which we can calculate proportions, rates, and the like.\n", + "\n", + "Let's try that out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (15,6)) \n", + "\n", + "# Plot percent hispanic as choropleth\n", + "counties.plot(column=(counties['HISPANIC']/counties['POP2012'] * 100), \n", + " legend=True, \n", + " cmap=\"Blues\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[20,40,60,80]},\n", + " edgecolor=\"grey\",\n", + " linewidth=0.5,\n", + " ax=ax)\n", + "\n", + "legend_labels_list = ['<20%','20% - 40%','40% - 60%','60% - 80%','80% - 100%']\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "ax.set_title(\"Percent Hispanic Population\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What new options and operations have we added to our code?\n", + "1. Based on our code, what title would you give this plot to describe what it displays?\n", + "1. How many bins do we specify in the `legend_labels_list` object, and how many bins are in the map legend? Why?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.4 Point maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choropleth maps are great, but mapping using point symbols enables us to visualize our spatial data in another way. \n", + "\n", + "If you know both mapping methods you can expand how much information you can show in one map. \n", + "\n", + "For example, point maps are a great way to map `counts` because the varying sizes of areas are deemphasized.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------\n", + "Let's read in some point data on Alameda County schools." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We got it from a plain CSV file, let's coerce it to a GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can map it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.plot()\n", + "plt.title('Alameda County Schools')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Proportional Color Maps\n", + "**Proportional color maps** linearly scale the `color` of a point symbol by the data values.\n", + "\n", + "Let's try this by creating a map of `API`. API stands for *Academic Performance Index*, which is a measurement system that looks at the performance of an individual school." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.plot(column=\"API\", cmap=\"gist_heat\", \n", + " edgecolor=\"grey\", figsize=(10,8), legend=True)\n", + "plt.title(\"Alameda County, School API scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you see that continuous color bar in the legend you know that the mapping of data values to colors is not classified.\n", + "\n", + "\n", + "### Graduated Color Maps\n", + "\n", + "We can also create **graduated color maps** by binning data values before associating them with colors. These are just like choropleth maps, except that the term \"choropleth\" is only used with polygon data. \n", + "\n", + "Graduated color maps use the same syntax as the choropleth maps above - you create them by setting a value for `scheme`. \n", + "\n", + "Below, we copy the code we used above to create a choropleth, but we change the name of the geodataframe to use the point gdf. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (15,6)) \n", + "\n", + "# Plot percent non-white with graduated colors\n", + "schools_gdf.plot(column='API', \n", + " legend=True, \n", + " cmap=\"Blues\",\n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[0,200,400,600,800]},\n", + " edgecolor=\"grey\",\n", + " linewidth=0.5,\n", + " #markersize=60,\n", + " ax=ax)\n", + "\n", + "# Create a custom legend\n", + "legend_labels_list = ['0','< 200','< 400','< 600','< 800','>= 800']\n", + "\n", + "# Apply the legend to the map\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "# Create the plot\n", + "plt.tight_layout()\n", + "plt.title(\"Alameda County, School API scores\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf['API'].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the syntax for a choropleth and graduated color map is the same,\n", + "although some options only apply to one or the other.\n", + "\n", + "For example, uncomment the `markersize` parameter above to see how you can further customize a graduated color map." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graduated symbol maps\n", + "\n", + "`Graduated symbol maps` are also a great method for mapping points. These are just like graduated color maps but instead of associating symbol color with data values they associate point size. Similarly,graduated symbol maps use `classification schemes` to set the size of point symbols. \n", + "\n", + "> We demonstrate how to make graduated symbol maps along with some other mapping techniques in the `Optional Mapping notebook` which we encourage you to explore on your own. (***Coming Soon***)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.5 Mapping Categorical Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mapping categorical data, also called qualitative data, is a bit more straightforward. There is no need to scale or classify data values. The goal of the color map is to provide a contrasting set of colors so as to clearly delineate different categories. Here's a point-based example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.plot(column='Org', categorical=True, legend=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.6 Recap\n", + "We learned about important data driven mapping strategies and mapping concepts and can leverage what many of us know about `matplotlib`\n", + "- Choropleth Maps\n", + "- Point maps\n", + "- Color schemes \n", + "- Classifications" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise: Data-Driven Mapping\n", + "\n", + "Point and polygons are not the only geometry-types that we can use in data-driven mapping!\n", + "\n", + "Run the next cell to load a dataset containing Berkeley's bicycle boulevards (which we'll be using more in the following notebook).\n", + "\n", + "Then in the following cell, write your own code to:\n", + "1. plot the bike boulevards;\n", + "2. color them by status (find the correct column in the head of the dataframe, displayed below);\n", + "3. color them using a fitting, good-looking qualitative colormap that you choose from [The Matplotlib Colormap Reference](https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html);\n", + "4. set the line width to 5 (check the plot method's documentation to find the right argument for this!);\n", + "4. add the argument `figsize=[20,20]`, to make your map nice and big and visible!\n", + " \n", + "Then answer the questions posed in the last cell.\n", + "\n", + "
\n", + "\n", + "\n", + "To see the solution, double-click the Markdown cell below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What does that map indicate about the status of the Berkeley bike boulevards?\n", + "1. What does that map indicate about the status of your Berkeley bike-boulevard *dataset*?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/05_Data-Driven_Mapping.py b/_build/jupyter_execute/lessons/05_Data-Driven_Mapping.py new file mode 100644 index 0000000..b50a624 --- /dev/null +++ b/_build/jupyter_execute/lessons/05_Data-Driven_Mapping.py @@ -0,0 +1,503 @@ +# Lesson 5. Data-driven Mapping + +*Data-driven mapping* refers to the process of using data values to determine the symbology of mapped features. Color, shape, and size are the three most common symbology types used in data-driven mapping. +Data-driven maps are often refered to as thematic maps. + + +- 5.1 Choropleth Maps +- 5.2 Issues with Visualization +- 5.3 Classification Schemes +- 5.4 Point Maps +- 5.5 Mapping Categorical Data +- 5.6 Recap +- **Exercise**: Data-Driven Mapping + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/california_counties/CaliforniaCounties.shp' + - 'notebook_data/alco_schools.csv' + - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson' +- Expected time to complete + - Lecture + Questions: 30 minutes + - Exercises: 15 minutes + + + +### Types of Thematic Maps + +There are two primary types of maps used to convey data values: + +- `Choropleth maps`: set the color of areas (polygons) by data value +- `Point symbol maps`: set the color or size of points by data value + +We will discuss both of these types of maps in more detail in this lesson. But let's take a quick look at choropleth maps. + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +# 5.1 Choropleth Maps +Choropleth maps are the most common type of thematic map. + +Let's take a look at how we can use a geodataframe to make a choropleth map. + +We'll start by reloading our counties dataset from Day 1. + +counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp') + +counties.head() + +counties.columns + +Here's a plain map of our polygons. + +counties.plot() + +Now, for comparison, let's create a choropleth map by setting the color of the county based on the values in the population per square mile (`POP12_SQMI`) column. + +counties.plot(column='POP12_SQMI', figsize=(10,10)) + +That's really the heart of it. To set the color of the features based on the values in a column, set the `column` argument to the column name in the gdf. +> **Protip:** +- You can quickly right-click on the plot and save to a file or open in a new browser window. + +By default map colors are linearly scaled to data values. This is called a `proportional color map`. + +- The great thing about `proportional color maps` is that you can visualize the full range of data values. + + + +We can also add a legend, and even tweak its display. + +counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True) +plt.show() + +counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True, + legend_kwds={'label': "Population Density per mile$^2$", + 'orientation': "horizontal"},) +plt.show() + +
+ +
+
+ +#### Question +
+ +Why are we plotting `POP12_SQMI` instead of `POP2012`? + +Your response here: + + + + + + +### Note: Types of Color Maps + +There are a few different types of color maps (or color palettes), each of which has a different purpose: +- *diverging* - a "diverging" set of colors are used so emphasize mid-range values as well as extremes. +- *sequential* - usually with a single color hue to emphasize changes in magnitude, where darker colors typically mean higher values +- *qualitative* - a diverse set of colors to identify categories and avoid implying quantitative significance. + + + +
Image Credit: Dsdugan at English Wikipedia
+ +> **Pro-tip**: You can actually see all your color map options if you misspell what you put in `cmap` and try to run-in. Try it out! + +> **Pro-tip**: Sites like [ColorBrewer](https://colorbrewer2.org/#type=sequential&scheme=Blues&n=3) let's you play around with different types of color maps. If you want to create your own, [The Python Graph Gallery](https://python-graph-gallery.com/python-colors/) is a way to see what your Python color options are. + + +# 5.2 Issues with Visualization + +### Types of choropleth data + +There are several types of quantitative data variables that can be used to create a choropleth map. Let's consider these in terms of our ACS data. + +- **Count** + - counts, aggregated by feature + - *e.g. population within a census tract* + +- **Density** + - count, aggregated by feature, normalized by feature area + - *e.g. population per square mile within a census tract* + +- **Proportions / Percentages** + - value in a specific category divided by total value across in all categories + - *e.g. proportion of the tract population that is white compared to the total tract population* + +- **Rates / Ratios** + - value in one category divided by value in another category + - *e.g. homeowner-to-renter ratio would be calculated as the number of homeowners (c_owners/ c_renters)* + +### Interpretability of plotted data +The goal of a choropleth map is to use color to visualize the spatial distribution of a quantitative variable. + +Brighter or richer colors are typically used to signify higher values. + +A big problem with choropleth maps is that our eyes are drawn to the color of larger areas, even if the values being mapped in one or more smaller areas are more important. + + + +We see just this sort of problem in our population-density map. + +***Why does our map not look that interesting?*** Take a look at the histogram below, then consider the following question. + +plt.hist(counties['POP12_SQMI'],bins=40) +plt.title('Population Density per mile$^2$') +plt.show() + +
+ +
+
+ +#### Question +
+ +What county does that outlier represent? What problem does that pose? + +Your response here: + + + + + + +# 5.3 Classification schemes + +Let's try to make our map more interpretable! + +The common alternative to a proportionial color map is to use a **classification scheme** to create a **graduated color map**. This is the standard way to create a **choropleth map**. + +A **classification scheme** is a method for binning continuous data values into 4-7 classes (the default is 5) and map those classes to a color palette. + +### The commonly used classifications schemes: + +- **Equal intervals** + - equal-size data ranges (e.g., values within 0-10, 10-20, 20-30, etc.) + - pros: + - best for data spread across entire range of values + - easily understood by map readers + - cons: + - but avoid if you have highly skewed data or a few big outliers + + +- **Quantiles** + - equal number of observations in each bin + - pros: + - looks nice, becuase it best spreads colors across full set of data values + - thus, it's often the default scheme for mapping software + - cons: + - bin ranges based on the number of observations, not on the data values + - thus, different classes can have very similar or very different values. + + +- **Natural breaks** + - minimize within-class variance and maximize between-class differences + - e.g. 'fisher-jenks' + - pros: + - great for exploratory data analysis, because it can identify natural groupings + - cons: + - class breaks are best fit to one dataset, so the same bins can't always be used for multiple years + + +- **Manual** + - classifications are user-defined + - pros: + - especially useful if you want to slightly change the breaks produced by another scheme + - can be used as a fixed set of breaks to compare data over time + - cons: + - more work involved + +### Classification schemes and GeoDataFrames + +Classification schemes can be implemented using the geodataframe `plot` method by setting a value for the **scheme** argument. This requires the [pysal](https://pysal.org/) and [mapclassify](https://pysal.org/mapclassify) libraries to be installed in your Python environment. + +Here is a list of the `classification schemes` names that we will use: +- `equalinterval`, `quantiles`,`fisherjenks`,`naturalbreaks`, and `userdefined`. + +For more information about these classification schemes see the [pysal mapclassifiers web page](https://pysal.org/mapclassify/api.html) or check out the help docs. + +-------------------------- + +### Classification schemes in action + +Let's redo the last map using the `quantile` classification scheme. + +- What is different about the code? About the output map? + +# Plot population density - mile^2 +fig, ax = plt.subplots(figsize = (10,5)) +counties.plot(column='POP12_SQMI', + scheme="quantiles", + legend=True, + ax=ax + ) +ax.set_title("Population Density per Sq Mile") + +Note: For interval notation +- A square bracket is *inclusive* +- A parentheses is *exclusive* + +### User Defined Classification Schemes + +You may get pretty close to your final map without being completely satisfied. In this case you can manually define a classification scheme. + +Let's customize our map with a `user-defined` classification scheme where we manually set the breaks for the bins using the `classification_kwds` argument. + +fig, ax = plt.subplots(figsize = (14,8)) +counties.plot(column='POP12_SQMI', + legend=True, + cmap="RdYlGn", + scheme='user_defined', + classification_kwds={'bins':[50,100,200,300,400]}, + ax=ax) +ax.set_title("Population Density per Sq Mile") + +Since we are customizing our plot, we can also edit our legend to specify and format the text so that it's easier to read. + +- We'll use `legend_labels_list` to customize the labels for group in the legend. + +fig, ax = plt.subplots(figsize = (14,8)) +counties.plot(column='POP12_SQMI', + legend=True, + cmap="RdYlGn", + scheme='user_defined', + classification_kwds={'bins':[50,100,200,300,400]}, + ax=ax) + +# Create the labels for the legend +legend_labels_list = ['<50','50 to 100','100 to 200','200 to 300','300 to 400','>400'] + +# Apply the labels to the plot +for j in range(0,len(ax.get_legend().get_texts())): + ax.get_legend().get_texts()[j].set_text(legend_labels_list[j]) + +ax.set_title("Population Density per Sq Mile") + +### Let's plot a ratio + +If we look at the columns in our dataset, we see we have a number of variables +from which we can calculate proportions, rates, and the like. + +Let's try that out: + +counties.head() + +fig, ax = plt.subplots(figsize = (15,6)) + +# Plot percent hispanic as choropleth +counties.plot(column=(counties['HISPANIC']/counties['POP2012'] * 100), + legend=True, + cmap="Blues", + scheme='user_defined', + classification_kwds={'bins':[20,40,60,80]}, + edgecolor="grey", + linewidth=0.5, + ax=ax) + +legend_labels_list = ['<20%','20% - 40%','40% - 60%','60% - 80%','80% - 100%'] +for j in range(0,len(ax.get_legend().get_texts())): + ax.get_legend().get_texts()[j].set_text(legend_labels_list[j]) + +ax.set_title("Percent Hispanic Population") +plt.tight_layout() + +
+ +
+
+ +#### Questions +
+ +1. What new options and operations have we added to our code? +1. Based on our code, what title would you give this plot to describe what it displays? +1. How many bins do we specify in the `legend_labels_list` object, and how many bins are in the map legend? Why? + +Your responses here: + + + + + + +# 5.4 Point maps + +Choropleth maps are great, but mapping using point symbols enables us to visualize our spatial data in another way. + +If you know both mapping methods you can expand how much information you can show in one map. + +For example, point maps are a great way to map `counts` because the varying sizes of areas are deemphasized. + + + +----------------------- +Let's read in some point data on Alameda County schools. + +schools_df = pd.read_csv('notebook_data/alco_schools.csv') +schools_df.head() + +We got it from a plain CSV file, let's coerce it to a GeoDataFrame. + +schools_gdf = gpd.GeoDataFrame(schools_df, + geometry=gpd.points_from_xy(schools_df.X, schools_df.Y)) +schools_gdf.crs = "epsg:4326" + +Then we can map it. + +schools_gdf.plot() +plt.title('Alameda County Schools') + +### Proportional Color Maps +**Proportional color maps** linearly scale the `color` of a point symbol by the data values. + +Let's try this by creating a map of `API`. API stands for *Academic Performance Index*, which is a measurement system that looks at the performance of an individual school. + +schools_gdf.plot(column="API", cmap="gist_heat", + edgecolor="grey", figsize=(10,8), legend=True) +plt.title("Alameda County, School API scores") + +When you see that continuous color bar in the legend you know that the mapping of data values to colors is not classified. + + +### Graduated Color Maps + +We can also create **graduated color maps** by binning data values before associating them with colors. These are just like choropleth maps, except that the term "choropleth" is only used with polygon data. + +Graduated color maps use the same syntax as the choropleth maps above - you create them by setting a value for `scheme`. + +Below, we copy the code we used above to create a choropleth, but we change the name of the geodataframe to use the point gdf. + +fig, ax = plt.subplots(figsize = (15,6)) + +# Plot percent non-white with graduated colors +schools_gdf.plot(column='API', + legend=True, + cmap="Blues", + scheme='user_defined', + classification_kwds={'bins':[0,200,400,600,800]}, + edgecolor="grey", + linewidth=0.5, + #markersize=60, + ax=ax) + +# Create a custom legend +legend_labels_list = ['0','< 200','< 400','< 600','< 800','>= 800'] + +# Apply the legend to the map +for j in range(0,len(ax.get_legend().get_texts())): + ax.get_legend().get_texts()[j].set_text(legend_labels_list[j]) + +# Create the plot +plt.tight_layout() +plt.title("Alameda County, School API scores") + +schools_gdf['API'].describe() + +As you can see, the syntax for a choropleth and graduated color map is the same, +although some options only apply to one or the other. + +For example, uncomment the `markersize` parameter above to see how you can further customize a graduated color map. + +### Graduated symbol maps + +`Graduated symbol maps` are also a great method for mapping points. These are just like graduated color maps but instead of associating symbol color with data values they associate point size. Similarly,graduated symbol maps use `classification schemes` to set the size of point symbols. + +> We demonstrate how to make graduated symbol maps along with some other mapping techniques in the `Optional Mapping notebook` which we encourage you to explore on your own. (***Coming Soon***) + +## 5.5 Mapping Categorical Data + +Mapping categorical data, also called qualitative data, is a bit more straightforward. There is no need to scale or classify data values. The goal of the color map is to provide a contrasting set of colors so as to clearly delineate different categories. Here's a point-based example: + +schools_gdf.plot(column='Org', categorical=True, legend=True) + +## 5.6 Recap +We learned about important data driven mapping strategies and mapping concepts and can leverage what many of us know about `matplotlib` +- Choropleth Maps +- Point maps +- Color schemes +- Classifications + +# Exercise: Data-Driven Mapping + +Point and polygons are not the only geometry-types that we can use in data-driven mapping! + +Run the next cell to load a dataset containing Berkeley's bicycle boulevards (which we'll be using more in the following notebook). + +Then in the following cell, write your own code to: +1. plot the bike boulevards; +2. color them by status (find the correct column in the head of the dataframe, displayed below); +3. color them using a fitting, good-looking qualitative colormap that you choose from [The Matplotlib Colormap Reference](https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html); +4. set the line width to 5 (check the plot method's documentation to find the right argument for this!); +4. add the argument `figsize=[20,20]`, to make your map nice and big and visible! + +Then answer the questions posed in the last cell. + +
+ + +To see the solution, double-click the Markdown cell below. + + +bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson') +bike_blvds.head() + +# YOUR CODE HERE: + + +## Double-click to see solution! + + + +------------------------------------- + +
+ +
+
+ +#### Questions +
+ +1. What does that map indicate about the status of the Berkeley bike boulevards? +1. What does that map indicate about the status of your Berkeley bike-boulevard *dataset*? + +Your responses here: + + + + + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + diff --git a/_build/jupyter_execute/lessons/06_Spatial_Queries.ipynb b/_build/jupyter_execute/lessons/06_Spatial_Queries.ipynb new file mode 100644 index 0000000..e1ff869 --- /dev/null +++ b/_build/jupyter_execute/lessons/06_Spatial_Queries.ipynb @@ -0,0 +1,1058 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 6. Spatial Queries\n", + "\n", + "In spatial analysis, our goal is not just to make nice maps,\n", + "but to actually run analyses that leverage the explicitly spatial\n", + "nature of our data. The process of doing this is known as \n", + "**spatial analysis**.\n", + "\n", + "To construct spatial analyses, we string together series of spatial\n", + "operations in such a way that the end result answers our question of interest.\n", + "There are many such spatial operations. These are known as **spatial queries**.\n", + "\n", + "\n", + "- 6.0 Load and prep some data\n", + "- 6.1 Measurement Queries\n", + "- 6.2 Relationship Queries\n", + "- **Exercise**: Spatial Relationship Query\n", + "- 6.3 Proximity Analysis\n", + "- **Exercise**: Proximity Analysis\n", + "- 6.4 Recap\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip'\n", + " - 'notebook_data/protected_areas/CPAD_2020a_Units.shp'\n", + " - 'notebook_data/berkeley/BerkeleyCityLimits.shp'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + " - 'notebook_data/transportation/bart.csv'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------\n", + "\n", + "We will start by reviewing the most\n", + "fundamental set, which we'll refer to as **spatial queries**.\n", + "These can be divided into:\n", + "\n", + "- Measurement queries\n", + " - What is feature A's **length**?\n", + " - What is feature A's **area**?\n", + " - What is feature A's **perimeter**?\n", + " - What is feature A's **distance** from feature B?\n", + " - etc.\n", + "- Relationship queries\n", + " - Is feature A **within** feature B?\n", + " - Does feature A **intersect** with feature B?\n", + " - Does feature A **cross** feature B?\n", + " - etc.\n", + " \n", + "We'll work through examples of each of those types of queries.\n", + "\n", + "Then we'll see an example of a very common spatial analysis that \n", + "is a conceptual amalgam of those two types: **proximity analysis**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.0 Load and prep some data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read in our census tracts data again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\")\n", + "census_tracts.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "census_tracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we'll grab just the Alameda Country tracts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac = census_tracts.loc[census_tracts['COUNTYFP']=='001'].reset_index(drop=True)\n", + "census_tracts_ac.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.1 Measurement Queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll start off with some simple measurement queries.\n", + "\n", + "For example, here's how we can get the areas of each of our census tracts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay! \n", + "\n", + "We got... \n", + "\n", + "numbers!\n", + "\n", + "...?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What do those numbers mean?\n", + "1. What are our units?\n", + "1. And if we're not sure, how might be find out?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at our CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ah-hah! We're working in an unprojected CRS, with units of decimal degrees.\n", + "\n", + "**When doing spatial analysis, we will almost always want to work in a projected CRS\n", + "that has natural distance units, such as meters!**\n", + "\n", + "Time to project!\n", + "\n", + "(As previously, we'll use UTM Zone 10N with a NAD83 data.\n", + "This is a good choice for our region of interest.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10 = census_tracts_ac.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's try our area calculation again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks much more reasonable!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What are our units now?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may have noticed that our census tracts already have an area column in them.\n", + "\n", + "Let's do a sanity check on our results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# calculate the area for the 0th feature\n", + "census_tracts_ac_utm10.area[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get the area for the 0th feature according to its 'ALAND' attribute\n", + "census_tracts['ALAND'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check equivalence of the calculated areas and the 'ALAND' column\n", + "census_tracts_ac_utm10['ALAND'].values == census_tracts_ac_utm10.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What explains this disagreement? Are the calculated areas incorrect?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also sum the area for Alameda county by adding `.sum()` to the end of our area calculation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.area.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can actually look up how large Alameda County is to check our work.The county is 739 miles2, which is around 1,914,001,213 meters2. I'd say we're pretty close!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As it turns out, we can similarly use another attribute\n", + "to get the features' lengths.\n", + "\n", + "**NOTE**: In this case, given we're\n", + "dealing with polygons, this is equivalent to getting the features' perimeters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.length" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.2 Relationship Queries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GBP2Co-TutCH" + }, + "source": [ + "\n", + "[Spatial relationship queries](https://en.wikipedia.org/wiki/Spatial_relation) consider how two geometries or sets of geometries relate to one another in space. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jgUkeehpCqnS" + }, + "source": [ + "Here is a list of the most commonly used GeoPandas methods to test spatial relationships.\n", + "\n", + "- [within](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.within)\n", + "- [contains](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.contains) (the inverse of `within`)\n", + "- [intersects](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.intersects)\n", + "\n", + "
\n", + "There several other GeoPandas spatial relationship predicates but they are more complex to properly employ. For example the following two operations only work with geometries that are completely aligned.\n", + "\n", + "- [touches](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.touches)\n", + "- [equals](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.equals)\n", + "\n", + "\n", + "All of these methods takes the form:\n", + "\n", + " Geoseries.(geometry)\n", + " \n", + "For example:\n", + "\n", + " Geoseries.contains(geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------------\n", + "\n", + "Let's load a new dataset to demonstrate these queries.\n", + "\n", + "This is a dataset containing all the protected areas (parks and the like) in California." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas = gpd.read_file('./notebook_data/protected_areas/CPAD_2020a_Units.shp')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Does this need to be reprojected too?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes it does!\n", + "\n", + "Let's reproject it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas_utm10 = pas.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One common use for spatial queries is for spatial subsetting of data.\n", + "\n", + "In our case, lets use **intersects** to\n", + "find all of the parks that have land in Alameda County.\n", + "\n", + "But before we do that, let's take another look at our geometries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.geometry.type.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because we nave census tracts, each of these rows is either a Polygon or a MultiPolygon. For our relationship query we can actually simplify our geometry to be one polygon by using `unary_union`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.geometry.unary_union" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(census_tracts_ac_utm10.geometry.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can go ahead and conduct our operation `intersects`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas_in_ac = pas_utm10.intersects(census_tracts_ac_utm10.geometry.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we scroll the resulting GeoDataFrame to the right we'll see that \n", + "the `COUNTY` column of our resulting subset gives us a good sanity check on our results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas_in_ac" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas_utm10[pas_in_ac].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So does this overlay plot!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = census_tracts_ac_utm10.plot(color='gray', figsize=[12,16])\n", + "pas_utm10[pas_in_ac].plot(ax=ax, column='ACRES', cmap='summer', legend=True,\n", + " edgecolor='black', linewidth=0.4, alpha=0.8,\n", + " legend_kwds={'label': \"acres\",\n", + " 'orientation': \"horizontal\"})\n", + "ax.set_title('Protected areas in Alameda County, colored by area', size=18);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# color by county?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Spatial Relationship Query\n", + "\n", + "Let's use a spatial relationship query to create a new dataset containing Berkeley schools!\n", + "\n", + "Run the next two cells to load datasets containing Berkeley's city boundary and Alameda County's\n", + "schools and to reproject them to EPSG: 26910.\n", + "\n", + "Then in the following cell, write your own code to:\n", + "1. subset the schools for only those `within` Berkeley\n", + "2. plot the Berkeley boundary and then the schools as an overlay map\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the Berkeley boundary\n", + "berkeley = gpd.read_file(\"notebook_data/berkeley/BerkeleyCityLimits.shp\")\n", + "\n", + "# transform to EPSG:26910\n", + "berkeley_utm10 = berkeley.to_crs(\"epsg:26910\")\n", + "\n", + "# display\n", + "berkeley_utm10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the Alameda County schools CSV\n", + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "\n", + "# coerce it to a GeoDataFrame\n", + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "# define its unprojected (EPSG:4326) CRS\n", + "schools_gdf.crs = \"epsg:4326\"\n", + "\n", + "# transform to EPSG:26910\n", + "schools_gdf_utm10 = schools_gdf.to_crs( \"epsg:26910\")\n", + "\n", + "# display\n", + "schools_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.3 Proximity Analysis\n", + "\n", + "Now that we've seen the basic idea of spatial measurement and relationship queries,\n", + "let's take a look at a common analysis that combines those concepts: **promximity analysis**.\n", + "\n", + "Proximity analysis seeks to identify all features in a focal feature set\n", + "that are within some maximum distance of features in a reference feature set.\n", + "\n", + "A common workflow for this analysis is:\n", + "\n", + "1. Buffer (i.e. add a margin around) the reference dataset, out to the maximum distance.\n", + "1. Run a spatial relationship query to find all focal features that intersect (or are within) the buffer.\n", + "\n", + "---------------------------------\n", + "\n", + "Let's read in our bike boulevard data again.\n", + "\n", + "Then we'll find out which of our Berkeley schools are within a block's distance (200 m) of the boulevards." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, we need to reproject the boulevards to our projected CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10 = bike_blvds.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create our 200 meter bike boulevard buffers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10.crs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_buf = bike_blvds_utm10.buffer(distance=200)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_buf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's overlay everything." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "berkeley_utm10.plot(color='lightgrey', ax=ax)\n", + "bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "bike_blvds_utm10.plot(ax=ax)\n", + "berkeley_schools.plot(color='purple',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Looks like we're all ready to run our intersection to complete the proximity analysis.\n", + "\n", + "\n", + "**NOTE**: In order to subset with our buffers we need to call the `unary_union` attribute of the buffer object.\n", + "This gives us a single unified polygon, rather than a series of multipolygons representing buffers around each of the points in our multilines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_near_blvds = berkeley_schools.within(bike_blvds_buf.unary_union)\n", + "blvd_schools = berkeley_schools[schools_near_blvds]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's overlay again, to see if the schools we subsetted make sense." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "berkeley_utm10.plot(color='lightgrey', ax=ax)\n", + "bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "bike_blvds_utm10.plot(ax=ax)\n", + "berkeley_schools.plot(color='purple',ax=ax)\n", + "blvd_schools.plot(color='yellow', markersize=50, ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to find the shortest distance from one school to the bike boulevards, we can use the `distance` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley_schools.distance(bike_blvds_utm10.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Proximity Analysis\n", + "\n", + "Now it's your turn to try out a proximity analysis!\n", + "\n", + "Run the next cell to load our BART-system data, reproject it to EPSG: 26910, and subset it to Berkeley.\n", + "\n", + "Then in the following cell, write your own code to find all schools within walking distance (1 km) of a BART station.\n", + "\n", + "As a reminder, let's break this into steps:\n", + "1. buffer your Berkeley BART stations to 1 km (**HINT**: remember your units!)\n", + "2. use the schools' `within` attribute to check whether or not they're within the buffers (**HINT**: don't forget the `unary_union`!)\n", + "3. subset the Berkeley schools using the object returned by your spatial relationship query\n", + "\n", + "4. as always, plot your results for a good visual check!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the BART stations from CSV\n", + "bart_stations = pd.read_csv('notebook_data/transportation/bart.csv')\n", + "# coerce to a GeoDataFrame\n", + "bart_stations_gdf = gpd.GeoDataFrame(bart_stations, \n", + " geometry=gpd.points_from_xy(bart_stations.lon, bart_stations.lat))\n", + "# define its unprojected (EPSG:4326) CRS\n", + "bart_stations_gdf.crs = \"epsg:4326\"\n", + "# transform to UTM Zone 10 N (EPSG:26910)\n", + "bart_stations_gdf_utm10 = bart_stations_gdf.to_crs( \"epsg:26910\")\n", + "# subset to Berkeley\n", + "berkeley_bart = bart_stations_gdf_utm10[bart_stations_gdf_utm10.within(berkeley_utm10.unary_union)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Double-click to see solution!\n", + "\n", + "\n", + "\n", + "----------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.4 Recap\n", + "Leveraging what we've learned in our earlier lessons, we got to work with map overlays and start answering questions related to proximity. Key concepts include:\n", + "- Measuring area and length\n", + "\t- `.area`, \n", + "\t- `.length`\n", + "- Relationship Queries\n", + "\t- `.intersects()`\n", + "\t- `.within()`\n", + "- Buffer analysis\n", + "\t- `.buffer()`\n", + "\t- `.distance()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/06_Spatial_Queries.py b/_build/jupyter_execute/lessons/06_Spatial_Queries.py new file mode 100644 index 0000000..dc93354 --- /dev/null +++ b/_build/jupyter_execute/lessons/06_Spatial_Queries.py @@ -0,0 +1,513 @@ +# Lesson 6. Spatial Queries + +In spatial analysis, our goal is not just to make nice maps, +but to actually run analyses that leverage the explicitly spatial +nature of our data. The process of doing this is known as +**spatial analysis**. + +To construct spatial analyses, we string together series of spatial +operations in such a way that the end result answers our question of interest. +There are many such spatial operations. These are known as **spatial queries**. + + +- 6.0 Load and prep some data +- 6.1 Measurement Queries +- 6.2 Relationship Queries +- **Exercise**: Spatial Relationship Query +- 6.3 Proximity Analysis +- **Exercise**: Proximity Analysis +- 6.4 Recap + + + + + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip' + - 'notebook_data/protected_areas/CPAD_2020a_Units.shp' + - 'notebook_data/berkeley/BerkeleyCityLimits.shp' + - 'notebook_data/alco_schools.csv' + - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson' + - 'notebook_data/transportation/bart.csv' + +- Expected time to complete + - Lecture + Questions: 45 minutes + - Exercises: 20 minutes + + +------------------- + +We will start by reviewing the most +fundamental set, which we'll refer to as **spatial queries**. +These can be divided into: + +- Measurement queries + - What is feature A's **length**? + - What is feature A's **area**? + - What is feature A's **perimeter**? + - What is feature A's **distance** from feature B? + - etc. +- Relationship queries + - Is feature A **within** feature B? + - Does feature A **intersect** with feature B? + - Does feature A **cross** feature B? + - etc. + +We'll work through examples of each of those types of queries. + +Then we'll see an example of a very common spatial analysis that +is a conceptual amalgam of those two types: **proximity analysis**. + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +## 6.0 Load and prep some data + +Let's read in our census tracts data again. + +census_tracts = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip") +census_tracts.plot() + +census_tracts.head() + +Then we'll grab just the Alameda Country tracts. + +census_tracts_ac = census_tracts.loc[census_tracts['COUNTYFP']=='001'].reset_index(drop=True) +census_tracts_ac.plot() + +## 6.1 Measurement Queries + +We'll start off with some simple measurement queries. + +For example, here's how we can get the areas of each of our census tracts. + +census_tracts_ac.area + +Okay! + +We got... + +numbers! + +...? + +
+ +
+
+ +#### Questions +
+ +1. What do those numbers mean? +1. What are our units? +1. And if we're not sure, how might be find out? + +Your responses here: + + + + + + + +Let's take a look at our CRS. + +census_tracts_ac.crs + +Ah-hah! We're working in an unprojected CRS, with units of decimal degrees. + +**When doing spatial analysis, we will almost always want to work in a projected CRS +that has natural distance units, such as meters!** + +Time to project! + +(As previously, we'll use UTM Zone 10N with a NAD83 data. +This is a good choice for our region of interest.) + +census_tracts_ac_utm10 = census_tracts_ac.to_crs( "epsg:26910") + +census_tracts_ac_utm10.crs + +Now let's try our area calculation again. + +census_tracts_ac_utm10.area + +That looks much more reasonable! + +
+ +
+
+ +#### Question +
+ +What are our units now? + + +Your response here: + + + + + + +You may have noticed that our census tracts already have an area column in them. + +Let's do a sanity check on our results. + +# calculate the area for the 0th feature +census_tracts_ac_utm10.area[0] + +# get the area for the 0th feature according to its 'ALAND' attribute +census_tracts['ALAND'][0] + +# check equivalence of the calculated areas and the 'ALAND' column +census_tracts_ac_utm10['ALAND'].values == census_tracts_ac_utm10.area + +
+ +
+
+ +#### Question +
+ +What explains this disagreement? Are the calculated areas incorrect? + + +Your response here: + + + + + + + +We can also sum the area for Alameda county by adding `.sum()` to the end of our area calculation. + +census_tracts_ac_utm10.area.sum() + +We can actually look up how large Alameda County is to check our work.The county is 739 miles2, which is around 1,914,001,213 meters2. I'd say we're pretty close! + +As it turns out, we can similarly use another attribute +to get the features' lengths. + +**NOTE**: In this case, given we're +dealing with polygons, this is equivalent to getting the features' perimeters. + +census_tracts_ac_utm10.length + +## 6.2 Relationship Queries + + +[Spatial relationship queries](https://en.wikipedia.org/wiki/Spatial_relation) consider how two geometries or sets of geometries relate to one another in space. + + + + +Here is a list of the most commonly used GeoPandas methods to test spatial relationships. + +- [within](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.within) +- [contains](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.contains) (the inverse of `within`) +- [intersects](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.intersects) + +
+There several other GeoPandas spatial relationship predicates but they are more complex to properly employ. For example the following two operations only work with geometries that are completely aligned. + +- [touches](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.touches) +- [equals](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.equals) + + +All of these methods takes the form: + + Geoseries.(geometry) + +For example: + + Geoseries.contains(geometry) + +-------------------------------- + +Let's load a new dataset to demonstrate these queries. + +This is a dataset containing all the protected areas (parks and the like) in California. + +pas = gpd.read_file('./notebook_data/protected_areas/CPAD_2020a_Units.shp') + +Does this need to be reprojected too? + +pas.crs + +Yes it does! + +Let's reproject it. + +pas_utm10 = pas.to_crs("epsg:26910") + +One common use for spatial queries is for spatial subsetting of data. + +In our case, lets use **intersects** to +find all of the parks that have land in Alameda County. + +But before we do that, let's take another look at our geometries. + +census_tracts_ac_utm10.geometry.type.unique() + +census_tracts_ac_utm10.plot() + +Because we nave census tracts, each of these rows is either a Polygon or a MultiPolygon. For our relationship query we can actually simplify our geometry to be one polygon by using `unary_union` + +census_tracts_ac_utm10.geometry.unary_union + +print(census_tracts_ac_utm10.geometry.unary_union) + +Now we can go ahead and conduct our operation `intersects` + +pas_in_ac = pas_utm10.intersects(census_tracts_ac_utm10.geometry.unary_union) + +If we scroll the resulting GeoDataFrame to the right we'll see that +the `COUNTY` column of our resulting subset gives us a good sanity check on our results. + +pas_in_ac + +pas_utm10[pas_in_ac].head() + +So does this overlay plot! + +ax = census_tracts_ac_utm10.plot(color='gray', figsize=[12,16]) +pas_utm10[pas_in_ac].plot(ax=ax, column='ACRES', cmap='summer', legend=True, + edgecolor='black', linewidth=0.4, alpha=0.8, + legend_kwds={'label': "acres", + 'orientation': "horizontal"}) +ax.set_title('Protected areas in Alameda County, colored by area', size=18); + +# color by county? + +## Exercise: Spatial Relationship Query + +Let's use a spatial relationship query to create a new dataset containing Berkeley schools! + +Run the next two cells to load datasets containing Berkeley's city boundary and Alameda County's +schools and to reproject them to EPSG: 26910. + +Then in the following cell, write your own code to: +1. subset the schools for only those `within` Berkeley +2. plot the Berkeley boundary and then the schools as an overlay map + +To see the solution, double-click the Markdown cell below. + +# load the Berkeley boundary +berkeley = gpd.read_file("notebook_data/berkeley/BerkeleyCityLimits.shp") + +# transform to EPSG:26910 +berkeley_utm10 = berkeley.to_crs("epsg:26910") + +# display +berkeley_utm10.head() + +# load the Alameda County schools CSV +schools_df = pd.read_csv('notebook_data/alco_schools.csv') + +# coerce it to a GeoDataFrame +schools_gdf = gpd.GeoDataFrame(schools_df, + geometry=gpd.points_from_xy(schools_df.X, schools_df.Y)) +# define its unprojected (EPSG:4326) CRS +schools_gdf.crs = "epsg:4326" + +# transform to EPSG:26910 +schools_gdf_utm10 = schools_gdf.to_crs( "epsg:26910") + +# display +schools_df.head() + +# YOUR CODE HERE: + + +### Double-click to see solution! + + + +------------------------------- + +## 6.3 Proximity Analysis + +Now that we've seen the basic idea of spatial measurement and relationship queries, +let's take a look at a common analysis that combines those concepts: **promximity analysis**. + +Proximity analysis seeks to identify all features in a focal feature set +that are within some maximum distance of features in a reference feature set. + +A common workflow for this analysis is: + +1. Buffer (i.e. add a margin around) the reference dataset, out to the maximum distance. +1. Run a spatial relationship query to find all focal features that intersect (or are within) the buffer. + +--------------------------------- + +Let's read in our bike boulevard data again. + +Then we'll find out which of our Berkeley schools are within a block's distance (200 m) of the boulevards. + +bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson') +bike_blvds.plot() + +Of course, we need to reproject the boulevards to our projected CRS. + +bike_blvds_utm10 = bike_blvds.to_crs( "epsg:26910") + +Now we can create our 200 meter bike boulevard buffers. + +bike_blvds_utm10.crs + +bike_blvds_buf = bike_blvds_utm10.buffer(distance=200) + +bike_blvds_utm10.head() + +bike_blvds_buf.head() + +Now let's overlay everything. + +fig, ax = plt.subplots(figsize=(10,10)) +berkeley_utm10.plot(color='lightgrey', ax=ax) +bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5) +bike_blvds_utm10.plot(ax=ax) +berkeley_schools.plot(color='purple',ax=ax) + +Great! Looks like we're all ready to run our intersection to complete the proximity analysis. + + +**NOTE**: In order to subset with our buffers we need to call the `unary_union` attribute of the buffer object. +This gives us a single unified polygon, rather than a series of multipolygons representing buffers around each of the points in our multilines. + +schools_near_blvds = berkeley_schools.within(bike_blvds_buf.unary_union) +blvd_schools = berkeley_schools[schools_near_blvds] + +Now let's overlay again, to see if the schools we subsetted make sense. + +fig, ax = plt.subplots(figsize=(10,10)) +berkeley_utm10.plot(color='lightgrey', ax=ax) +bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5) +bike_blvds_utm10.plot(ax=ax) +berkeley_schools.plot(color='purple',ax=ax) +blvd_schools.plot(color='yellow', markersize=50, ax=ax) + +If we want to find the shortest distance from one school to the bike boulevards, we can use the `distance` function. + +berkeley_schools.distance(bike_blvds_utm10.unary_union) + +## Exercise: Proximity Analysis + +Now it's your turn to try out a proximity analysis! + +Run the next cell to load our BART-system data, reproject it to EPSG: 26910, and subset it to Berkeley. + +Then in the following cell, write your own code to find all schools within walking distance (1 km) of a BART station. + +As a reminder, let's break this into steps: +1. buffer your Berkeley BART stations to 1 km (**HINT**: remember your units!) +2. use the schools' `within` attribute to check whether or not they're within the buffers (**HINT**: don't forget the `unary_union`!) +3. subset the Berkeley schools using the object returned by your spatial relationship query + +4. as always, plot your results for a good visual check! + +To see the solution, double-click the Markdown cell below. + +# load the BART stations from CSV +bart_stations = pd.read_csv('notebook_data/transportation/bart.csv') +# coerce to a GeoDataFrame +bart_stations_gdf = gpd.GeoDataFrame(bart_stations, + geometry=gpd.points_from_xy(bart_stations.lon, bart_stations.lat)) +# define its unprojected (EPSG:4326) CRS +bart_stations_gdf.crs = "epsg:4326" +# transform to UTM Zone 10 N (EPSG:26910) +bart_stations_gdf_utm10 = bart_stations_gdf.to_crs( "epsg:26910") +# subset to Berkeley +berkeley_bart = bart_stations_gdf_utm10[bart_stations_gdf_utm10.within(berkeley_utm10.unary_union)] + +# YOUR CODE HERE: + + +### Double-click to see solution! + + + +---------------------------------- + +## 6.4 Recap +Leveraging what we've learned in our earlier lessons, we got to work with map overlays and start answering questions related to proximity. Key concepts include: +- Measuring area and length + - `.area`, + - `.length` +- Relationship Queries + - `.intersects()` + - `.within()` +- Buffer analysis + - `.buffer()` + - `.distance()` + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + diff --git a/_build/jupyter_execute/lessons/07_Joins_and_Aggregation.ipynb b/_build/jupyter_execute/lessons/07_Joins_and_Aggregation.ipynb new file mode 100644 index 0000000..bd4d5e9 --- /dev/null +++ b/_build/jupyter_execute/lessons/07_Joins_and_Aggregation.ipynb @@ -0,0 +1,1180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 7. Attribute and Spatial Joins\n", + "\n", + "Now that we understand the logic of spatial relationship queries,\n", + "let's take a look at another fundamental spatial operation that relies on them.\n", + "\n", + "This operation, called a **spatial join**, is the process by which we can\n", + "leverage the spatial relationships between distinct datasets to merge\n", + "their information into a new, synthetic dataset.\n", + "\n", + "This operation can be thought as the spatial equivalent of an\n", + "**attribute join**, in which multiple tabular datasets can be merged by\n", + "aligning matching values in a common column that they both contain.\n", + "Thus, we'll start by developing an understanding of this operation first!\n", + "\n", + "- 7.0 Data Input and Prep\n", + "- 7.1 Attribute Joins\n", + "- **Exercise**: Choropleth Map\n", + "- 7.2 Spatial Joins\n", + "- 7.3 Aggregation\n", + "- **Exercise**: Aggregation\n", + "- 7.4 Recap\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/census/ACS5yr/census_variables_CA.csv'\n", + " - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip'\n", + " - 'notebook_data/alco_schools.csv'\n", + " \n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.0 Data Input and Prep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read in a table of data from the US Census' 5-year American Community Survey (ACS5)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the ACS5 data for CA into a pandas DataFrame.\n", + "# Note: We force the FIPS_11_digit to be read in as a string to preserve any leading zeroes.\n", + "acs5_df = pd.read_csv(\"notebook_data/census/ACS5yr/census_variables_CA.csv\", dtype={'FIPS_11_digit':str})\n", + "acs5_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Brief summary of the data**:\n", + "\n", + "Below is a table of the variables in this table. They were combined from \n", + "different ACS 5 year tables.\n", + "\n", + "NOTE:\n", + "- variables that start with `c_` are counts\n", + "- variables that start with `med_` are medians\n", + "- variables that end in `_moe` are margin of error estimates\n", + "- variables that start with `_p` are proportions calcuated from the counts divided by the table denominator (the total count for whom that variable was assessed)\n", + "\n", + "\n", + "| Variable | Description |\n", + "|-----------------|-------------------------------------------------|\n", + "|`c_race` |Total population \n", + "|`c_white` |Total white non-Latinx\n", + "| `c_black` | Total black and African American non-Latinx\n", + "| `c_asian` | Total Asian non-Latinx\n", + "| `c_latinx` | Total Latinx\n", + "| `state_fips` | State level FIPS code\n", + "| `county_fips` | County level FIPS code\n", + "| `tract_fips` |Tracts level FIPS code\n", + "| `med_rent` |Median rent\n", + "| `med_hhinc` |Median household income\n", + "| `c_tenants` |Total tenants\n", + "| `c_owners` |Total owners\n", + "| `c_renters` |Total renters\n", + "| `c_movers` |Total number of people who moved\n", + "| `c_stay` |Total number of people who stayed\n", + "| `c_movelocal` |Number of people who moved locally\n", + "| `c_movecounty` |Number of people who moved counties\n", + "| `c_movestate` | Number of people who moved states\n", + "| `c_moveabroad` |Number of people who moved abroad\n", + "| `c_commute` |Total number of commuters\n", + "| `c_car` | Number of commuters who use a car\n", + "| `c_carpool` | Number of commuters who carpool\n", + "| `c_transit` |Number of commuters who use public transit\n", + "| `c_bike` |Number of commuters who bike\n", + "| `c_walk` |Number of commuters who bike\n", + "| `year` | ACS data year\n", + "| `FIPS_11_digit` | 11-digit FIPS code\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're going to drop all of our `moe` columns by identifying all of those that end with `_moe`. We can do that in two steps, first by using `filter` to identify columns that contain the string `_moe`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "moe_cols = acs5_df.filter(like='_moe',axis=1).columns\n", + "moe_cols" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df.drop(moe_cols, axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And lastly, let's grab only the rows for year 2018 and county FIPS code 1 (i.e. Alameda County)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df_ac = acs5_df[(acs5_df['year']==2018) & (acs5_df['county_fips']==1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------------------\n", + "Now let's also read in our census tracts again!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001']\n", + "tracts_gdf_ac.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.1 Attribute Joins\n", + "\n", + "**Attribute Joins between Geodataframes and Dataframes**\n", + "\n", + "*We just mapped the census tracts. But what makes a map powerful is when you map the data associated with the locations.*\n", + "\n", + "- `tracts_gdf_ac`: These are polygon data in a GeoDataFrame. However, as we saw in the `head` of that dataset, they no attributes of interest!\n", + "\n", + "- `acs5_df_ac`: These are 2018 ACS data from a CSV file ('census_variables_CA.csv'), imported and read in as a `pandas` DataFrame. However, they have no geometries!\n", + "\n", + "In order to map the ACS data we need to associate it with the tracts. Let's do that now, by joining the columns from `acs5_df_ac` to the columns of `tracts_gdf_ac` using a common column as the key for matching rows. This process is called an **attribute join**.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "The image above gives us a nice conceptual summary of the types of joins we could run.\n", + "\n", + "1. In general, why might we choose one type of join over another?\n", + "1. In our case, do we want an inner, left, right, or outer (AKA 'full') join? \n", + "\n", + "(**NOTE**: You can read more about merging in `geopandas` [here](http://geopandas.org/mergingdata.html#attribute-joins).)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, here we go!\n", + "\n", + "Let's take a look at the common column in both our DataFrames.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_ac['GEOID'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df_ac['FIPS_11_digit'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Note that they are **not named the same thing**. \n", + " \n", + " That's okay! We just need to know that they contain the same information.\n", + "\n", + "Also note that they are **not in the same order**. \n", + " \n", + " That's not only okay... That's the point! (If they were in the same order already then we could just join them side by side, without having Python find and line up the matching rows from each!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------------------\n", + "\n", + "Let's do a `left` join to keep all of the census tracts in Alameda County and only the ACS data for those tracts.\n", + "\n", + "**NOTE**: To figure out how to do this we could always take a peek at the documentation by calling\n", + "`?tracts_gdf_ac.merge`, or `help(tracts_gdf_ac)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Left join keeps all tracts and the acs data for those tracts\n", + "tracts_acs_gdf_ac = tracts_gdf_ac.merge(acs5_df_ac, left_on='GEOID',\n", + " right_on=\"FIPS_11_digit\", how='left')\n", + "tracts_acs_gdf_ac.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check that we have all the variables we have in our dataset now." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "list(tracts_acs_gdf_ac.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "It's always important to run sanity checks on our results, at each step of the way!\n", + "\n", + "In this case, how many rows and columns should we have?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Rows and columns in the Alameda County Census tract gdf:\\n\\t\", tracts_gdf_ac.shape)\n", + "print(\"Row and columns in the ACS5 2018 data:\\n\\t\", acs5_df_ac.shape)\n", + "print(\"Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\\n\\t\", tracts_acs_gdf_ac.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's save out our merged data so we can use it in the final notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_acs_gdf_ac.to_file('outdata/tracts_acs_gdf_ac.json', driver='GeoJSON')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Choropleth Map\n", + "We can now make choropleth maps using our attribute joined geodataframe. Go ahead and pick one variable to color the map, then map it. You can go back to lesson 5 if you need a refresher on how to make this!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------\n", + "## 7.2 Spatial Joins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! We've wrapped our heads around the concept of an attribute join.\n", + "\n", + "Now let's extend that concept to its spatially explicit equivalent: the **spatial join**!\n", + "\n", + "\n", + "
\n", + "\n", + "To start, we'll read in some other data: The Alameda County schools data.\n", + "\n", + "Then we'll work with that data and our `tracts_acs_gdf_ac` data together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check if we have to transform the schools to match the`tracts_acs_gdf_ac`'s CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('schools_gdf CRS:', schools_gdf.crs)\n", + "print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes we do! Let's do that.\n", + "\n", + "**NOTE**: Explicit syntax aiming at that dataset's CRS leaves less room for human error!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf = schools_gdf.to_crs(tracts_acs_gdf_ac.crs)\n", + "\n", + "print('schools_gdf CRS:', schools_gdf.crs)\n", + "print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we're ready to combine the datasets in an analysis.\n", + "\n", + "**In this case, we want to get data from the census tract within which each school is located.**\n", + "\n", + "But how can we do that? The two datasets don't share a common column to use for a join." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_acs_gdf_ac.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, they do have a shared relationship by way of space! \n", + "\n", + "So, we'll use a spatial relationship query to figure out the census tract that\n", + "each school is in, then associate the tract's data with that school (as additional data in the school's row).\n", + "This is a **spatial join**!\n", + "\n", + "---------------------------------\n", + "\n", + "### Census Tract Data Associated with Each School\n", + "\n", + "In this case, let's say we're interested in the relationship between the median household income\n", + "in a census tract (`tracts_acs_gdf_ac['med_hhinc']`) and a school's Academic Performance Index\n", + "(`schools_gdf['API']`).\n", + "\n", + "To start, let's take a look at the distributions of our two variables of interest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_acs_gdf_ac.hist('med_hhinc')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.hist('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh, right! Those pesky schools with no reported APIs (i.e. API == 0)! Let's drop those." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf_api = schools_gdf.loc[schools_gdf['API'] > 0, ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf_api.hist('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much better!\n", + "\n", + "Now, maybe we think there ought to be some correlation between the two variables?\n", + "As a first pass at this possibility, let's overlay the two datasets, coloring each one by\n", + "its variable of interest. This should give us a sense of whether or not similar values co-occur." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = tracts_acs_gdf_ac.plot(column='med_hhinc', cmap='cividis', figsize=[18,18],\n", + " legend=True, legend_kwds={'label': \"median household income ($)\",\n", + " 'orientation': \"horizontal\"})\n", + "schools_gdf_api.plot(column='API', cmap='cividis', edgecolor='black', alpha=1, ax=ax,\n", + " legend=True, legend_kwds={'label': \"API\", 'orientation': \"horizontal\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatially Joining our Schools and Census Tracts\n", + "\n", + "Though it's hard to say for sure, it certainly looks possible.\n", + "It would be ideal to scatterplot the variables! But in order to do that, \n", + "we need to know the median household income in each school's tract, which\n", + "means we definitely need our **spatial join**!\n", + "\n", + "We'll first take a look at the documentation for the spatial join function, `gpd.sjoin`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(gpd.sjoin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like the key arguments to consider are:\n", + "- the two GeoDataFrames (**`left_df`** and **`right_df`**)\n", + "- the type of join to run (**`how`**), which can take the values `left`, `right`, or `inner`\n", + "- the spatial relationship query to use (**`op`**)\n", + "\n", + "**NOTE**:\n", + "- By default `sjoin` is an inner join. It keeps the data from both geodataframes only where the locations spatially intersect.\n", + "\n", + "- By default `sjoin` maintains the geometry of first geodataframe input to the operation. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Which GeoDataFrame are we joining onto which (i.e. which one is getting the other one's data added to it)?\n", + "1. What happened to 'outer' as a join type?\n", + "1. Thus, in our operation, which GeoDataFrame should be the `left_df`, which should be the `right_df`, and `how` do we want our join to run?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alright! Let's run our join!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts = gpd.sjoin(schools_gdf_api, tracts_acs_gdf_ac, how='left')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking Our Output\n", + "\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "As always, we want to sanity-check our intermediate result before we rush ahead.\n", + "\n", + "One way to do that is to introspect the structure of the result object a bit.\n", + "\n", + "1. What type of object should that have given us?\n", + "1. What should the dimensions of that object be, and why?\n", + "1. If we wanted a visual check of our results (i.e. a plot or map), what could we do?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(schools_jointracts.shape)\n", + "print(schools_gdf.shape)\n", + "print(tracts_acs_gdf_ac.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confirmed! The output of the our `sjoin` operation is a GeoDataFrame (`schools_jointracts`) with:\n", + "- a row for each school that is located inside a census tract (all of them are)\n", + "- the **point geometry** of that school\n", + "- all of the attribute data columns (non-geometry columns) from both input GeoDataFrames\n", + "\n", + "----------------------------\n", + "\n", + "Let's also take a look at an overlay map of the schools on the tracts.\n", + "If we color the schools categorically by their tracts IDs, then we should see\n", + "that all schools within a given tract polygon are the same color." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = tracts_acs_gdf_ac.plot(color='white', edgecolor='black', figsize=[18,18])\n", + "schools_jointracts.plot(column='GEOID', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assessing the Relationship between Median Household Income and API\n", + "\n", + "Fantastic! That looks right!\n", + "\n", + "Now we can create that scatterplot we were thinking about!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(6,6))\n", + "ax.scatter(schools_jointracts.med_hhinc, schools_jointracts.API)\n", + "ax.set_xlabel('median household income ($)')\n", + "ax.set_ylabel('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wow! Just as we suspected based on our overlay map,\n", + "there's a pretty obvious, strong, and positive correlation\n", + "between median household income in a school's tract\n", + "and the school's API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.3. Aggregation\n", + "\n", + "We just saw that a spatial join in one way to leverage the spatial relationship\n", + "between two datasets in order to create a new, synthetic dataset.\n", + "\n", + "An **aggregation** is another way we can generate new data from this relationship.\n", + "In this case, for each feature in one dataset we find all the features in another\n", + "dataset that satisfy our chosen spatial relationship query with it (e.g. within, intersects),\n", + "then aggregate them using some summary function (e.g. count, mean).\n", + "\n", + "------------------------------------\n", + "\n", + "### Getting the Aggregated School Counts\n", + "\n", + "Let's take this for a spin with our data. We'll count all the schools within each census tract.\n", + "\n", + "Note that we've already done the first step of spatially joining the data from the aggregating features\n", + "(the tracts) onto the data to be aggregated (our schools).\n", + "\n", + "The next step is to group our GeoDataFrame by census tract, and then summarize our data by group.\n", + "We do this using the DataFrame method `groupy`.\n", + "\n", + "To get the correct count, lets rejoin our schools on our tracts, this time keeping all schools\n", + "(not just those with APIs > 0, as before)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='left')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now for the `groupy` operation.\n", + "\n", + "**NOTE**: We could really use any column, since we're just taking a count. For now we'll just use the school names ('Site')." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_countsbytract = schools_jointracts[['GEOID','Site']].groupby('GEOID', as_index=False).count()\n", + "print(\"Counts, rows and columns:\", schools_countsbytract.shape)\n", + "print(\"Tracts, rows and columns:\", tracts_acs_gdf_ac.shape)\n", + "\n", + "# take a look at the data\n", + "schools_countsbytract.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting Tract Polygons with School Counts\n", + "\n", + "The above `groupby` and `count` operations give us the counts we wanted.\n", + "- We have the 263 (of 361) census tracts that have at least one school\n", + "- We have the number of schools within each of those tracts\n", + "\n", + "But the output of `groupby` is a plain DataFrame not a GeoDataFrame.\n", + "\n", + "If we want a GeoDataFrame then we have two options:\n", + "1. We could join the `groupby` output to `tracts_acs_gdf_ac` by the attribute `GEOID`\n", + "or\n", + "2. We could start over, using the GeoDataFrame `dissolve` method, which we can think of as a spatial `groupby`. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------------\n", + "\n", + "Since we already know how to do an attribute join, we'll do the `dissolve`!\n", + "\n", + "First, let's run a new spatial join." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_joinschools = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='right')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_joinschools.geometry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's run our dissolve!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_schoolcounts = tracts_joinschools[['GEOID', 'Site', 'geometry']].dissolve(by='GEOID', aggfunc='count')\n", + "print(\"Counts, rows and columns:\", tracts_schoolcounts.shape)\n", + "\n", + "# take a look\n", + "tracts_schoolcounts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice! Let's break that down.\n", + "\n", + "- The `dissolve` operation requires a geometry column and a grouping column (in our case, 'GEOID'). Any geometries within the **same group** will be dissolved if they have the same geometry or nested geometries. \n", + " \n", + "- The `aggfunc`, or aggregation function, of the dissolve operation will be applied to all numeric columns in the input geodataframe (unless the function is `count` in which case it will count rows.) \n", + "\n", + "Check out the Geopandas documentation on [dissolve](https://geopandas.org/aggregation_with_dissolve.html?highlight=dissolve) for more information.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Above we selected three columns from the input GeoDataFrame to create a subset as input to the dissolve operation. Why?\n", + "1. Why did we run a new spatial join? What would have happened if we had used the `schools_jointracts` object instead?\n", + "1. What explains the dimensions of the new object (361, 2)?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "You responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mapping our Spatial Join Output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also, because our `sjoin` plus `dissolve` pipeline outputs a GeoDataFrame, we can now easily map the school count by census tract!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "\n", + "# Display the output of our spatial join\n", + "tracts_schoolcounts.plot(ax=ax,column='Site', \n", + " scheme=\"user_defined\",\n", + " classification_kwds={'bins':[*range(9)]},\n", + " cmap=\"PuRd_r\",\n", + " edgecolor=\"grey\",\n", + " legend=True, \n", + " legend_kwds={'title':'Number of schools'})\n", + "schools_gdf.plot(ax=ax, color='black', markersize=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------\n", + "\n", + "## Exercise: Aggregation\n", + "\n", + "#### What is the mean API of each census tract?\n", + "\n", + "As we mentioned, the spatial aggregation workflow that we just put together above\n", + "could have been used not to generate a new count variable, but also\n", + "to generate any other new variable the results from calling an aggregation function\n", + "on an attribute column.\n", + "\n", + "In this case, we want to calculate and map the mean API of the schools in each census tract.\n", + "\n", + "Copy and paste code from above where useful, then tweak and/or add to that code such that your new code:\n", + "1. joins the schools onto the tracts (**HINT**: make sure to decide whether or not you want to include schools with API = 0!)\n", + "1. dissolves that joined object by the tract IDs, giving you a new GeoDataFrame with each tract's mean API (**HINT**: because this is now a different calculation, different problems may arise and need handling!)\n", + "1. plots the tracts, colored by API scores (**HINT**: overlay the schools points again, visualizing them in a way that will help you visually check your results!)\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Double-click to see solution!\n", + "\n", + "\n", + "\n", + "----------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.4 Recap\n", + "We discussed how we can combine datasets to enhance any geospatial data analyses you could do. Key concepts include:\n", + "- Attribute joins\n", + "\t- `.merge()`\n", + "- Spatial joins (order matters!)\n", + "\t- `gpd.sjoin()`\n", + "- Aggregation\n", + "\t-`.groupby()`\n", + "\t- `.dissolve()` (preserves geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/07_Joins_and_Aggregation.py b/_build/jupyter_execute/lessons/07_Joins_and_Aggregation.py new file mode 100644 index 0000000..a90499d --- /dev/null +++ b/_build/jupyter_execute/lessons/07_Joins_and_Aggregation.py @@ -0,0 +1,641 @@ +# Lesson 7. Attribute and Spatial Joins + +Now that we understand the logic of spatial relationship queries, +let's take a look at another fundamental spatial operation that relies on them. + +This operation, called a **spatial join**, is the process by which we can +leverage the spatial relationships between distinct datasets to merge +their information into a new, synthetic dataset. + +This operation can be thought as the spatial equivalent of an +**attribute join**, in which multiple tabular datasets can be merged by +aligning matching values in a common column that they both contain. +Thus, we'll start by developing an understanding of this operation first! + +- 7.0 Data Input and Prep +- 7.1 Attribute Joins +- **Exercise**: Choropleth Map +- 7.2 Spatial Joins +- 7.3 Aggregation +- **Exercise**: Aggregation +- 7.4 Recap + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/census/ACS5yr/census_variables_CA.csv' + - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip' + - 'notebook_data/alco_schools.csv' + +- Expected time to complete + - Lecture + Questions: 45 minutes + - Exercises: 20 minutes + + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +## 7.0 Data Input and Prep + +Let's read in a table of data from the US Census' 5-year American Community Survey (ACS5). + +# Read in the ACS5 data for CA into a pandas DataFrame. +# Note: We force the FIPS_11_digit to be read in as a string to preserve any leading zeroes. +acs5_df = pd.read_csv("notebook_data/census/ACS5yr/census_variables_CA.csv", dtype={'FIPS_11_digit':str}) +acs5_df.head() + + +**Brief summary of the data**: + +Below is a table of the variables in this table. They were combined from +different ACS 5 year tables. + +NOTE: +- variables that start with `c_` are counts +- variables that start with `med_` are medians +- variables that end in `_moe` are margin of error estimates +- variables that start with `_p` are proportions calcuated from the counts divided by the table denominator (the total count for whom that variable was assessed) + + +| Variable | Description | +|-----------------|-------------------------------------------------| +|`c_race` |Total population +|`c_white` |Total white non-Latinx +| `c_black` | Total black and African American non-Latinx +| `c_asian` | Total Asian non-Latinx +| `c_latinx` | Total Latinx +| `state_fips` | State level FIPS code +| `county_fips` | County level FIPS code +| `tract_fips` |Tracts level FIPS code +| `med_rent` |Median rent +| `med_hhinc` |Median household income +| `c_tenants` |Total tenants +| `c_owners` |Total owners +| `c_renters` |Total renters +| `c_movers` |Total number of people who moved +| `c_stay` |Total number of people who stayed +| `c_movelocal` |Number of people who moved locally +| `c_movecounty` |Number of people who moved counties +| `c_movestate` | Number of people who moved states +| `c_moveabroad` |Number of people who moved abroad +| `c_commute` |Total number of commuters +| `c_car` | Number of commuters who use a car +| `c_carpool` | Number of commuters who carpool +| `c_transit` |Number of commuters who use public transit +| `c_bike` |Number of commuters who bike +| `c_walk` |Number of commuters who bike +| `year` | ACS data year +| `FIPS_11_digit` | 11-digit FIPS code + + +We're going to drop all of our `moe` columns by identifying all of those that end with `_moe`. We can do that in two steps, first by using `filter` to identify columns that contain the string `_moe`. + +moe_cols = acs5_df.filter(like='_moe',axis=1).columns +moe_cols + +acs5_df.drop(moe_cols, axis=1, inplace=True) + +And lastly, let's grab only the rows for year 2018 and county FIPS code 1 (i.e. Alameda County) + +acs5_df_ac = acs5_df[(acs5_df['year']==2018) & (acs5_df['county_fips']==1)] + +--------------------------------- +Now let's also read in our census tracts again! + +tracts_gdf = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip") + +tracts_gdf.head() + +tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001'] +tracts_gdf_ac.plot() +plt.show() + +## 7.1 Attribute Joins + +**Attribute Joins between Geodataframes and Dataframes** + +*We just mapped the census tracts. But what makes a map powerful is when you map the data associated with the locations.* + +- `tracts_gdf_ac`: These are polygon data in a GeoDataFrame. However, as we saw in the `head` of that dataset, they no attributes of interest! + +- `acs5_df_ac`: These are 2018 ACS data from a CSV file ('census_variables_CA.csv'), imported and read in as a `pandas` DataFrame. However, they have no geometries! + +In order to map the ACS data we need to associate it with the tracts. Let's do that now, by joining the columns from `acs5_df_ac` to the columns of `tracts_gdf_ac` using a common column as the key for matching rows. This process is called an **attribute join**. + + + + + + +-------------------------- + + + + +
+ +
+
+ +#### Question +
+ +The image above gives us a nice conceptual summary of the types of joins we could run. + +1. In general, why might we choose one type of join over another? +1. In our case, do we want an inner, left, right, or outer (AKA 'full') join? + +(**NOTE**: You can read more about merging in `geopandas` [here](http://geopandas.org/mergingdata.html#attribute-joins).) + +Your responses here: + + + + + + + +Okay, here we go! + +Let's take a look at the common column in both our DataFrames. + + +tracts_gdf_ac['GEOID'].head() + +acs5_df_ac['FIPS_11_digit'].head() + + +Note that they are **not named the same thing**. + + That's okay! We just need to know that they contain the same information. + +Also note that they are **not in the same order**. + + That's not only okay... That's the point! (If they were in the same order already then we could just join them side by side, without having Python find and line up the matching rows from each!) + +------------------------------- + +Let's do a `left` join to keep all of the census tracts in Alameda County and only the ACS data for those tracts. + +**NOTE**: To figure out how to do this we could always take a peek at the documentation by calling +`?tracts_gdf_ac.merge`, or `help(tracts_gdf_ac)`. + +# Left join keeps all tracts and the acs data for those tracts +tracts_acs_gdf_ac = tracts_gdf_ac.merge(acs5_df_ac, left_on='GEOID', + right_on="FIPS_11_digit", how='left') +tracts_acs_gdf_ac.head(2) + +Let's check that we have all the variables we have in our dataset now. + +list(tracts_acs_gdf_ac.columns) + +
+ +
+
+ +#### Question +
+ +It's always important to run sanity checks on our results, at each step of the way! + +In this case, how many rows and columns should we have? + + +Your response here: + + + + + + + +print("Rows and columns in the Alameda County Census tract gdf:\n\t", tracts_gdf_ac.shape) +print("Row and columns in the ACS5 2018 data:\n\t", acs5_df_ac.shape) +print("Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\n\t", tracts_acs_gdf_ac.shape) + +Let's save out our merged data so we can use it in the final notebook. + +tracts_acs_gdf_ac.to_file('outdata/tracts_acs_gdf_ac.json', driver='GeoJSON') + +## Exercise: Choropleth Map +We can now make choropleth maps using our attribute joined geodataframe. Go ahead and pick one variable to color the map, then map it. You can go back to lesson 5 if you need a refresher on how to make this! + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE + + +### Double-click to see solution! + + + +------------------- +## 7.2 Spatial Joins + +Great! We've wrapped our heads around the concept of an attribute join. + +Now let's extend that concept to its spatially explicit equivalent: the **spatial join**! + + +
+ +To start, we'll read in some other data: The Alameda County schools data. + +Then we'll work with that data and our `tracts_acs_gdf_ac` data together. + +schools_df = pd.read_csv('notebook_data/alco_schools.csv') +schools_gdf = gpd.GeoDataFrame(schools_df, + geometry=gpd.points_from_xy(schools_df.X, schools_df.Y)) +schools_gdf.crs = "epsg:4326" + +Let's check if we have to transform the schools to match the`tracts_acs_gdf_ac`'s CRS. + +print('schools_gdf CRS:', schools_gdf.crs) +print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs) + +Yes we do! Let's do that. + +**NOTE**: Explicit syntax aiming at that dataset's CRS leaves less room for human error! + +schools_gdf = schools_gdf.to_crs(tracts_acs_gdf_ac.crs) + +print('schools_gdf CRS:', schools_gdf.crs) +print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs) + +Now we're ready to combine the datasets in an analysis. + +**In this case, we want to get data from the census tract within which each school is located.** + +But how can we do that? The two datasets don't share a common column to use for a join. + +tracts_acs_gdf_ac.columns + +schools_gdf.columns + +However, they do have a shared relationship by way of space! + +So, we'll use a spatial relationship query to figure out the census tract that +each school is in, then associate the tract's data with that school (as additional data in the school's row). +This is a **spatial join**! + +--------------------------------- + +### Census Tract Data Associated with Each School + +In this case, let's say we're interested in the relationship between the median household income +in a census tract (`tracts_acs_gdf_ac['med_hhinc']`) and a school's Academic Performance Index +(`schools_gdf['API']`). + +To start, let's take a look at the distributions of our two variables of interest. + +tracts_acs_gdf_ac.hist('med_hhinc') + +schools_gdf.hist('API') + +Oh, right! Those pesky schools with no reported APIs (i.e. API == 0)! Let's drop those. + +schools_gdf_api = schools_gdf.loc[schools_gdf['API'] > 0, ] + +schools_gdf_api.hist('API') + +Much better! + +Now, maybe we think there ought to be some correlation between the two variables? +As a first pass at this possibility, let's overlay the two datasets, coloring each one by +its variable of interest. This should give us a sense of whether or not similar values co-occur. + +ax = tracts_acs_gdf_ac.plot(column='med_hhinc', cmap='cividis', figsize=[18,18], + legend=True, legend_kwds={'label': "median household income ($)", + 'orientation': "horizontal"}) +schools_gdf_api.plot(column='API', cmap='cividis', edgecolor='black', alpha=1, ax=ax, + legend=True, legend_kwds={'label': "API", 'orientation': "horizontal"}) + +### Spatially Joining our Schools and Census Tracts + +Though it's hard to say for sure, it certainly looks possible. +It would be ideal to scatterplot the variables! But in order to do that, +we need to know the median household income in each school's tract, which +means we definitely need our **spatial join**! + +We'll first take a look at the documentation for the spatial join function, `gpd.sjoin`. + +help(gpd.sjoin) + +Looks like the key arguments to consider are: +- the two GeoDataFrames (**`left_df`** and **`right_df`**) +- the type of join to run (**`how`**), which can take the values `left`, `right`, or `inner` +- the spatial relationship query to use (**`op`**) + +**NOTE**: +- By default `sjoin` is an inner join. It keeps the data from both geodataframes only where the locations spatially intersect. + +- By default `sjoin` maintains the geometry of first geodataframe input to the operation. + + +
+ +
+
+ +#### Questions +
+ +1. Which GeoDataFrame are we joining onto which (i.e. which one is getting the other one's data added to it)? +1. What happened to 'outer' as a join type? +1. Thus, in our operation, which GeoDataFrame should be the `left_df`, which should be the `right_df`, and `how` do we want our join to run? + +Your responses here: + + + + + + + + + +Alright! Let's run our join! + +schools_jointracts = gpd.sjoin(schools_gdf_api, tracts_acs_gdf_ac, how='left') + +schools_jointracts.head() + +### Checking Our Output + +
+ +
+ +
+
+ +#### Questions +
+ +As always, we want to sanity-check our intermediate result before we rush ahead. + +One way to do that is to introspect the structure of the result object a bit. + +1. What type of object should that have given us? +1. What should the dimensions of that object be, and why? +1. If we wanted a visual check of our results (i.e. a plot or map), what could we do? + +Your responses here: + + + + + + + + +print(schools_jointracts.shape) +print(schools_gdf.shape) +print(tracts_acs_gdf_ac.shape) + +schools_jointracts.head() + +Confirmed! The output of the our `sjoin` operation is a GeoDataFrame (`schools_jointracts`) with: +- a row for each school that is located inside a census tract (all of them are) +- the **point geometry** of that school +- all of the attribute data columns (non-geometry columns) from both input GeoDataFrames + +---------------------------- + +Let's also take a look at an overlay map of the schools on the tracts. +If we color the schools categorically by their tracts IDs, then we should see +that all schools within a given tract polygon are the same color. + +ax = tracts_acs_gdf_ac.plot(color='white', edgecolor='black', figsize=[18,18]) +schools_jointracts.plot(column='GEOID', ax=ax) + +### Assessing the Relationship between Median Household Income and API + +Fantastic! That looks right! + +Now we can create that scatterplot we were thinking about! + +fig, ax = plt.subplots(figsize=(6,6)) +ax.scatter(schools_jointracts.med_hhinc, schools_jointracts.API) +ax.set_xlabel('median household income ($)') +ax.set_ylabel('API') + +Wow! Just as we suspected based on our overlay map, +there's a pretty obvious, strong, and positive correlation +between median household income in a school's tract +and the school's API. + +## 7.3. Aggregation + +We just saw that a spatial join in one way to leverage the spatial relationship +between two datasets in order to create a new, synthetic dataset. + +An **aggregation** is another way we can generate new data from this relationship. +In this case, for each feature in one dataset we find all the features in another +dataset that satisfy our chosen spatial relationship query with it (e.g. within, intersects), +then aggregate them using some summary function (e.g. count, mean). + +------------------------------------ + +### Getting the Aggregated School Counts + +Let's take this for a spin with our data. We'll count all the schools within each census tract. + +Note that we've already done the first step of spatially joining the data from the aggregating features +(the tracts) onto the data to be aggregated (our schools). + +The next step is to group our GeoDataFrame by census tract, and then summarize our data by group. +We do this using the DataFrame method `groupy`. + +To get the correct count, lets rejoin our schools on our tracts, this time keeping all schools +(not just those with APIs > 0, as before). + +schools_jointracts = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='left') + +Now for the `groupy` operation. + +**NOTE**: We could really use any column, since we're just taking a count. For now we'll just use the school names ('Site'). + +schools_countsbytract = schools_jointracts[['GEOID','Site']].groupby('GEOID', as_index=False).count() +print("Counts, rows and columns:", schools_countsbytract.shape) +print("Tracts, rows and columns:", tracts_acs_gdf_ac.shape) + +# take a look at the data +schools_countsbytract.head() + +### Getting Tract Polygons with School Counts + +The above `groupby` and `count` operations give us the counts we wanted. +- We have the 263 (of 361) census tracts that have at least one school +- We have the number of schools within each of those tracts + +But the output of `groupby` is a plain DataFrame not a GeoDataFrame. + +If we want a GeoDataFrame then we have two options: +1. We could join the `groupby` output to `tracts_acs_gdf_ac` by the attribute `GEOID` +or +2. We could start over, using the GeoDataFrame `dissolve` method, which we can think of as a spatial `groupby`. + + +--------------------------- + +Since we already know how to do an attribute join, we'll do the `dissolve`! + +First, let's run a new spatial join. + +tracts_joinschools = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='right') + +tracts_joinschools.geometry + +Now, let's run our dissolve! + +tracts_schoolcounts = tracts_joinschools[['GEOID', 'Site', 'geometry']].dissolve(by='GEOID', aggfunc='count') +print("Counts, rows and columns:", tracts_schoolcounts.shape) + +# take a look +tracts_schoolcounts.head() + +Nice! Let's break that down. + +- The `dissolve` operation requires a geometry column and a grouping column (in our case, 'GEOID'). Any geometries within the **same group** will be dissolved if they have the same geometry or nested geometries. + +- The `aggfunc`, or aggregation function, of the dissolve operation will be applied to all numeric columns in the input geodataframe (unless the function is `count` in which case it will count rows.) + +Check out the Geopandas documentation on [dissolve](https://geopandas.org/aggregation_with_dissolve.html?highlight=dissolve) for more information. + + +
+ +
+
+ +#### Questions +
+ +1. Above we selected three columns from the input GeoDataFrame to create a subset as input to the dissolve operation. Why? +1. Why did we run a new spatial join? What would have happened if we had used the `schools_jointracts` object instead? +1. What explains the dimensions of the new object (361, 2)? + +You responses here: + + + + + + + + +### Mapping our Spatial Join Output + +Also, because our `sjoin` plus `dissolve` pipeline outputs a GeoDataFrame, we can now easily map the school count by census tract! + +fig, ax = plt.subplots(figsize = (14,8)) + +# Display the output of our spatial join +tracts_schoolcounts.plot(ax=ax,column='Site', + scheme="user_defined", + classification_kwds={'bins':[*range(9)]}, + cmap="PuRd_r", + edgecolor="grey", + legend=True, + legend_kwds={'title':'Number of schools'}) +schools_gdf.plot(ax=ax, color='black', markersize=2) + +--------------------- + +## Exercise: Aggregation + +#### What is the mean API of each census tract? + +As we mentioned, the spatial aggregation workflow that we just put together above +could have been used not to generate a new count variable, but also +to generate any other new variable the results from calling an aggregation function +on an attribute column. + +In this case, we want to calculate and map the mean API of the schools in each census tract. + +Copy and paste code from above where useful, then tweak and/or add to that code such that your new code: +1. joins the schools onto the tracts (**HINT**: make sure to decide whether or not you want to include schools with API = 0!) +1. dissolves that joined object by the tract IDs, giving you a new GeoDataFrame with each tract's mean API (**HINT**: because this is now a different calculation, different problems may arise and need handling!) +1. plots the tracts, colored by API scores (**HINT**: overlay the schools points again, visualizing them in a way that will help you visually check your results!) + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE: + + + + + + +### Double-click to see solution! + + + +---------------------------- + +## 7.4 Recap +We discussed how we can combine datasets to enhance any geospatial data analyses you could do. Key concepts include: +- Attribute joins + - `.merge()` +- Spatial joins (order matters!) + - `gpd.sjoin()` +- Aggregation + -`.groupby()` + - `.dissolve()` (preserves geometry) + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + diff --git a/_build/jupyter_execute/lessons/08_Pulling_It_All_Together.ipynb b/_build/jupyter_execute/lessons/08_Pulling_It_All_Together.ipynb new file mode 100644 index 0000000..676850b --- /dev/null +++ b/_build/jupyter_execute/lessons/08_Pulling_It_All_Together.ipynb @@ -0,0 +1,449 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 08. Pulling it all Together\n", + "\n", + "For this last lesson, we'll practice going through a full workflow!! We'll answer the question:\n", + "## What is the total grocery-store sales volume of each census tract?\n", + "\n", + "\n", + "### WORKFLOW:\n", + "\n", + "
\n", + "Here's a set of steps that we will implement in the labeled cells below:\n", + "\n", + " 8.1 Read in and Prep Data\n", + "- read in tracts acs joined data\n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- transform it to match the CRS of `tracts_acs_gdf_ac`\n", + "- take a peek\n", + "\n", + "8.2 Spatial Join and Dissolve\n", + "- join the two datasets in such a way that you can then...\n", + "- group by tract and calculate the total grocery-store sales volume\n", + "- don't forget to check the dimensions, contents, and any other relevant aspects of your results\n", + "\n", + "8.3 Plot and Review\n", + "- plot the tracts, coloring them by total grocery-store sales volume\n", + "- plot the grocery stores on top\n", + "- bonus points for devising a nice visualization scheme that helps you heuristically check your results!\n", + "\n", + "\n", + "\n", + "### INSTRUCTIONS:\n", + "**We've written out some of the code for you, but you'll need to replace the ellipses with the correct\n", + "content.**\n", + "\n", + "*You can check your answers by double-clicking on the Markdown cells where indicated.*\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'outdata/tracts_acs_gdf_ac.json'\n", + " - 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: N/A\n", + " - Exercises: 30 minutes\n", + "\n", + "\n", + "\n", + "\n", + "-----------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "---------------------------------------\n", + "\n", + "\n", + "### Install Packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------------------\n", + "\n", + "## 8.1 Read in the Prep Data\n", + "\n", + "We first need to prepare our data by loading both our tracts/acs and grocery data, and conduct our usual steps to make there they have the same CRS.\n", + "\n", + "- read in our tracts acs joined data \n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- transform it to match the CRS of `tracts_acs_gdf_ac`\n", + "- take a peek\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in tracts acs data\n", + "\n", + "tracts_acs_gdf_ac = gpd.read_file(..)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read our grocery-data CSV into a Pandas DataFrame\n", + "\n", + "grocery_pts_df = pd.read_csv(...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# coerce it to a GeoDataFrame\n", + "\n", + "grocery_pts_gdf = gpd.GeoDataFrame(grocery_pts_df, \n", + " geometry=gpd.points_from_xy(...,...))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define its CRS (NOTE: Use EPSG:4326)\n", + "\n", + "grocery_pts_gdf.crs = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# transform it to match the CRS of tracts_acs_gdf_ac\n", + "\n", + "grocery_pts_gdf.to_crs(..., inplace=...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grocery_pts_gdf.crs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# take a peek\n", + "\n", + "print(grocery_pts_gdf.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "-----------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.2 Spatial Join and Dissolve\n", + "\n", + "Now that we have our data and they're in the same projection, we're going to conduct an *attribute join* to bring together the two datasets. From there we'll be able to actually *aggregate* our data to count the total sales volume.\n", + "\n", + "- join the two datasets in such a way that you can then...\n", + "- group by tract and calculate the total grocery-store sales volume\n", + "- don't forget to check the dimensions, contents, and any other relevant aspects of your results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# join the two datasets in such a way that you can then...\n", + "\n", + "tracts_joingrocery = gpd.sjoin(..., ..., how= ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# group by tract and calculate the total grocery-store sales volume\n", + "\n", + "tracts_totsalesvol = tracts_joingrocery[['GEOID','geometry','SALESVOL']].dissolve(by= ...,\n", + " aggfunc=..., as_index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# don't forget to check the dimensions, contents, and any other relevant aspects of your results\n", + "\n", + "# check the dimensions\n", + "print('Dimensions of result:', ...)\n", + "print('Dimesions of census tracts:', ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check the result\n", + "print(tracts_totsalesvol.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "----------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.3 Plot and Review\n", + "\n", + "With any time of geospatial analysis you do, it's always nice to plot and visualize your results to check your work and start to understand the full story of your analysis.\n", + "\n", + "- Plot the tracts, coloring them by total grocery-store sales volume\n", + "- Plot the grocery stores on top\n", + "- Bonus points for devising a nice visualization scheme that helps you heuristically check your results!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create the figure and axes\n", + "\n", + "fig, ax = plt.subplots(figsize = (20,20)) \n", + "\n", + "# plot the tracts, coloring by total SALESVOL\n", + "\n", + "tracts_totsalesvol.plot(ax=ax, column= ..., scheme=\"quantiles\", cmap=\"autumn\", edgecolor=\"grey\",\n", + " legend=True, legend_kwds={'title':...})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subset the stores for only those within our tracts, to keep map within region of interest\n", + "\n", + "grocery_pts_gdf_ac = grocery_pts_gdf.loc[..., ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# add the grocery stores, coloring by SALESVOL, for a visual check\n", + "\n", + "grocery_pts_gdf_ac.plot(ax=ax, column= ... , cmap= ..., linewidth= ..., markersize= ...,\n", + " legend=True, legend_kwds={'label': ... , 'orientation': \"horizontal\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "scrolled": false + }, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "-------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "***\n", + "\n", + "## Congrats!! Thanks for Joining Us for Geospatial Fundamentals!!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/08_Pulling_It_All_Together.py b/_build/jupyter_execute/lessons/08_Pulling_It_All_Together.py new file mode 100644 index 0000000..4a034a7 --- /dev/null +++ b/_build/jupyter_execute/lessons/08_Pulling_It_All_Together.py @@ -0,0 +1,267 @@ +# 08. Pulling it all Together + +For this last lesson, we'll practice going through a full workflow!! We'll answer the question: +## What is the total grocery-store sales volume of each census tract? + + +### WORKFLOW: + +
+Here's a set of steps that we will implement in the labeled cells below: + + 8.1 Read in and Prep Data +- read in tracts acs joined data +- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`) +- coerce it to a GeoDataFrame +- define its CRS (EPSG:4326) +- transform it to match the CRS of `tracts_acs_gdf_ac` +- take a peek + +8.2 Spatial Join and Dissolve +- join the two datasets in such a way that you can then... +- group by tract and calculate the total grocery-store sales volume +- don't forget to check the dimensions, contents, and any other relevant aspects of your results + +8.3 Plot and Review +- plot the tracts, coloring them by total grocery-store sales volume +- plot the grocery stores on top +- bonus points for devising a nice visualization scheme that helps you heuristically check your results! + + + +### INSTRUCTIONS: +**We've written out some of the code for you, but you'll need to replace the ellipses with the correct +content.** + +*You can check your answers by double-clicking on the Markdown cells where indicated.* + + +
+ + Instructor Notes + +- Datasets used + - 'outdata/tracts_acs_gdf_ac.json' + - 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv' + +- Expected time to complete + - Lecture + Questions: N/A + - Exercises: 30 minutes + + + + +----------------- + + +--------------------------------------- + + +### Install Packages + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +------------------ + +## 8.1 Read in the Prep Data + +We first need to prepare our data by loading both our tracts/acs and grocery data, and conduct our usual steps to make there they have the same CRS. + +- read in our tracts acs joined data +- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`) +- coerce it to a GeoDataFrame +- define its CRS (EPSG:4326) +- transform it to match the CRS of `tracts_acs_gdf_ac` +- take a peek + + + +# read in tracts acs data + +tracts_acs_gdf_ac = gpd.read_file(..) + +# read our grocery-data CSV into a Pandas DataFrame + +grocery_pts_df = pd.read_csv(...) + +# coerce it to a GeoDataFrame + +grocery_pts_gdf = gpd.GeoDataFrame(grocery_pts_df, + geometry=gpd.points_from_xy(...,...)) + +# define its CRS (NOTE: Use EPSG:4326) + +grocery_pts_gdf.crs = ... + +# transform it to match the CRS of tracts_acs_gdf_ac + +grocery_pts_gdf.to_crs(..., inplace=...) + +grocery_pts_gdf.crs + +# take a peek + +print(grocery_pts_gdf.head()) + +## Double-click here to see solution! + + + +----------------------- + +## 8.2 Spatial Join and Dissolve + +Now that we have our data and they're in the same projection, we're going to conduct an *attribute join* to bring together the two datasets. From there we'll be able to actually *aggregate* our data to count the total sales volume. + +- join the two datasets in such a way that you can then... +- group by tract and calculate the total grocery-store sales volume +- don't forget to check the dimensions, contents, and any other relevant aspects of your results + +# join the two datasets in such a way that you can then... + +tracts_joingrocery = gpd.sjoin(..., ..., how= ...) + +# group by tract and calculate the total grocery-store sales volume + +tracts_totsalesvol = tracts_joingrocery[['GEOID','geometry','SALESVOL']].dissolve(by= ..., + aggfunc=..., as_index=False) + +# don't forget to check the dimensions, contents, and any other relevant aspects of your results + +# check the dimensions +print('Dimensions of result:', ...) +print('Dimesions of census tracts:', ...) + +# check the result +print(tracts_totsalesvol.head()) + +## Double-click here to see solution! + + + +---------------------- + +## 8.3 Plot and Review + +With any time of geospatial analysis you do, it's always nice to plot and visualize your results to check your work and start to understand the full story of your analysis. + +- Plot the tracts, coloring them by total grocery-store sales volume +- Plot the grocery stores on top +- Bonus points for devising a nice visualization scheme that helps you heuristically check your results! + +# create the figure and axes + +fig, ax = plt.subplots(figsize = (20,20)) + +# plot the tracts, coloring by total SALESVOL + +tracts_totsalesvol.plot(ax=ax, column= ..., scheme="quantiles", cmap="autumn", edgecolor="grey", + legend=True, legend_kwds={'title':...}) + +# subset the stores for only those within our tracts, to keep map within region of interest + +grocery_pts_gdf_ac = grocery_pts_gdf.loc[..., ] + +# add the grocery stores, coloring by SALESVOL, for a visual check + +grocery_pts_gdf_ac.plot(ax=ax, column= ... , cmap= ..., linewidth= ..., markersize= ..., + legend=True, legend_kwds={'label': ... , 'orientation': "horizontal"}) + +## Double-click here to see solution! + + + +------------------- + +
+
+
+
+
+
+ +*** + +## Congrats!! Thanks for Joining Us for Geospatial Fundamentals!! + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + + diff --git a/_build/jupyter_execute/lessons/09_ON_YOUR_OWN_A_Full_Workflow.ipynb b/_build/jupyter_execute/lessons/09_ON_YOUR_OWN_A_Full_Workflow.ipynb new file mode 100644 index 0000000..540a0f8 --- /dev/null +++ b/_build/jupyter_execute/lessons/09_ON_YOUR_OWN_A_Full_Workflow.ipynb @@ -0,0 +1,680 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 9. On Your Own: A Full Workflow\n", + "Now is your chance to pull everything we've learned together and answer the questions: \n", + "- How many polling stations are in each census tract in Alameda County?\n", + "- Which polling stations are within walking distance (100m) from a bus route in Berkeley?\n", + "- How far are these polling stations from the bus routes in Berkeley?\n", + "\n", + "**All on your own!!**\n", + "\n", + "- 9.1 Polling Station Locations\n", + "- 9.2 Tracts data \n", + "- 9.3 Spatial Join \n", + "- 9.4 Aggregate number of stations by census tracts\n", + "- 9.5 Attribute join back to tracts data\n", + "- 9.6 Berkeley outline\n", + "- 9.7 Bus routes\n", + "- 9.8 Polling station distance from bus routes\n", + "\n", + "*We've written out some of the code for you, and you can check your answers by clicking on the toggle solution button*\n", + " \n", + "### Install Packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.1 Polling Station Locations\n", + "\n", + "We'll be using the 2020 General Election voting locations for Alameda County for this analysis. Since the data is *aspatial* we'll need to coerce it to be a geodataframe and define a CRS.\n", + "\n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/ac_voting_locations.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- plot it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pull in polling location\n", + "\n", + "# polling_ac_df = pd.read_csv(...)\n", + "# polling_ac_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make into geo data frame\n", + "\n", + "# polling_ac_gdf = gpd.GeoDataFrame(..., \n", + "# geometry=gpd.points_from_xy(...,...))\n", + "# polling_ac_gdf.crs = ...\n", + "\n", + "# plot it \n", + "\n", + "# polling_ac_gdf.plot(...)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.2 Tracts data\n", + "\n", + "Since we want to answer the question **How many polling stations are in each census tract?**, we'll pull in our tracts data.\n", + "\n", + "- Bring in the census tracts data which lives at `notebook_data/census/Tracts/cb_2013_06_tract_500k.zip`\n", + "- Narrow it down to Alameda County\n", + "- Check CRS\n", + "- Transform CRS to 26910 if needed\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bring in census tracts\n", + "# tracts_gdf = gpd.read_file(...)\n", + "\n", + "# Narrow it down to Alameda County\n", + "# tracts_gdf_ac = tracts_gdf[...]\n", + "# tracts_gdf_ac.plot()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check CRS\n", + "print('polling_ac_gdf:', ...)\n", + "print('tracts_gdf_ac CRS:', ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform CRS\n", + "polling_ac_gdf_utm10 = ...\n", + "tracts_gdf_ac_utm10 = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.3 Spatial Join\n", + "\n", + "Alright, now our data is all ready to go! We're going to do a *spatial join* to answer our question about polling stations in each tract.\n", + "\n", + "- Spatial join tracts/acs with the polling data (keep the tracts geometry!)\n", + "- Plot it to make sure you have the right geometry\n", + "- Check out your data and its dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Spatial join tracts/acs with the polling data (keep the tracts geometry!)\n", + "\n", + "# polls_jointracts = gpd.sjoin(..., ... , how=...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot it to make sure you have the right geometry\n", + "\n", + "# polls_jointracts.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check out your data and its dimensions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.4 Aggregate number of stations by census tracts\n", + "\n", + "Now that we have a GeoDataFrame with all our polling and tract data, we'll need to *aggregate* to actually count the number of stations we have\n", + "\n", + "- Use `dissolve` to count the number of polls we have\n", + "- Create a choropleth map base don the number of stations there are" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use `dissolve` to count the number of polls we have\n", + "\n", + "# polls_countsbytract = polls_jointracts[['TRACTCE', 'NAME_right', \n", + "# 'geometry']].dissolve(by=..., \n", + "# aggfunc=...).reset_index()\n", + "# polls_countsbytract.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# rename the column to be for the number of polling stations (you dont have to change anything here)\n", + "\n", + "# polls_countsbytract.rename(columns={'NAME_right': 'Num_Polling'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a choropleth map base don the number of stations there are\n", + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "\n", + "# polls_countsbytract.plot(ax=ax,\n", + "# column=..., \n", + "# cmap=...,\n", + "# edgecolor=\"grey\",\n", + "# legend=True)\n", + "\n", + "# polling_ac_gdf_utm10.plot(ax=ax, color=..., edgecolor=..., markersize= ...)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.5 Attribute join back to tracts data\n", + "\n", + "Amazing! Now that we have this information let's do an *attribute join* to add this data into our tracts data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# merge onto census tract data\n", + "\n", + "# tracts_gdf_ac = tracts_gdf_ac.merge(polls_countsbytract[['TRACTCE', 'Num_Polling']], left_on= ...,right_on= ... , how= ... ) \n", + "# tracts_gdf_ac.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.6 Berkeley outline\n", + "\n", + "To answer our question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* we'll need to know where Berkeley is! This is the perfect time to bring our Berkeley places data in.\n", + "\n", + "- Read in `outdata/berkeley_places.shp`\n", + "- Check the CRS\n", + "- Transform CRS if necessary to EPSG:26910" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in outdata/berkeley_places.shp\n", + "# berkeley_places = gpd.read_file(...)\n", + "\n", + "# Check the CRS\n", + "\n", + "\n", + "# Transform CRS if necessary to EPSG:26910\n", + "berkeley_places_utm10 = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.7 Bus routes\n", + "\n", + "- Bring in bus routes ('notebook_data/transportation/Fall20Routeshape.zip'), transform CRS to 26910\n", + "- Intersect bus routes with Berkeley\n", + "- Plot results of intersection\n", + "- Clip bus routes to everything that is inside the berkley outline\n", + "- Plot bus routes on top of Berkeley outline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Bring in bus routes, transform CRS to 26910\n", + "bus_routes = ...\n", + "# bus_routes_utm10 = bus_routes.to_crs(...)\n", + "# bus_routes_utm10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at intersection between bus routes and Berkeley\n", + "# bus_routes_berkeley = .intersects(... .geometry.squeeze())\n", + "\n", + "# Create new geodataframe from these results\n", + "# bus_berk = bus_routes_utm10.loc[bus_routes_berkeley].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot results of intersection\n", + "\n", + "# fig, ax = plt.subplots(figsize=(10,10))\n", + "# berkeley_places_utm10.plot(ax=ax)\n", + "# bus_berk.plot(ax=ax, column ='PUB_RTE')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# BONUS: Look at route length\n", + "# bus_berk.length" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Clip bus routes to everything that is inside the berkley outline\n", + "# bus_berk_clip = gpd.clip(...,...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Plot bus routes on top of Berkeley outline\n", + "# fig, ax = plt.subplots(figsize=(10,10))\n", + "# berkeley_places_utm10.plot(ax=ax)\n", + "# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.6 Polling stations within walking distance of bus routes\n", + "\n", + "Now we can really answer the question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* \n", + "\n", + "- Create buffer around bus route for 100m\n", + "- Intersect polling locations in Alameda County with Berkeley outline \n", + "- Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations\n", + "- Calculate the distance from polling stations to the closest bus route" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create buffer around bus route for 100m\n", + "# bus_berk_buf =bus_berk_clip.buffer(distance= ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Intersect polling locations in Alameda County with Berkeley outline\n", + "# polling_berk = ... .intersects(berkeley_places_utm10.geometry.squeeze())\n", + "\n", + "# polling_berk_gdf = polling_ac_gdf_utm10[polling_berk].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations\n", + "\n", + "# fig, ax = plt.subplots(figsize=(10,10))\n", + "# berkeley_places_utm10.plot(ax=ax)\n", + "# bus_berk_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')\n", + "# polling_berk_gdf.plot(ax=ax, color= 'yellow')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the distance from polling stations to the closest bus route\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## You're done!!!! \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/09_ON_YOUR_OWN_A_Full_Workflow.py b/_build/jupyter_execute/lessons/09_ON_YOUR_OWN_A_Full_Workflow.py new file mode 100644 index 0000000..737f65b --- /dev/null +++ b/_build/jupyter_execute/lessons/09_ON_YOUR_OWN_A_Full_Workflow.py @@ -0,0 +1,392 @@ +# Lesson 9. On Your Own: A Full Workflow +Now is your chance to pull everything we've learned together and answer the questions: +- How many polling stations are in each census tract in Alameda County? +- Which polling stations are within walking distance (100m) from a bus route in Berkeley? +- How far are these polling stations from the bus routes in Berkeley? + +**All on your own!!** + +- 9.1 Polling Station Locations +- 9.2 Tracts data +- 9.3 Spatial Join +- 9.4 Aggregate number of stations by census tracts +- 9.5 Attribute join back to tracts data +- 9.6 Berkeley outline +- 9.7 Bus routes +- 9.8 Polling station distance from bus routes + +*We've written out some of the code for you, and you can check your answers by clicking on the toggle solution button* + +### Install Packages + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +## 9.1 Polling Station Locations + +We'll be using the 2020 General Election voting locations for Alameda County for this analysis. Since the data is *aspatial* we'll need to coerce it to be a geodataframe and define a CRS. + +- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/ac_voting_locations.csv`) +- coerce it to a GeoDataFrame +- define its CRS (EPSG:4326) +- plot it + +# Pull in polling location + +# polling_ac_df = pd.read_csv(...) +# polling_ac_df.head() + +# Make into geo data frame + +# polling_ac_gdf = gpd.GeoDataFrame(..., +# geometry=gpd.points_from_xy(...,...)) +# polling_ac_gdf.crs = ... + +# plot it + +# polling_ac_gdf.plot(...) + + +## Double-click here to see solution! + + + +## 9.2 Tracts data + +Since we want to answer the question **How many polling stations are in each census tract?**, we'll pull in our tracts data. + +- Bring in the census tracts data which lives at `notebook_data/census/Tracts/cb_2013_06_tract_500k.zip` +- Narrow it down to Alameda County +- Check CRS +- Transform CRS to 26910 if needed + + +# Bring in census tracts +# tracts_gdf = gpd.read_file(...) + +# Narrow it down to Alameda County +# tracts_gdf_ac = tracts_gdf[...] +# tracts_gdf_ac.plot() +# plt.show() + +# Check CRS +print('polling_ac_gdf:', ...) +print('tracts_gdf_ac CRS:', ...) + +# Transform CRS +polling_ac_gdf_utm10 = ... +tracts_gdf_ac_utm10 = ... + +## Double-click here to see solution! + + + +## 9.3 Spatial Join + +Alright, now our data is all ready to go! We're going to do a *spatial join* to answer our question about polling stations in each tract. + +- Spatial join tracts/acs with the polling data (keep the tracts geometry!) +- Plot it to make sure you have the right geometry +- Check out your data and its dimensions + +# Spatial join tracts/acs with the polling data (keep the tracts geometry!) + +# polls_jointracts = gpd.sjoin(..., ... , how=...) + +# Plot it to make sure you have the right geometry + +# polls_jointracts.plot() + +# Check out your data and its dimensions + + +## Double-click here to see solution! + + + +## 9.4 Aggregate number of stations by census tracts + +Now that we have a GeoDataFrame with all our polling and tract data, we'll need to *aggregate* to actually count the number of stations we have + +- Use `dissolve` to count the number of polls we have +- Create a choropleth map base don the number of stations there are + +# Use `dissolve` to count the number of polls we have + +# polls_countsbytract = polls_jointracts[['TRACTCE', 'NAME_right', +# 'geometry']].dissolve(by=..., +# aggfunc=...).reset_index() +# polls_countsbytract.head() + +# rename the column to be for the number of polling stations (you dont have to change anything here) + +# polls_countsbytract.rename(columns={'NAME_right': 'Num_Polling'}, inplace=True) + +# Create a choropleth map base don the number of stations there are +fig, ax = plt.subplots(figsize = (14,8)) + +# polls_countsbytract.plot(ax=ax, +# column=..., +# cmap=..., +# edgecolor="grey", +# legend=True) + +# polling_ac_gdf_utm10.plot(ax=ax, color=..., edgecolor=..., markersize= ...) + +## Double-click here to see solution! + + + +## 9.5 Attribute join back to tracts data + +Amazing! Now that we have this information let's do an *attribute join* to add this data into our tracts data + +# merge onto census tract data + +# tracts_gdf_ac = tracts_gdf_ac.merge(polls_countsbytract[['TRACTCE', 'Num_Polling']], left_on= ...,right_on= ... , how= ... ) +# tracts_gdf_ac.head() + +## Double-click here to see solution! + + + +## 9.6 Berkeley outline + +To answer our question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* we'll need to know where Berkeley is! This is the perfect time to bring our Berkeley places data in. + +- Read in `outdata/berkeley_places.shp` +- Check the CRS +- Transform CRS if necessary to EPSG:26910 + +# Read in outdata/berkeley_places.shp +# berkeley_places = gpd.read_file(...) + +# Check the CRS + + +# Transform CRS if necessary to EPSG:26910 +berkeley_places_utm10 = ... + +## Double-click here to see solution! + + + + +## 8.7 Bus routes + +- Bring in bus routes ('notebook_data/transportation/Fall20Routeshape.zip'), transform CRS to 26910 +- Intersect bus routes with Berkeley +- Plot results of intersection +- Clip bus routes to everything that is inside the berkley outline +- Plot bus routes on top of Berkeley outline + +# Bring in bus routes, transform CRS to 26910 +bus_routes = ... +# bus_routes_utm10 = bus_routes.to_crs(...) +# bus_routes_utm10.head() + +# Look at intersection between bus routes and Berkeley +# bus_routes_berkeley = .intersects(... .geometry.squeeze()) + +# Create new geodataframe from these results +# bus_berk = bus_routes_utm10.loc[bus_routes_berkeley].reset_index(drop=True) + +# Plot results of intersection + +# fig, ax = plt.subplots(figsize=(10,10)) +# berkeley_places_utm10.plot(ax=ax) +# bus_berk.plot(ax=ax, column ='PUB_RTE') + +# BONUS: Look at route length +# bus_berk.length + +# Clip bus routes to everything that is inside the berkley outline +# bus_berk_clip = gpd.clip(...,...) + +# Plot bus routes on top of Berkeley outline +# fig, ax = plt.subplots(figsize=(10,10)) +# berkeley_places_utm10.plot(ax=ax) +# bus_berk_clip.plot(ax=ax, column ='PUB_RTE') + +## Double-click here to see solution! + + + +## 8.6 Polling stations within walking distance of bus routes + +Now we can really answer the question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* + +- Create buffer around bus route for 100m +- Intersect polling locations in Alameda County with Berkeley outline +- Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations +- Calculate the distance from polling stations to the closest bus route + +# Create buffer around bus route for 100m +# bus_berk_buf =bus_berk_clip.buffer(distance= ...) + +# Intersect polling locations in Alameda County with Berkeley outline +# polling_berk = ... .intersects(berkeley_places_utm10.geometry.squeeze()) + +# polling_berk_gdf = polling_ac_gdf_utm10[polling_berk].reset_index(drop=True) + +# Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations + +# fig, ax = plt.subplots(figsize=(10,10)) +# berkeley_places_utm10.plot(ax=ax) +# bus_berk_buf.plot(color='pink', ax=ax, alpha=0.5) +# bus_berk_clip.plot(ax=ax, column ='PUB_RTE') +# polling_berk_gdf.plot(ax=ax, color= 'yellow') + +# Calculate the distance from polling stations to the closest bus route + + +## Double-click here to see solution! + + + +## You're done!!!! + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + diff --git a/_build/jupyter_execute/lessons/10_OPTIONAL_Fetching_Data.ipynb b/_build/jupyter_execute/lessons/10_OPTIONAL_Fetching_Data.ipynb new file mode 100644 index 0000000..813c6bb --- /dev/null +++ b/_build/jupyter_execute/lessons/10_OPTIONAL_Fetching_Data.ipynb @@ -0,0 +1,745 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 10. Read in Data from Online Sources + CSV to Geodataframe\n", + "\n", + "In this optional notebook we'll be going over how to read data into a notebook from online sources.\n", + "\n", + "- [10.1 Introduction ](#section1)\n", + "- [10.2 Read File from a url](#section2)\n", + "- [10.3 Read File from an API](#section3)\n", + "- [10.4 Read in Data from a Pyhton Library](#section4)\n", + "- [10.5 Exercise](#section5)\n", + "- [10.6 Read in Data from a CSV and convert to geodataframe](#section6)\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + " \n", + "**DEVELOPER NOTES**:\n", + "- Datasets used:\n", + " - Census Data: https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_tract_500k.zip\n", + " - SF Bikeway Data: https://data.sfgov.org/api/geospatial/ygmz-vaxd?method=export&format=GeoJSON\n", + " - Berkeley Bikeway Data: https://data.cityofberkeley.info/api/geospatial/fgw9-98ic?method=export&format=GeoJSON\n", + " - OSMNX Library SF and Berkeley Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.1 Introduction\n", + "\n", + "In the past examples, the data we have imported into our notebooks has come either from previously downloaded and saved files or from the census API. The goal of this notebook is to present other ways of accessing data, either from **urls**, other **APIs** or from predetermined **Python libraries**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set-Up\n", + "Let's import the packages we need before we get started." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import collections\n", + "import requests \n", + "from urllib.request import urlopen, Request\n", + "\n", + "import json # for working with JSON data\n", + "import geojson # ditto for GeoJSON data - an extension of JSON with support for geographic data\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "%matplotlib inline \n", + "import matplotlib.pyplot as plt # more plotting stuff" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.2 Read File from a url\n", + "\n", + "The following link shows the different shapefile data available through the Census Bureau [website](https://www2.census.gov/geo/tiger/GENZ2018/shp/). Clicking on any of the files will dowload the .zip file unto your computer.\n", + "\n", + "This notebook will show a workaround to access the file directly from the notebook, without having to go through the process of previously downloading the shapefile.\n", + "\n", + "For this example, we will download the cities for the state of California ([cb_2018_06_tract_500k.zip](https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_tract_500k.zip)). Remember that California's State FIPS code is 06, which is how we recognize that this dataset is associated with the State of California." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the data from the url, read it using geopandas and create a subset of only Berkeley places" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll save the data from the url into a variable called *ca_places*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ca_places = \"https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_place_500k.zip\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll use geopandas to read the file and then we'll visualize it to make sure we read it properly" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "places = gpd.read_file(ca_places)\n", + "places.plot(); ### This takes a little bit, because the file is fairly large" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### CONFIRM THAT THIS IS TRUE\n", + "Notice that there are some spaces inside the boundaries of the state of California that are empty. These are unincorporated areas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, say we are only interested in the City of Berkeley. Let's examine the file to see how we could select the polygon fob Berkeley. We'll take a look at which columns are included in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "places.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try filtering by Name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "berkeley = places[places['NAME']=='Berkeley']\n", + "berkeley.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Awesome! This worked! Now we have a polygon with the boundaries of the City of Berkeley." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.3 Read in file from a API\n", + "\n", + "In this section, we will be reading a file using an API, or Application Programming Interface. APIs are very useful, because they allow two different portals to talk to each other. For more information on APIs, take a look [here](https://en.wikipedia.org/wiki/Application_programming_interface).\n", + "\n", + "In this case, we will be using the City of Berkeley Open Data Portal's API to read in information on the bike network to out notebook.\n", + "\n", + "Below you can find more information both on the City of Berkeley's Open Data portal and on the bike network data.\n", + "\n", + "### Berkeley Open Data portal\n", + "https://data.cityofberkeley.info/\n", + "\n", + "### Berkeley Bike Network data\n", + "https://data.cityofberkeley.info/Transportation/Bicycle-Boulevards/fgw9-98ic\n", + "\n", + "\n", + "We will be reading the geospatial data for the bike network of the City of Berkeley." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, first we'll save the data from the url into a variable called *berkeley_bike_ways* and then we'll read it using geopandas." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley_bike_ways = \"https://data.cityofberkeley.info/api/geospatial/fgw9-98ic?method=export&format=GeoJSON\"\n", + "bikes = gpd.read_file(berkeley_bike_ways)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll plot the bikeways on top of the City of Berkeley polygon that we imported from the Census Bureau url" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (10,8)) \n", + "berkeley.plot(ax=ax)\n", + "bikes.plot(ax=ax)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oops! Where did the bike lanes go? Well, python uses a default color for all plots, so the bike paths were plotted on top of the polygon in the exact same color. Let's try to plot the bike lanes yellow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (10,8)) \n", + "berkeley.plot(ax=ax)\n", + "bikes.plot(ax=ax, color=\"yellow\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have a map that shows where the bike network of the City of Berkeley is located." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.4 Read in data via a Python library (OSMnx)\n", + "\n", + "OSMnx is a Python library that lets you access Open Street Map's street networks through an API.\n", + "\n", + "You can explore more of Open Street Maps [here](https://www.openstreetmap.org/)\n", + "\n", + "You can access the full documentation of OSMnx [here](https://osmnx.readthedocs.io/en/stable/index.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to install library\n", + "# !pip install osmnx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the below cell does not run, you need to install the library first, by uncommmenting and running the cell above\n", + "\n", + "> **Note**\n", + ">\n", + "> If you get a `numpy` associated error you may need to uninstall and reinstall `numpy` as well as set up tools. Run the following lines of code in your terminal:\n", + ">\n", + " pip uninstall -y numpy\n", + " pip uninstall -y setuptools\n", + " pip install setuptools\n", + " pip install numpy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import osmnx as ox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the osmnx library to access data from Open Street Maps. Let's try to load the Berkeley street map. \n", + "We are using the graph_from_place function. To see the full documentation for the function, go to this link: https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.graph.graph_from_place.\n", + "\n", + "\n", + "We need to define two arguments for the function: the **query** and the **network type**\n", + "\n", + "- **Query**: For cities in the US, the query should follow the following format: \"City Name, State Abbreviation, USA\"\n", + " \n", + " \n", + "- **Network Type**: This is where we define which network we are interested in. Some of the available options are:\n", + " - all\n", + " - drive\n", + " - walk\n", + " - bike\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try to read the data for the vehicular network for Berkeley" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "place = \"Berkeley, CA, USA\"\n", + "graph = ox.graph_from_place(place, network_type='drive')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This took a while to read. Let's take a look at how many elements were loaded from OSM for Berkeley" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "len(graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the data type" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "type(graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a new format. To get this into something that is familiar to us, we are going to extract the nodes and links by using the *graph_to_gdfs* function, which converts our data from a graph to two geodataframes. Because a street network is made up from nodes and links, and our geodatraframes can only have one geography type, the *graph_to_gdfs* returns 2 geodataframes: a node (point) and a street (line) geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "nodes, streets = ox.graph_to_gdfs(graph)\n", + "streets.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's try to put everything together in the same map (the limits of the city, the bike lanes and the streets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (10,8)) \n", + "berkeley.plot(ax=ax)\n", + "streets.plot(ax=ax, color=\"grey\")\n", + "bikes.plot(ax=ax, color=\"yellow\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another feature that we can extract form OSMnx is the bus stops. To do this, we use the pois_from_place function (see full documentation [here](https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.pois.pois_from_place))\n", + "\n", + "This function requires two arguments: the **query** (same as above) and the **tag**:\n", + "\n", + "- **Query**: For cities in the US, the query should follow the following format: \"City Name, State Abbreviation, USA\"\n", + " \n", + " \n", + "- **Tag**: This is where we define which tags we are interested in. There are many options available. You can find a list of tag features [here](https://wiki.openstreetmap.org/wiki/Map_Features#Highway). These tags are coded as dictionaries. Bus stops are a value defined under the key highway, therefore, the format to call for bus stops looks like this: {'highway':'bus_stop'}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's access the bus stops using the same query defined for Berkeley\n", + "\n", + "> **Note**\n", + ">\n", + ">If you are using an older version of `osmnx` you would be able to use the function `pois_from_place`. This and other functions such as `footprints_from_place` are deprecated as of July 2020. `geometries_from_place` is meant to replace these functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### fetch and map POIs from osmnx\n", + "busstops = ox.geometries_from_place(place, tags = {'highway':'bus_stop'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's check the data type busstops was read as" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "type(busstops)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, busstops is already a geodataframe. Therefore, we can plot it as it is unto out map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (10,8)) \n", + "berkeley.plot(ax=ax)\n", + "streets.plot(ax=ax, color=\"grey\")\n", + "bikes.plot(ax=ax, color=\"yellow\")\n", + "busstops.plot(ax=ax, color=\"white\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.5 Exercise\n", + "\n", + "Repeat above for SF. The link for accessing the bikeways for SF is already given to you below.\n", + "\n", + "### SF Open Data portal\n", + "\n", + "https://datasf.org/opendata/\n", + "\n", + "#### SF Bike Network data\n", + "https://data.sfgov.org/Transportation/SFMTA-Bikeway-Network/ygmz-vaxd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sf_bike_ways = \"https://data.sfgov.org/api/geospatial/ygmz-vaxd?method=export&format=GeoJSON\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.6 Read in Data from a CSV and convert to geodataframe\n", + "\n", + "In this example, we'll learn how to read a csv file with latitude and longitude coordinates and convert it to a geodataframe for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Read in CSV file\n", + "stations = pd.read_csv(\"notebook_data/transportation/bart.csv\")\n", + "stations.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now want to convert the csv file into a Point geodataframe, so we can produce maps and access the geospatial analysis tools.\n", + "\n", + "We do this below with the geopandas `GeoDataFrame` function which takes as input\n", + "\n", + "1. a pandas dataframe here `stations`, and\n", + "2. `geometry` for each row in the dataframe.\n", + "\n", + "We create the geometry using the geopandas `points_from_xy` function, using the data in the `lon` and `lat` columns of the pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Convert the DataFrame to a GeoDataFrame. \n", + "bart_gdf = gpd.GeoDataFrame(stations, geometry=gpd.points_from_xy(stations.lon, stations.lat)) \n", + "\n", + "# and take a look\n", + "bart_gdf.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have a map of BART stations! You can use this approach with any CSV file that has columns of x,y coordinates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.7 Exercises\n", + "\n", + "\n", + "\n", + "Set the CRS for `bart_gdf` to WGS84" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is the url for the 2018 census county geographic boundary file.\n", + "\n", + "* Read in the county file\n", + "* Subset on Marin County\n", + "* Plot Marin County with the Bart stations you transformed\n", + "* Question: what should do if the county name is not unique?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Census Counties file for the USA\n", + "county_file = \"https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_500k.zip\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/10_OPTIONAL_Fetching_Data.py b/_build/jupyter_execute/lessons/10_OPTIONAL_Fetching_Data.py new file mode 100644 index 0000000..15505ae --- /dev/null +++ b/_build/jupyter_execute/lessons/10_OPTIONAL_Fetching_Data.py @@ -0,0 +1,340 @@ +# 10. Read in Data from Online Sources + CSV to Geodataframe + +In this optional notebook we'll be going over how to read data into a notebook from online sources. + +- [10.1 Introduction ](#section1) +- [10.2 Read File from a url](#section2) +- [10.3 Read File from an API](#section3) +- [10.4 Read in Data from a Pyhton Library](#section4) +- [10.5 Exercise](#section5) +- [10.6 Read in Data from a CSV and convert to geodataframe](#section6) + + + +
+ + +**DEVELOPER NOTES**: +- Datasets used: + - Census Data: https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_tract_500k.zip + - SF Bikeway Data: https://data.sfgov.org/api/geospatial/ygmz-vaxd?method=export&format=GeoJSON + - Berkeley Bikeway Data: https://data.cityofberkeley.info/api/geospatial/fgw9-98ic?method=export&format=GeoJSON + - OSMNX Library SF and Berkeley Data + + +## 10.1 Introduction + +In the past examples, the data we have imported into our notebooks has come either from previously downloaded and saved files or from the census API. The goal of this notebook is to present other ways of accessing data, either from **urls**, other **APIs** or from predetermined **Python libraries**. + +### Set-Up +Let's import the packages we need before we get started. + +import pandas as pd +import collections +import requests +from urllib.request import urlopen, Request + +import json # for working with JSON data +import geojson # ditto for GeoJSON data - an extension of JSON with support for geographic data +import geopandas as gpd + +import matplotlib # base python plotting library +%matplotlib inline +import matplotlib.pyplot as plt # more plotting stuff + + +## 10.2 Read File from a url + +The following link shows the different shapefile data available through the Census Bureau [website](https://www2.census.gov/geo/tiger/GENZ2018/shp/). Clicking on any of the files will dowload the .zip file unto your computer. + +This notebook will show a workaround to access the file directly from the notebook, without having to go through the process of previously downloading the shapefile. + +For this example, we will download the cities for the state of California ([cb_2018_06_tract_500k.zip](https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_tract_500k.zip)). Remember that California's State FIPS code is 06, which is how we recognize that this dataset is associated with the State of California. + +### Read the data from the url, read it using geopandas and create a subset of only Berkeley places + +First, we'll save the data from the url into a variable called *ca_places* + +ca_places = "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_place_500k.zip" + +Now, we'll use geopandas to read the file and then we'll visualize it to make sure we read it properly + +places = gpd.read_file(ca_places) +places.plot(); ### This takes a little bit, because the file is fairly large + +#### CONFIRM THAT THIS IS TRUE +Notice that there are some spaces inside the boundaries of the state of California that are empty. These are unincorporated areas. + +However, say we are only interested in the City of Berkeley. Let's examine the file to see how we could select the polygon fob Berkeley. We'll take a look at which columns are included in the dataset. + +places.head() + +Let's try filtering by Name + +berkeley = places[places['NAME']=='Berkeley'] +berkeley.plot(); + +Awesome! This worked! Now we have a polygon with the boundaries of the City of Berkeley. + + +## 10.3 Read in file from a API + +In this section, we will be reading a file using an API, or Application Programming Interface. APIs are very useful, because they allow two different portals to talk to each other. For more information on APIs, take a look [here](https://en.wikipedia.org/wiki/Application_programming_interface). + +In this case, we will be using the City of Berkeley Open Data Portal's API to read in information on the bike network to out notebook. + +Below you can find more information both on the City of Berkeley's Open Data portal and on the bike network data. + +### Berkeley Open Data portal +https://data.cityofberkeley.info/ + +### Berkeley Bike Network data +https://data.cityofberkeley.info/Transportation/Bicycle-Boulevards/fgw9-98ic + + +We will be reading the geospatial data for the bike network of the City of Berkeley. + +As before, first we'll save the data from the url into a variable called *berkeley_bike_ways* and then we'll read it using geopandas. + +berkeley_bike_ways = "https://data.cityofberkeley.info/api/geospatial/fgw9-98ic?method=export&format=GeoJSON" +bikes = gpd.read_file(berkeley_bike_ways) + +Now, we'll plot the bikeways on top of the City of Berkeley polygon that we imported from the Census Bureau url + +fig, ax = plt.subplots(figsize = (10,8)) +berkeley.plot(ax=ax) +bikes.plot(ax=ax) +plt.show() + +Oops! Where did the bike lanes go? Well, python uses a default color for all plots, so the bike paths were plotted on top of the polygon in the exact same color. Let's try to plot the bike lanes yellow. + +fig, ax = plt.subplots(figsize = (10,8)) +berkeley.plot(ax=ax) +bikes.plot(ax=ax, color="yellow") +plt.show() + +Now we have a map that shows where the bike network of the City of Berkeley is located. + + +## 10.4 Read in data via a Python library (OSMnx) + +OSMnx is a Python library that lets you access Open Street Map's street networks through an API. + +You can explore more of Open Street Maps [here](https://www.openstreetmap.org/) + +You can access the full documentation of OSMnx [here](https://osmnx.readthedocs.io/en/stable/index.html) + +# Uncomment to install library +# !pip install osmnx + +If the below cell does not run, you need to install the library first, by uncommmenting and running the cell above + +> **Note** +> +> If you get a `numpy` associated error you may need to uninstall and reinstall `numpy` as well as set up tools. Run the following lines of code in your terminal: +> + pip uninstall -y numpy + pip uninstall -y setuptools + pip install setuptools + pip install numpy + +import osmnx as ox + +Now we can use the osmnx library to access data from Open Street Maps. Let's try to load the Berkeley street map. +We are using the graph_from_place function. To see the full documentation for the function, go to this link: https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.graph.graph_from_place. + + +We need to define two arguments for the function: the **query** and the **network type** + +- **Query**: For cities in the US, the query should follow the following format: "City Name, State Abbreviation, USA" + + +- **Network Type**: This is where we define which network we are interested in. Some of the available options are: + - all + - drive + - walk + - bike + + +Let's try to read the data for the vehicular network for Berkeley + +place = "Berkeley, CA, USA" +graph = ox.graph_from_place(place, network_type='drive') + +This took a while to read. Let's take a look at how many elements were loaded from OSM for Berkeley + +len(graph) + +Let's check the data type + +type(graph) + +This is a new format. To get this into something that is familiar to us, we are going to extract the nodes and links by using the *graph_to_gdfs* function, which converts our data from a graph to two geodataframes. Because a street network is made up from nodes and links, and our geodatraframes can only have one geography type, the *graph_to_gdfs* returns 2 geodataframes: a node (point) and a street (line) geodataframe. + +nodes, streets = ox.graph_to_gdfs(graph) +streets.plot(); + +Now, let's try to put everything together in the same map (the limits of the city, the bike lanes and the streets) + +fig, ax = plt.subplots(figsize = (10,8)) +berkeley.plot(ax=ax) +streets.plot(ax=ax, color="grey") +bikes.plot(ax=ax, color="yellow") +plt.show() + +Another feature that we can extract form OSMnx is the bus stops. To do this, we use the pois_from_place function (see full documentation [here](https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.pois.pois_from_place)) + +This function requires two arguments: the **query** (same as above) and the **tag**: + +- **Query**: For cities in the US, the query should follow the following format: "City Name, State Abbreviation, USA" + + +- **Tag**: This is where we define which tags we are interested in. There are many options available. You can find a list of tag features [here](https://wiki.openstreetmap.org/wiki/Map_Features#Highway). These tags are coded as dictionaries. Bus stops are a value defined under the key highway, therefore, the format to call for bus stops looks like this: {'highway':'bus_stop'} + +Let's access the bus stops using the same query defined for Berkeley + +> **Note** +> +>If you are using an older version of `osmnx` you would be able to use the function `pois_from_place`. This and other functions such as `footprints_from_place` are deprecated as of July 2020. `geometries_from_place` is meant to replace these functions. + +### fetch and map POIs from osmnx +busstops = ox.geometries_from_place(place, tags = {'highway':'bus_stop'}) + +Now, let's check the data type busstops was read as + +type(busstops) + +As we can see, busstops is already a geodataframe. Therefore, we can plot it as it is unto out map. + +fig, ax = plt.subplots(figsize = (10,8)) +berkeley.plot(ax=ax) +streets.plot(ax=ax, color="grey") +bikes.plot(ax=ax, color="yellow") +busstops.plot(ax=ax, color="white") +plt.show() + + +## 10.5 Exercise + +Repeat above for SF. The link for accessing the bikeways for SF is already given to you below. + +### SF Open Data portal + +https://datasf.org/opendata/ + +#### SF Bike Network data +https://data.sfgov.org/Transportation/SFMTA-Bikeway-Network/ygmz-vaxd + +sf_bike_ways = "https://data.sfgov.org/api/geospatial/ygmz-vaxd?method=export&format=GeoJSON" + +# Your code here + +## Double-click here to see solution! + + + + +## 10.6 Read in Data from a CSV and convert to geodataframe + +In this example, we'll learn how to read a csv file with latitude and longitude coordinates and convert it to a geodataframe for plotting. + +# Read in CSV file +stations = pd.read_csv("notebook_data/transportation/bart.csv") +stations.head() + +We now want to convert the csv file into a Point geodataframe, so we can produce maps and access the geospatial analysis tools. + +We do this below with the geopandas `GeoDataFrame` function which takes as input + +1. a pandas dataframe here `stations`, and +2. `geometry` for each row in the dataframe. + +We create the geometry using the geopandas `points_from_xy` function, using the data in the `lon` and `lat` columns of the pandas dataframe. + +#Convert the DataFrame to a GeoDataFrame. +bart_gdf = gpd.GeoDataFrame(stations, geometry=gpd.points_from_xy(stations.lon, stations.lat)) + +# and take a look +bart_gdf.plot(); + +Now we have a map of BART stations! You can use this approach with any CSV file that has columns of x,y coordinates. + +### 10.7 Exercises + + + +Set the CRS for `bart_gdf` to WGS84 + + + +Below is the url for the 2018 census county geographic boundary file. + +* Read in the county file +* Subset on Marin County +* Plot Marin County with the Bart stations you transformed +* Question: what should do if the county name is not unique? + +# Census Counties file for the USA +county_file = "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_500k.zip" + +# Your code here + +## Double-click here to see solution! + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily.ipynb b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily.ipynb new file mode 100644 index 0000000..6b0cc90 --- /dev/null +++ b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily.ipynb @@ -0,0 +1,866 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 11. Adding Basemaps with Contextily\n", + "\n", + "If you work with geospatial data in Python, you most likely are familiar with the fantastic [GeoPandas](https://geopandas.org/) library. GeoPandas leverages the power of [Matplotlib](https://matplotlib.org/) to enable users to make maps of their data. However, until recently, it has not been easy to add basemaps to these maps. Basemaps are the contextual map data, like Google Maps, on top of which geospatial data are often displayed.\n", + "\n", + "\n", + "The new Python library [contextily](https://github.com/geopandas/contextily), which stands for *context map tiles*, now makes it possible and relatively straight forward to add basemaps to Geopandas maps. Below we walk through a few common workflows for doing this.\n", + "\n", + "First, let's load are libraries. This assumes you have the following Python libraries installed in your environment:\n", + "\n", + "- pandas\n", + "- matplotlib\n", + "- geopandas (and all dependancies)\n", + "- contextily\n", + "- descartes" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import contextily as cx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read data into a Geopandas GeoDataFrame\n", + "\n", + "Fetch the census places data to map. Census places includes cities and other populated places. Here we fetch the 2019 cartographic boundary (`cb_`) file of California (`06`) places." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ca_places = \"https://www2.census.gov/geo/tiger/GENZ2019/shp/cb_2019_06_place_500k.zip\"\n", + "places = gpd.read_file(ca_places)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the geodatarame `plot` method to make a quick map." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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10640130024116201600000US06401300640130Lancaster25244187339681671POLYGON ((-118.32517 34.75176, -118.32073 34.7...
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" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "places.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can subset the data by selecting a row or rows by place name. Let's select the city of Berkeley, CA." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley = places[places['NAME']=='Berkeley']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_10_0.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "berkeley.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Contextily to add a basemap\n", + "\n", + "Above we can see the map of the boundary of the city of Berkeley, CA. The axis labels display the longitude and latitude coordinates for the bounding extent of the city.\n", + "\n", + "Let's use `contextily` in it's most simple form to add a basemap to provide the geographic context for Berkeley. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_12_0.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = berkeley.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a few important things to note about the above code.\n", + "\n", + "- We use `matplotlib` to define the plot canvas as `ax`.\n", + "- We then add the contextily basemap to the map with the code `cx.add_basemap(ax)`\n", + "\n", + "Additionally, we **dynamically transform the coordinate reference system**, or CRS, of the Berkeley geodataframe from geographic lat/lon coordinates to `web mercator` using the method **to_crs('EPSG:3857')**. [Web mercator](https://en.wikipedia.org/wiki/Web_Mercator_projection) is the default CRS used by all web map tilesets. It is referenced by a the code `EPSG:3857` where [EPSG](https://en.wikipedia.org/wiki/EPSG_Geodetic_Parameter_Dataset) stands for the the initials of the organization that created these codes (the European Petroleum Survey Group).\n", + "\n", + "Let's clean up the map by adding some code to change the symbology of the Berkeley city boundary. This will highlight the value of adding a basemap.\n", + "\n", + "First, let's map the boundary with out a fill color." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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POeYY+2zbdue6e+1V8fKntWvhrrvgpJOgY0cb8vbuXdarS+7dbd1qnxs22K5UEyfCU09l8l/KqY7OneGxx8K2ImOIZsNHkiWKi4t11qxZYZuRWbZtsyHdrFm2lGiXXXa+J9ELKy9iq1bBvvuWiVj//nbeokXdt6Mytm+3Htjdd5tfL9Gry8uDhg2td1dQYJ/NmkGTJpbp9Ywzwra8fjF3rv0RfOedsC1JGRGZrarFFZa5sIXA4sXwzDOWYWHGDOv1bN1qPqzBgy2lTF5e2TAw0QtLiFdCyPbYw3xcjlNbVq60HvrXX4dtScpUJWw+FK0L1qyB6dPLxGzTJvM/nXgijB9fNsRbscKE7sUXzVk/YACceSb07VtxT85xMkXr1rakauNGSyEecVzYssWyZTBhggnZ3Lk2vDrySDj/fBOqihYbFxaaE/+EE+rcXKeeI2ITCEuXQo8eYVtTa1zYssVrr1n4xMMP2yxjkyZhW+Q4VRMjYavWQSMijUVkpojME5EFInJlcH2yiMwNjo9FZG4l9S8J6pWIyCQRaRxcLxKRN4L6s0SkhjEBOc4Pf2gBpi+84KLmRIN27WKz+iAVz/MWYIiq9geKgKNEZJCqnqSqRapaBDwCPFq+ooh0Ai4EilW1L9AQGB0UXwdcGdT/fXAeH0Qssv+xx+rFrkBODGjbFr74ImwrMkK1wqbG+uA0Pzi+mUoVEQFOBCZV8og8oImI5AFNgWWJRwMJj/iuSdfjQ6tWcP/9cPbZlobHcXKZdu3qj7ABiEjDYKj5JfCcqr6ZVDwY+EJV3y9fT1WXAjcAS4DlwBpVfTYovhi4XkQ+De75dSXvHhsMVWetWLEitVblEoMHw9ix8KMfZWddpeNkihgthE9J2FR1RzBk7AwMFJG+ScVjqKS3JiKtgJHAHkBHoJmInBoUnwdcoqq7A5cAEyp593hVLVbV4sLCwlTMzT0uu8xi13yDEieXqW89tgSquhqYARwFEAwvRwGTK6kyDPhIVVeo6jbMD3dQUHY6ZX65h4F4TR4kk5dncWsvvBC2JY5TOfVp8kBECkWkZfC9CSZWi4LiYcAiVa0sLcASYJCINA18cUOBhUHZMuCw4PsQYKehbKwYOtSFzcltYjR5kEocWwfgHhFpiAnhQ6o6LSgbTblhqIh0BO5U1RGq+qaITAHmANuBt4Hxwa3nALcEvb7NwNhatyaXOeIIm0TYtq0sq4bj5BIxGor6WtG6ZL/9LHfZwQeHbYnj7IyqJSJYsaLyTDA5RFVrRX0FdV3iw1EnlxGJzXDUha0uGTrUFrg7Tq4Sk+GoC1tdMniw5V2L0ca0TsyISSybC1td0rw5FBXBq6+GbYnjVIz32Jy0cD+bk8vEJJbNha2ucWFzchmfPHDSYtAgeO+9SKVgduoRPhR10qKgwLa+e+mlsC1xnJ3xoaiTNj4cdXIVH4rGlC1bLCTjH/+An/0sO9uRDRniwubkJjHpsdXvPQ+2bDHhmj277Fi4ELp3tx2iCgvhe9+DN9+EDh0y996iIvvhWbrU8sw7Tq6w22622XXE1zTXH2HbsgVKSqw3VpGIDRhgm/T27//t7cd22QVGjjSfWKb2LmjYEA4/3FYhnHZaZp7pOJmgQQNo08b+8Eb4j258hW3lSnj0UROwWbNMxPbaC4qLKxexivjtb2HRIvjxj2HSpMxtUJzws7mwOblGYjgaYWGLr4/t9tvhJz+Bffe1jBorV9qw8+674ac/tX0+U9kYVgTuvNN2ab/yyszZl1g3GqHsKk49IQYTCPEVtvPOs/+gAw9MXcQqo3Fj223q3nvhgQcyY9/ee0NpKXzwQWae5ziZIgaxbPEVtsJCuOkmOOss2Lq19s9r2xYefxwuuQRef732zxPxsA8nN4nBzGh8hQ3g5JNh993hugxtWdq3L0ycCMcfDx9/XPvnediHk4v4UDTHEYE77oBbbrHJg0wwYgT83//ZLu9r19buWUOHwvTpNiR1nFzBe2wRoEsXc/qffXbmBOTCCy2995gxsGNH+s/p3Blat4Z58zJjl+NkAu+xRYRzz7Uwjdtvz8zzROCvf7XYuF/8onbP8qy6Tq7hkwcRoUEDC9m44gr45JPMPDM/Hx5+GJ580pZfpYtPIDi5hg9FI0TPnjBunMW2ZSp2rFUrmDYNLr8cnn8+vWcccYRl1M3EzK3jZILCQtupKsK+3/ojbACXXgqffw7335+5Z/boAZMnwymnwP/+V/P6u+1mz5g5M3M2OU5tKCiAFi1g1aqwLUmb+iVs+flw113mF8tkV/uww+Caa+CYY+Crr2pe38M+nFwj4hMI1QqbiDQWkZkiMk9EFojIlcH1ySIyNzg+FpG5ldS/JKhXIiKTRKRxTepnnP33t3WfF16Y2eeeeSYcd5zFuFU1rFSFjz6CRx6B3/wGjjwSJkyAzz7LrD2OUxsiPoGQyiL4LcAQVV0vIvnAqyLylKqelLhBRG4E1pSvKCKdgAuB3qq6SUQeAkYDE1OpnzWuuAL69YOpU+EHP8jcc6+5BkaNsuVcd95p1z780Bbiz5lT9tm4sQnsgAG2bnX//aFjx8zZ4Ti1JeITCNUKm6oqsD44zQ+Ob7zvIiLAicCQKt7RRES2AU2BZcmFKdTPPE2amPCceqoNI3fdNTPPbdjQ1pIecggccAAsXmxb7iXSIl18sX22b5+Z9zlOtoj4UDSltEUi0hCYDXQH/qaqbyYVDwa+UNX3y9dT1aUicgOwBNgEPKuqz5a7rdL6wbvHAmMBunTpkoq5qXHYYZZE8pe/rF24RnmaN7cZ0jlzLKFk27aZe7bj1BURH4qmNHmgqjtUtQjoDAwUkb5JxWOASRXVE5FWwEhgD6Aj0ExETi13W6X1g3ePV9ViVS0uLCxMxdzUufZai0ObMSOzz23TBoYPd1Fzokt9ELYEqroamAEcBSAiecAoYHIlVYYBH6nqClXdBjwKHJQoTKF+dtl1V1uNcM45sHFjKCY4Tk7Stq3FskWUVGZFC0WkZfC9CSZWi4LiYcAiVa1sSm8JMEhEmga+tKFA8mr06upnn+9/37LqXnFFaCY4Ts5RD3psHYDpIjIfeAt4TlWnBWWjKTeMFJGOIvIkQOCLmwLMAd4J3jc+6fad6ofCLbfAPfdYCnHHcazHFuFZUdEIpaYuLi7WWdkSn/vvh+uvN3GL8O48jpMR1q2z2fsNG8K2pFJEZLaqFldUVr9WHlTFKafY5hXXXx+2JY4TPs2bWzB5DgtbVbiwJUgkpfzLX2xXKsepz4jYcDSiK2Jc2JLp2tUmETKZlNJxosqBB0Z2DbMLW3nOO88+77gjXDscJ2x23RXyorn1sAtbeZKTUi5ZErY1jhMemzfbuuYI4sJWEfvsY+s6zz3XNzR26i+bN0OjRmFbkRYubJXxy1/Cu+9CSUnYljhO3fPFF/DOO5lLEFHHuLBVRn4+DBoEc+eGbYnj1D2jR9v2knvuGbYlaeHCVhVFRS5sTv3kgQcsZ+HNN4dtSVq4sFVF//6+56dTP+nY0ZZUHX982JakhQtbVRQVmbD5BIJT31i40DY+OvzwsC1JCxe2qmjf3iKwly2r/l7HiRMPPABjxlhW6AjiwlYVImW9NsepL6iasJ1yStiWpI0LW3X07+8TCE794vXXbV+QoqKwLUkbF7bq8AkEp76RlwdbtkTat+zCVh0e8uHUNw44AFq1gqefDtuStHFhq46ePeHTTyObl8pxaoyI7Xd7221hW5I2LmzVkZ8PvXrZ8pJs8cILcMQRsGBB9t7hODXhpJMsm/QHH4RtSVq4sKVCtvxsy5bZlPrZZ9s7TjzRd8tycoMmTeDMM20XtwjiwpYKmQ752L7dNpDp39/W4i1YYJl7998fLrwwc+9xnNpw7rm2yVEE3TAubKmQyZCP//7Xtvt7/HF45RX44x+haVPza9x+u1178MHMvMtxakO3bjB4sG10FDFc2FKhXz/zsdUmXfjKlTbkPOEE+NWv4LnnLO9bMi1awOTJcNFF8P77tbPZcTJBYhIhYqEfLmyp0KoVtG4NixfXvG5pqWXk7dPHdv55911LCSNS8f1FRXDllea83bKlVmY7Tq0pLrachBHz/bqwpUo6Ewhvvw0HHwx33QXPPGMpYFJJ3HfeeeZ7u/TStEx1nIzRoIG5SiKWSbdaYRORxiIyU0TmicgCEbkyuD5ZROYGx8ciMreS+pcE9UpEZJKINE4q+5mI/C8ovy5jrcoGNQnUXbPGhpNHHWXDz1dfrdnyFBHr5T3+OPz732kY6zgZYpddYL/94IknwrakRqTSY9sCDFHV/kARcJSIDFLVk1S1SFWLgEeAR8tXFJFOwIVAsar2BRoCo4OyI4CRQD9V7QPckIH2ZI9Uemyq5vjv1Qs2bbJh51ln2V+9mtKyJfzrX/CTn8Ann6RlsuNkhLw8C/+IENXuraWqCqwPTvOD4xtPoogIcCIwpIp3NBGRbUBTIJED6Dzgz6q6JXjPl+k0oM6orse2cCFccAGsWgWPPmppxWvLgQfa3gujR8PLL1uwsOPUJTt2wOzZ5muLECl1JUSkYTDU/BJ4TlXfTCoeDHyhqjtN46nqUqwntgRYDqxR1WeD4r2BwSLypoi8JCIHVPLusSIyS0RmrVixIuWGZZxu3WyI+fXX376+YQP8+tdw6KFw3HHw1luZEbUE48bBbrvBZZdl7pmOkyoLF0KHDvYzGCFSEjZV3REMOTsDA0Wkb1LxGGBSRfVEpBU23NwD6Ag0E5FTg+I8oBUwCLgUeCjo/ZV/93hVLVbV4sLCwtRalQ0aNPj2cFQVHnvMZjuXLIH58+FnP8v8BrMNGliQ5AMPRHpRshNRZs6EgQPDtqLG1Oi3UFVXi8gM4CigRETygFHAgEqqDAM+UtUVACLyKHAQcD/wGfBoMNSdKSKlQBsgxG5ZNSQCdbt2tRUCixfbjOeQykbhGaJNGwuSHD3ahgUdO2b3fY6TIKLClsqsaKGItAy+N8HEalFQPAxYpKqfVVJ9CTBIRJoGvbGhwMKg7DECv5yI7A0UACvTa0Yd0b+/rQ4YOBAOOcR6b9kWtQSHHWZhIKecYn4Px6kL2ra1P6pf5rYLvDypDEU7ANNFZD7wFuZjmxaUjabcMFREOorIkwCBL24KMAd4J3jf+ODWu4A9RaQE+BdwetB7y12GDTNf2uzZtnqgoKBu3//b31ooyNVX1+17nfrLlVfCkUdaPGY6AeohIbmuJckUFxfrrFmzwjYjXJYvt8XykyZFdgchJ4L8/e9w1VUwdWrOzJCKyGxVrdAYX3kQNTp0gIkT4dRTIcxZYqd+ce65cMcdcPTR8NRTYVtTLRmewnPqhCOPNGE7/XSYNi29AGDHqSkjR0JhIYwaBVdcYcHn1cVWqloCiA8+gA8/NF/dpk028vjii7Jj9Gh7ZobwoWhU2bbNJhSOO87XlDp1y//+Z0sFFy2CY4+1kKeOHS3W7dNPTcSSj4YNoUcP2GMPaNfOVjG0b29Hu3a2QufJJ2u8bKuqoaj32KJKfr752QYOtJxZmQwKdpyq6NnT8gZ+8omtZf7wQ3jtNfjqK+jcGbp3t95d9+52VBfc26wZ3H13Rk10YYsyXbvCP/5h6cXnzLH0So5TV3TtChdfXPvntGuX8XASd85EnWOPhe9/34YGEXIrOM43FBaasGXw59eFLQ5cfz18/HFkN95w6jlNm5prZd26jD3ShS0ONGpkKY6uuMKSWzpO1GjbNqPDURe2uNCjB9x6q6UUX7+++vsdJ5do29bCPjKEC1ucGDPGptBffjlsSxynZmR4AsGFLW60bAlbt4ZthePUDB+KOlWSn2/Bu44TJVzYnCopKHBhc6KHC5tTJd5jc6KITx44VZKf7z62+oAqrF5t6yxffTVsa2pPhicPfElV3PAeW7zYsMEWiL/4IixbBp9/bj2bzz83t0P79vD++5ZBo3XrsK1NnwwPRV3Y4ob72KLLhg2WMWPhQvucPx9eegm+8x0YMcLSVbVrV5YVo2lTq3fQQVBSYtleoooLm1Ml3mPLfdavN+F69107FiywY/ly2Htv23C7Vy/LuTdxYvXZMfbdF955J9rCtttutr3l9u0Z2enNhS1uuI8td9i82XKXlZSUiVdJiQlYjx6Wx6x3bzjzTPu+557p/VInhC0qJPyDy5bB0qV2LFtmedtWrrQeaS1xYYsb3mOre7ZsgffeKxOvt98uS5rYu7eJVt++8KMf2ffu3TO7/2y/fvDgg5l7Xm3YvNmEOyFWycKV/JmfD506WYLKTp3suOUWy/SRAVzY4kZBgf2iOZlnxw7bqamkxHpIJSV2fPSRZYdN9MCGDLFkjL/4he1RkW169jRhzRYbN1pPauVKSyaZ+L5ixc6CtW6d9bgSYpUQrv32K/vesSM0b549e3Fhix/5+b4IPpN8/TX8/Oe2h+yiRfZL27evDf9GjYLf/978Yo0ahWdjYSGsXWu9pcaNq753y5YyYapIrCo6SkvtHW3a2NG6ddn3gQO/LWBt2uTEHhwubHHDfWyZZdUqS3/9zDPWI8tyTyMtGjSwLfEeecQ21E6wfbvtLHX33RYe0qMHzJoFu+5aJkzJR/fulmI+WbjatLHZV5Hw2pcGLmxxw31smaVbN/tDkauiluD6622npwMPtIDdZ5+F556D/v3hppvg9ddtODh4sO0xEHOqFTYRaQy8DDQK7p+iqpeLyGSgZ3BbS2C1qhZVUP8S4GxAsd3gz1DVzSJyBXAOkNgc8zeq+mStWuN4HFumSeyw9L//wYABYVtTOQcfbKI1cCAMGwbDh8M119i+BFDvNtdOpce2BRiiqutFJB94VUSeUtWTEjeIyI3AmvIVRaQTcCHQW1U3ichDwGhgYnDLX1T1hto2wkki13psqhZw+swzdhQXw9VXl5Vv2mTDvVWroEUL6NIlPFsro1cva0MuCxvAfffZZ8OG4dqRA1QrbGobjya80fnB8c2uCyIiwInAkCre0UREtgFNgWW1MdiphlwQtlWr4PnnTciefdb8M0ceaZvO/PSn5rNKiFlpqe2u1aqVhQksWQK77BKu/eXp0cNmQ3MdF7RvSMnHJiINgdlAd+BvqvpmUvFg4AtVfb98PVVdKiI3AEuATcCzqvps0i0/FZEfAbOAn6vqqgrePRYYC9AlF/+a5xphTB6sX2/7TE6fbmsa33vPhkXDh1vIQ8+eZc7nAQPMV5UQsyZNysqOOw6mTLGA1Vxijz08K3HESGleVlV3BP6zzsBAEembVDwGmFRRPRFpBYwE9gA6As1E5NSg+A5gL6AIWA7cWMm7x6tqsaoWF2YoeC/W1IWPbfNmE7HLLjPfTvv2cO21Jlg332whAk88ARddBPvs8+0ZtUGDLFyiU6edZ9tOPx3uvTe7tqdDt262C5gTGWo0K6qqq0VkBnAUUCIiecAooDLnwzDgI1VdASAijwIHAfer6jfJl0Tkn8C0mpvv7EQ2hqLbt1uYwAsvWI9s5kwTpyFD4MorbRF2YkF2bRgxAs45pyzgNVfo1s1sciJDKrOihcC2QNSaYGJ1bVA8DFikqp9VUn0JMEhEmmJD0aHYsBMR6aCqy4P7jgNK0m+G8w2ZELbSUouof/FFE7NXXrHZtaFD4ZJL4NBDs+MHKyiwkIX77rPA11xh990tsr60NCeCT53qSaXH1gG4J/CzNQAeUtVE72o05YahItIRuFNVR6jqmyIyBZgDbAfeBsYHt14nIkXYRMTHwE9q2RYHTBxq6mNTtXCG6dPtmDHDgjiHDLEMExMmWFqZuuD0003cLrssd4JCJ0yw7BM7driwRYRUZkXnA/tVUvbjCq4tA0YknV8OXF7BfafVxFAnRVLpsanaLF+ykOXnwxFHwPe+BzfcEF7YxYABJs7//a/578Jk+3YYN84CXV97zf6NnEjgKw/iRmXC9vnn8PTTNrycPt2GVUccYcPLq682n1Yu9JBErNd2zz3hC9tf/2q+xddft20NncjgwhY3EsKmahlYH3/cjvfeg+9+14Tsd7+z2KxcELKKOOUUS8Vzyy0WDlIdquYDS6QNmjfPMsxee231dauieXNb4O6iFjlc2OJGQQHMmWPO/vx8C4r9058srqygIGzrUqNzZ1uhMHUqnHRS2XVVy/efSNiYELIFCyy7Rq9eNlvbsyf85jfw5z/XTrx79TL/mhM5XNjiRs+ecPvtlia6V6/c7ZVVx+mnw223Wc6vZAEDW5Dep4/16k4+2b6Xj3G89Vb47DOb0UyXxFIq1ej+O9ZTXNjiRqNGcN55YVtRe449Fh56yBI69ukDP/yhfbZrl5rI9O5t+wmkI2yqJooLFlgw8sqVGcvs6tQNLmxObtKsGfznP+nXTwjbkUdWfk+ygCVvqvLuu/b+Pn3gggtyO12RUyEubE486dMHZs+276q2uD4hXondod5910QrsS/BwIFwxhl2Xt3OUE5O48LmxJPeveHyy22GdOFCS4mU8M0NGgRnnWU+NBewWOLC5sSTQYMs0HjPPU3kWrUK2yKnDnFhc+JJQQGc5otb6iu+8M1xnNjhwuY4TuxwYXMcJ3a4sDmOEztc2BzHiR0ubI7jxA4XNsdxYocLm+M4sUNsP+RoICIrgE+y/Jo2wMosv6Mu8fbkNt6e9OmqqhWmXYmUsNUFIjJLVYvDtiNTeHtyG29PdvChqOM4scOFzXGc2OHCtjPjq78lUnh7chtvTxZwH5vjOLHDe2yO48QOFzbHcWJHrIVNRE4QkQUiUioixUnXvysis0XkneBzSHC9qYg8ISKLgnp/ruS5A0VkbnDME5HjalI/Ku0JygYEz/1ARG4Vydw+dFlsT4X1g7IxwfX5IvK0iLSJeHsKRGS8iLwXPOf4KLcn6Z6pIlKStvGqGtsD6AX0BGYAxUnX9wM6Bt/7AkuD702BI4LvBcArwNEVPLcpkBd87wB8iWUjTql+VNoTnM8EvgMI8FRE2lNZ/bygbW2C8+uAK6LanuD8SuDq4HuDRNui2p7g2ijgQaAkXdtjnRpcVRcClO9kqOrbSacLgMYi0khVNwLTg3u2isgcoHMFz92YdNoY0KTr1daPSntEpAOwi6q+HpzfCxyLCVwut6fC+kApJtDNROQrYBfgg0y0JYz2qOoW4Exgn+C+UjIY9R9Ge0SkOTAOGAs8lK7tsR6KpsjxwNvBD8k3iEhL4PvACxVVEpEDRWQB8A5wrqpur0n9LJLJ9nQCPku67bPgWl2SVnsqqq+q24DzsDYuA3oDEzJucYr2JF9Mpz1BHYA/iMgcEXlYRNpl2uBU7Um+mE57gvM/ADcCGyuvkgKZ6raGdQDPAyUVHCOT7plBUlc66XofYDGwV7nreViv5OIU3t8LG641Tqd+LrcHOAB4PqlsMPB4VNpTvj6Qj/2i7YX13G4Dfhfh9rTBetfHB+fjgPsi3J6ixM8X0I1aDEXTqhS1o6L/GKyL/B5wcAX33wXcWoPnT+fbPoga1c/V9mD+tkVJ18cA/4hCeyqqjwn1C0nnhwJPRrg9AmwAGgTnuwMLItye87Ce9MfY6GArMCMtmzP9j5CLR/n/GKAlMI/gL125e68GHkn8sFTyvD0oc653Df4z2qRaP2LteQsYRNnkwYgItKfC+kBHYDlQGJz/Abgxqu0Jyv4FDAm+/xh4OMrtSbqnG95jq/Qf57hA+bcAXwDPBNd/h/2lm5t0tMX+iiiwMOn62UGdHwBXBd9Pw5yec4E5wLHB9UrrR7E9QVkxNjRZjA3dJALtqbB+UHZuUH8+8DjQOuLt6Qq8HLTnBaBLlNuT9O5u1ELYfEmV4zixw2dFHceJHS5sjuPEDhc2x3Fihwub4zixw4XNcZzY4cLmOE7scGFzHCd2/D+6039GslPGPAAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_14_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "berkeley.plot(edgecolor=\"red\", facecolor=\"none\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's build on those symbology options and add the contextily basemap." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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4-122.26712037.828705MACARTHURBARTALA
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" + ], + "text/plain": [ + " lon lat STATION OPERATOR COUNTY\n", + "0 -122.283348 37.874061 NORTH BERKELEY BART ALA\n", + "1 -122.268249 37.869689 DOWNTOWN BERKELEY BART ALA\n", + "2 -122.270119 37.853207 ASHBY BART ALA\n", + "3 -122.251777 37.844510 ROCKRIDGE BART ALA\n", + "4 -122.267120 37.828705 MACARTHUR BART ALA" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bart.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting Point Data in a dataframe to Geospatial Data in a geodataframe\n", + "\n", + "Because these data are in a CSV file we read them into a Pandas DataFrame.\n", + "\n", + "In order to map these data we need to convert these data to a GeoPandas GeoDataFame. To do this, we need to specify:\n", + "\n", + "- the data, here the geodataframe `bart`\n", + "- the coordinate data, here `bart['X']` and `bart['Y']`\n", + "- the CRS of the bart coordinate data, here `EPSG:4326`\n", + "\n", + "The CRS code 'EPSG:4326' stands for the World Geodectic System of 1984, or WGS84. This is the most commonly used CRS for geographic (lat/lon) coordinate data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_22_0.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Convert the DataFrame to a GeoDataFrame. \n", + "bart_gdf = gpd.GeoDataFrame(bart, geometry=gpd.points_from_xy(bart['lon'], \n", + " bart['lat']), \n", + " crs='EPSG:4326') \n", + "\n", + "# and take a look\n", + "bart_gdf.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have the BART data in a geodataframe we can use the same commands as we did above to map it with a contextily basemap." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_24_0.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have the full range of `matplotlib` style options to enhance the map, a few of which are shown in the example below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_26_0.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = bart_gdf.to_crs('EPSG:3857').plot(\n", + " color=\"red\",\n", + " edgecolor=\"black\",\n", + " markersize=50, \n", + " figsize=(9, 9))\n", + "\n", + "ax.set_title('Bay Area Bart Stations')\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Changing the Basemap\n", + "\n", + "By default `contextiley` returns maptiles from the OpenStreetmap Mapnik basemap. However, ther are other available tilesets from different providers. These tilesets are stored in the contextily `cx.providers` dictionary.\n", + "\n", + "That's a large dictionary and you can view it. Alternatively, and more simply, you can access the list of the providers in this dictionary using the command `cs.providers.keys`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['OpenStreetMap', 'OpenSeaMap', 'OpenPtMap', 'OpenTopoMap', 'OpenRailwayMap', 'OpenFireMap', 'SafeCast', 'Thunderforest', 'OpenMapSurfer', 'Hydda', 'MapBox', 'Stamen', 'Esri', 'OpenWeatherMap', 'HERE', 'FreeMapSK', 'MtbMap', 'CartoDB', 'HikeBike', 'BasemapAT', 'nlmaps', 'NASAGIBS', 'NLS', 'JusticeMap', 'Wikimedia', 'GeoportailFrance', 'OneMapSG'])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# change basemap - can be one of these\n", + "# first see available provider names\n", + "cx.providers.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Once you have the list of providers, you can find the names of their specific tilesets. \n", + "\n", + "Below, we retrieve the list of the tilesets available from the provider `CartoDB`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['Positron', 'PositronNoLabels', 'PositronOnlyLabels', 'DarkMatter', 'DarkMatterNoLabels', 'DarkMatterOnlyLabels', 'Voyager', 'VoyagerNoLabels', 'VoyagerOnlyLabels', 'VoyagerLabelsUnder'])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Then find the names of the tile sets for a specific provider\n", + "cx.providers.CartoDB.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can specify a different tileset using the **source** argument to the `add_basemap` method." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['WorldStreetMap', 'DeLorme', 'WorldTopoMap', 'WorldImagery', 'WorldTerrain', 'WorldShadedRelief', 'WorldPhysical', 'OceanBasemap', 'NatGeoWorldMap', 'WorldGrayCanvas'])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cx.providers.Esri.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/contextily/tile.py:632: UserWarning: The inferred zoom level of 11 is not valid for the current tile provider (valid zooms: 1 - 9).\n", + " warnings.warn(msg)\n" + ] + }, + { + "ename": "ConnectionError", + "evalue": "('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mConnectionResetError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m 700\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 446\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 441\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1346\u001b[0m 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Connection reset by peer", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mProtocolError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m resp = conn.urlopen(\n\u001b[0m\u001b[1;32m 440\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 754\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 755\u001b[0;31m retries = retries.increment(\n\u001b[0m\u001b[1;32m 756\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_method_retryable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 532\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m 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"\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m 700\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;31m# Otherwise it looks like a bug in the 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url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 540\u001b[0m }\n\u001b[1;32m 541\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 654\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 657\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mProtocolError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 500\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mMaxRetryError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mConnectionError\u001b[0m: ('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))" + ] + }, + { + "data": { + "image/png": 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GJpmgALtk22hgkpnMCLtk22hgkgkK0Ae2jQYmlaEHAKCToAAAdBIUAIBOggIA0ElQAAA6CQoAQCdBAQDoJCgAAJ0EBQCgk6AAAHQSFACAToICANDJplCwSwtLy3aOBCaWoAC7sLC0nNNnL2T1ytUkyfLKak6fvZAkwgIwEQw9wC6cOXfx5ZCwbvXK1Zw5d3FINQLoLz0KTJ1+DhVcWlndVjnAuNGjwFRZHypYXllNyytDBQtLyzt6v4OzM9sqBxg3ggJTpV9DBQtLyzn28GNZXllNXXdsZv++nDp+eJc1BRgNhh6YKv0YKrh+AmNLUr1/59z1AEwYQYGpcnB2JsubhILtDBVs1iuxHhI++4F7d1tFgJFi6IGpcur44czs33dN2XaHCkxgBKaJoMBUOXF0Lg/dfyRzszOprPUCPHT/kW0NFZjACEyTaq0Nuw43mJ+fb4uLi8OuBmzq+jkKyVqvxJ/89rn84m9ctkIjMHaq6nxrbX6zY+YowDat//HfuBbDd3/rgfyj88tWaAQmjqAAO3Di6Nw1AeDYw4913nYpKADjzBwF6AMTHIFJpUdhzNm5cDT047ZLgFGkR2GM9Xs5YnauH7ddAoyiLQeFqtpXVUtV9clNjt1TVV+rqid7jw9uOPZMVV3olbuVoY/sXDg6+nHbJcAo2s7Qw/uSPJ3kjR3HH2+t3ddx7Ltba1/ZVs24JePio+X6CY4Ak2BLQaGqDiV5R5L/Jcn/MNAacVMb5yS8qipXN1kHw7g4AP2y1aGHDyV5f5KXbnLO26vqqar6dFXdtaG8JfmnVXW+qh7oenFVPVBVi1W1ePny5S1Wa7pcPydhs5BgXByAfrplUKiq+5I811o7f5PTnkhyR2vtrUk+nGRhw7FjrbU/kuQHkvxYVX3XZm/QWnuktTbfWps/cODAli9gmmw2JyFJ9lUZFwdgILYy9HAsyTur6geTvDbJG6vqY62196yf0Fp7fsPzT1XVT1bV7a21r7TWLvXKn6uqn0tyd5Jf7u9lTIeuuQcvtZYvPfyOPa4NANPglj0KrbXTrbVDrbU7k5xM8tjGkJAkVfWmqqre87t77/vVqnp9Vf2BXvnrk/zXST7f52uYaAtLyzn28GN58wd+Pq9aa+IbmJMAwKDseMGlqnowSVprH0ny7iTvraoXk6wmOdlaa1X1nyf5uV6GuC3JP2ytfWb31Z4O128+ZE4CAHvN7pEj7NjDj2262t++qrzUmpUYAegLu0eOKXMSABg2SziPsK65B+YkALBXBIURZv8AAIbN0MOIuNkukHaHBGBYBIURcP3dDeu7QCb2DwBguAw9jAC7QAIwqgSFEWAXSABGlaGHEXBwdmbT9RLc3cB23GyeC8BO6VEYAe5uYLeu31l0fZ7LwtLysKsGjDlBYQScODqXh+4/krnZGbtAsiPmuQCDYuhhRLi7gd0wzwUYFD0KMAGs4gkMiqAAE8A8F2BQDD3ABLCKJzAoggJMCPNcgEEw9AAAdBIUAIBOhh6GxCp6AIwDQWEIbrVbJACMCkMPQ2AVPQDGhaAwBFbRA2BcCApDYBU9AMaFoDAEVtEDYFyYzDgEVtEDYFwICkNiFT0AxoGhBwCgk6AAAHQy9AB0soIoICgMkV/CjDIriAKJoYehWf8lvLyympZXfgkvLC0Pu2qQxAqiwBpBYUj8EmbUWUEUSASFofFLmFFnBVEgERSGxi9hRp0VRIFEUBgav4QZdSeOzuWh+49kbnYmlWRudiYP3X/EREaYMu56GBLLODMOrCAKCApD5JcwAKPO0AMA0ElQAAA6CQoAQCdBAQDoJCgAAJ0EBQCgk6AAAHQSFACAToICANBJUAAAOgkKAEAnez300cLSsk2eAJgogkKfLCwt5/TZC1m9cjVJsryymtNnLySJsADA2DL00Cdnzl18OSSsW71yNWfOXRxSjQBg9wSFPrm0srqtcgAYB4JCnxycndlWOQCMA0GhT04dP5yZ/fuuKZvZvy+njh8eUo0AYPdMZuyT9QmL7noAYJIICn104uicYADARDH0AAB0EhQAgE6CAgDQyRyFLbA0MwyenzMYTVvuUaiqfVW1VFWf3OTYPVX1tap6svf44FZfO+rWl2ZeXllNyytLMy8sLQ+7ajAx/JzB6NrO0MP7kjx9k+OPt9be1nv8tW2+dmRZmhkGz88ZjK4tBYWqOpTkHUk+ut1vsJvXjgJLM8Pg+TmD0bXVHoUPJXl/kpducs7bq+qpqvp0Vd21zdemqh6oqsWqWrx8+fIWqzV4lmaGwfNzBqPrlkGhqu5L8lxr7fxNTnsiyR2ttbcm+XCShW28NknSWnuktTbfWps/cODAliq/FyzNDIPn5wxG11Z6FI4leWdVPZPk0ST3VtXHNp7QWnu+tfZC7/mnkuyvqtu38tpRd+LoXB66/0jmZmdSSeZmZ/LQ/UfMxoY+8nMGo6taa1s/ueqeJD/RWrvvuvI3Jflya61V1d1JPpG1HoZ2q9duZn5+vi0uLm65XgDAzlXV+dba/GbHdryOQlU9mCSttY8keXeS91bVi0lWk5xs20kgAMBI2laPwl7RowAAe+dmPQqWcAYAOgkKAEAnQQEA6CQoAACdBAUAoNPEbzNt61oA2LmJDgrrW9eu70q3vnVtEmEBALZgoocebF0LALsz0UHB1rUAsDsTPfRwcHYmy5uEAlvXAjBuhjXnbqJ7FGxdC8AkWJ9zt7yympZX5twtLC0P/HtPdI/CetJy1wMAe2FQn/pvNudu0H/TJjooJGthQTAAYNAGeafdMOfcTfTQA8D1FpaWc+zhx/LmD/x8jj382J503TIdBnmnXdfcur2YcycoAFNjmOO8TL5Bfuof5pw7QQGYGtZWYZAG+an/xNG5PHT/kczNzqSSzM3O5KH7j+zJ0PrEz1EAWGdtFQbp1PHD18xRSPr7qX9Yc+70KABTY5jjvEy+YX7qHyQ9CsDUGPQnPpjEO+0EBWBqWFsFtk9QAKbKKH/iG9YSvXAzggLACBjkYj2wGyYzAowAt24yqgQFgBHg1k1GlaAAMALcusmoEhQARsAwl+iFm5mayYxmEwOjzK2bjKqpCApmEwPjYJRv3WR6TcXQg9nEALAzUxEUzCYGgJ2ZiqBgNjEA7MxUBAWziQFgZ6ZiMqPZxACwM1MRFBKziQFgJ6Zi6AEA2BlBAQDoJCgAAJ0EBQCgk6AAAHQSFACATlNzeyTAsNnFlnEkKADsAbvYMq4MPQDsAbvYMq4EBYA9YBdbxpWgALAH7GLLuBIUAPaAXWwZVyYzboMZy8BO2cWWcSUobJEZy8Bu2cWWcWToYYvMWAZgGgkKW2TGMgDTSFDYIjOWAZhGgsIWmbEMwDQymXGLzFgGYBoJCttgxjIA08bQAwDQSVAAADoJCgBAJ0EBAOgkKAAAnQQFAKDTloNCVe2rqqWq+uQmx+6pqq9V1ZO9xwd75a+tql+rqqeq6gtV9Vf7WXkAYLC2s47C+5I8neSNHccfb63dd13Z7ye5t7X2QlXtT/IvqurTrbVf3UFdAYA9tqUehao6lOQdST66nTdva17ofbm/92jbqiEAMDRbHXr4UJL3J3npJue8vTfE8Omqumu9sDdk8WSS55L8Qmvtc5u9uKoeqKrFqlq8fPnyFqsFAAzSLYNCVd2X5LnW2vmbnPZEkjtaa29N8uEkC+sHWmtXW2tvS3Ioyd1V9ZbN3qC19khrbb61Nn/gwIFtXAIAMChb6VE4luSdVfVMkkeT3FtVH9t4Qmvt+fUhhtbap5Lsr6rbrztnJckvJfn+3VcbANgLtwwKrbXTrbVDrbU7k5xM8lhr7T0bz6mqN1VV9Z7f3Xvfr1bVgaqa7ZXPJPneJL/R30sAAAZlx7tHVtWDSdJa+0iSdyd5b1W9mGQ1ycnWWquqb0jy96pqX9bCw8+01m64vRIAGE3V2ujdhDA/P98WFxeHXQ0AmApVdb61Nr/ZMSszAgCdBAUAoJOgAAB02vFkRvpnYWk5Z85dzKWV1Rycncmp44dz4ujcsKsFAILCsC0sLef02QtZvXI1SbK8sprTZy8kibAAwNAJCkN25tzFl0PCutUrV3Pm3EVBAdgyPZMMiqAwZJdWVrdVDnA9PZMMksmMQ3ZwdmZb5QDXu1nPJOyWoDBkp44fzsz+fdeUzezfl1PHDw+pRsC40TPJIAkKQ3bi6Fweuv9I5mZnUknmZmfy0P1HdBcCW6ZnkkEyR2EEnDg6JxgAO3bq+OFr5igkeibpH0EBYMytf9Bw1wODICgATAA9kwyKOQoAQCdBAQDoJCgAAJ0EBQCgk6AAAHQSFACAToICANBJUAAAOgkKAEAnQQEA6GQJ5wmzsLRsvXcA+kZQmCALS8vX7CC3vLKa02cvJImwAMCOGHqYIGfOXbxmm9kkWb1yNWfOXRxSjQAYd4LCBLm0srqtcgC4FUFhghycndlWOQDciqAwQU4dP5yZ/fuuKZvZvy+njh8eUo0AGHcmM06Q9QmL7noAoF8EhQlz4uicYABA3xh6AAA6CQoAQCdBAQDoJCgAAJ0EBQCgk6AAAHQSFACATtZRmGC2nAZgtwSFCWXLaQD6wdDDhLLlNAD9IChMKFtOA9APgsKEsuU0AP0gKEwoW04D0A8mM04oW04D0A+CwgSz5TQAu2XoAQDopEcBgB2zsNvkExQA2BELu00HQw8A7IiF3aaDoADAjljYbToICgDsiIXdpoOgAMCOWNhtOpjMCMCOWNhtOggKAOyYhd0mn6EHAKCToAAAdBIUAIBOggIA0GnLQaGq9lXVUlV9cpNj91TV16rqyd7jg73yP1hVv1hVT1fVF6rqff2sPAAwWNu56+F9SZ5O8saO44+31u67ruzFJH+5tfZEVf2BJOer6hdaa1/cQV0BgD22pR6FqjqU5B1JPrqdN2+t/U5r7Yne8/8va0HDfTQAMCa2OvTwoSTvT/LSTc55e1U9VVWfrqq7rj9YVXcmOZrkc9utJAAwHLcMClV1X5LnWmvnb3LaE0nuaK29NcmHkyxc9x5vSPKPkvzF1trzHd/ngaparKrFy5cvb7X+AMAAbaVH4ViSd1bVM0keTXJvVX1s4wmttedbay/0nn8qyf6quj1Jqmp/1kLCP2itne36Jq21R1pr8621+QMHDuzsagCAvrplUGitnW6tHWqt3ZnkZJLHWmvv2XhOVb2pqqr3/O7e+361V/Z3kzzdWvubfa89ADBQO97roaoeTJLW2keSvDvJe6vqxSSrSU621lpV/fEkfybJhap6svfS/7HX6wAAjLhqrQ27DjeYn59vi4uLw64GAEyFqjrfWpvf7JiVGQGAToICANBJUAAAOgkKAEAnQQEA6CQoAACdBAUAoJOgAAB0EhQAgE6CAgDQSVAAADrteFMoABiWhaXlnDl3MZdWVnNwdianjh/OiaNzw67WRBIUABgrC0vLOX32QlavXE2SLK+s5vTZC0kiLAyAoQcAxsqZcxdfDgnrVq9czZlzF4dUo8kmKAAwVi6trG6rnN0RFAAYKwdnZ7ZVzu4ICgCMlVPHD2dm/75rymb278up44eHVKPJZjIjAGNlfcKiux72hqAAwNg5cXROMNgjhh4AgE6CAgDQSVAAADoJCgBAJ0EBAOgkKAAAnQQFAKCToAAAdBIUAIBOggIA0ElQAAA6CQoAQCdBAQDoJCgAAJ0EBQCgk6AAAHQSFACATtVaG3YdblBVl5P89rDrsUW3J/nKsCsxYrTJtbTHjbTJtbTHjbTJtQbdHne01g5sdmAkg8I4qarF1tr8sOsxSrTJtbTHjbTJtbTHjbTJtYbZHoYeAIBOggIA0ElQ2L1Hhl2BEaRNrqU9bqRNrqU9bqRNrjW09jBHAQDopEcBAOgkKGyiqn6oqr5QVS9V1aazTKvqtVX1a1X1VO/cv3rd8R+vqou9Y/9br+zuqnqy93iqqv7EXlxPPwywTb6vqs5X1YXev/fuxfX0wwDb5D+rql+sqheq6m/vxbX0w6Dao1d+uqr+be/Y8UFfS7/stk2q6q9U1fKG3xs/2Ct/dVX9dO/n5qmqumdvrmh3Btge+6vq7/Xa4+mqOr1X17RbA2yTH9lQ9mTv/d+2o0q21jyueyT5w0kOJ/mlJPMd51SSN/Se70/yuSTf0fv6u5P8sySv6X399b1/X5fktt7zb0jy3PrXo/4YYJscTXKw9/wtSZaHfa0j0CavT/LHkzyY5G8P+zpHoD2+LclTSV6T5M1JfjPJvmFf7x61yV9J8hObvObHkvz0ejslOZ/kVcO+3iG2x59O8mjv+euSPJPkzmFf7zDb5LrXH0nyWzut423hBq21p5Okqm52TkvyQu/L/b3H+oSP9yZ5uLX2+71zn+v9+x83vMVrN5w/8gbYJksb3uILSV5bVa9ZP2+UDbBNfi/Jv6iq/2IwNR+MQbVHkndl7Y/A7yf5UlX92yR3J/mVfl9Dv/WhTbp8W5L/p/f656pqJcl8kl/bXY0Ha4Dt0ZK8vqpuSzKT5D8leX639d0LA2yTjX44ycd3WEVDD7tRVfuq6sms9Qz8Qmvtc71D35Lkv6qqz1XVP6+qP7rhNX+sqr6Q5EKSB1trL+55xQdoJ22ywZ9MsjQOIWE7dtkmE2cH7TGX5N9veItne2UT4yZtkiR/oap+vap+qqq+rlf2VJJ3VdVtVfXmJN+e5A/uba0HZwft8Ykkv5fkd5L8uyR/vbX2u3ta6QHbQZts9N9EUNi+qvpnVfX5TR7v2up7tNauttbeluRQkrur6i29Q7cl+bok35HkVJKfqV5cbK19rrV2V5I/muR0Vb22n9e1G8Nqk973vivJ/5rkz/frevphmG0yiobUHpu1ycj0xg24Tf5Okm9K8ras/RH8G73yn8paYFpM8qEk/zLJSHzoGFJ73J3kapKDWRue+stV9Yf6c0W7N6Q2Wf/efyzJf2ytfX6n9Z/aoYfW2vf28b1WquqXknx/ks9n7Qf4bK+76Neq6qWsrdN9ecNrnq6q38vauPxiv+qyG8Nqk6o6lOTnkvy3rbXf7Fcd+mHY/5+MmiG1x7O59tPyoSSX+lWP3Rpkm7TWvrx+rKr+zySf7J33YpK/tOHYv0zyb/pVj90YRntkbY7CZ1prV5I8V1WfzdpQzG/1qy67MaQ2WXcyu+hNSKa4R2G3qupAVc32ns8k+d4kv9E7vJDk3t6xb0ny6iRfqao319oYWqrqjqxNYHlmTys+QDtsk9kkP5/kdGvts3tc5YHbSZvsfS33zg7b458kOVlVr+l1s39zRnwsfjtu1iZV9Q0bTv0TWQtUqarXVdXre8+/L8mLrbUv7mW9B2Un7ZG14YZ7a83rs9Yr9RuZEDtsk1TVq5L8UJJHd1WBnc6CnORHr7GfTfL7Sb6c5Fyv/GCST/We/5dJlpL8eu8/zAc3vP7VST7WK38iyb298j+TtQl7T/bKTwz7WkegTf7nrI0tPrnh8fXDvt5htknv2DNJfjdrE5ieTfJtw77eIbfH/5S1ux0uJvmBYV/rHrbJ/521+Uy/nrXA9A298jt7bfF01u4UuWPY1zrk9nhDkp/N2u/XLyY5NexrHXab9I7dk+RXd1tHKzMCAJ0MPQAAnQQFAKCToAAAdBIUAIBOggIAjLFaW5Hxuaq65aJKVfW36pWNov51rS3/ffPXuOsBAMZXVX1X1m6l/vuttbfc6vwNr/vxJEdba3/2ZufpUQCAMdZa++Wsrbvysqr6pqr6TFWdr6rHq+pbN3npljaLmtolnAFggj2StY0H/01vv4efTG/l0+Tl1YHfnOSxW72RoAAAE6Sq3pDkO5P87IZ95l5z3Wknk3yitXb1Vu8nKADAZHlVkpW2tttkl5NJfmyrbwYATIjW2vNJvlRVP5Qkvc2y3rp+vKoOZ21L91/ZyvsJCgAwxqrq41n7o3+4qp6tqh9N8iNJfrSqnsraZlnv2vCSH07yaNvibY9ujwQAOulRAAA6CQoAQCdBAQDoJCgAAJ0EBQCgk6AAAHQSFACAToICANDp/wenhqZowe9UIAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_33_2.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Change the basemap provider and tileset\n", + "ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", + "cx.add_basemap(ax, source=cx.providers.NASAGIBS.ModisTerraTrueColorCR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning More\n", + "\n", + "Above, we prove a very short introduction to the excellent `contextily` library. You can find more detailed information on the `contextily` homepage, available at: [https://github.com/geopandas/contextily](https://github.com/geopandas/contextily). We especially encourage you to check out the notebook examples provided in that github repo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily.py b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily.py new file mode 100644 index 0000000..9814140 --- /dev/null +++ b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily.py @@ -0,0 +1,155 @@ +# 11. Adding Basemaps with Contextily + +If you work with geospatial data in Python, you most likely are familiar with the fantastic [GeoPandas](https://geopandas.org/) library. GeoPandas leverages the power of [Matplotlib](https://matplotlib.org/) to enable users to make maps of their data. However, until recently, it has not been easy to add basemaps to these maps. Basemaps are the contextual map data, like Google Maps, on top of which geospatial data are often displayed. + + +The new Python library [contextily](https://github.com/geopandas/contextily), which stands for *context map tiles*, now makes it possible and relatively straight forward to add basemaps to Geopandas maps. Below we walk through a few common workflows for doing this. + +First, let's load are libraries. This assumes you have the following Python libraries installed in your environment: + +- pandas +- matplotlib +- geopandas (and all dependancies) +- contextily +- descartes + +%matplotlib inline + +import pandas as pd +import geopandas as gpd +import contextily as cx +import matplotlib.pyplot as plt + +## Read data into a Geopandas GeoDataFrame + +Fetch the census places data to map. Census places includes cities and other populated places. Here we fetch the 2019 cartographic boundary (`cb_`) file of California (`06`) places. + +ca_places = "https://www2.census.gov/geo/tiger/GENZ2019/shp/cb_2019_06_place_500k.zip" +places = gpd.read_file(ca_places) + +Use the geodatarame `plot` method to make a quick map. + +places.plot(); + +Now that we can see those cities, let's take a look at the data in the geodataframe. + +places.head() + +We can subset the data by selecting a row or rows by place name. Let's select the city of Berkeley, CA. + +berkeley = places[places['NAME']=='Berkeley'] + +berkeley.plot(); + +## Use Contextily to add a basemap + +Above we can see the map of the boundary of the city of Berkeley, CA. The axis labels display the longitude and latitude coordinates for the bounding extent of the city. + +Let's use `contextily` in it's most simple form to add a basemap to provide the geographic context for Berkeley. + +ax = berkeley.to_crs('EPSG:3857').plot(figsize=(9, 9)) +cx.add_basemap(ax) + +There are a few important things to note about the above code. + +- We use `matplotlib` to define the plot canvas as `ax`. +- We then add the contextily basemap to the map with the code `cx.add_basemap(ax)` + +Additionally, we **dynamically transform the coordinate reference system**, or CRS, of the Berkeley geodataframe from geographic lat/lon coordinates to `web mercator` using the method **to_crs('EPSG:3857')**. [Web mercator](https://en.wikipedia.org/wiki/Web_Mercator_projection) is the default CRS used by all web map tilesets. It is referenced by a the code `EPSG:3857` where [EPSG](https://en.wikipedia.org/wiki/EPSG_Geodetic_Parameter_Dataset) stands for the the initials of the organization that created these codes (the European Petroleum Survey Group). + +Let's clean up the map by adding some code to change the symbology of the Berkeley city boundary. This will highlight the value of adding a basemap. + +First, let's map the boundary with out a fill color. + +berkeley.plot(edgecolor="red", facecolor="none") + +Now, let's build on those symbology options and add the contextily basemap. + +ax = berkeley.to_crs('EPSG:3857').plot(edgecolor="red", + facecolor="none", # or a color + alpha=0.95, # opacity value for colors, 0-1 + linewidth=2, # line, or stroke, thickness + figsize=(9, 9) + ) +cx.add_basemap(ax) + +## Mapping Point Data + +Let's expand on this example by mapping a point dataset of BART station locations. + +First we fetch these data from a D-Lab web mapping tutorial. + +bart_url = 'https://raw.githubusercontent.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/master/notebook_data/transportation/bart.csv' + +bart = pd.read_csv(bart_url) + +bart.head() + +### Converting Point Data in a dataframe to Geospatial Data in a geodataframe + +Because these data are in a CSV file we read them into a Pandas DataFrame. + +In order to map these data we need to convert these data to a GeoPandas GeoDataFame. To do this, we need to specify: + +- the data, here the geodataframe `bart` +- the coordinate data, here `bart['X']` and `bart['Y']` +- the CRS of the bart coordinate data, here `EPSG:4326` + +The CRS code 'EPSG:4326' stands for the World Geodectic System of 1984, or WGS84. This is the most commonly used CRS for geographic (lat/lon) coordinate data. + + +#Convert the DataFrame to a GeoDataFrame. +bart_gdf = gpd.GeoDataFrame(bart, geometry=gpd.points_from_xy(bart['lon'], + bart['lat']), + crs='EPSG:4326') + +# and take a look +bart_gdf.plot(); + +Now that we have the BART data in a geodataframe we can use the same commands as we did above to map it with a contextily basemap. + +ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9)) +cx.add_basemap(ax) + +We have the full range of `matplotlib` style options to enhance the map, a few of which are shown in the example below. + +ax = bart_gdf.to_crs('EPSG:3857').plot( + color="red", + edgecolor="black", + markersize=50, + figsize=(9, 9)) + +ax.set_title('Bay Area Bart Stations') +cx.add_basemap(ax) + +## Changing the Basemap + +By default `contextiley` returns maptiles from the OpenStreetmap Mapnik basemap. However, ther are other available tilesets from different providers. These tilesets are stored in the contextily `cx.providers` dictionary. + +That's a large dictionary and you can view it. Alternatively, and more simply, you can access the list of the providers in this dictionary using the command `cs.providers.keys`. + +# change basemap - can be one of these +# first see available provider names +cx.providers.keys() + + +Once you have the list of providers, you can find the names of their specific tilesets. + +Below, we retrieve the list of the tilesets available from the provider `CartoDB`. + +# Then find the names of the tile sets for a specific provider +cx.providers.CartoDB.keys() + +Now we can specify a different tileset using the **source** argument to the `add_basemap` method. + +cx.providers.Esri.keys() + +# Change the basemap provider and tileset +ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9)) +cx.add_basemap(ax, source=cx.providers.NASAGIBS.ModisTerraTrueColorCR) + +## Learning More + +Above, we prove a very short introduction to the excellent `contextily` library. You can find more detailed information on the `contextily` homepage, available at: [https://github.com/geopandas/contextily](https://github.com/geopandas/contextily). We especially encourage you to check out the notebook examples provided in that github repo. + + diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_10_0.png b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_10_0.png new file mode 100644 index 0000000..618e085 Binary files /dev/null and b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_10_0.png differ diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_12_0.png b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_12_0.png new file mode 100644 index 0000000..b25a1de Binary files /dev/null and b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_12_0.png differ diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_14_1.png b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_14_1.png new file mode 100644 index 0000000..e4cee7a Binary files /dev/null and b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_14_1.png differ diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_16_0.png b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_16_0.png new file mode 100644 index 0000000..cf67485 Binary files /dev/null and b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_16_0.png differ diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_22_0.png b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_22_0.png new file mode 100644 index 0000000..3695e05 Binary files /dev/null and b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_22_0.png differ diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_24_0.png b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_24_0.png new file mode 100644 index 0000000..c2e4ee9 Binary files /dev/null and b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_24_0.png differ diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_26_0.png b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_26_0.png new file mode 100644 index 0000000..2eab0b9 Binary files /dev/null and b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_26_0.png differ diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_33_2.png b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_33_2.png new file mode 100644 index 0000000..dc6906c Binary files /dev/null and b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_33_2.png differ diff --git a/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_5_0.png b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_5_0.png new file mode 100644 index 0000000..c113802 Binary files /dev/null and b/_build/jupyter_execute/lessons/11_OPTIONAL_Basemap_with_Contextily_5_0.png differ diff --git a/_build/jupyter_execute/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb b/_build/jupyter_execute/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb new file mode 100644 index 0000000..a073bc0 --- /dev/null +++ b/_build/jupyter_execute/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb @@ -0,0 +1,2042 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 12. Interactive Mapping with Folium\n", + "\n", + "In previous lessons we used `Geopandas` and `matplotlib` to create choropleth and point maps of our data. In this notebook we will take it to the next level by creating `interactive maps` with the **folium** library. \n", + "\n", + "\n", + "\n", + ">### References\n", + ">\n", + ">This notebook provides an introduction to `folium`. To see what else you can do, check out the references listed below.\n", + ">\n", + "> - [Folium web site](https://github.com/python-visualization/folium)\n", + ">\n", + "> - [Folium notebook examples](https://nbviewer.jupyter.org/github/python-visualization/folium/tree/master/examples/)\n", + "\n", + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline \n", + "\n", + "import folium # popular python web mapping tool for creating Leaflet maps\n", + "import folium.plugins\n", + "\n", + "# Supress minor warnings about the syntax of CRS definitions, \n", + "# ie \"init=epsg:4269\" vs \"epsg:4269\"\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Check your version of `folium` and `geopandas`.\n", + "\n", + "Folium is a new and evolving Python library so make sure you have version 0.10.1 or later installed." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unknown\n" + ] + } + ], + "source": [ + "print(folium.__version__) # Make sure you have version 0.10.1 or later of folium!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9.0\n" + ] + } + ], + "source": [ + "print(gpd.__version__) # Make sure you have version 0.7.0 or later of GeoPandas!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 12.1 Introduction\n", + "\n", + "Interactive maps serve two very important purposes in geospatial analysis. First, they provde new tools for exploratory data analysis. With an interactive map you can:\n", + "- `pan` over the mapped data, \n", + "- `zoom` into a smaller arear that is not easily visible when the full extent of the map is displayed, and \n", + "- `click` on or `hover` over a feature to see more information about it.\n", + "\n", + "Second, when saved and shared, interactive maps provide a new tool for communicating the results of your analysis and for inviting your online audience to actively explore your work.\n", + "\n", + "For those of you who work with tools like ArcGIS or QGIS, interactive maps also make working in the jupyter notebook environment a bit more like working in a desktop GIS.\n", + "\n", + "The goal of this notebook is to show you how to create an interactive map with your geospatial data so that you can better analyze your data and save your output to share with others. \n", + "\n", + "After completing this lesson you will be able to create an interactive map like the one shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.2 Interactive Mapping with Folium\n", + "\n", + "Under the hood, `folium` is a Python package for creating interactive maps with [Leaflet](https://leafletjs.com), a popular javascript web mapping library. \n", + "\n", + "Let's start by creating a interactive map with the `folium.Map` function and display it in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a new folium map and save it to the variable name map1\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " width=\"100%\", # the width & height of the output map\n", + " height=500, # in pixels (int) or in percent of available space (str)\n", + " zoom_start=13) # the zoom level for the data to be displayed (3-20)\n", + "\n", + "map1 # display the map in the notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's discuss the map above and the code we used to generate it.\n", + "\n", + "At any time you can enter the following command to get help with `folium.Map`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment to see help docs\n", + "?folium.Map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make another folium map using the code below:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new folium map and save it to the variable name map1\n", + "#\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " #width=800, # the width & height of the output map\n", + " #height=600, # in pixels or in percent of available space\n", + " zoom_start=13) # the zoom level for the data to be displayed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "- What's new in the code?\n", + "\n", + "- How do you think that will change the map?\n", + "\n", + "Let's display the map and see what changes..." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "map1 # display map in notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the map changes when you change the underlying **tileset** from the default, which is `OpenStreetMap`, to `CartoDB Positron`. \n", + "> [OpenStreetMap](https://www.openstreetmap.org/#map=5/38.007/-95.844) is the largest free and open source dataset of geographic information about the world. So it is the default basemap for a lot of mapping tools and libraries.\n", + "\n", + "- You can find a list of the available tilesets you can use in the help documentation (`folium.Map?`), a snippet of which is shown below:\n", + "\n", + "
\n",
+    "Generate a base map of given width and height with either default\n",
+    "tilesets or a custom tileset URL. The following tilesets are built-in\n",
+    "to Folium. Pass any of the following to the \"tiles\" keyword:\n",
+    "\n",
+    "    - \"OpenStreetMap\"\n",
+    "    - \"Mapbox Bright\" (Limited levels of zoom for free tiles)\n",
+    "    - \"Mapbox Control Room\" (Limited levels of zoom for free tiles)\n",
+    "    - \"Stamen\" (Terrain, Toner, and Watercolor)\n",
+    "    - \"Cloudmade\" (Must pass API key)\n",
+    "    - \"Mapbox\" (Must pass API key)\n",
+    "    - \"CartoDB\" (positron and dark_matter)\n",
+    "
\n", + "\n", + "\n", + "#### Exercise\n", + "\n", + "Take a few minutes to try some of the different tilesets in the code below and see how they change the output map. *Avoid the ones that don't require an API key*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make changes to the code below to change the folium Map\n", + "## Try changing the values for the zoom_start and tiles parameters.\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='Stamen Watercolor', # basemap aka baselay or tile set\n", + " width=800, # the width & height of the output map\n", + " height=500, # in pixels or percent of available space\n", + " zoom_start=13) # the zoom level for the data to be displayed\n", + "\n", + "#display the map\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.3 Adding a Map Layer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have created a folium map, let's add our California County data to the map. \n", + "\n", + "First, let's read that data into a Geopandas geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Alameda county census tract data with the associated ACS 5yr variables.\n", + "ca_counties_gdf = gpd.read_file(\"notebook_data/california_counties/CaliforniaCounties.shp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Take another brief look at the geodataframe to recall the contents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# take a look at first two rows\n", + "ca_counties_gdf.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# take a look at all column names\n", + "ca_counties_gdf.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding a layer with folium.GeoJson\n", + "\n", + "Folium provides a number of ways to add vector data - points, lines, and polygons - to a map. \n", + "\n", + "The data we are working with are in Geopandas geodataframes. The main folium function for adding these to the map is `folium.GeoJson`.\n", + "\n", + "Let's build on our last map and add the census tracts as a `folium.GeoJson` layer. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB positron', # basemap aka baselay or tile set\n", + " width=800, # the width & height of the output map\n", + " height=600, # in pixels or in percent of available space\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts to the map\n", + "folium.GeoJson(ca_counties_gdf).add_to(map1)\n", + "\n", + "#display the map\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That was pretty straight-forward, but `folium.GeoJSON` provides a lot of arguments for customizing the display of the data in the map. We will review some of these soon. However, at any time you can get more information about `folium.GeoJSON` by taking a look at the function documentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view documentation\n", + "# folium.GeoJson?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking and Transforming the CRS\n", + "\n", + "It's always a good idea to check the **CRS** of your geodata before doing anything with that data. This is true when we use `folium` to make an interactive map. \n", + "\n", + "Here is how folium deals with the CRS of a geodataframe before mapping it:\n", + "- Folium checks to see if the gdf has a defined CRS\n", + " - If the CRS is not defined, it assumes the data to be in the WGS84 CRS (epsg=4326).\n", + " - If the CRS is defined, it will be transformed dynamically to WGS84 before mapping.\n", + "\n", + "\n", + "So, if your map data doesn't show up where at all or where you think it should, check the CRS of your data!\n", + "- If it is not defined, define it.\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "- What is the CRS of the tract data?\n", + "- How is folium dealing with the CRS of this gdf?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check the CRS of the data \n", + "print(...)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for answers*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Styling features with `folium.GeoJson`\n", + "\n", + "Let's dive deeper into the `folium.GeoJson` function. Below is an excerpt from the help documentation for the function that shows all the available function arguments that we can set.\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "What argument do we use to style the color for our polygons?\n", + "\n", + "
\n",
+    "folium.GeoJson(\n",
+    "    data,\n",
+    "    style_function=None,\n",
+    "    highlight_function=None,\n",
+    "    name=None,\n",
+    "    overlay=True,\n",
+    "    control=True,\n",
+    "    show=True,\n",
+    "    smooth_factor=None,\n",
+    "    tooltip=None,\n",
+    "    embed=True,\n",
+    ")\n",
+    "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's examine the options for the `style_function` in more detail since we will use these to change the style of our mapped data.\n", + "\n", + "\n", + "`style_function = lambda x: {` apply to all features being mapped (ie, all rows in the geodataframe) \n", + "`'weight': line_weight,` set the thickness of a line or polyline where <1 is thin, >1 thick, 1 = default \n", + "`'opacity': line_opacity,` set opacity where 1 is solid, 0.5 is semi-opaque and 0 is transparent \n", + "`'color': line_color` set the color of the line, eg \"red\" or some hexidecimal color value\n", + "`'fillOpacity': opacity,` set opacity of the fill of a polygon \n", + "`'fillColor': color` set color of the fill of a polygon \n", + "`'dashArray': '5, 5'` set line pattern to a dash of 5 pixels on, off \n", + "`}`\n", + "\n", + "\n", + "\n", + "Ok! Let's try setting the style of our census tract by defining a style function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts gdf layer\n", + "# setting the style of the data\n", + "folium.GeoJson(ca_counties_gdf,\n", + " style_function = lambda x: {\n", + " 'weight':2,\n", + " 'color':\"white\",\n", + " 'opacity':1,\n", + " 'fillColor':\"red\",\n", + " 'fillOpacity':0.6\n", + " }\n", + " ).add_to(map1)\n", + "\n", + "\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "Copy the code from our last map and paste it below. Take a few minutes edit the code to change the style of the census tract polygons.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='Stamen Watercolor',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts gdf layer\n", + "# setting the style of the data\n", + "folium.GeoJson(ca_counties_gdf,\n", + " style_function = lambda x: {\n", + " 'weight':3,\n", + " 'color':\"black\",\n", + " 'opacity':1,\n", + " 'fillColor':\"none\",\n", + " 'fillOpacity':0.6\n", + " }\n", + " ).add_to(map1)\n", + "\n", + "\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding a Tooltip\n", + "\n", + "A `tooltip` can be added to a folium.GeoJson map layer to display data values when the mouse hovers over a feature.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Double check what columns we have\n", + "ca_counties_gdf.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?folium.GeoJsonTooltip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts gdf layer\n", + "folium.GeoJson(ca_counties_gdf,\n", + " style_function = lambda x: {\n", + " 'weight':2,\n", + " 'color':\"white\",\n", + " 'opacity':1,\n", + " 'fillColor':\"red\",\n", + " 'fillOpacity':0.6\n", + " },\n", + " \n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['NAME','POP2012','POP12_SQMI' ], \n", + " aliases=['County', 'Population', 'Population Density (mi2)'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map1)\n", + "\n", + "\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As always, you can get more help by reading the documentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view help\n", + "#folium.GeoJsonTooltip?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "\n", + "Edit the code in the cell below to `add` the median age(`MED_AGE`) to the tooltip." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts gdf layer\n", + "folium.GeoJson(ca_counties_gdf,\n", + " style_function = lambda x: {\n", + " 'weight':2,\n", + " 'color':\"white\",\n", + " 'opacity':1,\n", + " 'fillColor':\"red\",\n", + " 'fillOpacity':0.6\n", + " },\n", + " \n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['NAME','POP2012','POP12_SQMI','MED_AGE' ], \n", + " aliases=['County', 'Population', 'Population Density (mi2)', 'Median Age'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map1)\n", + "\n", + "\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for answers*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.4 Data Mapping\n", + "\n", + "Above, we set the style for all of the census tracts to the same fill and outline colors and opacity values. \n", + "\n", + "Let's take a look at how we would use the `data values` to set the color values for the polygons. This is called a `choropleth` map or, more generally, a `thematic map`.\n", + "\n", + "The `folium.Choropleth` function can be used for this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view help docs\n", + "## folium.Choropleth?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `folium.Choropleth`, we will use some of the same style parameters that we used with `folium.GeoJson`.\n", + "\n", + "We will also use some new parameters, as shown below.\n", + "\n", + "First, let's take a look at the data we will map to refresh our knowledge." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(ca_counties_gdf.columns)\n", + "ca_counties_gdf.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create a choropleth map of total population, which is in the `c_race` column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ca_counties_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map2 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "\n", + "# Add the Choropleth layer\n", + "folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), # The object with the geospatial data\n", + " data=ca_counties_gdf, # The object with the attribute data (can be same)\n", + " columns=['NAME','POP2012'], # the ID and data columns in the data objects\n", + " key_on=\"feature.id\", # the ID in the geo_data object (don't change)\n", + " fill_color=\"Reds\", # The color palette (or color map) - see help\n", + " fill_opacity=0.65,\n", + " line_color=\"grey\",\n", + " legend=True,\n", + " legend_name=\"Population\",\n", + " ).add_to(map2)\n", + "\n", + "# Display the map\n", + "map2 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choropleth Mapping with Folium - discussion\n", + "\n", + "Let's discuss the following lines from the code above in more detail.\n", + "\n", + "
\n",
+    "# Add the Choropleth layer\n",
+    "folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),\n",
+    "           data=ca_counties_gdf, \n",
+    "           columns=['NAME','POP2012'],\n",
+    "           key_on=\"feature.id\",\n",
+    "           fill_color=\"Reds\",                               \n",
+    "           ...)\n",
+    "\n",
+    "\n",
+    "
\n", + "\n", + "`geo_data` and the `data`: we need to identify the objects that contains both because they could be different objects. In our example they are in the same object.\n", + "\n", + "`ca_counties_gdf.set_index('NAME')`: We need to **set_index('NAME')** in order to identify the column in `geo_data` that will be used to `join` the geometries in the `geo_data` to the data values in `data`.\n", + "\n", + "`columns=['NAME','POP2012']`: we identify in `data` (1) the column that will join these `data` to `geo_data` and (2) the second column is the column with the values that will determine the color.\n", + "\n", + "`fill_color=\"Reds\":` Here we identify the name of the color palette that we will use to style the polygons. These will be the same as the `matplotlib` colormaps.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question\n", + "Recall our discussion about best practices for choropleth maps. Is population count an appropriate variable to plot as a choropleth? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your thoughts here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "\n", + "Copy and paste the code from above into the cell below to create a choropleth map of population density (`POP12_SQMI`).\n", + "\n", + "Feel free to experiment with any of the `folium.Choropleth` style parameters, especially the `fill_color` which needs to be one of the `color brewer palettes` listed below:\n", + "\n", + "
\n",
+    "fill_color: string, default 'blue'\n",
+    "    Area fill color. Can pass a hex code, color name, or if you are\n",
+    "    binding data, one of the following color brewer palettes:\n",
+    "    'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',\n",
+    "    'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.\n",
+    "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here\n", + "# Define the basemap\n", + "map2 = folium.Map(location=[37.7749, -122.4194], # lat, lon around which to center the map\n", + " tiles='Stamen Toner',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "\n", + "# Add the Choropleth layer \n", + "folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), # The object with the geospatial data\n", + " data=ca_counties_gdf, # The object with the attribute data (can be same)\n", + " columns=['NAME','POP12_SQMI'], # the ID and data columns in the data objects\n", + " key_on=\"feature.id\", # the ID in the geo_data object (don't change)\n", + " fill_color=\"RdPu\", # The color palette (or color map) - see help\n", + " fill_opacity=0.8).add_to(map2)\n", + "\n", + "map2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for answers*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choropleth Maps with Tooltips\n", + "\n", + "You can add a `tooltip` to a folium.Choropleth map but the process is not straigthforward. The `folium.Choropleth` function does not have a tooltip argument the way `folium.GeoJson` does.\n", + "\n", + "The workaround is to add the layer as both a `folium.Choropleth` layer and as a `folium.GeoJson` layer and bind the tooltip to the GeoJson layer.\n", + "\n", + "Let's check it out below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map3 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "\n", + "# Add the Choropleth layer\n", + "folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), # The object with the geospatial data\n", + " data=ca_counties_gdf, # The object with the attribute data (can be same)\n", + " columns=['NAME','POP2012'], # the ID and data columns in the data objects\n", + " key_on=\"feature.id\", # the ID in the geo_data object (don't change)\n", + " fill_color=\"Reds\", # The color palette (or color map) - see help\n", + " fill_opacity=0.65,\n", + " line_color=\"grey\",\n", + " legend=True,\n", + " legend_name=\"Population\",\n", + " ).add_to(map3)\n", + "\n", + "# ADD the same geodataframe to the map to display a tooltip\n", + "layer2 = folium.GeoJson(ca_counties_gdf,\n", + " style_function=lambda x: {'color':'transparent','fillColor':'transparent'},\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['NAME','POP2012'], \n", + " aliases=['County', 'Population'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " highlight_function=lambda x: {'weight':3,'color':'white'}\n", + ").add_to(map3)\n", + "\n", + "\n", + "\n", + "map3 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question \n", + "Do you notice anything different about the `style_function` for layer2 above?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "Redo the above choropleth map code to map population density. Add both population and population density to the tooltip. Don't forget to update the legend name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.5 Overlays\n", + "\n", + "We can overlay other geospatial data on our folium maps.\n", + "\n", + "Let's say we want to focus the previous choropleth map with tooltips (`map3`) on the City of Berkeley. We can fetch the border of the city from our census Places dataset. These data can be downloaded from the Census website. We use the cartographic boundary files not the TIGER line files as these look better on a map (clipped to shoreline). \n", + "\n", + "Specifically, we will fetch the city boundaries from the following census cartographic boundary file:\n", + "\n", + "- https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_place_500k.zip\n", + "\n", + "Then we can overlay the border of the city on the map and set the initial zoom to the center of the Berkeley boundary.\n", + "\n", + "Let's try that.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to read in the census places data and create a subset geodataframe for our city of interest, here Berkeley." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "places = gpd.read_file(\"zip://notebook_data/census/Places/cb_2018_06_place_500k.zip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "places.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley = places[places.NAME=='Berkeley'].copy()\n", + "berkeley.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the Berkeley geodataframe to make sure it looks ok." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "berkeley.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Create a new map centered on Berkeley\n", + "berkeley_map = folium.Map(location=[berkeley.centroid.y.mean(), \n", + " berkeley.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " width=800,height=600,\n", + " zoom_start=13)\n", + "\n", + "\n", + "# Add the census tract polygons as a choropleth map\n", + "layer1=folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),\n", + " data=ca_counties_gdf,\n", + " columns=['NAME','POP2012'],\n", + " fill_color=\"Reds\",\n", + " fill_opacity=0.65,\n", + " line_color=\"grey\", #\"white\",\n", + " line_weight=1,\n", + " line_opacity=1,\n", + " key_on=\"feature.id\",\n", + " legend=True,\n", + " legend_name=\"Population\",\n", + " highlight=True\n", + " ).add_to(berkeley_map)\n", + "\n", + "# Add the berkeley boundary - note the fill color\n", + "layer2 = folium.GeoJson(data=berkeley,\n", + " name='Berkeley',smooth_factor=2,\n", + " style_function=lambda x: {'color':'black',\n", + " 'opacity':1,\n", + " 'fillColor':\n", + " 'transparent',\n", + " 'weight':3},\n", + " ).add_to(berkeley_map)\n", + "\n", + "# Add the tooltip for the census tracts as its own layer\n", + "layer3 = folium.GeoJson(ca_counties_gdf,\n", + " style_function=lambda x: {'color':'transparent','fillColor':'transparent'},\n", + " tooltip=folium.features.GeoJsonTooltip(\n", + " fields=['NAME','POP2012'], \n", + " aliases=['County', 'Population'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " highlight_function=lambda x: {'weight':3,'color':'white'}\n", + ").add_to(berkeley_map)\n", + "\n", + "berkeley_map # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "Any questions about the above map?\n", + "\n", + "Does the code for the Berkeley map above differ from our previous choropleth map code?\n", + "\n", + "Does the order of layer2 & layer3 matter (can they be switched?)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "\n", + "Redo the above map with population density. Create and display the Oakland city boundary on the map instead of Berkeley and center the map on Oakland." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for solution*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.6 Mapping Points and Lines\n", + "\n", + "We can also add points and lines to a folium map.\n", + "\n", + "Let's overlay BART stations as points and BART lines as lines to the interactive map. For the Bay Area these are data are available from the [Metropoliton Transportation Commission (MTC) Open Data portal](http://opendata.mtc.ca.gov/datasets).\n", + "\n", + "We're going to try pulling in BART station data that we downloaded from the website and subsetted from the passenger-rail-stations. You can learn more about the dataset through here: http://opendata.mtc.ca.gov/datasets/passenger-rail-stations-2019\n", + "\n", + "As usual, let's try pulling in the data and inspect the first couple of rows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load light rail stop data\n", + "railstops = gpd.read_file(\"zip://notebook_data/transportation/Passenger_Rail_Stations_2019.zip\") \n", + "railstops.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Subset to keep just bart stations\n", + "bart_stations = railstops[railstops['agencyname']=='BART'].sort_values(by=\"station_na\")\n", + "bart_stations.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Repeat for the rail lines\n", + "rail_lines = gpd.read_file(\"zip://notebook_data/transportation/Passenger_Railways_2019.zip\") \n", + "rail_lines.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rail_lines.operator.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subset by operator to get the bart lines\n", + "bart_lines = rail_lines[rail_lines['operator']=='BART']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Check the CRS of the geodataframes\n", + "print(bart_stations.crs)\n", + "print(bart_lines.crs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Quick plot\n", + "bart_stations.plot()\n", + "bart_lines.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have fetched and checked the Bart data, let's do a quick folium map with it.\n", + "\n", + "We will use `folium.GeoJson` to add these data to the map, just as we used it previously for the census tract polygons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bart Map\n", + "map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " width=800,height=600,\n", + " zoom_start=10)\n", + "\n", + "\n", + "folium.GeoJson(bart_lines).add_to(map4)\n", + "\n", + "folium.GeoJson(bart_stations).add_to(map4)\n", + "\n", + "\n", + "map4 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also add tooltips, just as we did previously." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bart Map\n", + "map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " #width=800,height=600,\n", + " zoom_start=10)\n", + "\n", + "# Add Bart lines\n", + "folium.GeoJson(bart_lines,\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['operator' ],\n", + " aliases=['Line operator'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map4)\n", + "\n", + "# Add Bart stations\n", + "folium.GeoJson(bart_stations,\n", + " tooltip=folium.GeoJsonTooltip(fields=['ts_locatio'], \n", + " aliases=['Stop Name'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map4)\n", + "\n", + "\n", + "map4 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's pretty cool, but don't you just want to click on those marker points to get a `popup` rather than hovering over for a `tooltip`?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mapping Points\n", + "\n", + "So far we have used `folium.GeoJson` to map our BART points. By default this uses the push-pin marker symbology made popular by Google Maps. \n", + "\n", + "Under the hood, folium.GeoJson uses the default object type `folium.Marker` when the input data are points.\n", + "\n", + "This is helpful to know because `folium.Marker` has a few options that allow further customization of our points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view help docs\n", + "folium.Marker?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's explicitly add the Bart Stations as points so we can change the `tooltips` to `popups`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Bart Map\n", + "map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " #width=800,height=800,\n", + " zoom_start=10)\n", + "\n", + "# Add Bart lines\n", + "folium.GeoJson(bart_lines,\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['operator' ],\n", + " aliases=['Line operator'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map4)\n", + "\n", + "# Add Bart stations\n", + "bart_stations.apply(lambda row:\n", + " folium.Marker(\n", + " location=[row['geometry'].y, row['geometry'].x],\n", + " popup=row['ts_locatio'],\n", + " ).add_to(map4), axis=1)\n", + "\n", + "map4 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That `folium.Marker` code is a bit more complex than `folium.GeoJson` and may not be worth it unless you really want that popup behavior.\n", + "\n", + "But let's see what else we can do with a `folium.Marker` by viewing the next map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Bart Map\n", + "map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " #width=800,height=600,\n", + " zoom_start=10)\n", + "\n", + "# Add BART lines\n", + "folium.GeoJson(bart_lines,\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['operator' ],\n", + " aliases=['Line operator'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map4)\n", + "\n", + "# Add BART Stations\n", + "icon_url = \"https://gomentumstation.net/wp-content/uploads/2018/08/Bay-area-rapid-transit-1000.png\"\n", + "bart_stations.apply(lambda row:\n", + " folium.Marker(\n", + " location=[row['geometry'].y,row['geometry'].x],\n", + " popup=row['ts_locatio'],\n", + " icon=folium.features.CustomIcon(icon_url,icon_size=(20, 20)),\n", + " ).add_to(map4), axis=1)\n", + "\n", + "map4 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "\n", + "Copy and paste the code for the previous cell into the next cell and \n", + "1. change the bart icon to \"https://ya-webdesign.com/transparent450_/train-emoji-png-14.png\"\n", + "2. change the popup back to a tooltip." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for solution*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### folium.CircleMarkers\n", + "\n", + "You may prefer to customize points as `CircleMarkers` instead of the icon or pushpin Marker style. This allows you to set size and color of a marker, either manually or as a function of a data variable.\n", + "\n", + "Let's look at some code for doing this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " #width=1000, # the width & height of the output map\n", + " #height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "# Add BART Lines\n", + "folium.GeoJson(bart_lines).add_to(map5)\n", + "\n", + "\n", + "# Add BART Stations\n", + "bart_stations.apply(lambda row:\n", + " folium.CircleMarker(\n", + " location=[row['geometry'].y, row['geometry'].x],\n", + " radius=10,\n", + " color='purple',\n", + " fill=True,\n", + " fill_color='purple',\n", + " popup=row['ts_locatio'],\n", + " ).add_to(map5), \n", + " axis=1)\n", + "\n", + "\n", + "map5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### folium.Circle \n", + "\n", + "You can also set the size of your circles to a fixed radius, in meters, using `folium.Circle`. This is great for exploratory data analysis. For example, you can see what the census tract values are within 500 meters of a BART station." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view\n", + "#?folium.Circle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " #width=1000, # the width & height of the output map\n", + " #height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "# Add BART Lines\n", + "folium.GeoJson(bart_lines).add_to(map5)\n", + "\n", + "\n", + "# Add BART Stations\n", + "bart_stations.apply(lambda row:\n", + " folium.Circle(\n", + " location=[row['geometry'].y, row['geometry'].x],\n", + " radius=500,\n", + " color='purple',\n", + " fill=True,\n", + " fill_color='purple',\n", + " popup=row['ts_locatio'],\n", + " ).add_to(map5), \n", + " axis=1)\n", + "\n", + "\n", + "map5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What do you notice about the size of the circles as you zoom in/out when you compare folium.Circles and folium.CircleMarkers?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Proportional Symbol Maps\n", + "\n", + "One of the advantages of the `folium.CircleMarker` is that we can set the size of the map to vary based on a data value.\n", + "\n", + "To give this a try, let's add a fake column to the `bart_stations` gdf called millions_served and set it to a value between 1 and 10." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# add a column to the bart stations gdf\n", + "bart_stations['millions_served'] = np.random.randint(1,10, size=len(bart_stations))\n", + "bart_stations.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()],\n", + " tiles='CartoDB Positron',\n", + " #width=1000, # the width & height of the output map\n", + " #height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "folium.GeoJson(bart_lines).add_to(map5)\n", + "\n", + "# Add BART Stations as CircleMarkers\n", + "# Here, some knowlege of Python string formatting is useful\n", + "bart_stations.apply(lambda row:\n", + " folium.CircleMarker(\n", + " location=[row['geometry'].y, row['geometry'].x],\n", + " radius=row['millions_served'],\n", + " color='purple',\n", + " fill=True,\n", + " fill_color='purple',\n", + " tooltip = \"Bart Station: %s
Millions served: %s\" % (row['ts_locatio'], row['millions_served'])\n", + " \n", + " ).add_to(map5), axis=1)\n", + "map5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So if you hover over our BART stations, you see that we've formatted it nicely! Using some HTML and Python string formatting we can make our `tooltip` easier to read. \n", + "\n", + "If you want to learn more about customizing these, you can [go check this out to learn HTML basics](https://www.w3schools.com/html/html_basic.asp). You can then [go here to learn about Python string formatting](https://python-reference.readthedocs.io/en/latest/docs/str/formatting.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.7 Creating and Saving a folium Interactive Map\n", + "\n", + "Now that you have seen most of the ways you can add a geodataframe to a folium map, let's create one big map that includes several of our geodataframes.\n", + "\n", + "To control the display of the data layers, we will add a `folium.LayerControl`\n", + "\n", + "- A `folium.LayerControl` will allow you to toggle on/off a map's visible layers. \n", + "\n", + "- In order to add a layer to the LayerControl, the layer must have value set for its `name`.\n", + "\n", + "Let's take a look. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new map centered on the census tract data\n", + "map6 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " #width=800,height=600,\n", + " zoom_start=10)\n", + "\n", + "# Add the counties polygons as a choropleth map\n", + "layer1=folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),\n", + " data=ca_counties_gdf,\n", + " columns=['NAME','POP2012'],\n", + " fill_color=\"Reds\",\n", + " fill_opacity=0.65,\n", + " line_color=\"grey\", #\"white\",\n", + " line_weight=1,\n", + " line_opacity=1,\n", + " key_on=\"feature.id\",\n", + " legend=True,\n", + " legend_name=\"Population\",\n", + " highlight=True,\n", + " name=\"Counties\"\n", + " ).add_to(map6)\n", + "\n", + "# Add the tooltip for the counties as its own layer\n", + "# Don't display in the Layer control!\n", + "layer2 = folium.GeoJson(ca_counties_gdf,\n", + " style_function=lambda x: {'color':'transparent','fillColor':'transparent'},\n", + " tooltip=folium.features.GeoJsonTooltip(\n", + " fields=['NAME','POP2012'], \n", + " aliases=['Name', 'Population'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " highlight_function=lambda x: {'weight':3,'color':'white'}\n", + ").add_to(layer1.geojson)\n", + "\n", + "# Add Bart lines\n", + "folium.GeoJson(bart_lines,\n", + " name=\"Bart Lines\",\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['operator' ],\n", + " aliases=['Line operator'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map6)\n", + "\n", + "\n", + "# Add Bart stations\n", + "folium.GeoJson(bart_stations,\n", + " name=\"Bart stations\",\n", + " tooltip=folium.GeoJsonTooltip(fields=['ts_locatio' ], \n", + " aliases=['Stop Name'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map6)\n", + "\n", + "# ADD LAYER CONTROL\n", + "folium.LayerControl(collapsed=False).add_to(map6)\n", + "\n", + "map6 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Take a look at the help docs `folium.LayerControl?`. What parameter would move the location of the LayerControl? What parameter would allow it to be closed by default?\n", + "\n", + "2. Take a look at the way we added `layer2` above (this has the census tract tooltips). How has the code we use to add the layer to the map changed? Why do you think we made this change?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view\n", + "#folium.LayerControl?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving to an html file\n", + "\n", + "By saving our map to a html we can use it later as something to add to a website or email to a colleague.\n", + "\n", + "You can save any of the maps you have in the notebook using this syntax:\n", + "\n", + "> map_name.save(\"file_name.html\")\n", + "\n", + "Let's try that." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "map6.save('outdata/bartmap.html')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find your html file on your computer and double-click on it to open it in a browser." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extra Challenge\n", + "\n", + "Check out the notebook examples and find one to try with the data we have used in this notebook. I recommend the following.\n", + "\n", + "- [Mini-maps](https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/MiniMap.ipynb)\n", + "- [Dual-map](https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/plugin-DualMap.ipynb) (choropleth maps two census tract vars)\n", + "- [Search](https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/plugin-Search.ipynb) (e.g., for a Bart Station by name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.8 Recap\n", + "Here we learned about the wonderful world of `Folium`! We created interactive maps-- whether it be choropleth, points, lines, symbols... we mapped it all. \n", + "\n", + "Below you'll find a list of key functionalities we learned:\n", + "- Interactive mapping\n", + "\t- `folium.Map()`\n", + "- Adding a map layer\n", + "\t- `.add_to()`\n", + "\t- `folium.Choropleth()`\n", + "\t\t- `geo_data`\n", + "\t\t- `columns`\n", + "\t\t- `fill_color`\n", + "\t- `folium.GeoJson()`\n", + "\t\t- `style_function`\n", + "\t- `folium.Marker()`\n", + "\t\t- `icon`\n", + "\t- `folium.CircleMarker()`\n", + "\t\t- `radius`\n", + "- Adding a Tooltip\n", + "\t- `folium.GeoJsonTooltip`\n", + "\t- `folium.features.GeoJsonTooltip`\n", + "- Adding layer control\n", + "\t- `folium.LayerControl()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Important note\n", + "\n", + "The folium library changes often so I recommend you update your package frequently. This will give you increased functionality and may make future code easier to write. However, it might cause your existing code to break.\n", + "\n", + "### References\n", + "\n", + "This notebook provides an introduction to `folium`. To see what else you can do, check out the references listed below.\n", + "\n", + "- [Folium web site](https://github.com/python-visualization/folium)\n", + "\n", + "- [Folium notebook examples](https://nbviewer.jupyter.org/github/python-visualization/folium/tree/master/examples/)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.py b/_build/jupyter_execute/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.py new file mode 100644 index 0000000..0916884 --- /dev/null +++ b/_build/jupyter_execute/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.py @@ -0,0 +1,1238 @@ +# 12. Interactive Mapping with Folium + +In previous lessons we used `Geopandas` and `matplotlib` to create choropleth and point maps of our data. In this notebook we will take it to the next level by creating `interactive maps` with the **folium** library. + + + +>### References +> +>This notebook provides an introduction to `folium`. To see what else you can do, check out the references listed below. +> +> - [Folium web site](https://github.com/python-visualization/folium) +> +> - [Folium notebook examples](https://nbviewer.jupyter.org/github/python-visualization/folium/tree/master/examples/) + +### Import Libraries + +import pandas as pd +import geopandas as gpd +import numpy as np + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +import folium # popular python web mapping tool for creating Leaflet maps +import folium.plugins + +# Supress minor warnings about the syntax of CRS definitions, +# ie "init=epsg:4269" vs "epsg:4269" +import warnings +warnings.simplefilter(action='ignore', category=FutureWarning) + +#### Check your version of `folium` and `geopandas`. + +Folium is a new and evolving Python library so make sure you have version 0.10.1 or later installed. + +print(folium.__version__) # Make sure you have version 0.10.1 or later of folium! + +print(gpd.__version__) # Make sure you have version 0.7.0 or later of GeoPandas! + +## 12.1 Introduction + +Interactive maps serve two very important purposes in geospatial analysis. First, they provde new tools for exploratory data analysis. With an interactive map you can: +- `pan` over the mapped data, +- `zoom` into a smaller arear that is not easily visible when the full extent of the map is displayed, and +- `click` on or `hover` over a feature to see more information about it. + +Second, when saved and shared, interactive maps provide a new tool for communicating the results of your analysis and for inviting your online audience to actively explore your work. + +For those of you who work with tools like ArcGIS or QGIS, interactive maps also make working in the jupyter notebook environment a bit more like working in a desktop GIS. + +The goal of this notebook is to show you how to create an interactive map with your geospatial data so that you can better analyze your data and save your output to share with others. + +After completing this lesson you will be able to create an interactive map like the one shown below. + +%%html + + + +## 12.2 Interactive Mapping with Folium + +Under the hood, `folium` is a Python package for creating interactive maps with [Leaflet](https://leafletjs.com), a popular javascript web mapping library. + +Let's start by creating a interactive map with the `folium.Map` function and display it in the notebook. + +# Create a new folium map and save it to the variable name map1 +map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + width="100%", # the width & height of the output map + height=500, # in pixels (int) or in percent of available space (str) + zoom_start=13) # the zoom level for the data to be displayed (3-20) + +map1 # display the map in the notebook + +Let's discuss the map above and the code we used to generate it. + +At any time you can enter the following command to get help with `folium.Map`: + + +# uncomment to see help docs +?folium.Map + +Let's make another folium map using the code below: + +# Create a new folium map and save it to the variable name map1 +# +map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + tiles='CartoDB Positron', + #width=800, # the width & height of the output map + #height=600, # in pixels or in percent of available space + zoom_start=13) # the zoom level for the data to be displayed + +
+ +
+
+ +#### Questions +
+ +- What's new in the code? + +- How do you think that will change the map? + +Let's display the map and see what changes... + +map1 # display map in notebook + +Notice how the map changes when you change the underlying **tileset** from the default, which is `OpenStreetMap`, to `CartoDB Positron`. +> [OpenStreetMap](https://www.openstreetmap.org/#map=5/38.007/-95.844) is the largest free and open source dataset of geographic information about the world. So it is the default basemap for a lot of mapping tools and libraries. + +- You can find a list of the available tilesets you can use in the help documentation (`folium.Map?`), a snippet of which is shown below: + +
+Generate a base map of given width and height with either default
+tilesets or a custom tileset URL. The following tilesets are built-in
+to Folium. Pass any of the following to the "tiles" keyword:
+
+    - "OpenStreetMap"
+    - "Mapbox Bright" (Limited levels of zoom for free tiles)
+    - "Mapbox Control Room" (Limited levels of zoom for free tiles)
+    - "Stamen" (Terrain, Toner, and Watercolor)
+    - "Cloudmade" (Must pass API key)
+    - "Mapbox" (Must pass API key)
+    - "CartoDB" (positron and dark_matter)
+
+ + +#### Exercise + +Take a few minutes to try some of the different tilesets in the code below and see how they change the output map. *Avoid the ones that don't require an API key*. + +# Make changes to the code below to change the folium Map +## Try changing the values for the zoom_start and tiles parameters. +map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + tiles='Stamen Watercolor', # basemap aka baselay or tile set + width=800, # the width & height of the output map + height=500, # in pixels or percent of available space + zoom_start=13) # the zoom level for the data to be displayed + +#display the map +map1 + + +## 12.3 Adding a Map Layer + +Now that we have created a folium map, let's add our California County data to the map. + +First, let's read that data into a Geopandas geodataframe. + +# Alameda county census tract data with the associated ACS 5yr variables. +ca_counties_gdf = gpd.read_file("notebook_data/california_counties/CaliforniaCounties.shp") + +Take another brief look at the geodataframe to recall the contents. + +# take a look at first two rows +ca_counties_gdf.head(2) + +# take a look at all column names +ca_counties_gdf.columns + +### Adding a layer with folium.GeoJson + +Folium provides a number of ways to add vector data - points, lines, and polygons - to a map. + +The data we are working with are in Geopandas geodataframes. The main folium function for adding these to the map is `folium.GeoJson`. + +Let's build on our last map and add the census tracts as a `folium.GeoJson` layer. + +map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + tiles='CartoDB positron', # basemap aka baselay or tile set + width=800, # the width & height of the output map + height=600, # in pixels or in percent of available space + zoom_start=6) # the zoom level for the data to be displayed + +# Add the census tracts to the map +folium.GeoJson(ca_counties_gdf).add_to(map1) + +#display the map +map1 + +That was pretty straight-forward, but `folium.GeoJSON` provides a lot of arguments for customizing the display of the data in the map. We will review some of these soon. However, at any time you can get more information about `folium.GeoJSON` by taking a look at the function documentation. + +# Uncomment to view documentation +# folium.GeoJson? + +### Checking and Transforming the CRS + +It's always a good idea to check the **CRS** of your geodata before doing anything with that data. This is true when we use `folium` to make an interactive map. + +Here is how folium deals with the CRS of a geodataframe before mapping it: +- Folium checks to see if the gdf has a defined CRS + - If the CRS is not defined, it assumes the data to be in the WGS84 CRS (epsg=4326). + - If the CRS is defined, it will be transformed dynamically to WGS84 before mapping. + + +So, if your map data doesn't show up where at all or where you think it should, check the CRS of your data! +- If it is not defined, define it. + +
+ +
+
+ +#### Questions +
+ +- What is the CRS of the tract data? +- How is folium dealing with the CRS of this gdf? + +# Check the CRS of the data +print(...) + +*Click here for answers* + + + +### Styling features with `folium.GeoJson` + +Let's dive deeper into the `folium.GeoJson` function. Below is an excerpt from the help documentation for the function that shows all the available function arguments that we can set. + +
+ +
+
+ +#### Question +
+What argument do we use to style the color for our polygons? + +
+folium.GeoJson(
+    data,
+    style_function=None,
+    highlight_function=None,
+    name=None,
+    overlay=True,
+    control=True,
+    show=True,
+    smooth_factor=None,
+    tooltip=None,
+    embed=True,
+)
+
+ +Let's examine the options for the `style_function` in more detail since we will use these to change the style of our mapped data. + + +`style_function = lambda x: {` apply to all features being mapped (ie, all rows in the geodataframe) +`'weight': line_weight,` set the thickness of a line or polyline where <1 is thin, >1 thick, 1 = default +`'opacity': line_opacity,` set opacity where 1 is solid, 0.5 is semi-opaque and 0 is transparent +`'color': line_color` set the color of the line, eg "red" or some hexidecimal color value +`'fillOpacity': opacity,` set opacity of the fill of a polygon +`'fillColor': color` set color of the fill of a polygon +`'dashArray': '5, 5'` set line pattern to a dash of 5 pixels on, off +`}` + + + +Ok! Let's try setting the style of our census tract by defining a style function. + +# Define the basemap +map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + tiles='CartoDB Positron', + width=1000, # the width & height of the output map + height=600, # in pixels + zoom_start=6) # the zoom level for the data to be displayed + +# Add the census tracts gdf layer +# setting the style of the data +folium.GeoJson(ca_counties_gdf, + style_function = lambda x: { + 'weight':2, + 'color':"white", + 'opacity':1, + 'fillColor':"red", + 'fillOpacity':0.6 + } + ).add_to(map1) + + +map1 + +#### Exercise +Copy the code from our last map and paste it below. Take a few minutes edit the code to change the style of the census tract polygons. + + +# Your code here +map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + tiles='Stamen Watercolor', + width=1000, # the width & height of the output map + height=600, # in pixels + zoom_start=10) # the zoom level for the data to be displayed + +# Add the census tracts gdf layer +# setting the style of the data +folium.GeoJson(ca_counties_gdf, + style_function = lambda x: { + 'weight':3, + 'color':"black", + 'opacity':1, + 'fillColor':"none", + 'fillOpacity':0.6 + } + ).add_to(map1) + + +map1 + +### Adding a Tooltip + +A `tooltip` can be added to a folium.GeoJson map layer to display data values when the mouse hovers over a feature. + + +# Double check what columns we have +ca_counties_gdf.columns + +?folium.GeoJsonTooltip + +# Define the basemap +map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + tiles='CartoDB Positron', + width=1000, # the width & height of the output map + height=600, # in pixels + zoom_start=6) # the zoom level for the data to be displayed + +# Add the census tracts gdf layer +folium.GeoJson(ca_counties_gdf, + style_function = lambda x: { + 'weight':2, + 'color':"white", + 'opacity':1, + 'fillColor':"red", + 'fillOpacity':0.6 + }, + + tooltip=folium.GeoJsonTooltip( + fields=['NAME','POP2012','POP12_SQMI' ], + aliases=['County', 'Population', 'Population Density (mi2)'], + labels=True, + localize=True + ), + ).add_to(map1) + + +map1 + +As always, you can get more help by reading the documentation. + +# Uncomment to view help +#folium.GeoJsonTooltip? + +#### Exercise + +Edit the code in the cell below to `add` the median age(`MED_AGE`) to the tooltip. + +# Define the basemap +map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + tiles='CartoDB Positron', + width=1000, # the width & height of the output map + height=600, # in pixels + zoom_start=6) # the zoom level for the data to be displayed + +# Add the census tracts gdf layer +folium.GeoJson(ca_counties_gdf, + style_function = lambda x: { + 'weight':2, + 'color':"white", + 'opacity':1, + 'fillColor':"red", + 'fillOpacity':0.6 + }, + + tooltip=folium.GeoJsonTooltip( + fields=['NAME','POP2012','POP12_SQMI','MED_AGE' ], + aliases=['County', 'Population', 'Population Density (mi2)', 'Median Age'], + labels=True, + localize=True + ), + ).add_to(map1) + + +map1 + +*Click here for answers* + + + + +## 12.4 Data Mapping + +Above, we set the style for all of the census tracts to the same fill and outline colors and opacity values. + +Let's take a look at how we would use the `data values` to set the color values for the polygons. This is called a `choropleth` map or, more generally, a `thematic map`. + +The `folium.Choropleth` function can be used for this. + +# Uncomment to view help docs +## folium.Choropleth? + +With `folium.Choropleth`, we will use some of the same style parameters that we used with `folium.GeoJson`. + +We will also use some new parameters, as shown below. + +First, let's take a look at the data we will map to refresh our knowledge. + +print(ca_counties_gdf.columns) +ca_counties_gdf.head(2) + +Now let's create a choropleth map of total population, which is in the `c_race` column. + +ca_counties_gdf.head() + +# Define the basemap +map2 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + tiles='CartoDB Positron', + width=1000, # the width & height of the output map + height=600, # in pixels + zoom_start=6) # the zoom level for the data to be displayed + + +# Add the Choropleth layer +folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), # The object with the geospatial data + data=ca_counties_gdf, # The object with the attribute data (can be same) + columns=['NAME','POP2012'], # the ID and data columns in the data objects + key_on="feature.id", # the ID in the geo_data object (don't change) + fill_color="Reds", # The color palette (or color map) - see help + fill_opacity=0.65, + line_color="grey", + legend=True, + legend_name="Population", + ).add_to(map2) + +# Display the map +map2 + +### Choropleth Mapping with Folium - discussion + +Let's discuss the following lines from the code above in more detail. + +
+# Add the Choropleth layer
+folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),
+           data=ca_counties_gdf, 
+           columns=['NAME','POP2012'],
+           key_on="feature.id",
+           fill_color="Reds",                               
+           ...)
+
+
+
+ +`geo_data` and the `data`: we need to identify the objects that contains both because they could be different objects. In our example they are in the same object. + +`ca_counties_gdf.set_index('NAME')`: We need to **set_index('NAME')** in order to identify the column in `geo_data` that will be used to `join` the geometries in the `geo_data` to the data values in `data`. + +`columns=['NAME','POP2012']`: we identify in `data` (1) the column that will join these `data` to `geo_data` and (2) the second column is the column with the values that will determine the color. + +`fill_color="Reds":` Here we identify the name of the color palette that we will use to style the polygons. These will be the same as the `matplotlib` colormaps. + + +#### Question +Recall our discussion about best practices for choropleth maps. Is population count an appropriate variable to plot as a choropleth? + +# Write your thoughts here + +#### Exercise + +Copy and paste the code from above into the cell below to create a choropleth map of population density (`POP12_SQMI`). + +Feel free to experiment with any of the `folium.Choropleth` style parameters, especially the `fill_color` which needs to be one of the `color brewer palettes` listed below: + +
+fill_color: string, default 'blue'
+    Area fill color. Can pass a hex code, color name, or if you are
+    binding data, one of the following color brewer palettes:
+    'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
+    'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
+
+ +# Your code here +# Define the basemap +map2 = folium.Map(location=[37.7749, -122.4194], # lat, lon around which to center the map + tiles='Stamen Toner', + width=1000, # the width & height of the output map + height=600, # in pixels + zoom_start=10) # the zoom level for the data to be displayed + + +# Add the Choropleth layer +folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), # The object with the geospatial data + data=ca_counties_gdf, # The object with the attribute data (can be same) + columns=['NAME','POP12_SQMI'], # the ID and data columns in the data objects + key_on="feature.id", # the ID in the geo_data object (don't change) + fill_color="RdPu", # The color palette (or color map) - see help + fill_opacity=0.8).add_to(map2) + +map2 + +*Click here for answers* + + + +### Choropleth Maps with Tooltips + +You can add a `tooltip` to a folium.Choropleth map but the process is not straigthforward. The `folium.Choropleth` function does not have a tooltip argument the way `folium.GeoJson` does. + +The workaround is to add the layer as both a `folium.Choropleth` layer and as a `folium.GeoJson` layer and bind the tooltip to the GeoJson layer. + +Let's check it out below. + +# Define the basemap +map3 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map + tiles='CartoDB Positron', + width=1000, # the width & height of the output map + height=600, # in pixels + zoom_start=6) # the zoom level for the data to be displayed + + +# Add the Choropleth layer +folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), # The object with the geospatial data + data=ca_counties_gdf, # The object with the attribute data (can be same) + columns=['NAME','POP2012'], # the ID and data columns in the data objects + key_on="feature.id", # the ID in the geo_data object (don't change) + fill_color="Reds", # The color palette (or color map) - see help + fill_opacity=0.65, + line_color="grey", + legend=True, + legend_name="Population", + ).add_to(map3) + +# ADD the same geodataframe to the map to display a tooltip +layer2 = folium.GeoJson(ca_counties_gdf, + style_function=lambda x: {'color':'transparent','fillColor':'transparent'}, + tooltip=folium.GeoJsonTooltip( + fields=['NAME','POP2012'], + aliases=['County', 'Population'], + labels=True, + localize=True + ), + highlight_function=lambda x: {'weight':3,'color':'white'} +).add_to(map3) + + + +map3 # show map + +#### Question +Do you notice anything different about the `style_function` for layer2 above? + +#### Exercise +Redo the above choropleth map code to map population density. Add both population and population density to the tooltip. Don't forget to update the legend name. + +# Your code here + + +## 12.5 Overlays + +We can overlay other geospatial data on our folium maps. + +Let's say we want to focus the previous choropleth map with tooltips (`map3`) on the City of Berkeley. We can fetch the border of the city from our census Places dataset. These data can be downloaded from the Census website. We use the cartographic boundary files not the TIGER line files as these look better on a map (clipped to shoreline). + +Specifically, we will fetch the city boundaries from the following census cartographic boundary file: + +- https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_place_500k.zip + +Then we can overlay the border of the city on the map and set the initial zoom to the center of the Berkeley boundary. + +Let's try that. + + +First we need to read in the census places data and create a subset geodataframe for our city of interest, here Berkeley. + +places = gpd.read_file("zip://notebook_data/census/Places/cb_2018_06_place_500k.zip") + +places.head(2) + +berkeley = places[places.NAME=='Berkeley'].copy() +berkeley.head(2) + +Plot the Berkeley geodataframe to make sure it looks ok. + +berkeley.plot() + +# Create a new map centered on Berkeley +berkeley_map = folium.Map(location=[berkeley.centroid.y.mean(), + berkeley.centroid.x.mean()], + tiles='CartoDB Positron', + width=800,height=600, + zoom_start=13) + + +# Add the census tract polygons as a choropleth map +layer1=folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), + data=ca_counties_gdf, + columns=['NAME','POP2012'], + fill_color="Reds", + fill_opacity=0.65, + line_color="grey", #"white", + line_weight=1, + line_opacity=1, + key_on="feature.id", + legend=True, + legend_name="Population", + highlight=True + ).add_to(berkeley_map) + +# Add the berkeley boundary - note the fill color +layer2 = folium.GeoJson(data=berkeley, + name='Berkeley',smooth_factor=2, + style_function=lambda x: {'color':'black', + 'opacity':1, + 'fillColor': + 'transparent', + 'weight':3}, + ).add_to(berkeley_map) + +# Add the tooltip for the census tracts as its own layer +layer3 = folium.GeoJson(ca_counties_gdf, + style_function=lambda x: {'color':'transparent','fillColor':'transparent'}, + tooltip=folium.features.GeoJsonTooltip( + fields=['NAME','POP2012'], + aliases=['County', 'Population'], + labels=True, + localize=True + ), + highlight_function=lambda x: {'weight':3,'color':'white'} +).add_to(berkeley_map) + +berkeley_map # show map + +
+ +
+
+ +#### Questions +
+ +Any questions about the above map? + +Does the code for the Berkeley map above differ from our previous choropleth map code? + +Does the order of layer2 & layer3 matter (can they be switched?) + +#### Exercise + +Redo the above map with population density. Create and display the Oakland city boundary on the map instead of Berkeley and center the map on Oakland. + +# Your code here + +*Click here for solution* + + + + +## 12.6 Mapping Points and Lines + +We can also add points and lines to a folium map. + +Let's overlay BART stations as points and BART lines as lines to the interactive map. For the Bay Area these are data are available from the [Metropoliton Transportation Commission (MTC) Open Data portal](http://opendata.mtc.ca.gov/datasets). + +We're going to try pulling in BART station data that we downloaded from the website and subsetted from the passenger-rail-stations. You can learn more about the dataset through here: http://opendata.mtc.ca.gov/datasets/passenger-rail-stations-2019 + +As usual, let's try pulling in the data and inspect the first couple of rows. + +# Load light rail stop data +railstops = gpd.read_file("zip://notebook_data/transportation/Passenger_Rail_Stations_2019.zip") +railstops.tail() + +# Subset to keep just bart stations +bart_stations = railstops[railstops['agencyname']=='BART'].sort_values(by="station_na") +bart_stations.head() + +# Repeat for the rail lines +rail_lines = gpd.read_file("zip://notebook_data/transportation/Passenger_Railways_2019.zip") +rail_lines.head() + +rail_lines.operator.value_counts() + +# subset by operator to get the bart lines +bart_lines = rail_lines[rail_lines['operator']=='BART'] + +# Check the CRS of the geodataframes +print(bart_stations.crs) +print(bart_lines.crs) + +# Quick plot +bart_stations.plot() +bart_lines.plot() + +Now that we have fetched and checked the Bart data, let's do a quick folium map with it. + +We will use `folium.GeoJson` to add these data to the map, just as we used it previously for the census tract polygons. + +# Bart Map +map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], + tiles='CartoDB Positron', + width=800,height=600, + zoom_start=10) + + +folium.GeoJson(bart_lines).add_to(map4) + +folium.GeoJson(bart_stations).add_to(map4) + + +map4 # show map + +We can also add tooltips, just as we did previously. + +# Bart Map +map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], + tiles='CartoDB Positron', + #width=800,height=600, + zoom_start=10) + +# Add Bart lines +folium.GeoJson(bart_lines, + tooltip=folium.GeoJsonTooltip( + fields=['operator' ], + aliases=['Line operator'], + labels=True, + localize=True + ), + ).add_to(map4) + +# Add Bart stations +folium.GeoJson(bart_stations, + tooltip=folium.GeoJsonTooltip(fields=['ts_locatio'], + aliases=['Stop Name'], + labels=True, + localize=True + ), + ).add_to(map4) + + +map4 # show map + +That's pretty cool, but don't you just want to click on those marker points to get a `popup` rather than hovering over for a `tooltip`? + +### Mapping Points + +So far we have used `folium.GeoJson` to map our BART points. By default this uses the push-pin marker symbology made popular by Google Maps. + +Under the hood, folium.GeoJson uses the default object type `folium.Marker` when the input data are points. + +This is helpful to know because `folium.Marker` has a few options that allow further customization of our points. + +# Uncomment to view help docs +folium.Marker? + +Let's explicitly add the Bart Stations as points so we can change the `tooltips` to `popups`. + +# Bart Map +map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], + tiles='CartoDB Positron', + #width=800,height=800, + zoom_start=10) + +# Add Bart lines +folium.GeoJson(bart_lines, + tooltip=folium.GeoJsonTooltip( + fields=['operator' ], + aliases=['Line operator'], + labels=True, + localize=True + ), + ).add_to(map4) + +# Add Bart stations +bart_stations.apply(lambda row: + folium.Marker( + location=[row['geometry'].y, row['geometry'].x], + popup=row['ts_locatio'], + ).add_to(map4), axis=1) + +map4 # show map + +That `folium.Marker` code is a bit more complex than `folium.GeoJson` and may not be worth it unless you really want that popup behavior. + +But let's see what else we can do with a `folium.Marker` by viewing the next map. + +# Bart Map +map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], + tiles='CartoDB Positron', + #width=800,height=600, + zoom_start=10) + +# Add BART lines +folium.GeoJson(bart_lines, + tooltip=folium.GeoJsonTooltip( + fields=['operator' ], + aliases=['Line operator'], + labels=True, + localize=True + ), + ).add_to(map4) + +# Add BART Stations +icon_url = "https://gomentumstation.net/wp-content/uploads/2018/08/Bay-area-rapid-transit-1000.png" +bart_stations.apply(lambda row: + folium.Marker( + location=[row['geometry'].y,row['geometry'].x], + popup=row['ts_locatio'], + icon=folium.features.CustomIcon(icon_url,icon_size=(20, 20)), + ).add_to(map4), axis=1) + +map4 # show map + +#### Exercise + +Copy and paste the code for the previous cell into the next cell and +1. change the bart icon to "https://ya-webdesign.com/transparent450_/train-emoji-png-14.png" +2. change the popup back to a tooltip. + +# Your code here + +*Click here for solution* + + + +### folium.CircleMarkers + +You may prefer to customize points as `CircleMarkers` instead of the icon or pushpin Marker style. This allows you to set size and color of a marker, either manually or as a function of a data variable. + +Let's look at some code for doing this. + +# Define the basemap +map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], # lat, lon around which to center the map + tiles='CartoDB Positron', + #width=1000, # the width & height of the output map + #height=600, # in pixels + zoom_start=10) # the zoom level for the data to be displayed + +# Add BART Lines +folium.GeoJson(bart_lines).add_to(map5) + + +# Add BART Stations +bart_stations.apply(lambda row: + folium.CircleMarker( + location=[row['geometry'].y, row['geometry'].x], + radius=10, + color='purple', + fill=True, + fill_color='purple', + popup=row['ts_locatio'], + ).add_to(map5), + axis=1) + + +map5 + + +### folium.Circle + +You can also set the size of your circles to a fixed radius, in meters, using `folium.Circle`. This is great for exploratory data analysis. For example, you can see what the census tract values are within 500 meters of a BART station. + +# Uncomment to view +#?folium.Circle + +# Define the basemap +map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], # lat, lon around which to center the map + tiles='CartoDB Positron', + #width=1000, # the width & height of the output map + #height=600, # in pixels + zoom_start=10) # the zoom level for the data to be displayed + +# Add BART Lines +folium.GeoJson(bart_lines).add_to(map5) + + +# Add BART Stations +bart_stations.apply(lambda row: + folium.Circle( + location=[row['geometry'].y, row['geometry'].x], + radius=500, + color='purple', + fill=True, + fill_color='purple', + popup=row['ts_locatio'], + ).add_to(map5), + axis=1) + + +map5 + + +
+ +
+
+ +#### Question +
+ +What do you notice about the size of the circles as you zoom in/out when you compare folium.Circles and folium.CircleMarkers? + +### Proportional Symbol Maps + +One of the advantages of the `folium.CircleMarker` is that we can set the size of the map to vary based on a data value. + +To give this a try, let's add a fake column to the `bart_stations` gdf called millions_served and set it to a value between 1 and 10. + +# add a column to the bart stations gdf +bart_stations['millions_served'] = np.random.randint(1,10, size=len(bart_stations)) +bart_stations.head() + +# Define the basemap +map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], + tiles='CartoDB Positron', + #width=1000, # the width & height of the output map + #height=600, # in pixels + zoom_start=10) # the zoom level for the data to be displayed + +folium.GeoJson(bart_lines).add_to(map5) + +# Add BART Stations as CircleMarkers +# Here, some knowlege of Python string formatting is useful +bart_stations.apply(lambda row: + folium.CircleMarker( + location=[row['geometry'].y, row['geometry'].x], + radius=row['millions_served'], + color='purple', + fill=True, + fill_color='purple', + tooltip = "Bart Station: %s
Millions served: %s" % (row['ts_locatio'], row['millions_served']) + + ).add_to(map5), axis=1) +map5 + + +So if you hover over our BART stations, you see that we've formatted it nicely! Using some HTML and Python string formatting we can make our `tooltip` easier to read. + +If you want to learn more about customizing these, you can [go check this out to learn HTML basics](https://www.w3schools.com/html/html_basic.asp). You can then [go here to learn about Python string formatting](https://python-reference.readthedocs.io/en/latest/docs/str/formatting.html). + + +## 12.7 Creating and Saving a folium Interactive Map + +Now that you have seen most of the ways you can add a geodataframe to a folium map, let's create one big map that includes several of our geodataframes. + +To control the display of the data layers, we will add a `folium.LayerControl` + +- A `folium.LayerControl` will allow you to toggle on/off a map's visible layers. + +- In order to add a layer to the LayerControl, the layer must have value set for its `name`. + +Let's take a look. + +# Create a new map centered on the census tract data +map6 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], + tiles='CartoDB Positron', + #width=800,height=600, + zoom_start=10) + +# Add the counties polygons as a choropleth map +layer1=folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), + data=ca_counties_gdf, + columns=['NAME','POP2012'], + fill_color="Reds", + fill_opacity=0.65, + line_color="grey", #"white", + line_weight=1, + line_opacity=1, + key_on="feature.id", + legend=True, + legend_name="Population", + highlight=True, + name="Counties" + ).add_to(map6) + +# Add the tooltip for the counties as its own layer +# Don't display in the Layer control! +layer2 = folium.GeoJson(ca_counties_gdf, + style_function=lambda x: {'color':'transparent','fillColor':'transparent'}, + tooltip=folium.features.GeoJsonTooltip( + fields=['NAME','POP2012'], + aliases=['Name', 'Population'], + labels=True, + localize=True + ), + highlight_function=lambda x: {'weight':3,'color':'white'} +).add_to(layer1.geojson) + +# Add Bart lines +folium.GeoJson(bart_lines, + name="Bart Lines", + tooltip=folium.GeoJsonTooltip( + fields=['operator' ], + aliases=['Line operator'], + labels=True, + localize=True + ), + ).add_to(map6) + + +# Add Bart stations +folium.GeoJson(bart_stations, + name="Bart stations", + tooltip=folium.GeoJsonTooltip(fields=['ts_locatio' ], + aliases=['Stop Name'], + labels=True, + localize=True + ), + ).add_to(map6) + +# ADD LAYER CONTROL +folium.LayerControl(collapsed=False).add_to(map6) + +map6 # show map + +
+ +
+
+ +#### Questions +
+ +1. Take a look at the help docs `folium.LayerControl?`. What parameter would move the location of the LayerControl? What parameter would allow it to be closed by default? + +2. Take a look at the way we added `layer2` above (this has the census tract tooltips). How has the code we use to add the layer to the map changed? Why do you think we made this change? + +# Uncomment to view +#folium.LayerControl? + +### Saving to an html file + +By saving our map to a html we can use it later as something to add to a website or email to a colleague. + +You can save any of the maps you have in the notebook using this syntax: + +> map_name.save("file_name.html") + +Let's try that. + +map6.save('outdata/bartmap.html') + +Find your html file on your computer and double-click on it to open it in a browser. + +#### Extra Challenge + +Check out the notebook examples and find one to try with the data we have used in this notebook. I recommend the following. + +- [Mini-maps](https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/MiniMap.ipynb) +- [Dual-map](https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/plugin-DualMap.ipynb) (choropleth maps two census tract vars) +- [Search](https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/plugin-Search.ipynb) (e.g., for a Bart Station by name) + + +## 12.8 Recap +Here we learned about the wonderful world of `Folium`! We created interactive maps-- whether it be choropleth, points, lines, symbols... we mapped it all. + +Below you'll find a list of key functionalities we learned: +- Interactive mapping + - `folium.Map()` +- Adding a map layer + - `.add_to()` + - `folium.Choropleth()` + - `geo_data` + - `columns` + - `fill_color` + - `folium.GeoJson()` + - `style_function` + - `folium.Marker()` + - `icon` + - `folium.CircleMarker()` + - `radius` +- Adding a Tooltip + - `folium.GeoJsonTooltip` + - `folium.features.GeoJsonTooltip` +- Adding layer control + - `folium.LayerControl()` + +## Important note + +The folium library changes often so I recommend you update your package frequently. This will give you increased functionality and may make future code easier to write. However, it might cause your existing code to break. + +### References + +This notebook provides an introduction to `folium`. To see what else you can do, check out the references listed below. + +- [Folium web site](https://github.com/python-visualization/folium) + +- [Folium notebook examples](https://nbviewer.jupyter.org/github/python-visualization/folium/tree/master/examples/) + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + + diff --git a/_build/jupyter_execute/lessons/13_OPTIONAL_geocoding.ipynb b/_build/jupyter_execute/lessons/13_OPTIONAL_geocoding.ipynb new file mode 100644 index 0000000..5220cb9 --- /dev/null +++ b/_build/jupyter_execute/lessons/13_OPTIONAL_geocoding.ipynb @@ -0,0 +1,399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geocoding Addresses in Python\n", + "\n", + "This notebook demonstrates how to geocode a dataframe of addresses" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import our packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import contextily as cx\n", + "import matplotlib.pyplot as plt\n", + "import folium\n", + "\n", + "# FOR geocoding\n", + "import geopy\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sample Data\n", + "Let's use as our sample data a CSV file of Alameda County Schools." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df0 = pd.read_csv(\"./notebook_data/alco_schools.csv\")\n", + "df0.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that this datafile already has coordinates, but we will ignore those columns and subset it to Berkeley schools only for our geocoding example. We will also only keep public schools to limit the number of addresses to be geocoded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = df0[(df0['City']=='Berkeley' )& (df0['Org']== 'Public')][['Site','Address','City','State']].reset_index(drop=True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.shape # SEE HOW MANY SCHOOLS WILL BE GEOCODED" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we create a column that has all address components as this format is favored by many geocoders." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['full_address'] = df['Address'] +' '+ df['City']+ ' '+ df['State']\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a GeoDataFrame\n", + "We will create a Geopandas Geodataframe that has no geometry so that we can use GeoPandas functionality for geocoding." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = gpd.GeoDataFrame(data=df, \n", + " geometry=None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Geocoders and associated parameters\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##################################################################\n", + "## Geocoder to use \n", + "## see https://geopy.readthedocs.io/en/latest/\n", + "## and https://geopandas.org/geocoding.html\n", + "##################################################################\n", + "\n", + "# By default, the geocode function uses the GeoCode.Farm geocoding API with a rate limitation applied. \n", + "# But a different geocoding service can be specified (we really like the google geocoder!)\n", + "# Set your Google geocoding API Key if you want to geocode using that API\n", + "geocoder_name = 'Nominatim' # or \"GoogleV3\" or None to skip geocoding step\n", + "geocoder_apikey = None # None if not required or google api key, or other api key\n", + "geopy.geocoders.options.default_user_agent = 'D-Lab GeoFUN Workshop at UC Berkeley'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test the geocoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# test the geocoder\n", + "if geocoder_name is not None: \n", + " print(\"Geocoding is enabled with this geocoder:\", geocoder_name)\n", + " \n", + " if geocoder_apikey is None: \n", + " x= gpd.tools.geocode('1600 pennsylvania ave. washington, dc', provider=geocoder_name)['geometry'].squeeze()\n", + " \n", + " else:\n", + " x=gpd.tools.geocode('1600 pennsylvania ave. washington, dc', provider=geocoder_name, api_key=geocoder_apikey)['geometry'].squeeze()\n", + "else:\n", + " print(\"Geocoding is NOT enabled.\")\n", + " \n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make a Geocoding Function\n", + "\n", + "We can apply a geocoding function to a pandas dataframe to geocode all rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def geocode_one_address(addr, geocoder_name=geocoder_name, geocoder_apikey=geocoder_apikey):\n", + " '''\n", + " Function to geocode an input address IFF geom is None\n", + " Use geopy with google geocoder to geocode addresses.\n", + " Requires the api_key value to be set prior to running this function\n", + " \n", + " Parameters:\n", + " addr (str): address to geocode, eg \"1 Main St, Oakland, CA\"\n", + " geocoder_name (str): name of geocoder (\"nominatim\" or \"GoogleV3\")\n", + " geocoder_apikey (str): api_key if needed by geocoder\n", + " Returns: \n", + " geom (POINT): a point geometry or None if unsuccessful\n", + " \n", + " ''' \n", + " \n", + " if addr != None:\n", + " tempaddr = addr\n", + " \n", + " print(\"...geocoding this address: [%s]\" % tempaddr)\n", + " \n", + " try:\n", + " if geocoder_apikey == None:\n", + " return gpd.tools.geocode(tempaddr, provider=geocoder_name)['geometry'].squeeze()\n", + " else:\n", + " return gpd.tools.geocode(tempaddr, provider=geocoder_name, api_key=geocoder_apikey)['geometry'].squeeze()\n", + " except:\n", + " print(\"...Problem with address: \", tempaddr)\n", + " return None\n", + "\n", + " else: \n", + " print(\"No address to geocode\")\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# test geocoding function on one address\n", + "x = geocode_one_address('1600 pennsylvania ave. washington, dc')\n", + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#batch geocode addresses in a data frame\n", + "if geocoder_name is None:\n", + " print(\"Geocoding is NOT enabled.\")\n", + " print(\"Will NOT geocode addresses\")\n", + "else:\n", + " print(\"Geocoding is enabled with this geocoder:\", geocoder_name)\n", + " print(\"Ready to Geocode addresses\")\n", + " \n", + " if geocoder_apikey is None: \n", + " gdf['geometry'] = gdf.apply(lambda x: geocode_one_address(x['full_address']), axis=1)\n", + " else:\n", + " gdf['geometry'] = gdf.apply(lambda x: geocode_one_address(x['full_address']), axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set the CRS\n", + "Since we now have geographic coordinates we need to set the Coordinate Reference System of the data (WGS84)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = gdf.set_crs(epsg=4326)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Map the geocoded Addresses" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add basemap with Contextily\n", + "We can map the schools that were successfully geocoded, i.e. where the geometry is not equal to None." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = gdf[gdf.geometry!=None].to_crs('EPSG:3857').plot(figsize=(9, 9), color=\"red\")\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interactive Map with Folium\n", + "\n", + "We can create an interactive map of the schools that were successfully geocoded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "map1 = folium.Map(location=[gdf.geometry.y.mean(), gdf.geometry.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " zoom_start=12)\n", + "\n", + "folium.GeoJson(gdf[gdf.geometry!=None],\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['Site'], \n", + " aliases=[\"\"],\n", + " #labels=True,\n", + " localize=True)\n", + " ).add_to(map1)\n", + "\n", + "map1 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save output to GeoJson File" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save Geodataframe to file\n", + "#gdf.to_file(\"my_geocoded_schools.geojson\", driver='GeoJSON')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/13_OPTIONAL_geocoding.py b/_build/jupyter_execute/lessons/13_OPTIONAL_geocoding.py new file mode 100644 index 0000000..5c37418 --- /dev/null +++ b/_build/jupyter_execute/lessons/13_OPTIONAL_geocoding.py @@ -0,0 +1,172 @@ +# Geocoding Addresses in Python + +This notebook demonstrates how to geocode a dataframe of addresses + +# import our packages +import numpy as np +import pandas as pd +import geopandas as gpd +import contextily as cx +import matplotlib.pyplot as plt +import folium + +# FOR geocoding +import geopy + + +## Sample Data +Let's use as our sample data a CSV file of Alameda County Schools. + +df0 = pd.read_csv("./notebook_data/alco_schools.csv") +df0.head() + +We can see that this datafile already has coordinates, but we will ignore those columns and subset it to Berkeley schools only for our geocoding example. We will also only keep public schools to limit the number of addresses to be geocoded. + +df = df0[(df0['City']=='Berkeley' )& (df0['Org']== 'Public')][['Site','Address','City','State']].reset_index(drop=True) +df.head() + +df.shape # SEE HOW MANY SCHOOLS WILL BE GEOCODED + +Next we create a column that has all address components as this format is favored by many geocoders. + +df['full_address'] = df['Address'] +' '+ df['City']+ ' '+ df['State'] +df.head() + +## Create a GeoDataFrame +We will create a Geopandas Geodataframe that has no geometry so that we can use GeoPandas functionality for geocoding. + +gdf = gpd.GeoDataFrame(data=df, + geometry=None) + +gdf.head() + +gdf.info() + +## Define Geocoders and associated parameters + + +################################################################## +## Geocoder to use +## see https://geopy.readthedocs.io/en/latest/ +## and https://geopandas.org/geocoding.html +################################################################## + +# By default, the geocode function uses the GeoCode.Farm geocoding API with a rate limitation applied. +# But a different geocoding service can be specified (we really like the google geocoder!) +# Set your Google geocoding API Key if you want to geocode using that API +geocoder_name = 'Nominatim' # or "GoogleV3" or None to skip geocoding step +geocoder_apikey = None # None if not required or google api key, or other api key +geopy.geocoders.options.default_user_agent = 'D-Lab GeoFUN Workshop at UC Berkeley' + +## Test the geocoder + +# test the geocoder +if geocoder_name is not None: + print("Geocoding is enabled with this geocoder:", geocoder_name) + + if geocoder_apikey is None: + x= gpd.tools.geocode('1600 pennsylvania ave. washington, dc', provider=geocoder_name)['geometry'].squeeze() + + else: + x=gpd.tools.geocode('1600 pennsylvania ave. washington, dc', provider=geocoder_name, api_key=geocoder_apikey)['geometry'].squeeze() +else: + print("Geocoding is NOT enabled.") + +print(x) + +## Make a Geocoding Function + +We can apply a geocoding function to a pandas dataframe to geocode all rows + +def geocode_one_address(addr, geocoder_name=geocoder_name, geocoder_apikey=geocoder_apikey): + ''' + Function to geocode an input address IFF geom is None + Use geopy with google geocoder to geocode addresses. + Requires the api_key value to be set prior to running this function + + Parameters: + addr (str): address to geocode, eg "1 Main St, Oakland, CA" + geocoder_name (str): name of geocoder ("nominatim" or "GoogleV3") + geocoder_apikey (str): api_key if needed by geocoder + Returns: + geom (POINT): a point geometry or None if unsuccessful + + ''' + + if addr != None: + tempaddr = addr + + print("...geocoding this address: [%s]" % tempaddr) + + try: + if geocoder_apikey == None: + return gpd.tools.geocode(tempaddr, provider=geocoder_name)['geometry'].squeeze() + else: + return gpd.tools.geocode(tempaddr, provider=geocoder_name, api_key=geocoder_apikey)['geometry'].squeeze() + except: + print("...Problem with address: ", tempaddr) + return None + + else: + print("No address to geocode") + return None + +# test geocoding function on one address +x = geocode_one_address('1600 pennsylvania ave. washington, dc') +print(x) + +#batch geocode addresses in a data frame +if geocoder_name is None: + print("Geocoding is NOT enabled.") + print("Will NOT geocode addresses") +else: + print("Geocoding is enabled with this geocoder:", geocoder_name) + print("Ready to Geocode addresses") + + if geocoder_apikey is None: + gdf['geometry'] = gdf.apply(lambda x: geocode_one_address(x['full_address']), axis=1) + else: + gdf['geometry'] = gdf.apply(lambda x: geocode_one_address(x['full_address']), axis=1) + + +gdf.head() + +## Set the CRS +Since we now have geographic coordinates we need to set the Coordinate Reference System of the data (WGS84) + +gdf = gdf.set_crs(epsg=4326) + +## Map the geocoded Addresses + +gdf.plot(); + +## Add basemap with Contextily +We can map the schools that were successfully geocoded, i.e. where the geometry is not equal to None. + +ax = gdf[gdf.geometry!=None].to_crs('EPSG:3857').plot(figsize=(9, 9), color="red") +cx.add_basemap(ax) + +## Interactive Map with Folium + +We can create an interactive map of the schools that were successfully geocoded. + + +map1 = folium.Map(location=[gdf.geometry.y.mean(), gdf.geometry.x.mean()], + tiles='CartoDB Positron', + zoom_start=12) + +folium.GeoJson(gdf[gdf.geometry!=None], + tooltip=folium.GeoJsonTooltip( + fields=['Site'], + aliases=[""], + #labels=True, + localize=True) + ).add_to(map1) + +map1 # show map + +## Save output to GeoJson File + +# Save Geodataframe to file +#gdf.to_file("my_geocoded_schools.geojson", driver='GeoJSON') + diff --git a/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb new file mode 100644 index 0000000..b154890 --- /dev/null +++ b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb @@ -0,0 +1,1303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 14. Making Plots and Maps with Altair\n", + "\n", + "The Python Altair library is great because it works with both pandas dataframes and geopandas geodataframes. It allows you to create all kinds of plots and also to make makes. Moreover the plots can be linked to the maps (but not vice versa) so that selecting data on the plot in turn highlights the geographies for related areas. We demonstrate this below with census data.\n", + "\n", + "This is powerful because you can do all this with just one Python library - instead of learning one for plotting and one for mapping. You can do this with matplotlib as well but the Altair syntax is a bit less complex.\n", + "\n", + "\n", + "For more information see the Altair website: https://altair-viz.github.io/" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#Import libraries including altair\n", + "import numpy as np\n", + "import pandas as pd\n", + "import altair as alt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment & Install or Upgrade geopandas if necessary\n", + "#!pip install GeoPandas==0.8.2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import geopandas as gpd" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "census_income_CA_2018.csv census_variables_CA_2013.zip\r\n", + "census_mhhinc_CA_county_2018.csv census_variables_CA_2018.csv\r\n", + "census_tracts_CA_2018.zip census_variables_CA_2018.zip\r\n", + "census_variables_CA.csv s4_cenvars_CA.csv\r\n", + "census_variables_CA_2013.csv s4_cenvars_CA_2018.csv\r\n" + ] + } + ], + "source": [ + "!ls notebook_data/census/ACS5yr/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load ACS 5 year (2014 - 2018) data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"notebook_data/census/ACS5yr/census_variables_CA_2018.csv\", dtype={'FIPS_11_digit':str})" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1Census Tract 9.01, Merced County, California38361402973422204951864625...0.8284430.1490880.0195610.0015860.0013220.7879250.0671700.0000000.0000000.096604
2Census Tract 15.02, Merced County, California24931581812421542271052257...0.8537870.1049010.0182260.0097210.0133660.6448150.0941600.0083430.0119190.057211
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4Census Tract 12, Merced County, California543121871373582388450266104140...0.9201410.0588240.0053980.0107970.0048400.8387240.0642450.0004430.0000000.012406
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" + ], + "text/plain": [ + " NAME c_race c_white c_black \\\n", + "0 Census Tract 8.02, Merced County, California 3996 1609 50 \n", + "1 Census Tract 9.01, Merced County, California 3836 1402 97 \n", + "2 Census Tract 15.02, Merced County, California 2493 158 18 \n", + "3 Census Tract 9.02, Merced County, California 9811 3752 87 \n", + "4 Census Tract 12, Merced County, California 5431 2187 137 \n", + "\n", + " c_asian c_latinx c_race_moe c_white_moe c_black_moe c_asian_moe ... \\\n", + "0 231 2082 323 249 36 103 ... \n", + "1 34 2220 495 186 46 25 ... \n", + "2 124 2154 227 105 22 57 ... \n", + "3 1358 4172 796 863 83 621 ... \n", + "4 358 2388 450 266 104 140 ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.849861 0.146589 0.003551 0.000000 0.000000 0.824308 \n", + "1 0.828443 0.149088 0.019561 0.001586 0.001322 0.787925 \n", + "2 0.853787 0.104901 0.018226 0.009721 0.013366 0.644815 \n", + "3 0.891211 0.095677 0.004302 0.000000 0.008810 0.908548 \n", + "4 0.920141 0.058824 0.005398 0.010797 0.004840 0.838724 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.122142 0.012635 0.000000 0.000000 \n", + "1 0.067170 0.000000 0.000000 0.096604 \n", + "2 0.094160 0.008343 0.011919 0.057211 \n", + "3 0.043962 0.000000 0.000000 0.007598 \n", + "4 0.064245 0.000443 0.000000 0.012406 \n", + "\n", + "[5 rows x 66 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Take a look at the data\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 8057 entries, 0 to 8056\n", + "Data columns (total 66 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 NAME 8057 non-null object \n", + " 1 c_race 8057 non-null int64 \n", + " 2 c_white 8057 non-null int64 \n", + " 3 c_black 8057 non-null int64 \n", + " 4 c_asian 8057 non-null int64 \n", + " 5 c_latinx 8057 non-null int64 \n", + " 6 c_race_moe 8057 non-null int64 \n", + " 7 c_white_moe 8057 non-null int64 \n", + " 8 c_black_moe 8057 non-null int64 \n", + " 9 c_asian_moe 8057 non-null int64 \n", + " 10 c_latinx_moe 8057 non-null int64 \n", + " 11 state_fips 8057 non-null int64 \n", + " 12 county_fips 8057 non-null int64 \n", + " 13 tract_fips 8057 non-null int64 \n", + " 14 med_rent 7906 non-null float64\n", + " 15 med_hhinc 7965 non-null float64\n", + " 16 c_tenants 8057 non-null int64 \n", + " 17 c_owners 8057 non-null int64 \n", + " 18 c_renters 8057 non-null int64 \n", + " 19 med_rent_moe 7846 non-null float64\n", + " 20 med_hhinc_moe 7945 non-null float64\n", + " 21 c_tenants_moe 8057 non-null int64 \n", + " 22 c_owners_moe 8057 non-null int64 \n", + " 23 c_renters_moe 8057 non-null int64 \n", + " 24 c_movers 8057 non-null int64 \n", + " 25 c_stay 8057 non-null int64 \n", + " 26 c_movelocal 8057 non-null int64 \n", + " 27 c_movecounty 8057 non-null int64 \n", + " 28 c_movestate 8057 non-null int64 \n", + " 29 c_moveabroad 8057 non-null int64 \n", + " 30 c_movers_moe 8057 non-null int64 \n", + " 31 c_stay_moe 8057 non-null int64 \n", + " 32 c_movelocal_moe 8057 non-null int64 \n", + " 33 c_movecounty_moe 8057 non-null int64 \n", + " 34 c_movestate_moe 8057 non-null int64 \n", + " 35 c_moveabroad_moe 8057 non-null int64 \n", + " 36 c_commute 8057 non-null int64 \n", + " 37 c_car 8057 non-null int64 \n", + " 38 c_carpool 8057 non-null int64 \n", + " 39 c_transit 8057 non-null int64 \n", + " 40 c_bike 8057 non-null int64 \n", + " 41 c_walk 8057 non-null int64 \n", + " 42 c_commute_moe 8057 non-null int64 \n", + " 43 c_car_moe 8057 non-null int64 \n", + " 44 c_carpool_moe 8057 non-null int64 \n", + " 45 c_transit_moe 8057 non-null int64 \n", + " 46 c_bike_moe 8057 non-null int64 \n", + " 47 c_walk_moe 8057 non-null int64 \n", + " 48 year 8057 non-null int64 \n", + " 49 FIPS_11_digit 8057 non-null object \n", + " 50 p_white 8012 non-null float64\n", + " 51 p_black 8012 non-null float64\n", + " 52 p_asian 8012 non-null float64\n", + " 53 p_latinx 8012 non-null float64\n", + " 54 p_owners 7981 non-null float64\n", + " 55 p_renters 7981 non-null float64\n", + " 56 p_stay 8012 non-null float64\n", + " 57 p_movelocal 8012 non-null float64\n", + " 58 p_movecounty 8012 non-null float64\n", + " 59 p_movestate 8012 non-null float64\n", + " 60 p_moveabroad 8012 non-null float64\n", + " 61 p_car 7992 non-null float64\n", + " 62 p_carpool 7992 non-null float64\n", + " 63 p_transit 7992 non-null float64\n", + " 64 p_bike 7992 non-null float64\n", + " 65 p_walk 7992 non-null float64\n", + "dtypes: float64(20), int64(44), object(2)\n", + "memory usage: 4.1+ MB\n" + ] + } + ], + "source": [ + "# See what columns we have complete data for (no nulls) and what the datatypes are\n", + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Subset the data so we are only looking at Alameda County (fips code == 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df2 = df[df.county_fips==1]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " NAME c_race c_white \\\n", + "266 Census Tract 4415.01, Alameda County, California 6570 677 \n", + "267 Census Tract 4047, Alameda County, California 2079 1515 \n", + "\n", + " c_black c_asian c_latinx c_race_moe c_white_moe c_black_moe \\\n", + "266 111 4740 570 363 148 83 \n", + "267 134 199 175 133 137 62 \n", + "\n", + " c_asian_moe ... p_stay p_movelocal p_movecounty p_movestate \\\n", + "266 389 ... 0.925897 0.039593 0.010476 0.019874 \n", + "267 89 ... 0.891826 0.028390 0.037690 0.031816 \n", + "\n", + " p_moveabroad p_car p_carpool p_transit p_bike p_walk \n", + "266 0.004160 0.761761 0.113940 0.054812 0.012085 0.003453 \n", + "267 0.010279 0.532093 0.177674 0.158140 0.006512 0.005581 \n", + "\n", + "[2 rows x 66 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make an Altair scatter plot \n", + "\n", + "that visualizes the relationship between median household income and the percent of households that are owner-occupied.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(df2).mark_circle(size=50).encode(\n", + " x='med_hhinc',\n", + " y='p_owners'\n", + ").properties(\n", + " height=350, width=500\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(361, 66)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cb_2013_06_tract_500k.zip \u001b[31mcb_2018_06_tract_500k.shp.ea.iso.xml\u001b[m\u001b[m\r\n", + "cb_2017_06_tract_500k.zip \u001b[31mcb_2018_06_tract_500k.shp.iso.xml\u001b[m\u001b[m\r\n", + "cb_2018_06_tract_500k.cpg cb_2018_06_tract_500k.shx\r\n", + "cb_2018_06_tract_500k.dbf cb_2018_06_tract_500k.zip\r\n", + "cb_2018_06_tract_500k.prj oakland_tracts_2018.zip\r\n", + "cb_2018_06_tract_500k.shp\r\n" + ] + } + ], + "source": [ + "!ls notebook_data/census/Tracts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in the Census Tract geographic data\n", + "\n", + "into a GeoPandas GeoDataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "tracts = gpd.read_file('zip://./notebook_data/census/Tracts/cb_2018_06_tract_500k.zip')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", 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" + ], + "text/plain": [ + " STATEFP COUNTYFP TRACTCE AFFGEOID GEOID NAME_x LSAD \\\n", + "0 06 001 425101 1400000US06001425101 06001425101 4251.01 CT \n", + "1 06 001 428600 1400000US06001428600 06001428600 4286 CT \n", + "\n", + " ALAND AWATER geometry ... \\\n", + "0 590870 2045459 POLYGON ((-122.31419 37.84231, -122.29923 37.8... ... \n", + "1 898967 1080420 POLYGON ((-122.27993 37.76818, -122.27849 37.7... ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.865239 0.036524 0.035894 0.037154 0.025189 0.550998 \n", + "1 0.767469 0.067846 0.110467 0.036532 0.017686 0.550140 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.107539 0.169623 0.015521 0.062084 \n", + "1 0.019048 0.270588 0.034734 0.035294 \n", + "\n", + "[2 rows x 76 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tracts2.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Thematic Map\n", + "\n", + "Use the Geopandas Plot method to create a map of tracts colored by median household income values." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_27_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tracts2.plot(column='med_hhinc', legend=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make the same map with Altair" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(tracts2).mark_geoshape().encode(\n", + " color='med_hhinc'\n", + ").properties(\n", + " width=500,\n", + " height=300\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Link Atair Scatterplot and Map" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First create a selection object\n", + "my_selection = alt.selection_interval()\n", + "\n", + "# Create a background map\n", + "background_map = alt.Chart(tracts2).mark_geoshape(\n", + " fill= 'lightgray',\n", + " stroke = 'white'\n", + ").properties(\n", + " width=400,\n", + " height=300\n", + ")\n", + "\n", + "# Create the interactive scatterplot\n", + "# by addng the selection object\n", + "the_scatterplot = alt.Chart(tracts2).mark_circle(size=50).encode(\n", + " x='med_hhinc',\n", + " y='p_owners'\n", + ").properties(\n", + " width=375,\n", + " height=300\n", + ").add_selection(\n", + " my_selection\n", + ")\n", + "\n", + "# Create the interactive map\n", + "# by adding the selection object\n", + "income_map = alt.Chart(tracts2).mark_geoshape().encode(\n", + " color='med_hhinc'\n", + ").properties(\n", + " width=400,\n", + " height=350\n", + ").transform_filter(\n", + " my_selection\n", + ")\n", + "\n", + "# Link the maps (background_map and income_map)\n", + "# to the scatterplot (the_scatterplot)\n", + "the_scatterplot | (background_map + income_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try dragging a box around a subset of the points on the scatterplot and see what happens to the map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.py b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.py new file mode 100644 index 0000000..e1b5150 --- /dev/null +++ b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.py @@ -0,0 +1,133 @@ +# 14. Making Plots and Maps with Altair + +The Python Altair library is great because it works with both pandas dataframes and geopandas geodataframes. It allows you to create all kinds of plots and also to make makes. Moreover the plots can be linked to the maps (but not vice versa) so that selecting data on the plot in turn highlights the geographies for related areas. We demonstrate this below with census data. + +This is powerful because you can do all this with just one Python library - instead of learning one for plotting and one for mapping. You can do this with matplotlib as well but the Altair syntax is a bit less complex. + + +For more information see the Altair website: https://altair-viz.github.io/ + +#Import libraries including altair +import numpy as np +import pandas as pd +import altair as alt + +# Uncomment & Install or Upgrade geopandas if necessary +#!pip install GeoPandas==0.8.2 + +import geopandas as gpd + +!ls notebook_data/census/ACS5yr/ + +## Load ACS 5 year (2014 - 2018) data + +df = pd.read_csv("notebook_data/census/ACS5yr/census_variables_CA_2018.csv", dtype={'FIPS_11_digit':str}) + +# Take a look at the data +df.head() + +# See what columns we have complete data for (no nulls) and what the datatypes are +df.info() + +## Subset the data so we are only looking at Alameda County (fips code == 1) + +df2 = df[df.county_fips==1] + +df2.head(2) + +## Make an Altair scatter plot + +that visualizes the relationship between median household income and the percent of households that are owner-occupied. + + +alt.Chart(df2).mark_circle(size=50).encode( + x='med_hhinc', + y='p_owners' +).properties( + height=350, width=500 +) + +df2.shape + +!ls notebook_data/census/Tracts + +## Read in the Census Tract geographic data + +into a GeoPandas GeoDataFrame + +tracts = gpd.read_file('zip://./notebook_data/census/Tracts/cb_2018_06_tract_500k.zip') + +tracts.head(2) + +tracts.plot() + +## Subset to keep only the tracts for Alameda County + +tracts=tracts[tracts.COUNTYFP=='001'] + +tracts.plot() + +## Merge the ACS dataframe into the census tracts geodataframe + +tracts2 = tracts.merge(df2, how='left', left_on="GEOID", right_on="FIPS_11_digit") + +tracts2.head(2) + +## Create a Thematic Map + +Use the Geopandas Plot method to create a map of tracts colored by median household income values. + +tracts2.plot(column='med_hhinc', legend=True) + +## Make the same map with Altair + +alt.Chart(tracts2).mark_geoshape().encode( + color='med_hhinc' +).properties( + width=500, + height=300 +) + +## Link Atair Scatterplot and Map + +# First create a selection object +my_selection = alt.selection_interval() + +# Create a background map +background_map = alt.Chart(tracts2).mark_geoshape( + fill= 'lightgray', + stroke = 'white' +).properties( + width=400, + height=300 +) + +# Create the interactive scatterplot +# by addng the selection object +the_scatterplot = alt.Chart(tracts2).mark_circle(size=50).encode( + x='med_hhinc', + y='p_owners' +).properties( + width=375, + height=300 +).add_selection( + my_selection +) + +# Create the interactive map +# by adding the selection object +income_map = alt.Chart(tracts2).mark_geoshape().encode( + color='med_hhinc' +).properties( + width=400, + height=350 +).transform_filter( + my_selection +) + +# Link the maps (background_map and income_map) +# to the scatterplot (the_scatterplot) +the_scatterplot | (background_map + income_map) + +## Try dragging a box around a subset of the points on the scatterplot and see what happens to the map. + diff --git a/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_19_1.png b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_19_1.png new file mode 100644 index 0000000..bd12fdb Binary files /dev/null and b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_19_1.png differ diff --git a/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_22_1.png b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_22_1.png new file mode 100644 index 0000000..528d61c Binary files /dev/null and b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_22_1.png differ diff --git a/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_27_1.png b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_27_1.png new file mode 100644 index 0000000..045ad00 Binary files /dev/null and b/_build/jupyter_execute/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair_27_1.png differ diff --git a/_build/jupyter_execute/lessons/15_OPTIONAL_Voronoi_Tessellation.ipynb b/_build/jupyter_execute/lessons/15_OPTIONAL_Voronoi_Tessellation.ipynb new file mode 100644 index 0000000..332bcb9 --- /dev/null +++ b/_build/jupyter_execute/lessons/15_OPTIONAL_Voronoi_Tessellation.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 15. Voronoi Tessellation\n", + "\n", + "In some of the earlier lessons we dicussed how to conduct *proximity analyses* using buffer polygons. We looked at how accessible schools were via bike paths in Berkeley. Instead of using a buffers drawn at differnt radii around our locations or objects of interest, we could also use something called a **Voronoi diagram**.\n", + "\n", + "\n", + "\n", + "As seen above, we have a bunch of **Voronoi cells** that are delineated by encompassing all locations that are closest to our point of interest than any other points. \n", + "\n", + "In this notebook, we'll experiment with making these type of diagrams in Python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "import random\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll be using a Python package called `geovoronoi`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from geovoronoi.plotting import subplot_for_map, plot_voronoi_polys_with_points_in_area\n", + "from geovoronoi import voronoi_regions_from_coords, points_to_coords" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 15.1 Polling locations\n", + "\n", + "We'll be using the 2020 General Election voting locations for Alameda County for this analysis. Since the data is aspatial we'll need to coerce it to be a geodataframe and define a CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pull in polling location\n", + "polling_ac_df = pd.read_csv('notebook_data/ac_voting_locations.csv')\n", + "polling_ac_df.head()\n", + "\n", + "# Make into geo data frame\n", + "polling_ac_gdf = gpd.GeoDataFrame(polling_ac_df, \n", + " geometry=gpd.points_from_xy(polling_ac_df.X, polling_ac_df.Y))\n", + "polling_ac_gdf.crs = \"epsg:4326\"\n", + "\n", + "polling_ac_gdf.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 15.2 Census tracts\n", + "We'll also bring in our census tracts data for Alameda county." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bring in census tracts\n", + "tracts_gdf = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\")\n", + "\n", + "# Narrow it down to Alameda County\n", + "tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001']\n", + "tracts_gdf_ac.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make sure we can use it with our polling locations data, we'll check the Coordinate Reference System (CRS)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check CRS\n", + "print('polling_ac_gdf:', polling_ac_gdf.crs)\n", + "print('tracts_gdf_ac CRS:', tracts_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Uh oh! It looks like they have different CRS. We'll transform them both\n", + "> Note: If you need a refresher on CRS check out Lesson 3, Coordinate Reference Systems (CRS) & Map Projections" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform CRS\n", + "polling_ac_gdf_utm10 = polling_ac_gdf.to_crs(\"epsg:26910\")\n", + "tracts_gdf_ac_utm10 = tracts_gdf_ac.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now let's plot them together to see how the polling locations are spread across the county." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "\n", + "tracts_gdf_ac_utm10.plot(ax=ax,color='lightgrey',\n", + " legend=True)\n", + "polling_ac_gdf_utm10.plot(ax=ax, color='seagreen', markersize=9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 15.3 Voronoi Tessellation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make our Voronoi geometries, we'll be using the `voronoi_regions_from_coords` from the `geovoronai` package. Let's check the helper function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?voronoi_regions_from_coords" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll see that the helper function says *enerate Voronoi regions from NumPy array of 2D coordinates or list of Shapely Point objects in `coord`*. That means instead of GeoDataframe as an input, we'll need to first convert all our geometries to numpy arrays. \n", + "\n", + "We can easily do this by using `points_to_coords`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "polling_array = points_to_coords(polling_ac_gdf_utm10.geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now we're ready to run our voronoi region creation! We put in two inputs: our polling locations as a numpy array and our tracts boundary (which we created using `unary_union`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "region_polys, region_pts = voronoi_regions_from_coords(polling_array, tracts_gdf_ac_utm10.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll also notice we get two outputs from our line of code. The first object, in our case `region_polys` gives us the shape of the Voronoi geometry, while `region_pts` gives us the list of points.\n", + "\n", + "To easily plot our points, we can use the `plot_voronoi_polys_with_points_in_area` which takes the following arguments:\n", + "- `ax`: Matplotlib axes object on which you want to plot\n", + "- `area_shape`: the boundary shape that encompasses our Voronoi regions. In our case this is the shape of Alameda County.\n", + "- `region_polys`: The dictionary that we got from above that gives the IDs and the polygons of our Voronoi geoemtries.\n", + "- `points`: The numpy array of our shapely point objects, which we got above as `region_pts`\n", + "\n", + "There are more arguments than this that you can use to customize your plot. Uncomment the code below to see the helper file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ?plot_voronoi_polys_with_points_in_area" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplot_for_map(figsize=(10,10))\n", + "plot_voronoi_polys_with_points_in_area(ax, tracts_gdf_ac_utm10.unary_union, \n", + " region_polys, \n", + " polling_array, \n", + " region_pts,\n", + " points_markersize=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ta-da!!!! \n", + "\n", + "## 15.4 Voronoi colored by an attribute\n", + "\n", + "Now we can go a step beyond this by changing the colors of each of our Voronoi regions based on a certain attribute.\n", + "\n", + "To do that, let's first get all of our region geometries as a list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "list_polys = list(region_polys.values())\n", + "list_polys[0:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we'll replace our point geometries in our original polling locations geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "polling_v = gpd.GeoDataFrame(polling_ac_gdf_utm10.drop('geometry',axis=1),\n", + " geometry=list_polys)\n", + "polling_v.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Say we had a number of votes cast count for every polling location. We'll randomly generate it here..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "polling_v['votes_cast'] = random.sample(range(10000,50000), polling_v.shape[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can now color our polygons based on the number of votes cast there." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax= plt.subplots(figsize=(10,6))\n", + "polling_v.plot(column='votes_cast', cmap='Purples', legend=True, ax=ax)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/15_OPTIONAL_Voronoi_Tessellation.py b/_build/jupyter_execute/lessons/15_OPTIONAL_Voronoi_Tessellation.py new file mode 100644 index 0000000..a2b0619 --- /dev/null +++ b/_build/jupyter_execute/lessons/15_OPTIONAL_Voronoi_Tessellation.py @@ -0,0 +1,148 @@ +# 15. Voronoi Tessellation + +In some of the earlier lessons we dicussed how to conduct *proximity analyses* using buffer polygons. We looked at how accessible schools were via bike paths in Berkeley. Instead of using a buffers drawn at differnt radii around our locations or objects of interest, we could also use something called a **Voronoi diagram**. + + + +As seen above, we have a bunch of **Voronoi cells** that are delineated by encompassing all locations that are closest to our point of interest than any other points. + +In this notebook, we'll experiment with making these type of diagrams in Python. + +import pandas as pd +import geopandas as gpd +import random + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +We'll be using a Python package called `geovoronoi` + +from geovoronoi.plotting import subplot_for_map, plot_voronoi_polys_with_points_in_area +from geovoronoi import voronoi_regions_from_coords, points_to_coords + +## 15.1 Polling locations + +We'll be using the 2020 General Election voting locations for Alameda County for this analysis. Since the data is aspatial we'll need to coerce it to be a geodataframe and define a CRS. + +# Pull in polling location +polling_ac_df = pd.read_csv('notebook_data/ac_voting_locations.csv') +polling_ac_df.head() + +# Make into geo data frame +polling_ac_gdf = gpd.GeoDataFrame(polling_ac_df, + geometry=gpd.points_from_xy(polling_ac_df.X, polling_ac_df.Y)) +polling_ac_gdf.crs = "epsg:4326" + +polling_ac_gdf.plot() + +## 15.2 Census tracts +We'll also bring in our census tracts data for Alameda county. + +# Bring in census tracts +tracts_gdf = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip") + +# Narrow it down to Alameda County +tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001'] +tracts_gdf_ac.plot() +plt.show() + +To make sure we can use it with our polling locations data, we'll check the Coordinate Reference System (CRS). + +# Check CRS +print('polling_ac_gdf:', polling_ac_gdf.crs) +print('tracts_gdf_ac CRS:', tracts_gdf_ac.crs) + +Uh oh! It looks like they have different CRS. We'll transform them both +> Note: If you need a refresher on CRS check out Lesson 3, Coordinate Reference Systems (CRS) & Map Projections + +# Transform CRS +polling_ac_gdf_utm10 = polling_ac_gdf.to_crs("epsg:26910") +tracts_gdf_ac_utm10 = tracts_gdf_ac.to_crs("epsg:26910") + +And now let's plot them together to see how the polling locations are spread across the county. + +fig, ax = plt.subplots(figsize = (14,8)) + +tracts_gdf_ac_utm10.plot(ax=ax,color='lightgrey', + legend=True) +polling_ac_gdf_utm10.plot(ax=ax, color='seagreen', markersize=9) + +## 15.3 Voronoi Tessellation + +To make our Voronoi geometries, we'll be using the `voronoi_regions_from_coords` from the `geovoronai` package. Let's check the helper function. + +?voronoi_regions_from_coords + +You'll see that the helper function says *enerate Voronoi regions from NumPy array of 2D coordinates or list of Shapely Point objects in `coord`*. That means instead of GeoDataframe as an input, we'll need to first convert all our geometries to numpy arrays. + +We can easily do this by using `points_to_coords` + +polling_array = points_to_coords(polling_ac_gdf_utm10.geometry) + +And now we're ready to run our voronoi region creation! We put in two inputs: our polling locations as a numpy array and our tracts boundary (which we created using `unary_union`). + +region_polys, region_pts = voronoi_regions_from_coords(polling_array, tracts_gdf_ac_utm10.unary_union) + +You'll also notice we get two outputs from our line of code. The first object, in our case `region_polys` gives us the shape of the Voronoi geometry, while `region_pts` gives us the list of points. + +To easily plot our points, we can use the `plot_voronoi_polys_with_points_in_area` which takes the following arguments: +- `ax`: Matplotlib axes object on which you want to plot +- `area_shape`: the boundary shape that encompasses our Voronoi regions. In our case this is the shape of Alameda County. +- `region_polys`: The dictionary that we got from above that gives the IDs and the polygons of our Voronoi geoemtries. +- `points`: The numpy array of our shapely point objects, which we got above as `region_pts` + +There are more arguments than this that you can use to customize your plot. Uncomment the code below to see the helper file. + +# ?plot_voronoi_polys_with_points_in_area + +fig, ax = subplot_for_map(figsize=(10,10)) +plot_voronoi_polys_with_points_in_area(ax, tracts_gdf_ac_utm10.unary_union, + region_polys, + polling_array, + region_pts, + points_markersize=10) + +Ta-da!!!! + +## 15.4 Voronoi colored by an attribute + +Now we can go a step beyond this by changing the colors of each of our Voronoi regions based on a certain attribute. + +To do that, let's first get all of our region geometries as a list. + +list_polys = list(region_polys.values()) +list_polys[0:5] + +And we'll replace our point geometries in our original polling locations geodataframe. + +polling_v = gpd.GeoDataFrame(polling_ac_gdf_utm10.drop('geometry',axis=1), + geometry=list_polys) +polling_v.plot() + +Say we had a number of votes cast count for every polling location. We'll randomly generate it here... + +polling_v['votes_cast'] = random.sample(range(10000,50000), polling_v.shape[0]) + +And we can now color our polygons based on the number of votes cast there. + +fig, ax= plt.subplots(figsize=(10,6)) +polling_v.plot(column='votes_cast', cmap='Purples', legend=True, ax=ax) +plt.show() + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + + diff --git a/_build/jupyter_execute/lessons/16_OPTIONAL_Introduction_to_Raster_Data.ipynb b/_build/jupyter_execute/lessons/16_OPTIONAL_Introduction_to_Raster_Data.ipynb new file mode 100644 index 0000000..e07007a --- /dev/null +++ b/_build/jupyter_execute/lessons/16_OPTIONAL_Introduction_to_Raster_Data.ipynb @@ -0,0 +1,877 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 16. Introduction to Raster Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a very brief introduction to reading raster data and basic manipulations in Python. We'll walk through one of the most commonly used raster python packages, `rasterio`. We'll be using the [National Land Cover Database (NLCD)](https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend) from 2011 that was downloaded from [here](https://viewer.nationalmap.gov/basic).\n", + "\n", + "\n", + "\n", + "> Note: They also have a [cool online viewer](https://www.mrlc.gov/viewer/) that is free and open access." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "from matplotlib.patches import Patch\n", + "\n", + "import json\n", + "import numpy as np\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use raster data we'll be using the `rasterio` package, which is a popular package that helps you read, write, and manipulate raster data. We'll also be using `rasterstats`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import rasterio\n", + "from rasterio.plot import show, plotting_extent\n", + "from rasterio.mask import mask\n", + "\n", + "from rasterstats import zonal_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.1 Import data and plot\n", + "\n", + "To open our NLCD subset data, we'll use the `rasterio.open` function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011 = rasterio.open('notebook_data/raster/nlcd2011_sf.tif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check out what we get." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's dissect this output here. We can look at the helper documentation for clues." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?rasterio.open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which reads that the function returns a ``DatasetReader`` or ``DatasetWriter`` object. Unlike in `GeoPandas` which we've been utilizing a lot of, we don't have a directly editable object here. However, `rasterio` does have functions in place where we can still use this returned object directly.\n", + "\n", + "For example, we can easily plot our NLCD data using `rasterio.plot.show`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rasterio.plot.show(nlcd_2011)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And just like how we formatted our `matplotlib` plots when we were using GeoDataFrames, we can still do that with this raster plotting function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?rasterio.plot.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,8))\n", + "plt_nlcd = rasterio.plot.show(nlcd_2011, cmap='Pastel2', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Take note of what you think could be improved here... we'll come back to this)\n", + "\n", + "We can also plot a histogram of our data in a very similar way." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rasterio.plot.show_hist(nlcd_2011, bins=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that we have more values on the lower end than on the higher end. To really understand the values that we see here let's [take a look at the legend](https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend).\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.2 Raster data structure\n", + "\n", + "> *Note:* If you need a refresher on what raster data is and relevant terminology. Check out the first lesson that covers geospatial topics\n", + "\n", + "Now that we have a basic grasp on how to pull in and plot raster data, we can dig a little deeper to see what information we have.\n", + "\n", + "First let's check the number of bands there are in our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011.count" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case we only have 1 band. If you're pulling in aerial image, you might have 3 bands (red, green, blue). In the case you're bringing in remote sensing data like Landsat or MODIS you might have more!\n", + "\n", + "Not let's check out what meta data we have." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "nlcd_2011.meta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we have a lot of good information here. Let's unpack it:\n", + "\n", + "- `driver`: the file type (simialr to what we see in `open` and Geopandas `open`)\n", + "- `dtype`: the data type of each of your pixels\n", + "- `nodata`: the value that is set for no data pixels\n", + "- `width`: the number of pixels wide your dataset is\n", + "- `height`: the number of pixels high your dataset is\n", + "- `count`: the number of bands in your dataset\n", + "- `crs`: the coordiante reference system (CRS) of your data\n", + "- `transform`: the affine transform matrix that tell us which pixel locations in each row and column align with spatial locations (longitude, latitude)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also get similar information by calling `profile`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011.profile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, but now we want to actually access our data. We can read in our data as a Numpy ndarray." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011_array = nlcd_2011.read()\n", + "nlcd_2011_array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can call shape and see we have a 3D array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011_array.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much like other Numpy arrays, we can look at the min, mean, and max of our data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Minimum: \", np.nanmin(nlcd_2011_array))\n", + "print(\"Max: \", np.nanmean(nlcd_2011_array))\n", + "print(\"Mean: \", np.nanmax(nlcd_2011_array))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And since we have our data in an array form now, we can plot it using not a `rasterio` function, but simply `plt.imshow`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(nlcd_2011_array[0,:,:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that we specified this plotting by making our array 2D. This gives us more flexibility about how we want to create our plots. You can do something like this:\n", + "\n", + "> This definitely looks more scary than it actually is. Essentially we are:\n", + "> 1. constructing a full color spectrum with all the colors we want\n", + "> 2. If values are outside of this range, we set the color tot white\n", + "> 3. we set the boudnaries for each of these colors so we know which color to assign to what value\n", + "> 4. we create legend labels for our legend\n", + ">\n", + "> This process is only really needed if we want to have a color map for specific values outside of a specific named `matplotlib` named color map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the colors you want\n", + "cmap = matplotlib.colors.ListedColormap(['royalblue', #11\n", + " 'white', #12\n", + " 'beige', #21\n", + " 'salmon', #22\n", + " 'red', #23\n", + " 'darkred', #24\n", + " 'grey', #31\n", + " 'yellowgreen', #41\n", + " 'darkgreen', #42\n", + " 'lightgreen', # 43\n", + " 'darkgoldenrod', #51\n", + " 'tan', # 52\n", + " 'wheat', # 71\n", + " 'darkkhaki', #72\n", + " 'darkseagreen', #73\n", + " 'mediumseagreen', #74\n", + " 'gold', #81\n", + " 'chocolate', #82\n", + " 'lightsteelblue', #90\n", + " 'steelblue', #95\n", + " ])\n", + "cmap.set_under('#FFFFFF')\n", + "cmap.set_over('#FFFFFF')\n", + "# Define a normalization from values -> colors\n", + "norm = matplotlib.colors.BoundaryNorm([10.5,\n", + " 11.5,\n", + " 12.5,\n", + " 21.5,\n", + " 22.5,\n", + " 23.5,\n", + " 24.5,\n", + " 31.5,\n", + " 41.5, \n", + " 42.5,\n", + " 43.5,\n", + " 51.5,\n", + " 52.5,\n", + " 71.5,\n", + " 72.5,\n", + " 73.5,\n", + " 74.5,\n", + " 81.5,\n", + " 82.5,\n", + " 90.5,\n", + " 95.5,\n", + " ],20)\n", + "\n", + "\n", + "legend_labels = { 'royalblue':'Open Water', \n", + " 'white':'Perennial Ice/Snow',\n", + " 'beige':'Developed, Open Space',\n", + " 'salmon':'Developed, Low Intensity',\n", + " 'red':'Developed, Medium Intensity',\n", + " 'darkred':'Developed High Intensity',\n", + " 'grey':'Barren Land (Rock/Sand/Clay)',\n", + " 'yellowgreen':'Deciduous Forest',\n", + " 'darkgreen':'Evergreen Forest',\n", + " 'lightgreen':'Mixed Forest',\n", + " 'darkgoldenrod':'Dwarf Scrub',\n", + " 'tan':'Shrub/Scrub',\n", + " 'wheat':'Grassland/Herbaceous',\n", + " 'darkkhaki':'Sedge/Herbaceous',\n", + " 'darkseagreen':'Lichens',\n", + " 'mediumseagreen':'Moss',\n", + " 'gold':'Pasture/Hay',\n", + " 'chocolate':'Cultivated Crops',\n", + " 'lightsteelblue':'Woody Wetlands',\n", + " 'steelblue':'Emergent Herbaceous Wetlands'}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 8))\n", + "plt_nlcd = ax.imshow(nlcd_2011_array[0,:,:], cmap=cmap, norm=norm)\n", + "ax.set_title('NLCD 2011', fontsize=30)\n", + "\n", + "# Remove axes\n", + "ax.set_frame_on(False)\n", + "plt.setp(ax.get_xticklabels(), visible=False)\n", + "plt.setp(ax.get_yticklabels(), visible=False)\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "\n", + "# Add color bar\n", + "patches = [Patch(color=color, label=label)\n", + " for color, label in legend_labels.items()]\n", + "\n", + "fig.legend(handles=patches, facecolor=\"white\",bbox_to_anchor=(1.1, 1.05))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.2 Mask raster data\n", + "\n", + "*Masking* is a common action that is done with raster data where you \"mask\" everything outside of a certain geometry.\n", + "\n", + "To do this let's first bring in the san francisco county data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Bring in census tracts\n", + "tracts_gdf = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\").to_crs('epsg:4326')\n", + "\n", + "# Narrow it down to San Francisco County\n", + "tracts_gdf_sf = tracts_gdf[tracts_gdf['COUNTYFP']=='075']\n", + "\n", + "tracts_gdf_sf.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We forgot about the Farollon islands! Let's crop those out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Crop out Farallon\n", + "tracts_gdf_sf = tracts_gdf_sf.cx[-122.8:-122.35, 37.65:37.85].copy().reset_index(drop=True)\n", + "\n", + "tracts_gdf_sf.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll want to check the crs of our GeoDataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_sf.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will call the `mask` function from `rasterio`. Let's look at the documentation first." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We actually recommend using the `rioxarray` method instesd. So we'll import a new package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import rioxarray as rxr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Open our same NLCD data..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011 = rxr.open_rasterio('notebook_data/raster/nlcd2011_sf.tif',\n", + " masked=True).squeeze()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reproject our NLCD to be in the same coordinate reference system as the san francisco data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from rasterio.crs import CRS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!rio --version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Currently doesn't work\n", + "# Issue: https://github.com/mapbox/rasterio/issues/2103\n", + "test = nlcd_2011.rio.reproject(tracts_gdf_sf.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And clip our data to the san francisco geometry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clipped = test.rio.clip(tracts_gdf_sf.geometry, tracts_gdf_sf.crs, drop=False, invert=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can easily plot this using `.plot()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clipped.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can also make a pretty map like we did before." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 8))\n", + "clipped.plot(cmap=cmap, norm=norm, ax=ax, add_colorbar=False)\n", + "ax.set_title('NLCD 2011 (Cropped)', fontsize=30)\n", + "\n", + "# Add color bar\n", + "patches = [Patch(color=color, label=label)\n", + " for color, label in legend_labels.items()]\n", + "\n", + "fig.legend(handles=patches, facecolor=\"white\",bbox_to_anchor=(1.1, 1.05))\n", + "\n", + "# Remove axes\n", + "ax.set_frame_on(False)\n", + "plt.setp(ax.get_xticklabels(), visible=False)\n", + "plt.setp(ax.get_yticklabels(), visible=False)\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and you can save your work out to a new file!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clipped.rio.to_raster(\"outdata/nlcd2011_sf_cropped.tif\", tiled=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.3 Aggregate raster to vector\n", + "\n", + "Another common step we see in a lot of raster work flows is questions that go along the lines of \"How do I find the average of my raster within my vector data shapes\"?\n", + "\n", + "We can do this by *aggregating* to our vector data. For this example we'll ask the question, \"What is the majority class I have in each of the census tracts in San Francisco?\"\n", + "\n", + "For this we'll turn to the `rasterstas` pacakge which has a handy function called `zonal_stats`. By default, the function will give us the minimum, maximum, mean, and count. But there also a lot more statistics that the function can return beyond this:\n", + "- sum\n", + "- std\n", + "- median\n", + "- majority\n", + "- minority\n", + "- unique\n", + "- range\n", + "- nodata\n", + "- percentile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we'll first bring back our clipped census tracts shapefile we have for san francisco." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_sf.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we'll check out the `zonal_stats` documentation to get a better sense of how we can customize the arguments to better fit our needs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?zonal_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which doesn't tell us a ton. Since we don't have `gen_zonal_stas` loaded, we can go look at the documentation online: https://pythonhosted.org/rasterstats/rasterstats.html\n", + "\n", + "After we check that out, let's get on rolling and actually get our zonal stats by census tract." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with rasterio.open('notebook_data/raster/nlcd2011_sf.tif') as src:\n", + " affine = src.transform\n", + " array = src.read(1)\n", + " df_zonal_stats = pd.DataFrame(zonal_stats(tracts_gdf_sf, array, affine=affine, stats=['majority', 'unique']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's a lot going on in the cell above, let's break it down:\n", + "- `affine` object grabbed the transform of our raster data\n", + "- `array` object read the first band we have in our raster dataset\n", + "- `df_zonal_stats` has the results of our `zonal_stats` and then coerced it to be a dataframe.\n", + "\n", + "So from that caell, we get `df_zonal_stats` which looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_zonal_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So now, we can merge this back onto our geodataframe so we can add the majority classes and unique number of classes as attributes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_sf_zs = pd.concat([tracts_gdf_sf, df_zonal_stats[['majority','unique']]], axis=1) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can make a map that shows, for example, the majority class we have in each census tract." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,8))\n", + "tracts_gdf_sf_zs.plot(column='majority', cmap=cmap, norm=norm, ax=ax)\n", + "\n", + "# Add color bar\n", + "patches = [Patch(color=color, label=label)\n", + " for color, label in legend_labels.items()]\n", + "\n", + "fig.legend(handles=patches, facecolor=\"white\",bbox_to_anchor=(1.1, 1.05))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.4 Other resources\n", + "We really only grazed the surface here. We've linked a couple of resources that dive into raster data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [EarthLab](https://www.earthdatascience.org)\n", + "- [Software Carpentry](https://carpentries-incubator.github.io/geospatial-python/aio/index.html)\n", + "- [Intro to Python GIS](https://automating-gis-processes.github.io/CSC/index.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/lessons/16_OPTIONAL_Introduction_to_Raster_Data.py b/_build/jupyter_execute/lessons/16_OPTIONAL_Introduction_to_Raster_Data.py new file mode 100644 index 0000000..a8a2ef4 --- /dev/null +++ b/_build/jupyter_execute/lessons/16_OPTIONAL_Introduction_to_Raster_Data.py @@ -0,0 +1,381 @@ +# 16. Introduction to Raster Data + +This is a very brief introduction to reading raster data and basic manipulations in Python. We'll walk through one of the most commonly used raster python packages, `rasterio`. We'll be using the [National Land Cover Database (NLCD)](https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend) from 2011 that was downloaded from [here](https://viewer.nationalmap.gov/basic). + + + +> Note: They also have a [cool online viewer](https://www.mrlc.gov/viewer/) that is free and open access. + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib +from matplotlib.patches import Patch + +import json +import numpy as np + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +To use raster data we'll be using the `rasterio` package, which is a popular package that helps you read, write, and manipulate raster data. We'll also be using `rasterstats`. + +import rasterio +from rasterio.plot import show, plotting_extent +from rasterio.mask import mask + +from rasterstats import zonal_stats + +## 16.1 Import data and plot + +To open our NLCD subset data, we'll use the `rasterio.open` function + +nlcd_2011 = rasterio.open('notebook_data/raster/nlcd2011_sf.tif') + +Let's check out what we get. + +nlcd_2011 + +Let's dissect this output here. We can look at the helper documentation for clues. + +?rasterio.open + +Which reads that the function returns a ``DatasetReader`` or ``DatasetWriter`` object. Unlike in `GeoPandas` which we've been utilizing a lot of, we don't have a directly editable object here. However, `rasterio` does have functions in place where we can still use this returned object directly. + +For example, we can easily plot our NLCD data using `rasterio.plot.show`. + +rasterio.plot.show(nlcd_2011) + +And just like how we formatted our `matplotlib` plots when we were using GeoDataFrames, we can still do that with this raster plotting function. + +?rasterio.plot.show + +fig, ax = plt.subplots(figsize=(8,8)) +plt_nlcd = rasterio.plot.show(nlcd_2011, cmap='Pastel2', ax=ax) + +(Take note of what you think could be improved here... we'll come back to this) + +We can also plot a histogram of our data in a very similar way. + +rasterio.plot.show_hist(nlcd_2011, bins=30) + +We can see that we have more values on the lower end than on the higher end. To really understand the values that we see here let's [take a look at the legend](https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend). + + + +## 16.2 Raster data structure + +> *Note:* If you need a refresher on what raster data is and relevant terminology. Check out the first lesson that covers geospatial topics + +Now that we have a basic grasp on how to pull in and plot raster data, we can dig a little deeper to see what information we have. + +First let's check the number of bands there are in our dataset. + +nlcd_2011.count + +In this case we only have 1 band. If you're pulling in aerial image, you might have 3 bands (red, green, blue). In the case you're bringing in remote sensing data like Landsat or MODIS you might have more! + +Not let's check out what meta data we have. + +nlcd_2011.meta + +So we have a lot of good information here. Let's unpack it: + +- `driver`: the file type (simialr to what we see in `open` and Geopandas `open`) +- `dtype`: the data type of each of your pixels +- `nodata`: the value that is set for no data pixels +- `width`: the number of pixels wide your dataset is +- `height`: the number of pixels high your dataset is +- `count`: the number of bands in your dataset +- `crs`: the coordiante reference system (CRS) of your data +- `transform`: the affine transform matrix that tell us which pixel locations in each row and column align with spatial locations (longitude, latitude). + +We can also get similar information by calling `profile`. + +nlcd_2011.profile + +nlcd_2011.crs + +Okay, but now we want to actually access our data. We can read in our data as a Numpy ndarray. + +nlcd_2011_array = nlcd_2011.read() +nlcd_2011_array + +And we can call shape and see we have a 3D array. + +nlcd_2011_array.shape + +Much like other Numpy arrays, we can look at the min, mean, and max of our data + +print("Minimum: ", np.nanmin(nlcd_2011_array)) +print("Max: ", np.nanmean(nlcd_2011_array)) +print("Mean: ", np.nanmax(nlcd_2011_array)) + +And since we have our data in an array form now, we can plot it using not a `rasterio` function, but simply `plt.imshow`. + +plt.imshow(nlcd_2011_array[0,:,:]) + +Notice that we specified this plotting by making our array 2D. This gives us more flexibility about how we want to create our plots. You can do something like this: + +> This definitely looks more scary than it actually is. Essentially we are: +> 1. constructing a full color spectrum with all the colors we want +> 2. If values are outside of this range, we set the color tot white +> 3. we set the boudnaries for each of these colors so we know which color to assign to what value +> 4. we create legend labels for our legend +> +> This process is only really needed if we want to have a color map for specific values outside of a specific named `matplotlib` named color map. + +# Define the colors you want +cmap = matplotlib.colors.ListedColormap(['royalblue', #11 + 'white', #12 + 'beige', #21 + 'salmon', #22 + 'red', #23 + 'darkred', #24 + 'grey', #31 + 'yellowgreen', #41 + 'darkgreen', #42 + 'lightgreen', # 43 + 'darkgoldenrod', #51 + 'tan', # 52 + 'wheat', # 71 + 'darkkhaki', #72 + 'darkseagreen', #73 + 'mediumseagreen', #74 + 'gold', #81 + 'chocolate', #82 + 'lightsteelblue', #90 + 'steelblue', #95 + ]) +cmap.set_under('#FFFFFF') +cmap.set_over('#FFFFFF') +# Define a normalization from values -> colors +norm = matplotlib.colors.BoundaryNorm([10.5, + 11.5, + 12.5, + 21.5, + 22.5, + 23.5, + 24.5, + 31.5, + 41.5, + 42.5, + 43.5, + 51.5, + 52.5, + 71.5, + 72.5, + 73.5, + 74.5, + 81.5, + 82.5, + 90.5, + 95.5, + ],20) + + +legend_labels = { 'royalblue':'Open Water', + 'white':'Perennial Ice/Snow', + 'beige':'Developed, Open Space', + 'salmon':'Developed, Low Intensity', + 'red':'Developed, Medium Intensity', + 'darkred':'Developed High Intensity', + 'grey':'Barren Land (Rock/Sand/Clay)', + 'yellowgreen':'Deciduous Forest', + 'darkgreen':'Evergreen Forest', + 'lightgreen':'Mixed Forest', + 'darkgoldenrod':'Dwarf Scrub', + 'tan':'Shrub/Scrub', + 'wheat':'Grassland/Herbaceous', + 'darkkhaki':'Sedge/Herbaceous', + 'darkseagreen':'Lichens', + 'mediumseagreen':'Moss', + 'gold':'Pasture/Hay', + 'chocolate':'Cultivated Crops', + 'lightsteelblue':'Woody Wetlands', + 'steelblue':'Emergent Herbaceous Wetlands'} + +fig, ax = plt.subplots(figsize=(8, 8)) +plt_nlcd = ax.imshow(nlcd_2011_array[0,:,:], cmap=cmap, norm=norm) +ax.set_title('NLCD 2011', fontsize=30) + +# Remove axes +ax.set_frame_on(False) +plt.setp(ax.get_xticklabels(), visible=False) +plt.setp(ax.get_yticklabels(), visible=False) +ax.set_xticks([]) +ax.set_yticks([]) + +# Add color bar +patches = [Patch(color=color, label=label) + for color, label in legend_labels.items()] + +fig.legend(handles=patches, facecolor="white",bbox_to_anchor=(1.1, 1.05)) + + + + +## 16.2 Mask raster data + +*Masking* is a common action that is done with raster data where you "mask" everything outside of a certain geometry. + +To do this let's first bring in the san francisco county data. + +# Bring in census tracts +tracts_gdf = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip").to_crs('epsg:4326') + +# Narrow it down to San Francisco County +tracts_gdf_sf = tracts_gdf[tracts_gdf['COUNTYFP']=='075'] + +tracts_gdf_sf.plot() +plt.show() + +We forgot about the Farollon islands! Let's crop those out. + +# Crop out Farallon +tracts_gdf_sf = tracts_gdf_sf.cx[-122.8:-122.35, 37.65:37.85].copy().reset_index(drop=True) + +tracts_gdf_sf.plot() +plt.show() + +We'll want to check the crs of our GeoDataFrame + +tracts_gdf_sf.crs + +Now we will call the `mask` function from `rasterio`. Let's look at the documentation first. + +?mask + +We actually recommend using the `rioxarray` method instesd. So we'll import a new package. + +import rioxarray as rxr + +Open our same NLCD data... + +nlcd_2011 = rxr.open_rasterio('notebook_data/raster/nlcd2011_sf.tif', + masked=True).squeeze() + +Reproject our NLCD to be in the same coordinate reference system as the san francisco data + +from rasterio.crs import CRS + +!rio --version + +# Currently doesn't work +# Issue: https://github.com/mapbox/rasterio/issues/2103 +test = nlcd_2011.rio.reproject(tracts_gdf_sf.crs) + +And clip our data to the san francisco geometry + +clipped = test.rio.clip(tracts_gdf_sf.geometry, tracts_gdf_sf.crs, drop=False, invert=False) + +We can easily plot this using `.plot()` + +clipped.plot() + +And we can also make a pretty map like we did before. + +fig, ax = plt.subplots(figsize=(8, 8)) +clipped.plot(cmap=cmap, norm=norm, ax=ax, add_colorbar=False) +ax.set_title('NLCD 2011 (Cropped)', fontsize=30) + +# Add color bar +patches = [Patch(color=color, label=label) + for color, label in legend_labels.items()] + +fig.legend(handles=patches, facecolor="white",bbox_to_anchor=(1.1, 1.05)) + +# Remove axes +ax.set_frame_on(False) +plt.setp(ax.get_xticklabels(), visible=False) +plt.setp(ax.get_yticklabels(), visible=False) +ax.set_xticks([]) +ax.set_yticks([]) + + + +and you can save your work out to a new file! + +clipped.rio.to_raster("outdata/nlcd2011_sf_cropped.tif", tiled=True) + +## 16.3 Aggregate raster to vector + +Another common step we see in a lot of raster work flows is questions that go along the lines of "How do I find the average of my raster within my vector data shapes"? + +We can do this by *aggregating* to our vector data. For this example we'll ask the question, "What is the majority class I have in each of the census tracts in San Francisco?" + +For this we'll turn to the `rasterstas` pacakge which has a handy function called `zonal_stats`. By default, the function will give us the minimum, maximum, mean, and count. But there also a lot more statistics that the function can return beyond this: +- sum +- std +- median +- majority +- minority +- unique +- range +- nodata +- percentile + +So we'll first bring back our clipped census tracts shapefile we have for san francisco. + +tracts_gdf_sf.plot() +plt.show() + +And we'll check out the `zonal_stats` documentation to get a better sense of how we can customize the arguments to better fit our needs. + +?zonal_stats + +Which doesn't tell us a ton. Since we don't have `gen_zonal_stas` loaded, we can go look at the documentation online: https://pythonhosted.org/rasterstats/rasterstats.html + +After we check that out, let's get on rolling and actually get our zonal stats by census tract. + +with rasterio.open('notebook_data/raster/nlcd2011_sf.tif') as src: + affine = src.transform + array = src.read(1) + df_zonal_stats = pd.DataFrame(zonal_stats(tracts_gdf_sf, array, affine=affine, stats=['majority', 'unique'])) + +There's a lot going on in the cell above, let's break it down: +- `affine` object grabbed the transform of our raster data +- `array` object read the first band we have in our raster dataset +- `df_zonal_stats` has the results of our `zonal_stats` and then coerced it to be a dataframe. + +So from that caell, we get `df_zonal_stats` which looks like: + +df_zonal_stats + +So now, we can merge this back onto our geodataframe so we can add the majority classes and unique number of classes as attributes. + +tracts_gdf_sf_zs = pd.concat([tracts_gdf_sf, df_zonal_stats[['majority','unique']]], axis=1) + +And we can make a map that shows, for example, the majority class we have in each census tract. + +fig, ax = plt.subplots(figsize=(8,8)) +tracts_gdf_sf_zs.plot(column='majority', cmap=cmap, norm=norm, ax=ax) + +# Add color bar +patches = [Patch(color=color, label=label) + for color, label in legend_labels.items()] + +fig.legend(handles=patches, facecolor="white",bbox_to_anchor=(1.1, 1.05)) + +plt.show() + +## 16.4 Other resources +We really only grazed the surface here. We've linked a couple of resources that dive into raster data. + +- [EarthLab](https://www.earthdatascience.org) +- [Software Carpentry](https://carpentries-incubator.github.io/geospatial-python/aio/index.html) +- [Intro to Python GIS](https://automating-gis-processes.github.io/CSC/index.html) + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1.ipynb b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1.ipynb new file mode 100644 index 0000000..fd0b31b --- /dev/null +++ b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1.ipynb @@ -0,0 +1,1199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 2. Introduction to Geopandas\n", + "\n", + "In this lesson we'll learn about a package that is core to using geospatial data in Python. We'll go through the structure of the data (it's not too different from regular DataFrames!), geometries, shapefiles, and how to save your hard work.\n", + "\n", + "- 2.1 What is GeoPandas?\n", + "- 2.2 Read in a shapefile\n", + "- 2.3 Explore the GeoDataFrame\n", + "- 2.4 Plot the GeoDataFrame\n", + "- 2.5 Subset the GeoDataFrame\n", + "- 2.6 Save your data\n", + "- 2.7 Recap\n", + "- **Exercise**: IO, Manipulation, and Mapping\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/california_counties/CaliforniaCounties.shp'\n", + " - 'notebook_data/census/Places/cb_2018_06_place_500k.zip'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 5 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 What is GeoPandas?\n", + "\n", + "### GeoPandas and related Geospatial Packages\n", + "\n", + "[GeoPandas](http://geopandas.org/) is a relatively new package that makes it easier to work with geospatial data in Python. In the last few years it has grown more powerful and stable. This is really great because previously it was quite complex to work with geospatial data in Python. GeoPandas is now the go-to package for working with `vector` geospatial data in Python. \n", + "\n", + "> **Protip**: If you work with `raster` data you will want to checkout the [rasterio](https://rasterio.readthedocs.io/en/latest/) package. We will not cover raster data in this tutorial.\n", + "\n", + "### GeoPandas = pandas + geo\n", + "GeoPandas gives you access to all of the functionality of [pandas](https://pandas.pydata.org/), which is the primary data analysis tool for working with tabular data in Python. GeoPandas extends pandas with attributes and methods for working with geospatial data.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries\n", + "\n", + "Let's start by importing the libraries that we will use." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Read in a shapefile\n", + "\n", + "As we discussed in the initial geospatial overview, a *shapefile* is one type of geospatial data that holds vector data. \n", + "\n", + "> To learn more about ESRI Shapefiles, this is a good place to start: [ESRI Shapefile Wiki Page](https://en.wikipedia.org/wiki/Shapefile) \n", + "\n", + "The tricky thing to remember about shapefiles is that they're actually a collection of 3 to 9+ files together. Here's a list of all the files that can make up a shapefile:\n", + " \n", + ">`shp`: The main file that stores the feature geometry\n", + ">\n", + ">`shx`: The index file that stores the index of the feature geometry \n", + ">\n", + ">`dbf`: The dBASE table that stores the attribute information of features \n", + ">\n", + ">`prj`: The file that stores the coordinate system information. (should be required!)\n", + ">\n", + ">`xml`: Metadata —Stores information about the shapefile.\n", + ">\n", + ">`cpg`: Specifies the code page for identifying the character set to be used.\n", + "\n", + "But it remains the most commonly used file format for vector spatial data, and it's really easy to visualize in one go!\n", + "\n", + "Let's try it out with California counties, and use `geopandas` for the first time. `gpd.read_file` is a flexible function that let's you read in many different types of geospatial data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the counties shapefile\n", + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_6_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot out California counties\n", + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bam! Amazing! We're off to a running start." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Explore the GeoDataFrame\n", + "\n", + "Before we get in too deep, let's discuss what a *GeoDataFrame* is and how it's different from `pandas` *DataFrames*.\n", + "\n", + "### The GeoPandas GeoDataFrame\n", + "\n", + "A [GeoPandas GeoDataFrame](https://geopandas.org/data_structures.html#geodataframe), or `gdf` for short, is just like a pandas dataframe (`df`) but with an extra geometry column and methods & attributes that work on that column. I repeat because it's important:\n", + "\n", + "> `A GeoPandas GeoDataFrame is a pandas DataFrame with a geometry column and methods & attributes that work on that column.`\n", + "\n", + "> This means all the methods and attributes of a pandas DataFrame also work on a Geopandas GeoDataFrame!!\n", + "\n", + "With that in mind, let's start exploring out dataframe just like we would do in `pandas`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(58, 59)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find the number of rows and columnds in counties\n", + "counties.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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10KingsCalifornia152982109.9155039111.42742183027110142562...1203.201391.3906089Fresno,Kern,Tulare2212260NoneNoneNoneNonePOLYGON ((12524.028 -179431.328, 12358.142 -17...
20LakeCalifornia6466548.66525349.0823345203312322049...72.311329.4606106None0NoneNoneNoneNoneMULTIPOLYGON (((-240632.150 93056.104, -240669...
30LassenCalifornia348957.4350397.4228562553228341234...120.924720.4206086None0NoneNoneNoneNonePOLYGON ((-45364.032 352060.633, -45248.844 35...
40Los AngelesCalifornia98186052402.399043412423.264150493659985687472828...187.944087.1906073San Bernardino,Kern2874841NoneNoneNoneNoneMULTIPOLYGON (((173874.519 -471855.293, 173852...
\n", + "

5 rows × 59 columns

\n", + "
" + ], + "text/plain": [ + " FID_ NAME STATE_NAME POP2010 POP10_SQMI POP2012 POP12_SQMI \\\n", + "0 0 Kern California 839631 102.9 851089 104.282870 \n", + "1 0 Kings California 152982 109.9 155039 111.427421 \n", + "2 0 Lake California 64665 48.6 65253 49.082334 \n", + "3 0 Lassen California 34895 7.4 35039 7.422856 \n", + "4 0 Los Angeles California 9818605 2402.3 9904341 2423.264150 \n", + "\n", + " WHITE BLACK AMERI_ES ... AVG_SALE07 SQMI CountyFIPS \\\n", + "0 499766 48921 12676 ... 1513.53 8161.35 06103 \n", + "1 83027 11014 2562 ... 1203.20 1391.39 06089 \n", + "2 52033 1232 2049 ... 72.31 1329.46 06106 \n", + "3 25532 2834 1234 ... 120.92 4720.42 06086 \n", + "4 4936599 856874 72828 ... 187.94 4087.19 06073 \n", + "\n", + " NEIGHBORS PopNeigh NEIGHBOR_1 PopNeigh_1 NEIGHBOR_2 \\\n", + "0 San Bernardino,Tulare,Inyo 2495935 None None None \n", + "1 Fresno,Kern,Tulare 2212260 None None None \n", + "2 None 0 None None None \n", + "3 None 0 None None None \n", + "4 San Bernardino,Kern 2874841 None None None \n", + "\n", + " PopNeigh_2 geometry \n", + "0 None POLYGON ((193446.035 -244342.585, 194033.795 -... \n", + "1 None POLYGON ((12524.028 -179431.328, 12358.142 -17... \n", + "2 None MULTIPOLYGON (((-240632.150 93056.104, -240669... \n", + "3 None POLYGON ((-45364.032 352060.633, -45248.844 35... \n", + "4 None MULTIPOLYGON (((173874.519 -471855.293, 173852... \n", + "\n", + "[5 rows x 59 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the first couple of rows in our geodataframe\n", + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FID_', 'NAME', 'STATE_NAME', 'POP2010', 'POP10_SQMI', 'POP2012',\n", + " 'POP12_SQMI', 'WHITE', 'BLACK', 'AMERI_ES', 'ASIAN', 'HAWN_PI',\n", + " 'HISPANIC', 'OTHER', 'MULT_RACE', 'MALES', 'FEMALES', 'AGE_UNDER5',\n", + " 'AGE_5_9', 'AGE_10_14', 'AGE_15_19', 'AGE_20_24', 'AGE_25_34',\n", + " 'AGE_35_44', 'AGE_45_54', 'AGE_55_64', 'AGE_65_74', 'AGE_75_84',\n", + " 'AGE_85_UP', 'MED_AGE', 'MED_AGE_M', 'MED_AGE_F', 'HOUSEHOLDS',\n", + " 'AVE_HH_SZ', 'HSEHLD_1_M', 'HSEHLD_1_F', 'MARHH_CHD', 'MARHH_NO_C',\n", + " 'MHH_CHILD', 'FHH_CHILD', 'FAMILIES', 'AVE_FAM_SZ', 'HSE_UNITS',\n", + " 'VACANT', 'OWNER_OCC', 'RENTER_OCC', 'NO_FARMS07', 'AVG_SIZE07',\n", + " 'CROP_ACR07', 'AVG_SALE07', 'SQMI', 'CountyFIPS', 'NEIGHBORS',\n", + " 'PopNeigh', 'NEIGHBOR_1', 'PopNeigh_1', 'NEIGHBOR_2', 'PopNeigh_2',\n", + " 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at all the variables included in our data\n", + "counties.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like we have a good amount of information about the total population for different years and the densities, as well as race, age, and occupancy info." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.4 Plot the GeoDataFrame\n", + "\n", + "We're able to plot our GeoDataFrame because of the extra `geometry` column.\n", + "\n", + "### Geopandas Geometries\n", + "There are three main types of geometries that can be associated with your geodataframe: points, lines and polygons:\n", + "\n", + "\n", + "\n", + "In the geodataframe these geometries are encoded in a format known as [Well-Known Text (WKT)](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry). For example:\n", + "\n", + "> - POINT (30 10)\n", + "> - LINESTRING (30 10, 10 30, 40 40)\n", + "> - POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))\n", + ">\n", + "> *where coordinates are separated by a space and coordinate pairs by a comma*\n", + "\n", + "Your geodataframe may also include the variants **multipoints, multilines, and multipolgyons** if the row-level feature of interest is comprised of multiple parts. For example, a geodataframe of states, where one row represents one state, would have a POLYGON geometry for Utah but MULTIPOLYGON for Hawaii, which includes many islands.\n", + "\n", + "> It's ok to mix and match geometries of the same family, e.g., POLYGON and MULTIPOLYGON, in the same geodatafame.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " **Question** What kind of geometry would a roads geodataframe have? What about one that includes landmarks in the San Francisco Bay Area?\n", + "\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the types of geometries in a geodataframe or a subset of the geodataframe by combining the `type` and `unique` methods." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 POLYGON ((193446.035 -244342.585, 194033.795 -...\n", + "1 POLYGON ((12524.028 -179431.328, 12358.142 -17...\n", + "2 MULTIPOLYGON (((-240632.150 93056.104, -240669...\n", + "3 POLYGON ((-45364.032 352060.633, -45248.844 35...\n", + "4 MULTIPOLYGON (((173874.519 -471855.293, 173852...\n", + "Name: geometry, dtype: geometry" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's check what geometries we have in our counties geodataframe\n", + "counties['geometry'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Polygon', 'MultiPolygon'], dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's check to make sure that we only have polygons and multipolygons \n", + "counties['geometry'].type.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_19_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like with other plots you can make in Python, we can start customizing our map with colors, size, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# We can run the following line of code to get more info about the parameters we can specify:\n", + "\n", + "# ?counties.plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_23_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties.plot(figsize=(6,9), \n", + " edgecolor='grey', # grey colored border lines\n", + " facecolor='pink' , # fill in our counties as pink\n", + " linewidth=2) # make the linedwith a width of 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.5 Subset the GeoDataframe\n", + "\n", + "Since we'll be focusing on Berkeley later in the workshop, let's subset our GeoDataFrame to just be for Alameda County." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Kern', 'Kings', 'Lake', 'Lassen', 'Los Angeles', 'Madera',\n", + " 'Marin', 'Mariposa', 'Mendocino', 'Merced', 'Modoc', 'Mono',\n", + " 'Monterey', 'Napa', 'Nevada', 'Orange', 'Placer', 'Plumas',\n", + " 'Riverside', 'Sacramento', 'San Benito', 'San Bernardino',\n", + " 'San Diego', 'San Francisco', 'San Joaquin', 'San Luis Obispo',\n", + " 'San Mateo', 'Santa Barbara', 'Santa Clara', 'Santa Cruz',\n", + " 'Shasta', 'Sierra', 'Siskiyou', 'Solano', 'Alameda', 'Alpine',\n", + " 'Sonoma', 'Amador', 'Stanislaus', 'Sutter', 'Butte', 'Calaveras',\n", + " 'Tehama', 'Colusa', 'Trinity', 'Tulare', 'Contra Costa',\n", + " 'Del Norte', 'Tuolumne', 'Ventura', 'El Dorado', 'Yolo', 'Fresno',\n", + " 'Glenn', 'Yuba', 'Humboldt', 'Imperial', 'Inyo'], dtype=object)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# See all county names included in our dataset\n", + "counties['NAME'].values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like Alameda county is specified as \"Alameda\" in this dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
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" + ], + "text/plain": [ + " FID_ NAME STATE_NAME POP2010 POP10_SQMI POP2012 POP12_SQMI \\\n", + "34 0 Alameda California 1510271 2029.8 1534551 2062.402226 \n", + "\n", + " WHITE BLACK AMERI_ES ... AVG_SALE07 SQMI CountyFIPS NEIGHBORS \\\n", + "34 649122 190451 9799 ... 95.92 744.06 06068 None \n", + "\n", + " PopNeigh NEIGHBOR_1 PopNeigh_1 NEIGHBOR_2 PopNeigh_2 \\\n", + "34 0 None None None None \n", + "\n", + " geometry \n", + "34 MULTIPOLYGON (((-197580.800 -24065.060, -19763... \n", + "\n", + "[1 rows x 59 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.loc[counties['NAME'] == 'Alameda']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create a new geodataframe called `alameda_county` that is a subset of our counties geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county = counties.loc[counties['NAME'] == 'Alameda'].copy().reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_30_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot our newly subsetted geodataframe\n", + "alameda_county.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice! Looks like we have what we were looking for.\n", + "\n", + "*FYI*: You can also make dynamic plots of one or more county without saving to a new gdf." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_32_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bay_area_counties = ['Alameda', 'Contra Costa', 'Marin', 'Napa', 'San Francisco', \n", + " 'San Mateo', 'Santa Clara', 'Santa Cruz', 'Solano', 'Sonoma']\n", + "counties.loc[counties['NAME'].isin(bay_area_counties)].plot()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.6 Save your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's not forget to save out our Alameda County geodataframe `alameda_county`. This way we won't need to repeat the processing steps and attribute join we did above.\n", + "\n", + "We can save it as a shapefile." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.shp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the problems of saving to a shapefile is that our column names get truncated to 10 characters (a shapefile limitation.) \n", + "\n", + "Instead of renaming all columns with obscure names that are less than 10 characters, we can save our GeoDataFrame to a spatial data file format that does not have this limation - [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) or [GPKG](https://en.wikipedia.org/wiki/GeoPackage) (geopackage) file.\n", + "- These formats have the added benefit of outputting only one file in contrast tothe multi-file shapefile format." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.json\", driver=\"GeoJSON\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.gpkg\", driver=\"GPKG\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can read these in, just as you would a shapefile with `gpd.read_file`" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_41_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alameda_county_test2 = gpd.read_file(\"outdata/alameda_county.json\")\n", + "alameda_county_test2.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are also many other formats we could use for data output.\n", + "\n", + "**NOTE**: If you're working with point data (i.e. a single latitude and longitude value per feature),\n", + "then CSV might be a good option!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.7 Recap\n", + "\n", + "In this lesson we learned about...\n", + "- The `geopandas` package \n", + "- Reading in shapefiles \n", + " - `gpd.read_file`\n", + "- GeoDataFrame structures\n", + " - `shape`, `head`, `columns`\n", + "- Plotting GeoDataFrames\n", + " - `plot`\n", + "- Subsetting GeoDatFrames\n", + " - `loc`\n", + "- Saving out GeoDataFrames\n", + " - `to_file`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: IO, Manipulation, and Mapping\n", + "\n", + "Now you'll get a chance to practice the operations we learned above.\n", + "\n", + "In the following cell, compose code to:\n", + "\n", + "1. Read in the California places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`)\n", + "2. Subset the data to Berkeley\n", + "3. Plot, and customize as desired\n", + "4. Save out as a shapefile (`outdata/berkeley_places.shp`)\n", + "\n", + "\n", + "*Note: pulling in a zipped shapefile has the same syntax as just pulling in a shapefile. The only difference is that insead of just putting in the filepath you'll want to write `zip://notebook_data/census/Places/cb_2018_06_place_500k.zip`*\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
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 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1.py b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1.py new file mode 100644 index 0000000..91f76bd --- /dev/null +++ b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1.py @@ -0,0 +1,299 @@ +# Lesson 2. Introduction to Geopandas + +In this lesson we'll learn about a package that is core to using geospatial data in Python. We'll go through the structure of the data (it's not too different from regular DataFrames!), geometries, shapefiles, and how to save your hard work. + +- 2.1 What is GeoPandas? +- 2.2 Read in a shapefile +- 2.3 Explore the GeoDataFrame +- 2.4 Plot the GeoDataFrame +- 2.5 Subset the GeoDataFrame +- 2.6 Save your data +- 2.7 Recap +- **Exercise**: IO, Manipulation, and Mapping + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/california_counties/CaliforniaCounties.shp' + - 'notebook_data/census/Places/cb_2018_06_place_500k.zip' + +- Expected time to complete + - Lecture + Questions: 30 minutes + - Exercises: 5 minutes + + +## 2.1 What is GeoPandas? + +### GeoPandas and related Geospatial Packages + +[GeoPandas](http://geopandas.org/) is a relatively new package that makes it easier to work with geospatial data in Python. In the last few years it has grown more powerful and stable. This is really great because previously it was quite complex to work with geospatial data in Python. GeoPandas is now the go-to package for working with `vector` geospatial data in Python. + +> **Protip**: If you work with `raster` data you will want to checkout the [rasterio](https://rasterio.readthedocs.io/en/latest/) package. We will not cover raster data in this tutorial. + +### GeoPandas = pandas + geo +GeoPandas gives you access to all of the functionality of [pandas](https://pandas.pydata.org/), which is the primary data analysis tool for working with tabular data in Python. GeoPandas extends pandas with attributes and methods for working with geospatial data. + + + + +### Import Libraries + +Let's start by importing the libraries that we will use. + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +## 2.2 Read in a shapefile + +As we discussed in the initial geospatial overview, a *shapefile* is one type of geospatial data that holds vector data. + +> To learn more about ESRI Shapefiles, this is a good place to start: [ESRI Shapefile Wiki Page](https://en.wikipedia.org/wiki/Shapefile) + +The tricky thing to remember about shapefiles is that they're actually a collection of 3 to 9+ files together. Here's a list of all the files that can make up a shapefile: + +>`shp`: The main file that stores the feature geometry +> +>`shx`: The index file that stores the index of the feature geometry +> +>`dbf`: The dBASE table that stores the attribute information of features +> +>`prj`: The file that stores the coordinate system information. (should be required!) +> +>`xml`: Metadata —Stores information about the shapefile. +> +>`cpg`: Specifies the code page for identifying the character set to be used. + +But it remains the most commonly used file format for vector spatial data, and it's really easy to visualize in one go! + +Let's try it out with California counties, and use `geopandas` for the first time. `gpd.read_file` is a flexible function that let's you read in many different types of geospatial data. + +# Read in the counties shapefile +counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp') + +# Plot out California counties +counties.plot() + +Bam! Amazing! We're off to a running start. + +## 2.3 Explore the GeoDataFrame + +Before we get in too deep, let's discuss what a *GeoDataFrame* is and how it's different from `pandas` *DataFrames*. + +### The GeoPandas GeoDataFrame + +A [GeoPandas GeoDataFrame](https://geopandas.org/data_structures.html#geodataframe), or `gdf` for short, is just like a pandas dataframe (`df`) but with an extra geometry column and methods & attributes that work on that column. I repeat because it's important: + +> `A GeoPandas GeoDataFrame is a pandas DataFrame with a geometry column and methods & attributes that work on that column.` + +> This means all the methods and attributes of a pandas DataFrame also work on a Geopandas GeoDataFrame!! + +With that in mind, let's start exploring out dataframe just like we would do in `pandas`. + +# Find the number of rows and columnds in counties +counties.shape + +# Look at the first couple of rows in our geodataframe +counties.head() + +# Look at all the variables included in our data +counties.columns + +It looks like we have a good amount of information about the total population for different years and the densities, as well as race, age, and occupancy info. + +## 2.4 Plot the GeoDataFrame + +We're able to plot our GeoDataFrame because of the extra `geometry` column. + +### Geopandas Geometries +There are three main types of geometries that can be associated with your geodataframe: points, lines and polygons: + + + +In the geodataframe these geometries are encoded in a format known as [Well-Known Text (WKT)](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry). For example: + +> - POINT (30 10) +> - LINESTRING (30 10, 10 30, 40 40) +> - POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10)) +> +> *where coordinates are separated by a space and coordinate pairs by a comma* + +Your geodataframe may also include the variants **multipoints, multilines, and multipolgyons** if the row-level feature of interest is comprised of multiple parts. For example, a geodataframe of states, where one row represents one state, would have a POLYGON geometry for Utah but MULTIPOLYGON for Hawaii, which includes many islands. + +> It's ok to mix and match geometries of the same family, e.g., POLYGON and MULTIPOLYGON, in the same geodatafame. + + + + + **Question** What kind of geometry would a roads geodataframe have? What about one that includes landmarks in the San Francisco Bay Area? + + + + +Your response here: + + + + + + +You can check the types of geometries in a geodataframe or a subset of the geodataframe by combining the `type` and `unique` methods. + +# Let's check what geometries we have in our counties geodataframe +counties['geometry'].head() + +# Let's check to make sure that we only have polygons and multipolygons +counties['geometry'].type.unique() + +counties.plot() + +Just like with other plots you can make in Python, we can start customizing our map with colors, size, etc. + +# We can run the following line of code to get more info about the parameters we can specify: + +# ?counties.plot + +# Make the figure size bigger +counties.plot(figsize=(6,9)) + +counties.plot(figsize=(6,9), + edgecolor='grey', # grey colored border lines + facecolor='pink' , # fill in our counties as pink + linewidth=2) # make the linedwith a width of 2 + +## 2.5 Subset the GeoDataframe + +Since we'll be focusing on Berkeley later in the workshop, let's subset our GeoDataFrame to just be for Alameda County. + +# See all county names included in our dataset +counties['NAME'].values + +It looks like Alameda county is specified as "Alameda" in this dataset. + +counties.loc[counties['NAME'] == 'Alameda'] + +Now we can create a new geodataframe called `alameda_county` that is a subset of our counties geodataframe. + +alameda_county = counties.loc[counties['NAME'] == 'Alameda'].copy().reset_index(drop=True) + +# Plot our newly subsetted geodataframe +alameda_county.plot() + +Nice! Looks like we have what we were looking for. + +*FYI*: You can also make dynamic plots of one or more county without saving to a new gdf. + +bay_area_counties = ['Alameda', 'Contra Costa', 'Marin', 'Napa', 'San Francisco', + 'San Mateo', 'Santa Clara', 'Santa Cruz', 'Solano', 'Sonoma'] +counties.loc[counties['NAME'].isin(bay_area_counties)].plot() + + +## 2.6 Save your Data + +Let's not forget to save out our Alameda County geodataframe `alameda_county`. This way we won't need to repeat the processing steps and attribute join we did above. + +We can save it as a shapefile. + +alameda_county.to_file("outdata/alameda_county.shp") + +One of the problems of saving to a shapefile is that our column names get truncated to 10 characters (a shapefile limitation.) + +Instead of renaming all columns with obscure names that are less than 10 characters, we can save our GeoDataFrame to a spatial data file format that does not have this limation - [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) or [GPKG](https://en.wikipedia.org/wiki/GeoPackage) (geopackage) file. +- These formats have the added benefit of outputting only one file in contrast tothe multi-file shapefile format. + +alameda_county.to_file("outdata/alameda_county.json", driver="GeoJSON") + +alameda_county.to_file("outdata/alameda_county.gpkg", driver="GPKG") + +You can read these in, just as you would a shapefile with `gpd.read_file` + +alameda_county_test = gpd.read_file("outdata/alameda_county.gpkg") +alameda_county_test.plot() + +alameda_county_test2 = gpd.read_file("outdata/alameda_county.json") +alameda_county_test2.plot() + +There are also many other formats we could use for data output. + +**NOTE**: If you're working with point data (i.e. a single latitude and longitude value per feature), +then CSV might be a good option! + +## 2.7 Recap + +In this lesson we learned about... +- The `geopandas` package +- Reading in shapefiles + - `gpd.read_file` +- GeoDataFrame structures + - `shape`, `head`, `columns` +- Plotting GeoDataFrames + - `plot` +- Subsetting GeoDatFrames + - `loc` +- Saving out GeoDataFrames + - `to_file` + +## Exercise: IO, Manipulation, and Mapping + +Now you'll get a chance to practice the operations we learned above. + +In the following cell, compose code to: + +1. Read in the California places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`) +2. Subset the data to Berkeley +3. Plot, and customize as desired +4. Save out as a shapefile (`outdata/berkeley_places.shp`) + + +*Note: pulling in a zipped shapefile has the same syntax as just pulling in a shapefile. The only difference is that insead of just putting in the filepath you'll want to write `zip://notebook_data/census/Places/cb_2018_06_place_500k.zip`* + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE + + + + + + + + +## Double-click to see solution! + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_19_1.png b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_19_1.png new file mode 100644 index 0000000..0b6aa0c Binary files /dev/null and b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_19_1.png differ diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_22_1.png b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_22_1.png new file mode 100644 index 0000000..c82a17a Binary files /dev/null and b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_22_1.png differ diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_23_1.png b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_23_1.png new file mode 100644 index 0000000..2b7df2b Binary files /dev/null and b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_23_1.png differ diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_30_1.png b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_30_1.png new file mode 100644 index 0000000..9c3093e Binary files /dev/null and b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_30_1.png differ diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_32_1.png b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_32_1.png new file mode 100644 index 0000000..48300a2 Binary files /dev/null and b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_32_1.png differ diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_40_1.png b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_40_1.png new file mode 100644 index 0000000..9c3093e Binary files /dev/null and b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_40_1.png differ diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_41_1.png b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_41_1.png new file mode 100644 index 0000000..9c3093e Binary files /dev/null and b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_41_1.png differ diff --git a/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_6_1.png b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_6_1.png new file mode 100644 index 0000000..0b6aa0c Binary files /dev/null and b/_build/jupyter_execute/ran/02_Introduction_to_GeoPandas-Copy1_6_1.png differ diff --git a/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1.ipynb b/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1.ipynb new file mode 100644 index 0000000..d5dda9f --- /dev/null +++ b/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1.ipynb @@ -0,0 +1,2357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 3. Coordinate Reference Systems (CRS) & Map Projections\n", + "\n", + "Building off of what we learned in the previous notebook, we'll get to understand an integral aspect of geospatial data: Coordinate Reference Systems.\n", + "\n", + "- 3.1 California County Shapefile\n", + "- 3.2 USA State Shapefile\n", + "- 3.3 Plot the Two Together\n", + "- 3.4 Coordinate Reference System (CRS)\n", + "- 3.5 Getting the CRS\n", + "- 3.6 Setting the CRS\n", + "- 3.7 Transforming or Reprojecting the CRS\n", + "- 3.8 Plotting States and Counties Togther\n", + "- 3.9 Recap\n", + "- **Exercise**: CRS Management\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - ‘notebook_data/california_counties/CaliforniaCounties.shp’\n", + " - ‘notebook_data/us_states/us_states.shp’\n", + " - ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 10 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 California County shapefile\n", + "Let's go ahead and bring back in our California County shapefile. As before, we can read the file in using `gpd.read_file` and plot it straight away." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1_4_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')\n", + "counties.plot(color='darkgreen')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even if we have an awesome map like this, sometimes we want to have more geographical context, or we just want additional information. We're going to try **overlaying** our counties GeoDataFrame on our USA states shapefile." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 USA State shapefile\n", + "\n", + "We're going to bring in our states geodataframe, and let's do the usual operations to start exploring our data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in states shapefile\n", + "states = gpd.read_file('notebook_data/us_states/us_states.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
STATEGEOIDABBREVgeometry
0Alabama01ALMULTIPOLYGON (((-88.05338 30.50699, -88.05109 ...
1Alaska02AKMULTIPOLYGON (((-134.73726 58.26135, -134.7344...
2Arizona04AZPOLYGON ((-114.81629 32.50804, -114.81432 32.5...
3Arkansas05ARPOLYGON ((-94.61783 36.49941, -94.61765 36.499...
4California06CAMULTIPOLYGON (((-118.60442 33.47855, -118.5987...
\n", + "
" + ], + "text/plain": [ + " STATE GEOID ABBREV geometry\n", + "0 Alabama 01 AL MULTIPOLYGON (((-88.05338 30.50699, -88.05109 ...\n", + "1 Alaska 02 AK MULTIPOLYGON (((-134.73726 58.26135, -134.7344...\n", + "2 Arizona 04 AZ POLYGON ((-114.81629 32.50804, -114.81432 32.5...\n", + "3 Arkansas 05 AR POLYGON ((-94.61783 36.49941, -94.61765 36.499...\n", + "4 California 06 CA MULTIPOLYGON (((-118.60442 33.47855, -118.5987..." + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the first few rows\n", + "states.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(56, 4)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count how many rows and columns we have\n", + "states.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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y1IrI/4nIMRF5TUQ+lGz/hIi0iMiB5OmdmQw6dqS+aDRKPB6f5ta5ZXV5kI/cugGA+65fzVUrE2O411UE+PGHbuIDN68n3+ehNOhj5xUV/MGtW8aLN4Db7UZE2FFXht/r4of7mrPxMowx82wmY8eiwB+r6pXAdcAfiMjm5HX/oKrbk6dnM5aSC8PoPB4PLtfSHfLmcbv49F1bAXj3tmo2VhZOc48LyoN5XFFRwJGWfpp7ZrfEmjFm8Zm2Eqpqm6ruS54fAI4BKzMdbDpje+TpjqGdy65aWcw/3XvNhBEnM+F2u/jr2zfj87iIxpbe+2ZMrpnVrqyI1AHXAC8nmx4WkUMi8g0RKU1/z/k3tkeeOsFlqRRzEeH2bdXceW3trO4Xj8dZV+bjwY1xaksvf7V6Y8ziIDMteiJSALwIfFpVnxCRFUAXieOjPgpUqeoDU9xvN7AbYNWqVTvOnDkzX9nTikQieL3Tz240xhgnEJG9qlp/cfuM9sBFxAv8EPiOqj4BoKodqhpT1TjwNWDnVPdV1cdUtV5V6ysqFmaJl9TivVT2yo0xS89MRqEI8HXgmKp+IaW9KuVmdwFH5j/e3I0d0Ols9xCRaIz+0dlNWzfGmMVqJuPAdwG/CxwWkQPJto8B94rIdhJdKKeBD2Qg37wQEWrLgqgq4eEIx7qHubK6ONuxjDELTFUnfG/mdNMWcFX9OTDVK87osMFMEBGWFfkZjsT5yguN/P7N67IdyRizgESEaDSKx5Mbcxhz41XMUkVhHr9xTU22YziCqtLQMUChK0pxwEdBQUG2IxkzJ2537ixesiRnxOT73FQW2zC6mWrrHSHm9uH12QGwjPPl0kzuJbkHbmZORHjzphXZjmHMvMmlPvAluQdujFm6cmlksRVwY8ySEovlTheKFXBjzJIxNBrB48mdsmd94MaYJSPoz61DbOTOnyJjjFlirIAbY4xDWQE3xhiHsgJujDEOZQXcGGMcygq4McY4lBVwY4xxKCvgxhjjUFbAjTHGoeZUwEXk7SLSICKNIvLIfIUyxhgzvcsu4CLiBr4MvAPYTGKJtc3zFcwYY8ylzWUPfCfQqKqvq2oYeBy4Y35iGWOMmc5cCvhK4GzK5eZk2wQisltE9ojIns7Ozjk8nTHGmFRzKeBTLWsx6VDpqvqYqtaran1FRcUcns4YY0yquRTwZqA25XIN0Dq3OMYYY2ZqLgX8VWC9iKwRER9wD/DU/MQyxhgzncte0EFVoyLyMPATwA18Q1Vfm7dkxhhjLmlOK/Ko6rPAs/OUxRhjzCzYTExjjHEoK+DGGONQVsCNMcahrIAbY4xD5UQBV500f8gYY3JeThRwkakmhRpjTG7LiQJujDFLUU4U8GgsTjQata4UY8ySMqeJPIuFx+0iR/4WGWPMjFnVM8YYh7ICbowxDmUF3BhjHEoW8os/EekEzizYE87eMqAr2yFmwCk5wTlZLef8c0pWJ+RcraqTVsRZ0AK+2InIHlWtz3aO6TglJzgnq+Wcf07J6pScU7EuFGOMcSgr4MYY41BWwCd6LNsBZsgpOcE5WS3n/HNKVqfknMT6wI0xxqFsD9wYYxzKCrgxxjjUkizgInK3iLwmInERqU9prxORERE5kDz9c8p1O0TksIg0isgXZYGOYZsua/K6P0/maRCRt2U7a8rzf0JEWlLex3dOlzmbROTtyTyNIvJItvOkEpHTyc/ygIjsSbaVichzInIi+bM0C7m+ISLnRORISlvaXNn83NNkddQ2mpaqLrkTcCWwEXgBqE9prwOOpLnPK8D1gAA/At6R5aybgYNAHrAGOAm4s5k1JdsngI9O0Z42cxa3BXcyx1rAl8y3OZuZLsp3Glh2UdtngUeS5x8B/i4LuW4Crk39fUmXK9ufe5qsjtlGL3VaknvgqnpMVRtmensRqQKKVPWXmviUvwXcmal8qS6R9Q7gcVUNqeopoBHYmc2sMzBl5ixn2gk0qurrqhoGHieRczG7A/hm8vw3ycLnq6ovAd0XNafLldXPPU3WdBbjNprWkizg01gjIvtF5EURuTHZthJoTrlNc7Itm1YCZ1Muj2VaLFkfFpFDyX9fx/6VTpc5mxZjplQK/FRE9orI7mTbClVtA0j+XJ61dBOly7VY32OnbKNp5cTxwKciIv8DVE5x1cdV9b/S3K0NWKWq50VkB/CfIrKFRFfExeZt/OVlZk2XKaNZx5/8EpmBrwKPJp/3UeDvgQcWKtssLcZMqXapaquILAeeE5Hj2Q50GRbje+ykbTStnC3gqvrWy7hPCAglz+8VkZPABhJ/hWtSbloDtM5HzuRzzToriUy1KZfHMmU065iZZhaRrwFPJy+my5xNizHTOFVtTf48JyJPkvh3vkNEqlS1Ldlldi6rIS9Il2vRvceq2jF23gHbaFrWhZJCRCpExJ08vxZYD7ye/HdwQESuS47ouA9It2e8UJ4C7hGRPBFZQyLrK4sha/KXd8xdwNi3/1NmXshsU3gVWC8ia0TEB9xDImfWiUhQRArHzgO3kXgvnwLuT97sfrK/LY5Jl2vRfe4O20bTy/a3qNk4kfjAmknsbXcAP0m2/ybwGolvofcBt6fcp57Eh3wS+BLJWazZypq87uPJPA2kjDTJVtaU5/82cBg4ROIXomq6zFneHt4J/CqZ6+PZzpOSa21yWzyY3C4/nmwvB54HTiR/lmUh2/dIdDlGktvn710qVzY/9zRZHbWNpjvZVHpjjHEo60IxxhiHsgJujDEOZQXcGGMcygq4McY4lBVwY4xxKCvgxhjjUFbAjTHGof4flSbC7zOR4XMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1_10_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot our states data\n", + "states.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might have noticed that our plot extends beyond the 50 states (which we also saw when we executed the `shape` method). Let's double check what states we have included in our data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California',\n", + " 'Colorado', 'Connecticut', 'Delaware', 'District of Columbia',\n", + " 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa',\n", + " 'Kansas', 'Maryland', 'Minnesota', 'Mississippi', 'Montana',\n", + " 'Nevada', 'New Jersey', 'New Mexico', 'North Dakota', 'Oklahoma',\n", + " 'Pennsylvania', 'South Carolina', 'South Dakota', 'Utah',\n", + " 'Vermont', 'West Virginia', 'Wyoming', 'American Samoa',\n", + " 'Puerto Rico', 'Florida', 'Kentucky', 'Louisiana', 'Maine',\n", + " 'Massachusetts', 'Michigan', 'Missouri', 'Nebraska',\n", + " 'New Hampshire', 'New York', 'North Carolina', 'Ohio', 'Oregon',\n", + " 'Rhode Island', 'Tennessee', 'Texas', 'Virginia', 'Washington',\n", + " 'Wisconsin', 'Guam',\n", + " 'Commonwealth of the Northern Mariana Islands',\n", + " 'United States Virgin Islands'], dtype=object)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states['STATE'].values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beyond the 50 states we seem to have American Samoa, Puerto Rico, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands included in this geodataframe. To make our map cleaner, let's limit the states to the contiguous states (so we'll also exclude Alaska and Hawaii)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Define list of non-contiguous states\n", + "non_contiguous_us = [ 'American Samoa','Puerto Rico','Guam',\n", + " 'Commonwealth of the Northern Mariana Islands',\n", + " 'United States Virgin Islands', 'Alaska','Hawaii']\n", + "# Limit data according to above list\n", + "states_limited = states.loc[~states['STATE'].isin(non_contiguous_us)]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1_15_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot it\n", + "states_limited.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To prepare for our mapping overlay, let's make our states a nice, light grey color." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1_17_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "states_limited.plot(color='lightgrey', figsize=(10,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Plot the two together\n", + "\n", + "Now that we have both geodataframes in our environment, we can plot both in the same figure.\n", + "\n", + "**NOTE**: To do this, note that we're getting a Matplotlib Axes object (`ax`), then explicitly adding each our layers to it\n", + "by providing the `ax=ax` argument to the `plot` method." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1_19_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh no, what happened here?\n", + "\n", + " **Question** Without looking ahead, what do you think happened?\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "If you look at the numbers we have on the x and y axes in our two plots, you'll see that the county data has much larger numbers than our states data. It's represented in some different type of unit other than decimal degrees! \n", + "\n", + "In fcat, that means if we zoom in really close into our plot we'll probably see the states data plotted. We can explore this in two ways:\n", + "\n", + "- Set our matplotlib preferences to `%matplotlib notebook` to zoom in and out of our plot\n", + "- Limit the extent of our plot using `set_xlim` and `set_ylim`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.'\n", + " );\n", + " }\n", + "};\n", + "\n", + "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = document.createElement('div');\n", + " titlebar.classList =\n", + " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", + " var titletext = document.createElement('div');\n", + " titletext.classList = 'ui-dialog-title';\n", + " titletext.setAttribute(\n", + " 'style',\n", + " 'width: 100%; text-align: center; padding: 3px;'\n", + " );\n", + " titlebar.appendChild(titletext);\n", + " this.root.appendChild(titlebar);\n", + " this.header = titletext;\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute(\n", + " 'style',\n", + " 'border: 1px solid #ddd;' +\n", + " 'box-sizing: content-box;' +\n", + " 'clear: both;' +\n", + " 'min-height: 1px;' +\n", + " 'min-width: 1px;' +\n", + " 'outline: 0;' +\n", + " 'overflow: hidden;' +\n", + " 'position: relative;' +\n", + " 'resize: both;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'keydown',\n", + " on_keyboard_event_closure('key_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'keyup',\n", + " on_keyboard_event_closure('key_release')\n", + " );\n", + "\n", + " this._canvas_extra_style(canvas_div);\n", + " this.root.appendChild(canvas_div);\n", + "\n", + " var canvas = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute('style', 'box-sizing: content-box;');\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\n", + " var backingStore =\n", + " this.context.backingStorePixelRatio ||\n", + " this.context.webkitBackingStorePixelRatio ||\n", + " this.context.mozBackingStorePixelRatio ||\n", + " this.context.msBackingStorePixelRatio ||\n", + " this.context.oBackingStorePixelRatio ||\n", + " this.context.backingStorePixelRatio ||\n", + " 1;\n", + "\n", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + " if (this.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n", + " }\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", + " var nentries = entries.length;\n", + " for (var i = 0; i < nentries; i++) {\n", + " var entry = entries[i];\n", + " var width, height;\n", + " if (entry.contentBoxSize) {\n", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\n", + " width = entry.contentRect.width;\n", + " height = entry.contentRect.height;\n", + " }\n", + "\n", + " // Keep the size of the canvas and rubber band canvas in sync with\n", + " // the canvas container.\n", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'width: ' + width + 'px; height: ' + height + 'px;'\n", + " );\n", + "\n", + " rubberband_canvas.setAttribute('width', width);\n", + " rubberband_canvas.setAttribute('height', height);\n", + "\n", + " // And update the size in Python. We ignore the initial 0/0 size\n", + " // that occurs as the element is placed into the DOM, which should\n", + " // otherwise not happen due to the minimum size styling.\n", + " if (width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband_canvas.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\n", + " this._resize_canvas = function (width, height, forward) {\n", + " if (forward) {\n", + " canvas_div.style.width = width + 'px';\n", + " canvas_div.style.height = height + 'px';\n", + " }\n", + " };\n", + "\n", + " // Disable right mouse context menu.\n", + " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " continue;\n", + " }\n", + "\n", + " var button = (fig.buttons[name] = document.createElement('button'));\n", + " button.classList = 'mpl-widget';\n", + " button.setAttribute('role', 'button');\n", + " button.setAttribute('aria-disabled', 'false');\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib notebook\n", + "\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20.0, 50.0)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1_24_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)\n", + "ax.set_xlim(-140,-50)\n", + "ax.set_ylim(20,50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a key issue that you'll have to resolve time and time again when working with geospatial data!\n", + "\n", + "It all revolves around **coordinate reference systems** and **projections**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----------------------------\n", + "\n", + "## 3.4 Coordinate Reference Systems (CRS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **Question** Do you have experience with Coordinate Reference Systems?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

As a refresher, a CRS describes how the coordinates in a geospatial dataset relate to locations on the surface of the earth. \n", + "\n", + "A `geographic CRS` consists of: \n", + "- a 3D model of the shape of the earth (a **datum**), approximated as a sphere or spheroid (aka ellipsoid)\n", + "- the **units** of the coordinate system (e.g, decimal degrees, meters, feet) and \n", + "- the **origin** (i.e. the 0,0 location), specified as the meeting of the **equator** and the **prime meridian**( \n", + "\n", + "A `projected CRS` consists of\n", + "- a geographic CRS\n", + "- a **map projection** and related parameters used to transform the geographic coordinates to `2D` space.\n", + " - a map projection is a mathematical model used to transform coordinate data\n", + "\n", + "### A Geographic vs Projected CRS\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### There are many, many CRSs\n", + "\n", + "Theoretically the number of CRSs is unlimited!\n", + "\n", + "Why? Primariy, because there are many different definitions of the shape of the earth, multiplied by many different ways to cast its surface into 2 dimensions. Our understanding of the earth's shape and our ability to measure it has changed greatly over time.\n", + "\n", + "#### Why are CRSs Important?\n", + "\n", + "- You need to know the data about your data (or `metadata`) to use it appropriately.\n", + "\n", + "\n", + "- All projected CRSs introduce distortion in shape, area, and/or distance. So understanding what CRS best maintains the characteristics you need for your area of interest and your analysis is important.\n", + "\n", + "\n", + "- Some analysis methods expect geospatial data to be in a projected CRS\n", + " - For example, `geopandas` expects a geodataframe to be in a projected CRS for area or distance based analyses.\n", + "\n", + "\n", + "- Some Python libraries, but not all, implement dynamic reprojection from the input CRS to the required CRS and assume a specific CRS (WGS84) when a CRS is not explicitly defined.\n", + "\n", + "\n", + "- Most Python spatial libraries, including Geopandas, require geospatial data to be in the same CRS if they are being analysed together.\n", + "\n", + "#### What you need to know when working with CRSs\n", + "\n", + "- What CRSs used in your study area and their main characteristics\n", + "- How to identify, or `get`, the CRS of a geodataframe\n", + "- How to `set` the CRS of geodataframe (i.e. define the projection)\n", + "- Hot to `transform` the CRS of a geodataframe (i.e. reproject the data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Codes for CRSs commonly used with CA data\n", + "\n", + "CRSs are typically referenced by an [EPSG code](http://wiki.gis.com/wiki/index.php/European_Petroleum_Survey_Group). \n", + "\n", + "It's important to know the commonly used CRSs and their EPSG codes for your geographic area of interest. \n", + "\n", + "For example, below is a list of commonly used CRSs for California geospatial data along with their EPSG codes.\n", + "\n", + "##### Geographic CRSs\n", + "-`4326: WGS84` (units decimal degrees) - the most commonly used geographic CRS\n", + "\n", + "-`4269: NAD83` (units decimal degrees) - the geographic CRS customized to best fit the USA. This is used by all Census geographic data.\n", + "\n", + "> `NAD83 (epsg:4269)` are approximately the same as `WGS84(epsg:4326)` although locations can differ by up to 1 meter in the continental USA and elsewhere up to 3m. That is not a big issue with census tract data as these data are only accurate within +/-7meters.\n", + "##### Projected CRSs\n", + "\n", + "-`5070: CONUS NAD83` (units meters) projected CRS for mapping the entire contiguous USA (CONUS)\n", + "\n", + "-`3857: Web Mercator` (units meters) conformal (shape preserving) CRS used as the default in web mapping\n", + "\n", + "-`3310: CA Albers Equal Area, NAD83` (units meters) projected CRS for CA statewide mapping and spatial analysis\n", + "\n", + "-`26910: UTM Zone 10N, NAD83` (units meters) projected CRS for northern CA mapping & analysis\n", + "\n", + "-`26911: UTM Zone 11N, NAD83` (units meters) projected CRS for Southern CA mapping & analysis\n", + "\n", + "-`102641 to 102646: CA State Plane zones 1-6, NAD83` (units feet) projected CRS used for local analysis.\n", + "\n", + "You can find the full CRS details on the website https://www.spatialreference.org" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.5 Getting the CRS\n", + "\n", + "### Getting the CRS of a gdf\n", + "\n", + "GeoPandas GeoDataFrames have a `crs` attribute that returns the CRS of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83 / California Albers\n", + "Axis Info [cartesian]:\n", + "- X[east]: Easting (metre)\n", + "- Y[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: USA - California\n", + "- bounds: (-124.45, 32.53, -114.12, 42.01)\n", + "Coordinate Operation:\n", + "- name: California Albers\n", + "- method: Albers Equal Area\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.crs" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: WGS 84\n", + "Axis Info [ellipsoidal]:\n", + "- Lat[north]: Geodetic latitude (degree)\n", + "- Lon[east]: Geodetic longitude (degree)\n", + "Area of Use:\n", + "- name: World\n", + "- bounds: (-180.0, -90.0, 180.0, 90.0)\n", + "Datum: World Geodetic System 1984\n", + "- Ellipsoid: WGS 84\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can clearly see from those two printouts (even if we don't understand all the content!),\n", + "the CRSs of our two datasets are different! **This explains why we couldn't overlay them correctly!**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------------------------\n", + "The above CRS definition specifies \n", + "- the name of the CRS (`WGS84`), \n", + "- the axis units (`degree`)\n", + "- the shape (`datum`),\n", + "- and the origin (`Prime Meridian`, and the equator)\n", + "- and the area for which it is best suited (`World`)\n", + "\n", + "> Notes:\n", + "> - `geocentric` latitude and longitude assume a spherical (round) model of the shape of the earth\n", + "> - `geodetic` latitude and longitude assume a spheriodal (ellipsoidal) model, which is closer to the true shape.\n", + "> - `geodesy` is the study of the shape of the earth." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE**: If you print a `crs` call, Python will just display the EPSG code used to initiate the CRS object. Depending on your versions of Geopandas and its dependencies, this may or may not look different from what we just saw above." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epsg:4326\n" + ] + } + ], + "source": [ + "print(states_limited.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.6 Setting the CRS\n", + "\n", + "You can also set the CRS of a gdf using the `crs` attribute. You would set the CRS if is not defined or if you think it is incorrectly defined.\n", + "\n", + "> In desktop GIS terminology setting the CRS is called **defining the CRS**\n", + "\n", + "As an example, let's set the CRS of our data to `None`" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# first set the CRS to None\n", + "states_limited.crs = None" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Check it again\n", + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "...hummm...\n", + "\n", + "If a variable has a null value (None) then displaying it without printing it won't display anything!" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "# Check it again\n", + "print(states_limited.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll set it back to its correct CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Set it to 4326\n", + "states_limited.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: WGS 84\n", + "Axis Info [ellipsoidal]:\n", + "- Lat[north]: Geodetic latitude (degree)\n", + "- Lon[east]: Geodetic longitude (degree)\n", + "Area of Use:\n", + "- name: World\n", + "- bounds: (-180.0, -90.0, 180.0, 90.0)\n", + "Datum: World Geodetic System 1984\n", + "- Ellipsoid: WGS 84\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Show it\n", + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE**: You can set the CRS to anything you like, but **that doesn't make it correct**! This is because setting the CRS does not change the coordinate data; it just tells the software how to interpret it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.7 Transforming or Reprojecting the CRS\n", + "You can transform the CRS of a geodataframe with the `to_crs` method.\n", + "\n", + "\n", + "> In desktop GIS terminology transforming the CRS is called **projecting the data** (or **reprojecting the data**)\n", + "\n", + "When you do this you want to save the output to a new GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited_utm10 = states_limited.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now take a look at the CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83 / UTM zone 10N\n", + "Axis Info [cartesian]:\n", + "- E[east]: Easting (metre)\n", + "- N[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: North America - 126°W to 120°W and NAD83 by country\n", + "- bounds: (-126.0, 30.54, -119.99, 81.8)\n", + "Coordinate Operation:\n", + "- name: UTM zone 10N\n", + "- method: Transverse Mercator\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_limited_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see the result immediately by plotting the data." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(134312.9521453322, 5295973.096958174, 2936443.847710154, 8098103.992522996)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1_53_2.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot geographic gdf\n", + "states_limited.plot();\n", + "plt.axis('square');\n", + "\n", + "# plot utm gdf\n", + "states_limited_utm10.plot();\n", + "plt.axis('square')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Your thoughts here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What two key differences do you see between the two plots above?\n", + "1. Do either of these plotted USA maps look good?\n", + "1. Try looking at the common CRS EPSG codes above and see if any of them look better for the whole country than what we have now. Then try transforming the states data to the CRS that you think would be best and plotting it. (Use the code cell two cells below.)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Double-click to see solution!**\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.8 Plotting states and counties together\n", + "\n", + "Now that we know what a CRS is and how we can set them, let's convert our counties GeoDataFrame to match up with out states' crs." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert counties data to NAD83 \n", + "counties_utm10 = counties.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1_62_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot it together!\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "states_limited_utm10.plot(color='lightgrey', ax=ax)\n", + "counties_utm10.plot(color='darkgreen',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we know that the best CRS to plot the contiguous US from the above question is 5070, let's also transform and plot everything in that CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "counties_conus = counties.to_crs(\"epsg:5070\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'states_limited_conus' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstates_limited_conus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lightgrey'\u001b[0m\u001b[0;34m,\u001b[0m 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1_65_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "states_limited_conus.plot(color='lightgrey', ax=ax)\n", + "counties_conus.plot(color='darkgreen',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.9 Recap\n", + "\n", + "In this lesson we learned about...\n", + "- Coordinate Reference Systems \n", + "- Getting the CRS of a geodataframe\n", + " - `crs`\n", + "- Transforming/repojecting CRS\n", + " - `to_crs`\n", + "- Overlaying maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: CRS Management\n", + "\n", + "Now it's time to take a crack and managing the CRS of a new dataset. In the code cell below, write code to:\n", + "\n", + "1. Bring in the CA places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`)\n", + "2. Check if the CRS is EPSG code 26910. If not, transform the CRS\n", + "3. Plot the California counties and places together.\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1.py b/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1.py new file mode 100644 index 0000000..6c4279f --- /dev/null +++ b/_build/jupyter_execute/ran/03_CRS_Map_Projections-Copy1.py @@ -0,0 +1,430 @@ +# Lesson 3. Coordinate Reference Systems (CRS) & Map Projections + +Building off of what we learned in the previous notebook, we'll get to understand an integral aspect of geospatial data: Coordinate Reference Systems. + +- 3.1 California County Shapefile +- 3.2 USA State Shapefile +- 3.3 Plot the Two Together +- 3.4 Coordinate Reference System (CRS) +- 3.5 Getting the CRS +- 3.6 Setting the CRS +- 3.7 Transforming or Reprojecting the CRS +- 3.8 Plotting States and Counties Togther +- 3.9 Recap +- **Exercise**: CRS Management + +
+ + Instructor Notes + +- Datasets used + - ‘notebook_data/california_counties/CaliforniaCounties.shp’ + - ‘notebook_data/us_states/us_states.shp’ + - ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’ + +- Expected time to complete + - Lecture + Questions: 45 minutes + - Exercises: 10 minutes + + +### Import Libraries + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +## 3.1 California County shapefile +Let's go ahead and bring back in our California County shapefile. As before, we can read the file in using `gpd.read_file` and plot it straight away. + +counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp') +counties.plot(color='darkgreen') + +Even if we have an awesome map like this, sometimes we want to have more geographical context, or we just want additional information. We're going to try **overlaying** our counties GeoDataFrame on our USA states shapefile. + +## 3.2 USA State shapefile + +We're going to bring in our states geodataframe, and let's do the usual operations to start exploring our data. + +# Read in states shapefile +states = gpd.read_file('notebook_data/us_states/us_states.shp') + +# Look at the first few rows +states.head() + +# Count how many rows and columns we have +states.shape + +# Plot our states data +states.plot() + +You might have noticed that our plot extends beyond the 50 states (which we also saw when we executed the `shape` method). Let's double check what states we have included in our data. + +states['STATE'].values + +Beyond the 50 states we seem to have American Samoa, Puerto Rico, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands included in this geodataframe. To make our map cleaner, let's limit the states to the contiguous states (so we'll also exclude Alaska and Hawaii). + +# Define list of non-contiguous states +non_contiguous_us = [ 'American Samoa','Puerto Rico','Guam', + 'Commonwealth of the Northern Mariana Islands', + 'United States Virgin Islands', 'Alaska','Hawaii'] +# Limit data according to above list +states_limited = states.loc[~states['STATE'].isin(non_contiguous_us)] + +# Plot it +states_limited.plot() + +To prepare for our mapping overlay, let's make our states a nice, light grey color. + +states_limited.plot(color='lightgrey', figsize=(10,10)) + +## 3.3 Plot the two together + +Now that we have both geodataframes in our environment, we can plot both in the same figure. + +**NOTE**: To do this, note that we're getting a Matplotlib Axes object (`ax`), then explicitly adding each our layers to it +by providing the `ax=ax` argument to the `plot` method. + +fig, ax = plt.subplots(figsize=(10,10)) +counties.plot(color='darkgreen',ax=ax) +states_limited.plot(color='lightgrey', ax=ax) + +Oh no, what happened here? + + **Question** Without looking ahead, what do you think happened? + + + +Your response here: + + + + + + + +
+
+If you look at the numbers we have on the x and y axes in our two plots, you'll see that the county data has much larger numbers than our states data. It's represented in some different type of unit other than decimal degrees! + +In fcat, that means if we zoom in really close into our plot we'll probably see the states data plotted. We can explore this in two ways: + +- Set our matplotlib preferences to `%matplotlib notebook` to zoom in and out of our plot +- Limit the extent of our plot using `set_xlim` and `set_ylim` + +%matplotlib notebook + +fig, ax = plt.subplots(figsize=(10,10)) +counties.plot(color='darkgreen',ax=ax) +states_limited.plot(color='lightgrey', ax=ax) + +%matplotlib inline +fig, ax = plt.subplots(figsize=(10,10)) +counties.plot(color='darkgreen',ax=ax) +states_limited.plot(color='lightgrey', ax=ax) +ax.set_xlim(-140,-50) +ax.set_ylim(20,50) + +This is a key issue that you'll have to resolve time and time again when working with geospatial data! + +It all revolves around **coordinate reference systems** and **projections**. + +---------------------------- + +## 3.4 Coordinate Reference Systems (CRS) + + **Question** Do you have experience with Coordinate Reference Systems? + +Your response here: + + + + + + + +

As a refresher, a CRS describes how the coordinates in a geospatial dataset relate to locations on the surface of the earth. + +A `geographic CRS` consists of: +- a 3D model of the shape of the earth (a **datum**), approximated as a sphere or spheroid (aka ellipsoid) +- the **units** of the coordinate system (e.g, decimal degrees, meters, feet) and +- the **origin** (i.e. the 0,0 location), specified as the meeting of the **equator** and the **prime meridian**( + +A `projected CRS` consists of +- a geographic CRS +- a **map projection** and related parameters used to transform the geographic coordinates to `2D` space. + - a map projection is a mathematical model used to transform coordinate data + +### A Geographic vs Projected CRS + + +#### There are many, many CRSs + +Theoretically the number of CRSs is unlimited! + +Why? Primariy, because there are many different definitions of the shape of the earth, multiplied by many different ways to cast its surface into 2 dimensions. Our understanding of the earth's shape and our ability to measure it has changed greatly over time. + +#### Why are CRSs Important? + +- You need to know the data about your data (or `metadata`) to use it appropriately. + + +- All projected CRSs introduce distortion in shape, area, and/or distance. So understanding what CRS best maintains the characteristics you need for your area of interest and your analysis is important. + + +- Some analysis methods expect geospatial data to be in a projected CRS + - For example, `geopandas` expects a geodataframe to be in a projected CRS for area or distance based analyses. + + +- Some Python libraries, but not all, implement dynamic reprojection from the input CRS to the required CRS and assume a specific CRS (WGS84) when a CRS is not explicitly defined. + + +- Most Python spatial libraries, including Geopandas, require geospatial data to be in the same CRS if they are being analysed together. + +#### What you need to know when working with CRSs + +- What CRSs used in your study area and their main characteristics +- How to identify, or `get`, the CRS of a geodataframe +- How to `set` the CRS of geodataframe (i.e. define the projection) +- Hot to `transform` the CRS of a geodataframe (i.e. reproject the data) + +### Codes for CRSs commonly used with CA data + +CRSs are typically referenced by an [EPSG code](http://wiki.gis.com/wiki/index.php/European_Petroleum_Survey_Group). + +It's important to know the commonly used CRSs and their EPSG codes for your geographic area of interest. + +For example, below is a list of commonly used CRSs for California geospatial data along with their EPSG codes. + +##### Geographic CRSs +-`4326: WGS84` (units decimal degrees) - the most commonly used geographic CRS + +-`4269: NAD83` (units decimal degrees) - the geographic CRS customized to best fit the USA. This is used by all Census geographic data. + +> `NAD83 (epsg:4269)` are approximately the same as `WGS84(epsg:4326)` although locations can differ by up to 1 meter in the continental USA and elsewhere up to 3m. That is not a big issue with census tract data as these data are only accurate within +/-7meters. +##### Projected CRSs + +-`5070: CONUS NAD83` (units meters) projected CRS for mapping the entire contiguous USA (CONUS) + +-`3857: Web Mercator` (units meters) conformal (shape preserving) CRS used as the default in web mapping + +-`3310: CA Albers Equal Area, NAD83` (units meters) projected CRS for CA statewide mapping and spatial analysis + +-`26910: UTM Zone 10N, NAD83` (units meters) projected CRS for northern CA mapping & analysis + +-`26911: UTM Zone 11N, NAD83` (units meters) projected CRS for Southern CA mapping & analysis + +-`102641 to 102646: CA State Plane zones 1-6, NAD83` (units feet) projected CRS used for local analysis. + +You can find the full CRS details on the website https://www.spatialreference.org + +## 3.5 Getting the CRS + +### Getting the CRS of a gdf + +GeoPandas GeoDataFrames have a `crs` attribute that returns the CRS of the data. + +counties.crs + +states_limited.crs + +As we can clearly see from those two printouts (even if we don't understand all the content!), +the CRSs of our two datasets are different! **This explains why we couldn't overlay them correctly!** + +----------------------------------------- +The above CRS definition specifies +- the name of the CRS (`WGS84`), +- the axis units (`degree`) +- the shape (`datum`), +- and the origin (`Prime Meridian`, and the equator) +- and the area for which it is best suited (`World`) + +> Notes: +> - `geocentric` latitude and longitude assume a spherical (round) model of the shape of the earth +> - `geodetic` latitude and longitude assume a spheriodal (ellipsoidal) model, which is closer to the true shape. +> - `geodesy` is the study of the shape of the earth. + +**NOTE**: If you print a `crs` call, Python will just display the EPSG code used to initiate the CRS object. Depending on your versions of Geopandas and its dependencies, this may or may not look different from what we just saw above. + +print(states_limited.crs) + +## 3.6 Setting the CRS + +You can also set the CRS of a gdf using the `crs` attribute. You would set the CRS if is not defined or if you think it is incorrectly defined. + +> In desktop GIS terminology setting the CRS is called **defining the CRS** + +As an example, let's set the CRS of our data to `None` + +# first set the CRS to None +states_limited.crs = None + +# Check it again +states_limited.crs + +...hummm... + +If a variable has a null value (None) then displaying it without printing it won't display anything! + +# Check it again +print(states_limited.crs) + +Now we'll set it back to its correct CRS. + +# Set it to 4326 +states_limited.crs = "epsg:4326" + +# Show it +states_limited.crs + +**NOTE**: You can set the CRS to anything you like, but **that doesn't make it correct**! This is because setting the CRS does not change the coordinate data; it just tells the software how to interpret it. + +## 3.7 Transforming or Reprojecting the CRS +You can transform the CRS of a geodataframe with the `to_crs` method. + + +> In desktop GIS terminology transforming the CRS is called **projecting the data** (or **reprojecting the data**) + +When you do this you want to save the output to a new GeoDataFrame. + +states_limited_utm10 = states_limited.to_crs( "epsg:26910") + +Now take a look at the CRS. + +states_limited_utm10.crs + +You can see the result immediately by plotting the data. + +# plot geographic gdf +states_limited.plot(); +plt.axis('square'); + +# plot utm gdf +states_limited_utm10.plot(); +plt.axis('square') + +# Your thoughts here + +
+ +
+
+ +#### Questions +
+ +1. What two key differences do you see between the two plots above? +1. Do either of these plotted USA maps look good? +1. Try looking at the common CRS EPSG codes above and see if any of them look better for the whole country than what we have now. Then try transforming the states data to the CRS that you think would be best and plotting it. (Use the code cell two cells below.) + +Your responses here: + + + + + + + +# YOUR CODE HERE + + + + + + + +**Double-click to see solution!** + + + +## 3.8 Plotting states and counties together + +Now that we know what a CRS is and how we can set them, let's convert our counties GeoDataFrame to match up with out states' crs. + +# Convert counties data to NAD83 +counties_utm10 = counties.to_crs("epsg:26910") + +counties_utm10.plot() + +# Plot it together! +fig, ax = plt.subplots(figsize=(10,10)) +states_limited_utm10.plot(color='lightgrey', ax=ax) +counties_utm10.plot(color='darkgreen',ax=ax) + +Since we know that the best CRS to plot the contiguous US from the above question is 5070, let's also transform and plot everything in that CRS. + +counties_conus = counties.to_crs("epsg:5070") + +fig, ax = plt.subplots(figsize=(10,10)) +states_limited_conus.plot(color='lightgrey', ax=ax) +counties_conus.plot(color='darkgreen',ax=ax) + +## 3.9 Recap + +In this lesson we learned about... +- Coordinate Reference Systems +- Getting the CRS of a geodataframe + - `crs` +- Transforming/repojecting CRS + - `to_crs` +- Overlaying maps + +## Exercise: CRS Management + +Now it's time to take a crack and managing the CRS of a new dataset. In the code cell below, write code to: + +1. Bring in the CA places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`) +2. Check if the CRS is EPSG code 26910. If not, transform the CRS +3. Plot the California counties and places together. + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE + + + + + + + +## Double-click to see solution! + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
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\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/parcels/parcel_pts_rand30pct.geojson'\n", + " - ‘notebook_data/berkeley/BerkeleyCityLimits.shp’\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Berkeley Bike Boulevards\n", + "\n", + "We're going to bring in data bike boulevards in Berkeley. Note two things that are different from our previous data:\n", + "- We're bringing in a [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) this time and not a shapefile\n", + "- We have a **line** geometry GeoDataFrame (our county and states data had **polygon** geometries)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_4_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual, we'll want to do our usual data exploration..." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "Why are there 211 features when we only have 8 bike boulevards?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your reponse here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now take a look at our CRS..." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: WGS 84 / UTM zone 10N\n", + "Axis Info [cartesian]:\n", + "- E[east]: Easting (metre)\n", + "- N[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: World - N hemisphere - 126°W to 120°W - by country\n", + "- bounds: (-126.0, 0.0, -120.0, 84.0)\n", + "Coordinate Operation:\n", + "- name: UTM zone 10N\n", + "- method: Transverse Mercator\n", + "Datum: World Geodetic System 1984\n", + "- Ellipsoid: WGS 84\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_blvds.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's tranform our CRS to UTM Zone 10N, NAD83 that we used in the last lesson." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10 = bike_blvds.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " BB_STRNAM BB_STRID BB_FRO BB_TO BB_SECID DIR_ Status \\\n", + "0 Heinz/Russell RUS 7th 8th RUS01 E/W Existing \n", + "1 Heinz/Russell RUS 8th 9th RUS02 E/W Ezisting \n", + "2 Heinz/Russell RUS 9th 10th RUS03 E/W Existing \n", + "3 Heinz/Russell RUS 10th San Pablo RUS04 E/W Existing \n", + "4 San Pablo RUS Heinz Russell RUS05 N/S Existing \n", + "\n", + " ALT_bikeCA Shape_len len_km \\\n", + "0 No 101.128166 0.101 \n", + "1 No 100.814072 0.101 \n", + "2 No 100.037396 0.100 \n", + "3 No 106.592878 0.107 \n", + "4 No 89.563478 0.090 \n", + "\n", + " geometry \n", + "0 MULTILINESTRING ((562293.837 4189794.938, 5623... \n", + "1 MULTILINESTRING ((562391.603 4189820.796, 5624... \n", + "2 MULTILINESTRING ((562489.067 4189846.568, 5625... \n", + "3 MULTILINESTRING ((562585.773 4189872.168, 5626... \n", + "4 MULTILINESTRING ((562688.904 4189899.113, 5627... " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_blvds_utm10.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Alameda County Schools\n", + "\n", + "Alright! Now that we have our bike boulevard data squared away, we're going to bring in our Alameda County school data." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYSiteAddressCityStateTypeAPIOrg
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933Public
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932Public
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853Public
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927Public
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894Public
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" + ], + "text/plain": [ + " X Y Site Address \\\n", + "0 -122.238761 37.744764 Amelia Earhart Elementary 400 Packet Landing Rd \n", + "1 -122.251856 37.738999 Bay Farm Elementary 200 Aughinbaugh Way \n", + "2 -122.258915 37.762058 Donald D. Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org \n", + "0 Alameda CA ES 933 Public \n", + "1 Alameda CA ES 932 Public \n", + "2 Alameda CA ES 853 Public \n", + "3 Alameda CA ES 927 Public \n", + "4 Alameda CA ES 894 Public " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(550, 9)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **Questions** \n", + "\n", + "Without looking ahead:\n", + "\n", + "1. Is this a geodataframe? \n", + "2. How do you know?\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your reponse here:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "This is not a GeoDataFrame! A couple of clues to figure that out are..\n", + "\n", + "1. We're pulling in a Comma Separated Value (CSV) file, which is not a geospatial data format\n", + "2. There is no geometry column (although we do have latitude and longitude values)\n", + "\n", + "\n", + "-------------------------------\n", + "\n", + "Although our school data is not starting off as a GeoDataFrame, we actually have the tools and information to make it one. Using the `gpd.GeoDataFrame` constructor, we can transform our plain DataFrame into a GeoDataFrame (specifying the geometry information and then the CRS)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYSiteAddressCityStateTypeAPIOrggeometry
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933PublicPOINT (-122.23876 37.74476)
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932PublicPOINT (-122.25186 37.73900)
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853PublicPOINT (-122.25892 37.76206)
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927PublicPOINT (-122.23484 37.76525)
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894PublicPOINT (-122.23808 37.75396)
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" + ], + "text/plain": [ + " X Y Site Address \\\n", + "0 -122.238761 37.744764 Amelia Earhart Elementary 400 Packet Landing Rd \n", + "1 -122.251856 37.738999 Bay Farm Elementary 200 Aughinbaugh Way \n", + "2 -122.258915 37.762058 Donald D. Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org geometry \n", + "0 Alameda CA ES 933 Public POINT (-122.23876 37.74476) \n", + "1 Alameda CA ES 932 Public POINT (-122.25186 37.73900) \n", + "2 Alameda CA ES 853 Public POINT (-122.25892 37.76206) \n", + "3 Alameda CA ES 927 Public POINT (-122.23484 37.76525) \n", + "4 Alameda CA ES 894 Public POINT (-122.23808 37.75396) " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"\n", + "schools_gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll notice that the shape is the same from what we had as a dataframe, just with the added `geometry` column." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(550, 10)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_gdf.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And with it being a GeoDataFrame, we can plot it as we did for our other data sets.\n", + "Notice that we have our first **point** geometry GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_29_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf_utm10 = schools_gdf.to_crs( \"epsg:26910\")\n", + "schools_gdf_utm10.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*In Lesson 2 we discussed that you can save out GeoDataFrames in multiple file formats. You could opt for a GeoJSON, a shapefile, etc... for point data sets it is also an option to save it out as a CSV since the geometry isn't complicated*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Even More Data!\n", + "Let's play around with another point GeoDataFrame.\n", + "\n", + "In the code cell provided below, compose code to:\n", + "\n", + "1. Read in the parcel points data (`notebook_data/parcels/parcel_pts_rand30pct.geojson`)\n", + "1. Set the CRS to be 4326\n", + "1. Transform the CRS to 26910\n", + "1. Plot and customize as desired!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.3 Map Overlays with Matplotlib\n", + "\n", + "No matter the geometry type we have for our GeoDataFrame, we can create overlay plots.\n", + "\n", + "Since we've already done the legwork of transforming our CRS, we can go ahead and plot them together." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_35_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds_utm10.plot(ax=ax, color='red')\n", + "schools_gdf_utm10 .plot(ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to answer questions like *\"What schools are close to bike boulevards in Berkeley?\"*, the above plot isn't super helpful, since the extent covers all of Alameda county.\n", + "\n", + "Luckily, GeoDataFrames have an easy method to extract the minimium and maximum values for both x and y, so we can use that information to set the bounds for our plot." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "561541.1531499997 4189007.11635 566451.5549499998 4193483.09445\n" + ] + } + ], + "source": [ + "minx, miny, maxx, maxy = bike_blvds.total_bounds\n", + "print(minx, miny, maxx, maxy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `xlim` and `ylim` we can zoom in to see if there are schools proximal to the bike boulevards." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4189007.11635, 4193483.09445)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_39_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds_utm10.plot(ax=ax, color='red')\n", + "schools_gdf_utm10 .plot(ax=ax)\n", + "plt.xlim(minx, maxx)\n", + "plt.ylim(miny, maxy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.4 Recap\n", + "\n", + "In this lesson we learned a several new skills:\n", + "- Transformed an a-spatial dataframe into a geospatial one\n", + " - `gpd.GeoDataFrame`\n", + "- Worked with point and line GeoDataFrames\n", + "- Overlayed point and line GeoDataFrames\n", + "- Limited the extent of a map\n", + " - `total_bounds`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Overlay Mapping\n", + "\n", + "Let's take some time to practice reading in and reconciling new datasets, then mapping them together.\n", + "\n", + "In the code cell provided below, write code to:\n", + "\n", + "1. Bring in your Berkeley places shapefile (and don't forget to check/transform the crs!) (`notebook_data/berkeley/BerkeleyCityLimits.shp`)\n", + "1. Overlay the parcel points on top of the bike boulevards\n", + "1. Create the same plot but limit it to the extent of Berkeley city limits\n", + "\n", + "***BONUS***: *Add the Berkeley outline to your last plot!*\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click the see the solution!\n", + "\n", + "\n", + "\n", + "-----------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.5 Teaser for Day 2...\n", + "\n", + "You may be wondering if and how we could make our maps more interesting and informative than this.\n", + "\n", + "To give you a tantalizing taste of Day 2, the answer is: Yes, we can! And here's how!" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Public and Private Schools, Alameda County')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_45_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = schools_gdf_utm10.plot(column='Org', cmap='winter', \n", + " markersize=35, edgecolor='black',\n", + " linewidth=0.5, alpha=1, figsize=[9, 9],\n", + " legend=True)\n", + "ax.set_title('Public and Private Schools, Alameda County')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env", + "language": "python", + "name": "geo_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1.py b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1.py new file mode 100644 index 0000000..6a6948e --- /dev/null +++ b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1.py @@ -0,0 +1,317 @@ +# Lesson 4. More Data, More Maps! + +Now that we know how to pull in data, check and transform Coordinate Reference Systems (CRS), and plot GeoDataFrames together - let's practice doing the same thing with other geometry types. In this notebook we'll be bringing in bike boulevards and schools, which will get us primed to think about spatial relationship questions. + +- 4.1 Berkeley Bike Boulevards +- 4.2 Alameda County Schools +- **Exercise**: Even More Data! +- 4.3 Map Overlays with Matplotlib +- 4.4 Recap +- **Exercise**: Overlay Mapping +- 4.5 Teaser for Day 2 + + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson' + - 'notebook_data/alco_schools.csv' + - 'notebook_data/parcels/parcel_pts_rand30pct.geojson' + - ‘notebook_data/berkeley/BerkeleyCityLimits.shp’ + +- Expected time to complete + - Lecture + Questions: 30 minutes + - Exercises: 20 minutes + + +### Import Libraries + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +## 4.1 Berkeley Bike Boulevards + +We're going to bring in data bike boulevards in Berkeley. Note two things that are different from our previous data: +- We're bringing in a [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) this time and not a shapefile +- We have a **line** geometry GeoDataFrame (our county and states data had **polygon** geometries) + +bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson') +bike_blvds.plot() + +As usual, we'll want to do our usual data exploration... + +bike_blvds.head() + +bike_blvds.shape + +bike_blvds.columns + +Our bike boulevard data includes the following information: + - `BB_STRNAM` - bike boulevard Streetname + - `BB_STRID` - bike boulevard Street ID + - `BB_FRO` - bike boulevard origin street + - `BB_TO` - bike boulevard end street + - `BB_SECID`- bike boulevard section id + - `DIR_` - cardinal directions the bike boulevard runs + - `Status` - status on whether the bike boulevard exists + - `ALT_bikeCA` - ? + - `Shape_len` - length of the boulevard in meters + - `len_km` - length of the boulevard in kilometers + - `geometry` + + +
+ +
+
+ +#### Question +
+ +Why are there 211 features when we only have 8 bike boulevards? + +Your reponse here: + + + + + + + +And now take a look at our CRS... + +bike_blvds.crs + +Let's tranform our CRS to UTM Zone 10N, NAD83 that we used in the last lesson. + +bike_blvds_utm10 = bike_blvds.to_crs( "epsg:26910") + +bike_blvds_utm10.head() + +## 4.2 Alameda County Schools + +Alright! Now that we have our bike boulevard data squared away, we're going to bring in our Alameda County school data. + +schools_df = pd.read_csv('notebook_data/alco_schools.csv') +schools_df.head() + +schools_df.shape + + **Questions** + +Without looking ahead: + +1. Is this a geodataframe? +2. How do you know? + + + +Your reponse here: + + + + + + + +
+
+This is not a GeoDataFrame! A couple of clues to figure that out are.. + +1. We're pulling in a Comma Separated Value (CSV) file, which is not a geospatial data format +2. There is no geometry column (although we do have latitude and longitude values) + + +------------------------------- + +Although our school data is not starting off as a GeoDataFrame, we actually have the tools and information to make it one. Using the `gpd.GeoDataFrame` constructor, we can transform our plain DataFrame into a GeoDataFrame (specifying the geometry information and then the CRS). + +schools_gdf = gpd.GeoDataFrame(schools_df, + geometry=gpd.points_from_xy(schools_df.X, schools_df.Y)) +schools_gdf.crs = "epsg:4326" +schools_gdf.head() + +You'll notice that the shape is the same from what we had as a dataframe, just with the added `geometry` column. + +schools_gdf.shape + +And with it being a GeoDataFrame, we can plot it as we did for our other data sets. +Notice that we have our first **point** geometry GeoDataFrame. + +schools_gdf.plot() + +But of course we'll want to transform the CRS, so that we can later plot it with our bike boulevard data. + +schools_gdf_utm10 = schools_gdf.to_crs( "epsg:26910") +schools_gdf_utm10.plot() + +*In Lesson 2 we discussed that you can save out GeoDataFrames in multiple file formats. You could opt for a GeoJSON, a shapefile, etc... for point data sets it is also an option to save it out as a CSV since the geometry isn't complicated* + +## Exercise: Even More Data! +Let's play around with another point GeoDataFrame. + +In the code cell provided below, compose code to: + +1. Read in the parcel points data (`notebook_data/parcels/parcel_pts_rand30pct.geojson`) +1. Set the CRS to be 4326 +1. Transform the CRS to 26910 +1. Plot and customize as desired! + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE: + + + + + + +## Double-click to see solution! + + + +------------------------- + +## 4.3 Map Overlays with Matplotlib + +No matter the geometry type we have for our GeoDataFrame, we can create overlay plots. + +Since we've already done the legwork of transforming our CRS, we can go ahead and plot them together. + +fig, ax = plt.subplots(figsize=(10,10)) +bike_blvds_utm10.plot(ax=ax, color='red') +schools_gdf_utm10 .plot(ax=ax) + +If we want to answer questions like *"What schools are close to bike boulevards in Berkeley?"*, the above plot isn't super helpful, since the extent covers all of Alameda county. + +Luckily, GeoDataFrames have an easy method to extract the minimium and maximum values for both x and y, so we can use that information to set the bounds for our plot. + +minx, miny, maxx, maxy = bike_blvds.total_bounds +print(minx, miny, maxx, maxy) + +Using `xlim` and `ylim` we can zoom in to see if there are schools proximal to the bike boulevards. + +fig, ax = plt.subplots(figsize=(10,10)) +bike_blvds_utm10.plot(ax=ax, color='red') +schools_gdf_utm10 .plot(ax=ax) +plt.xlim(minx, maxx) +plt.ylim(miny, maxy) + +## 4.4 Recap + +In this lesson we learned a several new skills: +- Transformed an a-spatial dataframe into a geospatial one + - `gpd.GeoDataFrame` +- Worked with point and line GeoDataFrames +- Overlayed point and line GeoDataFrames +- Limited the extent of a map + - `total_bounds` + + +## Exercise: Overlay Mapping + +Let's take some time to practice reading in and reconciling new datasets, then mapping them together. + +In the code cell provided below, write code to: + +1. Bring in your Berkeley places shapefile (and don't forget to check/transform the crs!) (`notebook_data/berkeley/BerkeleyCityLimits.shp`) +1. Overlay the parcel points on top of the bike boulevards +1. Create the same plot but limit it to the extent of Berkeley city limits + +***BONUS***: *Add the Berkeley outline to your last plot!* + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE: + + + + + +## Double-click the see the solution! + + + +----------------------------------- + +## 4.5 Teaser for Day 2... + +You may be wondering if and how we could make our maps more interesting and informative than this. + +To give you a tantalizing taste of Day 2, the answer is: Yes, we can! And here's how! + +ax = schools_gdf_utm10.plot(column='Org', cmap='winter', + markersize=35, edgecolor='black', + linewidth=0.5, alpha=1, figsize=[9, 9], + legend=True) +ax.set_title('Public and Private Schools, Alameda County') + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + diff --git a/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_27_1.png b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_27_1.png new file mode 100644 index 0000000..2295d02 Binary files /dev/null and b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_27_1.png differ diff --git a/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_29_1.png b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_29_1.png new file mode 100644 index 0000000..1514c93 Binary files /dev/null and b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_29_1.png differ diff --git a/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_35_1.png b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_35_1.png new file mode 100644 index 0000000..fa0c732 Binary files /dev/null and b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_35_1.png differ diff --git a/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_39_1.png b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_39_1.png new file mode 100644 index 0000000..e40adb8 Binary files /dev/null and b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_39_1.png differ diff --git a/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_45_1.png b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_45_1.png new file mode 100644 index 0000000..8e8c891 Binary files /dev/null and b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_45_1.png differ diff --git a/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_4_1.png b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_4_1.png new file mode 100644 index 0000000..5392482 Binary files /dev/null and b/_build/jupyter_execute/ran/04_More_Data_More_Maps-Copy1_4_1.png differ diff --git a/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1.ipynb b/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1.ipynb new file mode 100644 index 0000000..57e1b62 --- /dev/null +++ b/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1.ipynb @@ -0,0 +1,1860 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 5. Data-driven Mapping\n", + "\n", + "*Data-driven mapping* refers to the process of using data values to determine the symbology of mapped features. Color, shape, and size are the three most common symbology types used in data-driven mapping.\n", + "Data-driven maps are often refered to as thematic maps.\n", + "\n", + "\n", + "- 5.1 Choropleth Maps\n", + "- 5.2 Issues with Visualization\n", + "- 5.3 Classification Schemes\n", + "- 5.4 Point Maps\n", + "- 5.5 Mapping Categorical Data\n", + "- 5.6 Recap\n", + "- **Exercise**: Data-Driven Mapping\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/california_counties/CaliforniaCounties.shp'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 15 minutes\n", + "\n", + "\n", + "\n", + "### Types of Thematic Maps\n", + "\n", + "There are two primary types of maps used to convey data values:\n", + "\n", + "- `Choropleth maps`: set the color of areas (polygons) by data value\n", + "- `Point symbol maps`: set the color or size of points by data value\n", + "\n", + "We will discuss both of these types of maps in more detail in this lesson. But let's take a quick look at choropleth maps. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.1 Choropleth Maps\n", + "Choropleth maps are the most common type of thematic map.\n", + "\n", + "Let's take a look at how we can use a geodataframe to make a choropleth map.\n", + "\n", + "We'll start by reloading our counties dataset from Day 1." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
00KernCalifornia839631102.9851089104.2828704997664892112676...1513.538161.3506103San Bernardino,Tulare,Inyo2495935NoneNoneNoneNonePOLYGON ((193446.035 -244342.585, 194033.795 -...
10KingsCalifornia152982109.9155039111.42742183027110142562...1203.201391.3906089Fresno,Kern,Tulare2212260NoneNoneNoneNonePOLYGON ((12524.028 -179431.328, 12358.142 -17...
20LakeCalifornia6466548.66525349.0823345203312322049...72.311329.4606106None0NoneNoneNoneNoneMULTIPOLYGON (((-240632.150 93056.104, -240669...
30LassenCalifornia348957.4350397.4228562553228341234...120.924720.4206086None0NoneNoneNoneNonePOLYGON ((-45364.032 352060.633, -45248.844 35...
40Los AngelesCalifornia98186052402.399043412423.264150493659985687472828...187.944087.1906073San Bernardino,Kern2874841NoneNoneNoneNoneMULTIPOLYGON (((173874.519 -471855.293, 173852...
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5 rows × 59 columns

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" + ], + "text/plain": [ + " FID_ NAME STATE_NAME POP2010 POP10_SQMI POP2012 POP12_SQMI \\\n", + "0 0 Kern California 839631 102.9 851089 104.282870 \n", + "1 0 Kings California 152982 109.9 155039 111.427421 \n", + "2 0 Lake California 64665 48.6 65253 49.082334 \n", + "3 0 Lassen California 34895 7.4 35039 7.422856 \n", + "4 0 Los Angeles California 9818605 2402.3 9904341 2423.264150 \n", + "\n", + " WHITE BLACK AMERI_ES ... AVG_SALE07 SQMI CountyFIPS \\\n", + "0 499766 48921 12676 ... 1513.53 8161.35 06103 \n", + "1 83027 11014 2562 ... 1203.20 1391.39 06089 \n", + "2 52033 1232 2049 ... 72.31 1329.46 06106 \n", + "3 25532 2834 1234 ... 120.92 4720.42 06086 \n", + "4 4936599 856874 72828 ... 187.94 4087.19 06073 \n", + "\n", + " NEIGHBORS PopNeigh NEIGHBOR_1 PopNeigh_1 NEIGHBOR_2 \\\n", + "0 San Bernardino,Tulare,Inyo 2495935 None None None \n", + "1 Fresno,Kern,Tulare 2212260 None None None \n", + "2 None 0 None None None \n", + "3 None 0 None None None \n", + "4 San Bernardino,Kern 2874841 None None None \n", + "\n", + " PopNeigh_2 geometry \n", + "0 None POLYGON ((193446.035 -244342.585, 194033.795 -... \n", + "1 None POLYGON ((12524.028 -179431.328, 12358.142 -17... \n", + "2 None MULTIPOLYGON (((-240632.150 93056.104, -240669... \n", + "3 None POLYGON ((-45364.032 352060.633, -45248.844 35... \n", + "4 None MULTIPOLYGON (((173874.519 -471855.293, 173852... \n", + "\n", + "[5 rows x 59 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FID_', 'NAME', 'STATE_NAME', 'POP2010', 'POP10_SQMI', 'POP2012',\n", + " 'POP12_SQMI', 'WHITE', 'BLACK', 'AMERI_ES', 'ASIAN', 'HAWN_PI',\n", + " 'HISPANIC', 'OTHER', 'MULT_RACE', 'MALES', 'FEMALES', 'AGE_UNDER5',\n", + " 'AGE_5_9', 'AGE_10_14', 'AGE_15_19', 'AGE_20_24', 'AGE_25_34',\n", + " 'AGE_35_44', 'AGE_45_54', 'AGE_55_64', 'AGE_65_74', 'AGE_75_84',\n", + " 'AGE_85_UP', 'MED_AGE', 'MED_AGE_M', 'MED_AGE_F', 'HOUSEHOLDS',\n", + " 'AVE_HH_SZ', 'HSEHLD_1_M', 'HSEHLD_1_F', 'MARHH_CHD', 'MARHH_NO_C',\n", + " 'MHH_CHILD', 'FHH_CHILD', 'FAMILIES', 'AVE_FAM_SZ', 'HSE_UNITS',\n", + " 'VACANT', 'OWNER_OCC', 'RENTER_OCC', 'NO_FARMS07', 'AVG_SIZE07',\n", + " 'CROP_ACR07', 'AVG_SALE07', 'SQMI', 'CountyFIPS', 'NEIGHBORS',\n", + " 'PopNeigh', 'NEIGHBOR_1', 'PopNeigh_1', 'NEIGHBOR_2', 'PopNeigh_2',\n", + " 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a plain map of our polygons." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_7_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for comparison, let's create a choropleth map by setting the color of the county based on the values in the population per square mile (`POP12_SQMI`) column." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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This is called a `proportional color map`.\n", + "\n", + "- The great thing about `proportional color maps` is that you can visualize the full range of data values.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also add a legend, and even tweak its display." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_14_0.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True,\n", + " legend_kwds={'label': \"Population Density per m$^2$\",\n", + " 'orientation': \"horizontal\"},)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "Why are we plotting `POP12_SQMI` instead of `POP2012`?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note: Types of Color Maps\n", + "\n", + "There are a few different types of color maps (or color palettes), each of which has a different purpose:\n", + "- *diverging* - a \"diverging\" set of colors are used so emphasize mid-range values as well as extremes.\n", + "- *sequential* - usually with a single color hue to emphasize changes in magnitude, where darker colors typically mean higher values\n", + "- *qualitative* - a diverse set of colors to identify categories and avoid implying quantitative significance.\n", + "\n", + "\n", + "\n", + "> **Pro-tip**: You can actually see all your color map options if you misspell what you put in `cmap` and try to run-in. Try it out!\n", + "\n", + "> **Pro-tip**: Sites like [ColorBrewer](https://colorbrewer2.org/#type=sequential&scheme=Blues&n=3) let's you play around with different types of color maps. If you want to create your own, [The Python Graph Gallery](https://python-graph-gallery.com/python-colors/) is a way to see what your Python color options are.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.2 Issues with Visualization\n", + "\n", + "### Types of choropleth data\n", + "\n", + "There are several types of quantitative data variables that can be used to create a choropleth map. Let's consider these in terms of our ACS data.\n", + "\n", + "- **Count**\n", + " - counts, aggregated by feature\n", + " - *e.g. population within a census tract*\n", + "\n", + "- **Density**\n", + " - count, aggregated by feature, normalized by feature area\n", + " - *e.g. population per square mile within a census tract*\n", + "\n", + "- **Proportions / Percentages**\n", + " - value in a specific category divided by total value across in all categories\n", + " - *e.g. proportion of the tract population that is white compared to the total tract population*\n", + "\n", + "- **Rates / Ratios**\n", + " - value in one category divided by value in another category\n", + " - *e.g. homeowner-to-renter ratio would be calculated as the number of homeowners (c_owners/ c_renters)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretability of plotted data\n", + "The goal of a choropleth map is to use color to visualize the spatial distribution of a quantitative variable.\n", + "\n", + "Brighter or richer colors are typically used to signify higher values.\n", + "\n", + "A big problem with choropleth maps is that our eyes are drawn to the color of larger areas, even if the values being mapped in one or more smaller areas are more important.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see just this sort of problem in our population-density map. \n", + "\n", + "***Why does our map not look that interesting?*** Take a look at the histogram below, then consider the following question." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_21_0.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(counties['POP12_SQMI'],bins=40)\n", + "plt.title('Population Density per m$^2$')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What county does that outlier represent? What problem does that pose?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.3 Classification schemes\n", + "\n", + "Let's try to make our map more interpretable!\n", + "\n", + "The common alternative to a proportionial color map is to use a **classification scheme** to create a **graduated color map**. This is the standard way to create a **choropleth map**.\n", + "\n", + "A **classification scheme** is a method for binning continuous data values into 4-7 classes (the default is 5) and map those classes to a color palette. \n", + "\n", + "### The commonly used classifications schemes:\n", + "\n", + "- **Equal intervals**\n", + " - equal-size data ranges (e.g., values within 0-10, 10-20, 20-30, etc.)\n", + " - pros:\n", + " - best for data spread across entire range of values\n", + " - easily understood by map readers\n", + " - cons:\n", + " - but avoid if you have highly skewed data or a few big outliers\n", + " \n", + " \n", + "- **Quantiles**\n", + " - equal number of observations in each bin\n", + " - pros:\n", + " - looks nice, becuase it best spreads colors across full set of data values\n", + " - thus, it's often the default scheme for mapping software\n", + " - cons:\n", + " - bin ranges based on the number of observations, not on the data values\n", + " - thus, different classes can have very similar or very different values.\n", + " \n", + " \n", + "- **Natural breaks**\n", + " - minimize within-class variance and maximize between-class differences\n", + " - e.g. 'fisher-jenks'\n", + " - pros:\n", + " - great for exploratory data analysis, because it can identify natural groupings\n", + " - cons:\n", + " - class breaks are best fit to one dataset, so the same bins can't always be used for multiple years\n", + " \n", + " \n", + "- **Manual** \n", + " - classifications are user-defined\n", + " - pros: \n", + " - especially useful if you want to slightly change the breaks produced by another scheme\n", + " - can be used as a fixed set of breaks to compare data over time\n", + " - cons:\n", + " - more work involved" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classification schemes and GeoDataFrames\n", + "\n", + "Classification schemes can be implemented using the geodataframe `plot` method by setting a value for the **scheme** argument. This requires the [pysal](https://pysal.org/) and [mapclassify](https://pysal.org/mapclassify) libraries to be installed in your Python environment. \n", + "\n", + "Here is a list of the `classification schemes` names that we will use:\n", + "- `equalinterval`, `quantiles`,`fisherjenks`,`naturalbreaks`, and `userdefined`.\n", + "\n", + "For more information about these classification schemes see the [pysal mapclassifiers web page](https://pysal.org/mapclassify/api.html) or check out the help docs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------\n", + "\n", + "### Classification schemes in action\n", + "\n", + "Let's redo the last map using the `quantile` classification scheme.\n", + "\n", + "- What is different about the code? About the output map?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Population Density per Sq Mile')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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///3vycrKYtKkSbZrw4cP5/e//z2rV68mMTGRb775BoB58+axYsUKABYsWEBaWhqZmZksWLDANrWNjIzkscceY/DgwQwePJh58+bZnPfnE8q00U2EEKhUKrp1iSY6Mpji0paNUmD3zvDD3nboXetRdYCj3p6GMIiPnlveyHnvH6Tj+jmTvQ6T8AWDBw8mLy/P4bWGEIygoCC2b9/eYl3207FNmzZx7733et2v7793fqTnhg0bHKY/9dRTtufPPvsszz77rMN8t956K7feeqvXfWsPFOPlAUII+qclsez1O7l77odkHz7lMr/Uubc7/nzA6OZosi2ZMXcK1/xpAhuWbeJMoZ6ouAiGTx3aqhGXWq3m7NmzZGVltSrWqy1wFUB6vjJjxgx++uknpk6d2tFd8Z3xEkKogW1AvpTySiFEJPAxkALkAtdJKfXWvI8At2HxDP9ZSvmNNX0gsAjL8fQrgb9IKaUQQoflWPOBwBlgmpQy11pmJvCotRtPSynPhQO3EWq1ih4pMS0ar/pWxAW1N+Ul5e3fqIMZU1BooE/DIZKSkjh58qTP6vuts2TJko7ugg1f/nX9Bci2ez0HWC2l7AGstr5GCNEXuB5IA8YD/7YaPoCFwJ1AD+tjvDX9NkAvpUwFXgKet9YVCTwOXAQMAR4XQrTLklR6r/gW85j2nWiHnvgGlboDDO35t/qucAHhk2+sECIRuAJ4yy75aqBhFPQecI1d+kdSyjop5TEgBxgihIgDQqWUP0uLF3NxkzINdS0DxgiLF30c8J2UstQ6qvuOcwavTbkoKwW1iz/4zpHBlOxRjJeCQlvhq2/sy8BswH6tvbOUshDA+n/DWm4CYD+Oz7OmJVifN01vVEZKaQTOAlEu6mpzoiKCCQ8JcHrdZJYERIW0R1d8glrbsQ57BQVPabXxEkJcCZyWUra81GIt4iBNukj3tkzjRoW4UwixTQixrbi42K2OtsRF/VOcXispqyIyo4tP2mkPWrNvz2scfFKVVXV89f0eFi37ma++30NlC9uGFH67+OIbewkwSQiRC3wE/E4I8QFQZJ0KYv2/QR8kD0iyK58IFFjTEx2kNyojhNAAYUCpi7qaIaV8Q0o5SEo5KDq62fmVXhEVEezy+kGVZQvNhcD54PN6b9kmJt/5Os8t/Ia3lm7kuYXfMPnO13lv2aZWNVNTU8PIkSMxmdzf1jVjxgx69epFeno6t956KwaDZbfBgQMHGDZsGDqdzuU+xZtvvpmuXbva5GYaVjqllPz5z38mNTWVjIwMduzY0ap7s2ft2rVkZWWRlpbGyJEjben/+te/SE9PJy0tjZdfftlp2bCwMFt/7UMqbr31VmJiYkhPT29UZtasWcTGxrbZfs2WaPU3Vkr5iJQyUUqZgsURv0ZKeSOwAphpzTYT+NL6fAVwvRBCJ4ToisUxv8U6tawQQgy1+rNualKmoa6p1jYk8A1wuRAiwuqov9ya1i4kJ0TSr1c8Gb0TyOyTQFbfRDL7JBLfORSA2jojySP6tFd3WkVH+7zeW7aJN5f+SE1t4y1JNbUG3lz6Y6sM2DvvvMO1117r0abzGTNmcODAAfbs2UNNTY1N6yoyMpIFCxbw0EMPtVjHCy+8wK5du9i1a5ctmHXVqlUcPnyYw4cP88Ybb3DPPfd4d1NNKCsr449//CMrVqxg3759fPrppwDs3buXN998ky1btvDLL7/w1VdfcfjwYYd1DB8+3NbfefPm2dJvvvlmvv76a4f3d/fdd/uk/97Qlt/Y54CxQojDwFjra6SU+4BPgP3A18C9UsqGn8R7sDj9c4AjwCpr+ttAlBAiB/gr1pVLKWUp8Hdgq/XxlDWtXdCXVbHnYAG7D+TzS3Y+u/bn8Ut2HpHhlhFZYIAfZqPnm7g7go4ceVVW1fHB8s0us36wfDNV1d5NIZcsWcLVV1/tUZmJEycihEAIwZAhQ2xBqjExMQwePNjtE26a8uWXX3LTTTchhGDo0KGUlZU12qbjLR9++CHXXnstycnJtn4CZGdnM3ToUAIDA9FoNIwcOZLly13LDzVlxIgR52WEvU+/sVLKtVLKK63Pz0gpx0gpe1j/L7XL94yUsruUspeUcpVd+jYpZbr12n3W0RVSylop5e+llKlSyiFSyqN2Zd6xpqdKKd/15f20xEknaggatYquSVH0ToykvqKWyNRY/CPPbynjjpjdCqv1WvvzoWYjrqbU1Br44edDHrdRX1/P0aNHbZpcnmIwGHj//fcZP97zRey5c+eSkZHBAw88QF2dxfC2ldzMoUOH0Ov1jBo1ioEDB7J48WIA0tPTWb9+PWfOnKG6upqVK1c6jXv7+eefyczMZMKECezbt6/VfWprlAj7VnDgiOMg1V37Lb/SxwC6xwLg76dB9dkmZCtHYmqNCpPR9xuoO2pTNkBJmXv7P8/oqzyvu6SE8HD3VCsc8cc//pERI0YwfPhwj8o9++yzxMbGUl9fz5133snzzz/PvHnz2kxuxmg0sn37dlavXk1NTQ3Dhg1j6NCh9OnTh4cffpixY8cSHBxMZmYmGk3zP/sBAwZw/PhxgoODWblyJddcc43T6eX5ghLc4yU1tfUcOe7+qmVtvZHo3i0HtroirFMIfS/uRd9hvQiO8O1ITnTgboBO4a4XPhqI8uKeAwICvD7w9sknn6S4uJj58+d7XDYuLg4hBDqdjltuuYUtW7YAlpGW/cgnLy+P+PjWfS8a6h0/fjxBQUF06tSJESNG8MsvvwBw2223sWPHDtavX09kZCQ9evRoVj40NJTgYMvnMHHiRAwGg00l43xFMV5esu9QIWazZ4oAUYNSvW5Pq9MQHhPGnvXZ7P/5ILFdPZdAcYWrgNu2ZtSwngT4u/YhBfhrGT2sp8d1R0REYDKZPDZgb731Ft988w1Lly71KoykwY8lpeSLL76wrdRNmjSJxYsXI6Vk06ZNhIWFERcXB1ikm72dQl599dVs2LABo9FIdXU1mzdvtkncNBwEcuLECT7//HOHcs6nTp2yjQq3bNmC2WxupPd1PqJMG71k5z4v9ssF+HnVVnz3zlRX1HLcOh0NjwklZ8cxr+pyhlB1XEhHcJCOGydfxJtLf3Sa58bJFxEU6JnYXgOXX345P/74I5dddpnbZe6++266dOnCsGHDALj22muZN28ep06dYtCgQZSXl6NSqXj55ZfZv38/oaGhTJw4kbfeeov4+HhmzJhBcXExUkqysrJ4/fXXAcuoZuXKlaSmphIYGMi771rctGazmZycHK8d43369GH8+PFkZGSgUqm4/fbbbQZzypQpnDlzBq1Wy2uvvUZEhGUHXUOf7r77bpYtW8bChQvRaDQEBATw0Ucf2aaz06dPZ+3atZSUlJCYmMiTTz7Jbbfd5lU/fYlojZ7QhcqgQYNka46fklJy41/e5Xi+ZwubcZ1CqFi8FlNdywoOKrUKXYAfXTOSObT1CEY76en41FgKclxvCveUzl2iKfJgGuwuc1f+ifhOjjc9JPSIIyjsnGLEe8s28cHyzY2c9wH+Wm6cfBEzWyFEuHPnTubPn8/777/vdR1tzd69e3nnnXe8mqJ2JE888QTBwcFuhY64Q3Z2tm3E2IAQYruUspnsrDLy8oK1mw55bLgAqmsN9B2dTuHuE5S4kJLOGNGXQ9uPYKgzsP+n5itsZwpK6X1pb+qq6zAbzQiVQJolxnojJoOJuuo6y3OjCZPBhMlgbGT8HNHRcV4AM6cOZerE/vzw8yHO6KuIighi9LCeXo+4Gujfvz+jR4/GZDJ1yAEj7pCenn7BGa5Zs2axfPlyHnzwwQ5pXxl5ecHf/7WSb9Z7rknfPT6Cgte+JjA0gG6ZXdi74UCzPEIlCAj2p7q8xmVdfgF+1F8xkBp3VwmlRC1ALQRqlUAlBGohUAEalUCtUkGgHwJB+LbD1JfXIlQghAqV2iLEKIRAqKwPISzhFeLccykt/RdYXwPTn72KpNhkh12KSojsMHVUhfMTZeTVxkRFePcHF1x8FoDq8hr2bzxIp/jIZiOwxJ5xnDzgcIdTI6RZYvLkh0cITIBJNvzjoGxNPQB1uSXUlnkeluAIQ53R+bFmv73fTQUf0vFzhQuQO28YTnK857Jh5s7nypjNkuqKGsKiQwmPCSXt4l4AFOScImNEX5f1pF01EPU1g6n3cLXTXWQHxnwpKLiLMvLyArVa1WJEuCPyyqvRhQRQV2GZElZX1ID1ednpclLSkig/U8GeH7PxD/anttLx8r7090Nf33bbjn6LrgSFCw/FeHnBifxStw7gaEpXtYrsCue+rJMHCzAZTaT270rOTsehEF0HduOkWg1tEGXfQEfuxyyvq2NVziGKq6qIDgpiQmpPQj08j1Dht4EybfSCH7cdcStfYIAfA6JDGdA5jKSYMArW7HGZv+EQVmeGKywmlP1JnTjtg8NgXfbD0DGHcby2dRMXv/MfHln9LfM3beSR1d9y8Tv/4bWt7S+JI6Vk7ty59OzZkz59+rBgwQIA9Ho9kydPJiMjgyFDhrB3r+PToZxJ4rgrqeMO69evZ8CAAWg0GpYtW2ZLP378OAMHDrTJ4zTEc7m6r6bMnj2btLQ0+vTpw5///GfbaNzZfX388cekpqa266EiysjLQ8xmyQ8/HXQrb01tPSfX7aMyvxShUbd6X2NoTBin2mEHtdkk20VeXtp57F/buol//ryxWZ5qg8GWfu9g72K9vJHEWbRoESdPnuTAgQOoVCpblPo//vEPsrKyWL58OQcOHODee+9l9erVDut44YUXmp2y0yCp88UXX3h1L/YkJyezaNGiZkYwLi6On376CZ1OR2VlJenp6UyaNIn4+Hin92XPTz/9xMaNG9m9ezcAl156KevWrbOdmu3ovqZNm0bnzp3bVdtLGXl5yJ6D+fzz0SnccM3gFvNKCYlXW/K11nABhCT7RkTRFUJAZLfObd4OYFttLK+rY+G2LS6zLty2hYq69pPEWbhwIfPmzbNtDWqQmNm/fz9jxowBLKdd5+bmUlRU5Ha9rZXUsSclJcUWUW+Pn58fOutUu66uDrP5nIvB2X3ZI4SgtraW+vp66urqMBgMdO7cTt8JD1CMlwfUG4x8u24/899azRff/OJWmUIfOdbjUmM5ENT2vh8pQRfqXJu/LViVc4hqg+upcLXBwKqc9pPEOXLkCB9//DGDBg1iwoQJNoWFzMxMPv/8c8CyB/D48eNOD6R1JInTXpw8eZKMjAySkpJ4+OGHbZu/nd2XPcOGDWP06NHExcURFxfHuHHjGsVedeR92aMYLw/IK9Rz4GgRqzcepNoaE9USlTX1PhHLComPoKoNnfT2tLc8TnGVezFlxdXtJ4lTV1eHv78/27Zt44477rCdHj1nzhz0ej1ZWVm88sor9O/f36HEzLPPPsuBAwfYunUrpaWlPP/88x73oTUkJSWxe/ducnJyeO+992yjQ2f3ZU9OTg7Z2dnk5eWRn5/PmjVrWL9+/XlxX/YoxstNqmvq0Z+t4eAR96cIAJXVdQR1at0pQhFxEVQkdWpVHZ7Q3quN0UHuSd1EB7afJE5iYiJTpkwBYPLkyTb/T2hoKO+++y67du1i8eLFFBcX07Vr12blnUnitDfx8fGkpaWxYcMGwPl92bN8+XKGDh1KcHAwwcHBTJgwgU2bLIsm58t9gWK83Oalt1bz78XrvCqr8mvdukj8wG4crXZvpOcLzO00wmtgQmpPAlvwAQVqtUxIbT9JnGuuuYY1a9YAsG7dOnr2tLRdVlZGfb3ls3jrrbcYMWIEoaGhzco7k8RxRWskcezJy8ujpsYSkqPX69m4cSO9evVyeV/2JCcns27dOoxGIwaDgXXr1tmmjd7cV1uhGC83OHS0iK/X7ePgUc9GXQ0YnASbukNopxBOBLevD6q9QiWkdYdAqE7HPYOGuMx7z6AhhHgZ79UgieMJc+bM4bPPPqNfv3488sgjtgM4srOzSUtLo3fv3qxatYp//etftjITJ06koMCytWvGjBn069ePfv36UVJSwqOPPgpYdLMSExOZP38+Tz/9NImJiZSXl3slibN161YSExP59NNPueuuu0hLS7P18aKLLiIzM5ORI0fy0EMP0a9fP5f3tW3bNm6//XYApiwjD6gAACAASURBVE6dSvfu3enXrx+ZmZlkZmZy1VVXubyvjkDZmO0Gjzz3BRu25njdXuB/t7VqtTHqhuHkuelj8wUxmw9RUaD3SV2uJHHiunUmJPKciuprWzexcNuWRs77QK2WewYN8TpMAhRJnPZi7dq1vPjii3z11Vde16FszPYheYV6ftzmveFSq1WtDpPorK8kz987IUNvMLZxEKwz7h08lJsy+lsi7KuriA60RNh7O+JqQJHEaXs+/vhjnnzySQYOHNhubSrGqwWWfLGF1gxOdVp1q8QTwjuHYQ4PAi/2UnqLO2KJbUWITsd1af18Xq+jVTUF3zFt2jSmTZvWrm0qxssFUkq27T7Rqjr8tBq8jYQJjgjizKV9KGxHwwXt6PP6DbosFHyHYrxcsO9QAYWnz7aqDq1G5ZXxSrtqILu1mjaTvXGFqQ0VKxQUfIWy2uiCxLgIIsJap/Tpp/HMx6LWqEmbMpRtGg31HTAwCQ32p1XzZAWFdkIZebkgPDSQCaPS+PDLrV7XofVAG77rgK5U9k5kWytXFtVqQedOoYQG+3O2vJaI8ECEAJPJjE6nofB0ObGdQhEqQV2dgTA/DdV7T4BahThbhdYqjGhRebbsDsjLKaSsqHWjUHeoNlazVb+dsvoywv3CGRwxkECNIhWt0BzFeLVAYpzniqn2qN08Uiw4PIjy3onNQiKCg3RUWmWUhYAeKTEE+GupN5ioqa2nptaARqMmIiwQKS1+pKMnSigoOkuB1dgUFjc3OqdLKmzPu8SGc3qF69CR+O6d29x4rSj4iq8KVlJnPjfRXnJ8KVfGT2RSvPdSKzU1NYwfP541a9a4XG2cO3cuixcvRq/XU1l5Tq/t9ddf57XXXkOtVhMcHMwbb7xB374WtdsTJ05w++23c/LkSYQQrFy5stk+yhMnTjBz5kzKysowmUw899xzTJw4EQC1Wm2LwUpOTmbFihVe32dDW3379uWJJ57goYceoqKiotFp33l5edx44428/PLLPPDAA/zwww8AVFdXc/r0acrKyprVOWrUKAoLCwkIsMQbfvvtt8TExHD8+HFuvfVWiouLiYyM5IMPPiAxMZEjR44wZcoUcnJyGr2PvkYxXi1QVdO6jacaN41X5ysGsrfa0laXhEh0Og0lpZVUVNaR2SeRyupaQHDoWHMJE4D8U82/dO5SVllLTL9kivaccCqFY27jqeSKgq/4LG95s/Q6c50t3VsD5q4kzlVXXcV9993X7ETpG264gbvvvtvSzxUr+Otf/8rXX38NwE033cTcuXMZO3YslZWVDg+offrpp7nuuuu455572L9/PxMnTiQ3NxewbF9q0MTyBQ888AATJkywvQ4JCWlU/8CBA7n22msBeOmll2zpr7zyCjt37nRa75IlSxg0qHGo1UMPPcRNN93EzJkzWbNmDY888gjvv/8+3bt3Z9euXbYTuNsKxXi1gGilspXazfIhkcEkhQVSbzBSWlZFhd2hFb9kO1Yt8BVnK2s5mxpHaFZXOmXnUehIbNHJwkF4TBhlrVzUqDZW81XBSpd5vipYydjOYwhQe77bYMmSJXz44Yct5hs61HEgrP32n6qqKttUev/+/RiNRsaOHQvg9I9VCEF5eTkAZ8+etSk8+JovvviCbt26EeRkr+jhw4c5ffp0o5FYA0uXLuXJJ5/0qL39+/fbDODo0aO55pprPO90K1Ac9i1w/aRBXHVZhtfl3XXXF63aQSd9JUUlFY0MV3tS5cLXZnKgNNFvRB/KS8oJDA2gz9CedMvs4lW7W/XbG00VHVFnrmNrqefH1XkridOU1157je7duzN79myb+uihQ4cIDw/n2muvpX///syaNcuhWusTTzxhm1JNnDiRV155xXattraWQYMGMXTo0FYJFFZVVfH888/z+OOPO82zdOlSpk2bZjO+DRw/fpxjx47xu9/9zmnZW265haysLP7+97/bQlwyMzP57LPPAMtm7oqKCs6cOeP1PXiKYrzcYPbdY7l9+iVelRVuhqgW5pwi/xvfTR+8wWQyY3ISXd+5S7TtYFqVWkX6pb3Zsz7bcgpSeQ3Zmw5RcKSIfsP7oPFwI3pZvXtT3jKD5yM8byVxmnLvvfdy5MgRnn/+eZ5++mkAjEYjGzZs4MUXX2Tr1q0cPXqURYsWNSu7dOlSbr75ZvLy8li5ciV/+MMfbAKBJ06cYNu2bXz44Yfcf//9HDninsR4Ux5//HEeeOABl1O1jz76iOnTpztMnzp1qtNp9ZIlS9izZw8bNmxgw4YNtm1WL774IuvWraN///6sW7eOhIQEh/JAbYVivNxACMHNU4dx0xTP99epPBBo0PprOyxMIThQR1piFKWHCx1e3/vjAZJ6xZN2SW+ik6LY+2PzA3NrK2vZsyGbHv27ogt0fztTuJ97xiVcG+Z2nQ14K4njjOuvv942QkpMTKR///5069YNjUbDNddcw44dO5qVefvtt7nuuusAi9BfbW0tJSUlALYpZLdu3Rg1apRLv5MrNm/ezOzZs0lJSeHll1/mH//4B6+++qrt+i+//ILRaHS4fceZUWsgIcGyNzUkJIQbbrjBJoMTHx/P559/zs6dO3nmmWcACAvz/DPyFsV4ecAd0y9hwRPXEeOBPpcwu2+9/NtZPcKeniH+HHllJUYX0fzH9+eR/fNBzrSwaTt782F6DuzeogZjw/RlcMRAdCrX+xd1Kh2DI5vtzW0RbyVx7LFXG/3f//5nc+gPHjwYvV5PcXExAGvWrLGtQtqTnJxs07nPzs6mtraW6Oho9Hq9TYm0pKSEjRs32so/8sgjLF/efAHDGRs2bCA3N5fc3Fzuv/9+/va3v3HffffZri9dutShgTp48CB6vZ5hw4Y5rNdoNNoMrcFg4KuvvrLJ4JSUlNhGkM8++2y7b8FSjJcHCCEY0C+Zv9wy2v0yHqiSFhwqIMjDoFZfEB4WQEnfQOKvy2wxr9ksMda3vH1oz4Zs0of3aeZfcUSgJpAr4ye6zHNl/ESvnPXgviTO7NmzSUxMpLq6msTERJ544gkAXn31VdLS0sjKymL+/Pm89957gCXM4cUXX2TMmDH069cPKSV33HEHAPPmzbOFPfzzn//kzTffJDMzk+nTp7No0SKEEGRnZzNo0CAyMzMZPXo0c+bMsRmvPXv2EBsb69X9OuKTTz5xaLyWLl3K9ddf3+xzysrKAizKq+PGjSMjI4OsrCwSEhJs97h27Vp69epFz549KSoqYu7cuT7rrzsokjhecOR4Mfc/+Sn6s9Ut5h0QGUT222vcqlelVhFy3cWcaqe9jH0HJqAPM5BjLuesrCMlKBT1Hd/6tI1Hv/4zSbHJqDVq6qobO+WbSuI4ivPSqXStjvO6ECRxmjJu3Di++eabju5GqwgODvY4zkuRxGljuneJ5t9PX88/31yNwWhCYD0IR0rMUmI2S4vz2ywRRe7HX5lNZsLUglNt1XE7dDoNJ8NrOGg8NwVMrFTj2OPlPdI6UjPWG1FpVC5VWifFX8nYzmPYWrqNMsNZwrVhDI4c5PWIq4ELQRKnKRey4WoIUm3rE4cU4+UlSfGRjBvZl2deWeUyX68gP/peNwxhkmAyg8mENJqRJjPSaMJsNGE2WB4mg5FQPy1Utb3wYPf0znxvLLC9DtRoKXv2pzZt00+npdboOiQiQB3AiOjmcUitRZHEaT8aglTbGsV4tYJxI/qy4rvd7DngXHf8oL0hEgI0GpfvekqQP+g9PyXHXSIjggjsH8IeWQZ2gyCD2YSxjY2m0aCoVSj4jlY77IUQSUKIH4QQ2UKIfUKIv1jTI4UQ3wkhDlv/j7Ar84gQIkcIcVAIMc4ufaAQYo/12gJh9SIKIXRCiI+t6ZuFECl2ZWZa2zgshJjZ2vvxBJVKcP9tzgP7vCEs1N+n9TUlNCuMLaYiisyN/XXhfv7Un61p07aN9Ub8g/2xbTpoj2O5FX61+GK10Qg8KKXsAwwF7hVC9AXmAKullD2A1dbXWK9dD6QB44F/CyEaHBELgTuBHtbHeGv6bYBeSpkKvAQ8b60rEngcuAgYAjxubyTbg55dY/jDtRf54mhGAIqKK1rO5AZxcWF07daJLl2iSEqKJCkpkl5psWRT6jC/RqjcWhlsLbWVtWh1lpOCWrv1SuG3TauNl5SyUEq5w/q8AsgGEoCrgfes2d4DGjY+XQ18JKWsk1IeA3KAIUKIOCBUSvmztCyBLm5SpqGuZcAY66hsHPCdlLJUSqkHvuOcwWsXhBDcNWM4Pbr6xjlp9MGBrwH+Wo72rmNj0hl+Tillczc9m7vp+aFTEXon23AKaypJfGpMqySr3aXh1CBHtkuaK5DVnyIr/2353+wbY67w68OncV7W6Vx/YDPQWUpZCBYDB8RYsyUAJ+2K5VnTEqzPm6Y3KiOlNAJngSgXdbU70ZG+2UHfS6tiEFgeahUDtWoG6LRkBWjJCPQjPdCPPkF+9ArSkRroR0qgH8kBfnT21xLhpyZIo0ItwejFoR/rgivpMmuET+7DFQ3hOU1HerJyIbJ4OLJ8LrLyZcv/xcORlQtb1V5NTQ0jR450uO/QEZMmTWp0HuEDDzxAVlYWWVlZ9OzZ07bd6IcffrClZ2Vl4e/v73B/4qJFi4iOjrblazhuDODhhx8mPT2d9PR0Pv744xb7tn79egYMGIBGo2HZsmW2dFd9ue2228jMzCQjI4OpU6fawhf0ej2TJ08mIyODIUOGsHfvXlt9L730EmlpaaSnpzN9+nSHQb5ffvmlLf5r0KBBjWLpUlJS6Nevn+1aA7NmzSI2NpYXX3yxxXttCZ857IUQwcBnwP1SynIXUxBHF6SLdG/LNO3fnVimpCQnJzvrm9dERXh+mrMj6vL17Puv9zFoDVxS0Y+vx3rep5+STQz5fQYFnzY/SbkBoRLnRk9e4Ci0UFYuRFa+5OBCtS1dBN/jVXvuSuIAfP755832BzqTjhk9erRtVa20tJTU1FQuv/xyh/VOmzat0XYdsETr79ixg127dlFXV8fIkSOZMGGCw0NsG0hOTmbRokXN/vhd9eWll16y1fnXv/6VV199lTlz5vCPf/yDrKwsli9fzoEDB7j33ntZvXo1+fn5LFiwgP379xMQEMB1113HRx99xM0339yozTFjxjBp0iSEEOzevZvrrruOAwfObRv74Ycf6NSp8UnvL7zwglPVC0/xychLCKHFYriWSCk/tyYXWaeCWP9vEKLKA5LsiicCBdb0RAfpjcoIITRAGFDqoq5mSCnfkFIOklIOio6O9uY2XXLnDZeia+XJ2AAFwQE2n1BrOPy/PVyeLQgweeZXqjOZ2DUqnM5X9HF4PbFnHDHJneg33PH1ltDoNJgbRoXWrklzBbLqPy7Lyar/IM3eCdstWbKEq6++usV8lZWVzJ8/3+VBqs622SxbtowJEyYQGOi+6uv+/fsZOXIkGo2GoKAgMjMzbTphzkhJSSEjI8OhbpizvjQYLiklNTU1jSR9xowZA0Dv3r3Jzc2lqMhysLLRaKSmpgaj0Uh1dbVDGZ/g4GBbXfZSQe2FL1YbBfA2kC2ltD94bgXQsPo3E/jSLv166wpiVyyO+S3WqWWFEGKotc6bmpRpqGsqsMbqF/sGuFwIEWF11F9uTWt3wkMDW626ClBQW0/nbr7xnx17azNJNZ4bwgpDPbsnRhOQ0HyTbWRsBEW5xezZkE2/EX1Iv8QiGd0lLZFuGV3o2i+Zrv2S6TGgKz0GdEXjp0EX6Gd5BPjhp9NaX+vOfdlrvwbZwm4FWW3J5yGeSOI89thjPPjgg04NkCvpmJY2N3/22We2advJkxZPR2ZmJqtWraK6upqSkhJ++OEH27XW4Kgvt9xyC7GxsRw4cIA//elPtvY//9wy1tiyZQvHjx8nLy+PhIQEHnroIZKTk4mLiyMsLMzpiHL58uX07t2bK664gnfeeceWLoTg8ssvZ+DAgbzxxhutvidH+GLkdQnwB+B3Qohd1sdE4DlgrBDiMDDW+hop5T7gE2A/8DVwr5SywRlxD/AWFif+EaAhAvRtIEoIkQP8FevKpZSyFPg7sNX6eMqa1iFoNL5xIfpqy1bqtAEcCvYudqvSUI/hiWFo7UI3NH4a9HbCg3vWZ7N340FSs1LIO1jI0d3HObbnBMf2nODwjmMc3nEMY72Ruup6y6OmnuryGuvrunMTfHOxe51yN58d7kri7Nq1i5ycHCZPnuw0jzPpmMLCQvbs2cO4ceMclrvqqqvIzc1l9+7dXHbZZcycafkdvvzyy5k4cSIXX3wx06dPZ9iwYa2WlHHWl3fffZeCggL69Olj863NmTMHvV5PVlYWr7zyCv3790ej0aDX6/nyyy85duwYBQUFVFVV8cEHHzhsb/LkyRw4cIAvvviCxx57zJa+ceNGduzYwapVq3jttddYv359q+7LEb5YbfxRSimklBlSyizrY6WU8oyUcoyUsof1/1K7Ms9IKbtLKXtJKVfZpW+TUqZbr91nHV0hpayVUv5eSpkqpRwipTxqV+Yda3qqlPLd1t5PawgP9Y0qhC7YN7FelQmt68/BqjKinhqNyl9D7GU9SegRx0kHAbk5u3IxteZUcJWb03h389nhriTOzz//zPbt20lJSeHSSy/l0KFDjBo1qlEeZ6OrTz75hMmTJ6PVOh7lRkVFobOe+n3HHXewfft227W5c+eya9cuvvvuO6SUzSSoPcVVX9RqNdOmTbMJCIaGhvLuu++ya9cuFi9eTHFxMV27duX777+na9euREdHo9Vqufbaa/npJ9e7L0aMGMGRI0eaSf3ExMQwefJkm4yOL1FUJXxIXLRvtIw0F/ckbsYIkm8cTnwv7yWDD0W3PuxiI3qS3ria09O6cXxf66c0jWhwkfiPB9GCr0gEWvJ5iLuSOPfccw8FBQXk5uby448/0rNnT9auXWu77ko6xpkfrIHCwnM7RlesWGHbeGwymWzKo7t372b37t226ZmnkjjO+iKlJCcnx/b8v//9L7179wagrKyM+nrLyPytt95ixIgRhIaGkpyczKZNm6iurkZKyerVq5ttlgbIycmxzRJ27NhBfX09UVFRVFVVUVFhCXGpqqri22+/bbR66yuU7UE+oqq6jrWbDnldPiUxCiEgNCSAnNzTVFVbvlS9WrEIEGxUEWpQYxSSY4HeKVUMiU9kTUEeI2tCaSslfaEKgaC7HK82NuQJuguh8i4cpUES57LLLvO2i06lY3Jzczl58iQjR45slD5v3jwGDRrEpEmTWLBgAStWrECj0RAZGWlTWzUYDDY9+dDQUD744APbtHHPnj1MmjSpWT+2bt3K5MmT0ev1/Pe//+Xxxx9n3759TvsipWTmzJmUl5cjpSQzM5OFCy2hJ9nZ2dx0002o1Wr69u3L22+/DcBFF13E1KlTbSEZ/fv358477wQsJykB3H333Xz22WcsXrwYrVZLQEAAH3/8MUIIioqKbNNvo9HIDTfcwPjxvg+/VCRxfMTyr3fxzze/96psv97x7DngcJGUPrmnyd15rDVdo+e1/Vk1wrXYnz2D4xMtv6gC9p0+TY3RwLBVek5/c9Bh/sSMJAL6xyOqDRxZ/kujKeTclX8ivpPj0LvEXvEEhpyb2srKhZZVR3vnvQi0GC4vwyRAkcQ533jiiScIDg7moYceanZNkcTpAJwdSeYOKuF89m4ytCz81yJ+7nsH0mM6s7Wg+RhL+7sUcGC84vsmsHlmIrVqCfgxwdCPQ1+4pyjQdGVdBN8DgX+wrCqaiy0+Lv/xXo+4GlAkcc4fZs2axfLly3nwwQdbXZdivHxE/inX0sgucREec/qoZ0YxKCwQaZbU1dTbRkCmMD+cxO42w+hEttqkO2cAg8ID6dwvEXWPCH7uq6ZWfa7MzmHBZIUOsUgAIdEG+aGLtPNn2XfDwWniQhUMgVPd6qsnKJI45wcvvPACL7zwgk/qUoyXjyiv9F4jXV/mWAInJVBHzKButmh2IcQ5Q2c1AlJK6wOQktMnSyjJK6VTYiTB4UGYTWaKEvyAlo9Tiw0O5khp86OrogOD0Kh0dJo7guJgyV6dgd02u9PY2J3SGfl6wDmjdLUfnA1svhUIINpHoSUKvw48dWEpxstHjBiSSk6u53FIACcK9ESFB3GmiRGLNJnZsz7bqzpL8kopyStFCEFpgHvnKSaGhnGqiWzv4PgEthXks44q8GJjwomqSqKqqtAGBTUzYIqmhEIDUkrOnDmDv7/7YUKK8fIRcTGtC5OI6RTSzHipqlqvrxXbKxZ9qB/hUmKyjuAkEOKnw2Q2ERscgk6joaS6mm0F52K41ELQLyaWrQXOhRbtCdGp0KhFM5mb948fRKsSJAQGN4vLkcUlaC8QH5RC2+Pv709iYmLLGa0oxstHDBvYFZVKYG7FhuWm7PXT0ud3/TiwZo/XdSTMjEJkNj9LUHtmJGsOF3O6uvm2nECtlvToGLa4YbguTg0kNj6PkvoipBO/2hqatw/wTLcnSQx0/8uqoGCP4nTwEWEhgXTu5FwNoCXMDhzltWbJztAAeo9Oo0ume1M/ezR+GgqHOw4srax1bmR7REa5ZbiuyAzEr9MOiutPOTVcrlEmjgreoxgvHyGE4A/XDvGqbGbfRA46W1UUgp3hwRzoFov/tIvpPf1St+s11hsJP+V4s3hkqPOgVa3K9VROo4Lpw02UB2zDROuj+BUUvEExXj5k7PDeBHlwzD1AaLA/v+x3L3ZdX29iX52BqIRIt/ILIVAZHX/Eaq13/rROQWp+f7Ekz+A4YNUTzIrhU2gFivHyIQH+OvqkxnlUJjoqxKP8yYE6zuS7J5whpcQ/1/GeQaPW+cqosylgfJiWYQPyyTN6twLaFJNUThNS8B7FePmY3t090+KqqKr1SIW1zEP1hpwFeQhjc9+SQVVGiM7xKNFZvM3IjGrKDL5THDJLZeSl4D2K8fIxpU4CTp1xuqSCxFj3RQzP1ptIu3Kg2/n1BWWcHlONX3XjvY0Bhi5U1DXX+uoWGebQj941SkeRMdftdh0hJQRXDkVdMgJ1yQiqa5TFbgXvUb49PiQ37wyrN3ruC8rOKSSzbwK/7G95hc8gJdu0GgaM6Uf2avdCKAx1BqK/iSV/8nFbWo02j1Bdd8rrLJH3IX5+jOgrOav7mUhNJ5Lju6BVm9H41bLnaAB9U89QWN/YyS+kBokJRPORWkj1YIwGLWrr1qG6Oi2llfBj4bkI/rsylNVGBe9RjJcPOXayhLp6zzdS1xtMHDlegkqAu2FiwsPTikzdGhsegygnzN+PISlBELaPpKA4cmr2IwC9qQS0FlE5jBCRDIX2gzQp8NNfypbjFcSHBNE9pZAKjU0fEq0MYsMBAxX1rkehnx/Yz8D4BDQu9NgVFJyhGC8fMmpoT674XTr/W7O35cxNqKyqI7NvIvsOFrh3duPl/oy89mJqT1VirpOgAmmUSIPlYaozY641Y6o1Y6wxoe/b3FfVL7WMCu1BAtQBqDUtG90Ac2fO5PeixmBi/2nLCOpgXT36mkgGdetETcBuzJg4mz+AivrmeySb8un+vZRUV/PK+CsI9PNslVZBQdHz8jFms+QPD7zL8TzvHNspiVHUGwwUFJW7zDfmr5CrdX/Vr2dwDw5VHm6UJqVFlqZXcA8ONrnWCLOKqvxL2F2od6o6AdAjKpxTFdVU1Humm391pxTmjRlDROeWteYVfnsoel7thEoleHne77nzkSUUn3F9VNfA8UEE9S+mSObjrwrADz806FGjJm5HLNu/dn6iTuWhYLoMTqbebKCwttBpvgZK6/X0CE6lsOYUlSZLvxr2Sbf08xVsTmZDfssjqcNnylrM44hTZ8v588Vz+cfKv5HUq0PODFa4AFGMVxsQHRXCG8/OYNbznxLRu466aklwtOB01+2ohRoVKjRCQw0myswWh3mlqcmx9pn59DcNZOd3jg1YSZ6JkrQTqFARqA6k2uT66LCS+hJK6kuI84+zGS93EFJNUWES0LLx8oZwPx1Vz/5Ezcky/nLJozz15cOkX9K7TdpS+HWheErbiOioEOb87VKMWYeoGrKdoq7bkEiM0ki9rKfaXE2d2bXGVsDgAtQODqQRKknniZbRlhkziQHuj1ZqTTVohZaUwHN7JV3tSwwwx7HDjVGXt/Q/Iag5aRmxVZRWMvuyp1i/7Oc2a0/h14NivNqQ3uE9eSLtMcK03m3YLjIVkjm6eYT8pdODKDKdmypWGCqa5XGG3lBGsCYYszSQEhBDkn80AdTgbDdjfVlXT7vtFgIYVajjxHNrG6Ub6gw8Pe0lPn/5f23SrsKvB2Xa2MZE6SKZnjSN14++6VX58v47GNMrnV8+E/QY6kdgShUFfnsaOaoK607RWRdDUZ17ktF6g57romoY6vfducRIgVlEYRAR1BJKpTmIsyYd/yn2gYZ+E1JCwumyWc/JN9Y5vC6lZOFfF3GmsJTbnp3h8mh7hd8uyreiHRgadRE9g707TFQiMUaWEvaH3ZzusY1cbTb1svlqnp/KjwC1+4fMBoimstUSlSxBZz5MmHk7Caynr/o75qR95FPhmh5hkcRuNZP3xuYW82ZvPszT0+ZTV9OyhLXCbw/FeLUDQghu6TqTILX7exjtCdK0XO5kTR7xAe5vCn+9SMf6uokt5ovzP8HAWO91yhpQC0FqaCSdz/g51LN3xMmDBWz4bDOzx/6dsyWuQ0cUfnsoxqudiA+IY3rydc1kkt3BKN2buunr3A9VqDXXsbJMyzo3DNioBO9CIOwZFBZH9X/PcHRjy2EdDQSHWfx9+386yF8ueZTCY0Wt7ofCrwfFeLUjw6Mv5b7Uezya3gEcqTpKcmASOuE6Cr3UUEqw5ty2IYEgQB1AqMYiu6MWKtTinGu+qK4UtRuaWuPjNjR6rVOrGRweR1p4NIFaB8uhdmhVKoaEx3NyhXta+PYEhZ0bceYfLuT+Sx7l4NYcj+tRsXA2xwAAIABJREFU+HWiGK92ZlDkQJ7t93eywjM9Knei+iTJQS1LQXcNSqFncA9i/WMJ1YZSY6qh3FhBJ78o/IRfoxCJ1MDOZGlb9j2lBB5gTHw0Q8PiuTQ4kT65QeR/WYD+y9PEbxUMDYtHLQRalYrogEAGh8cxXBNP7/3+dPrBRN6X+Y0jYd3c1XFwaw7xqeckhkpPlfHgqMf5acVWt8or/LpRVhs7gAi/CP6U+kdeyfk3u8p+cbtccd1pYnWxnKo75TTPieqTnDWcbZZeYt1raK+hJYFimUQwLR+YG3GgluydzVczq8/WcWJFPkkBaqQEQ20V+VSh1agwGM2oBSQnRBIWEkB1jQG1WmCodS5B3ZSo+CgKcs5NF+tq6nny2he475XbuOqecW7Xo/DrQxl5dRAalYbpydM88oGVGc66jBkL1YQ6NFyNsHOWH6ku4oTRvdN7WlK7qK8xYag14adVk9knkZDgAMJCAtD6acjNK+WX7HwO557mwJEi9JW19JhykVvtVp9trkxhNksW3PsWbz+yxOHBJQq/DRTj1YHE+nfmkk4Xe1QmryYff5XjgznLjeX0CE51WV7VxFhGqd1zxrdkI7RaNRl9EogMD+KX7DxKy6o4W1FDXV3jxYaQIH/iY8M5ExNOz/sm4Bd87l6SR/Wl558m0G1Cf8vrPgmcLXYegPvR81/w7I0LqK/1bCO4wq8DZdrYwXQP7samM5vdXlGsMlW5VIEoqXO9lcc+TMFPpSVB5Z4DvCU3Vc+uMezOdu2U79E1hhP5pew9WABAPhA//VISjhej7hXPvnw95rxS+qfEoNGqqSitRF/keiS59qONlBbqeeqL2Y0c/Aq/fpSRVweTFZ7J/al38o+EIp5PPMmIiJb3KR6qzCFM2/iE7h7BqfQK6UmMfzQa4fw3yWg2khCQQN/gZJ6P30+Eyr3QBWcSYyqVIK1nHMWlrjd7++s0VFbVNhNrLCipYH+QP3vySjFbLeSuojIybh3douFqYPe6/dw//DFOnyxxK7/CrwPFeHUwkX4RpIcPIC6oNzGqXG4O/ogro2JdlpFIQjXnfF8qVFQZqzhYcYiDFYdcjuJyq4+TX5NP34AawlXOHf821F1AO5jQoMbTzc6dQujXO4HgQB37DhVyusT1/spuyZ0oPO1eoKmUUK32bFKQu/ck91/yKDm7jnlUTuHCRTFe5wFSmhHa3qDqjBAwJehTro9xfTZjjencuYtmzKiEZx/luVlgCGj6gYiyvFQlgHYQaAeCpheYjoNhKxf3O6d/371LJyqq6thzIJ/yyqbbjJqT0SeB/YfdMJR2ZJdWEJYU5VGZ4rwzPDD8MTav3OFROYULE8V4nQeoVGpQRYJulC1tnP+XvJBwnCs7xZIaFEuPoMajsSpTFSrrx6dCxVmD+9tnNEJDksZ6bqOmKxj3gCwF/MCcD4ZtYNgOxnOHiVzS51sy+sTTs1sMeafKqK5xz0keER7I0RNeTOckGGrcD6looLaqjnlXP8+qt1d73qbCBYXisD9fUCeB8UPLaMdqNDqpTzAl8AQEQqU5gn9xGTlVpxAI/r+9M4+Ps6r3//v7PLNl39NsbdN9o5S2KZSWpWChBZEqohavwlUvKG64gQh4AYGL4E9RLwp6r1xBQUAW2UEQkCJQWkpL6ZIudEu6JG3S7JntOb8/5kmbpllmJrNkkvN+veaVmfM8Z5nOzKfnfM/3fL9TMyfTFmxHYaGAA53hRZTIdWZxw6gPKDBqgCwIfGBfUUDfgpSVdph0VysfbIrsjGFpUQ4bt4Z/JKgLf9Bi9PIFVN/9YsR1raDFLy6/l4M1DXzhPy8O+yylJrUYFjMvEVkqItUisk1Erk32eKJBeVeA/wMwikGO3zXLNBq5PG8Vi4rm882JX+fy8V+hMmMsW1u3s611Oy2B8GJ6nZadcVS4zKKIxrjwxMjOFs6YXBqVcAEEgxZraxsZ+63zmHbFORjOyP+ffeDmR/nF5ffi90U+g9MMfVJevETEBH4DnAdMBy4RkenJHVVkKBUA/wYwysC3AlTvdqRiYyf/XjaDqvw5ZDgz+Lexl3BG0ekDtu+U0PnDKRmlLMzYDs554JwEwb0RjXPh9NcxjPBmMbnZaewNc7ewPzbXNLCmvplJVyxm6pVLyCjOGbhSN16871VuuOB22pr7D5OtST2Gw7LxZGCbUuojABF5GFgGbEzqqCLB+wY4pyDuM1HtD0Bwdx83CjiPPRN5aUkll6Xfww5rNn8/7OKi/BbyZD8O1ciW4Byagw5OTGvigLeFseaKkHD5V4E5DhjY2N6dwuw9LPt0Nk/+dWBRKi/JZcOW6GZdvbF2b+gIU87Sk8h+dT3NEWRnWvPKer6/6Eb+6/nryI8gO7lmaJPyMy+gHNjT7XWNXZY6iAvJ/DaScSlS+By4z+nzVtV6d+ivfzNWy88xm3+IoRqZIK9yZd6LjFL/wmVtx1ANTDVe4WTni3iCGxjrcYbsan77UHNwHxDZLAag84RdVJ1W2O89E8YUxVS4utPU0kn5eXMirrd97U6uWnA9uzbVxGFUmmQwHMSrt3XMcf7gInKFiKwWkdX19fUJGFb4iPs0RELHZETckH0HSG+uEgrci1H+alTLT6Htd6AGPlSNagb/+xDspvFGNhDFsk4Ujae+yay5vQvYiVPL2RnHhB0Awcbwsx91Z//Oer6z8AbW/XNDjEekSQbDQbxqgNHdXlcAxxlzlFK/V0pVKaWqiooiM1QnGsPMRAqfCtnAnPO6XShEzByQtH6WlmFi1Yd8uiKtphQYivaz3mHSpOMF1jCEYDgZvweBjMpDHH2lDOmf1sNt/GjJrfzz0bdiPCpNohkO4rUKmCQi40TEBSwHnk7ymAaNmKOQwmeR/D+A2w79YhShOp5EGSWQ/wxk/xc4poHRv0d+7zjBCmPW1oOg/dcy/Tg//gH5eUezG02qLGLz9thGOx0z6vil7dq9DYy58lxKqyZE1abfF+DW5Xfx2C+eGezwNEkk5cVLKRUAvgm8BGwCHlVKDYt1gRiZiHiQ3F8jmd+HwGZwnQbBPYiRjrhOgsAmEA/Qe6SJPjFyInaVgGO/MF53C5XL95OZ4WLGpFK27qyn0xs7t4RpFfnU3ft35hRlM62igIy0o5FkN9c0kD0/uqQmXfzuBw/w2+/8nw6rk6IMh91GlFLPA88nexzxQkQg86vgWYLyr4NgLUpc0PFC6IbgTnDMBnUQgnVAGNl2lBeM0cCuAW/tjtHD4bMpu5YZy9NZ/1D4bYzKz+RgU3u/y8s5RdlsvufvCLDp96EUbRULpmDMHU+1vdOoYpDX6MlfP8/B2kP88IFv4U5zD7o9TeJI+ZlXKmMFIsuJqDpegqarofm60JaEORncF4BZCYEug7wXnCfR6z6GWRmyoZlTQLXaR4Iio2c8MICDhVuZubwTj7v//wtHj8phbEkurrc2U7ZhNydWHG8zy89JZ3ZpHvteXocKBI+5VvNWNY1PrGRiWcjdwYjRjGnF4yu5ZvFPdIaiFGNYzLxSFcPhQCmFsiwMc2ADtBieI9uo4v8Xkr4cyzkOmq459kb/djDLQzYtxwTAAYFdoRlacGe3GweOJd+hsjikytnsm8x4x64+6xwsrmbmpePZ8Oes4849Oh0GM/OzOLhiE3Ubju54HtxUy4zzZuMqzMbq8GKke9jz3Htsru9bRDxOg+bxiumOAjb/+oUBxx8uG9/ewlULb+C/nr+OsgnR2BA1iUZUmMkQhhNVVVVq9erVyR7GEaxAJ4ZjYJuV1bkCWm4KHR/KewjDDGUKUp2voZp/DFb3840OwLIffWBUgNW731PAMnjB+0lePdzJYX94R48A8hvHseFP2UcEbE5+Jntf/oCm3YN3TykcU8Ceb0xjrzvA6E4nlQ2welSAEw85afjpCqyBYlWHQW5xDrc+cy1T5vUfkVaTOETkPaVUVc9yvWwcAoQjXABiZIWWhoHtCEcFRTxnIdk39Lg7QL/CBRzdOzyWRquMmw8u44n6+oiEC6AhbwfTLz1Mbk4aJ1YU4Et3xUS4CiryqbGFC2CPx8+KMj8dpmJlsY/Kr5466D4ADtc18YOzbtIZilIALV6pxBHXBj/4Nx97zRHFcU51vLjtDU7iJwdOoqYz+qikjbm7qPxiLc62Rlre2BjG4rR/8svz2PvN6dS6+7YRflhpkF00+MzeAJ3tXm6+6Gc8c89LMWlPEx+0eKUQqv2Bo89bfopqvQcV2BkqMIuA/pPSHs+xM6891jR+emBCxLOt7hgYVDimsutQBXVz86hbt2tQe4J5pbkc+OYMajz9b27scwconhteJqRw0BmKhj5avFIJ1c2HKrgD1XoX6tCnsNofQyQN0i6OtMEjz3YET+SOfWNoCRyfaiwcTAwqHNPZueVE/vKmwYa9HawONJJ9QvTGb3e6m/pvz2R32sC7stNLPHhu6WDhn2czftnoAe8Pl4fv+Bt3Xna3DqszBNHilUJIxhfAHH9soWqD5htRbQ+EXCQc08Jv0MgGcwIfBWbxs/2jaAtGHjbGIQ7KjZns2HIif3kTdjUc3WkMKkX6f8yOuM0uMvIz2JU2sGhMGeVhzIQtNAQPsWPidoLXtTP538ZF3W9P/vHgCm644HaaG6KfkWpij3aVSCHEsxSsFlTz9T2u+FEtv4DMq5Dc36CabwHlg+BHYFaA6gAMECP0F6HLD2y7r5T/t7+DTiuy8Dgew0O+NYUVG+G1Jj99Gf83ujsoL8+hozbyQ+D5501hIHeOCYVuJkzaRkvg6GFtv/LjvKqDU0+bReM7LWy+/6Nj6oiAYRo4XA4M08B0mJimceS54TBxOE1Mh4FpmjhcDtqa2rnrit/x1Z9fSsnY4ojfiyb2aPFKNRy9beE7wEiH1ttR7X8Eq4EjXvZW/6FpCimi05oadvfpZjrZgSm8sjZAY/vAs6JGbyczv3UKtdf+Pew+ACYvm8WbU4T+xKuywMW0qTtoChzvF9ZutbNr9g5yRufgesSJFbQIBqyQX52CYMAiGIgsWW31qu1semcLt79wPeNmjo2orib2aPFKNZwngWMyBLYcLTOKjorUAGLVkzTVzNSMUja39V/PZbjJ8c/g5fd9tHjDOH7UDX9GeF+zjLwMir5wEpZD+KjAoMXRt7iMyXcxc9ouDgf6z/id/mIuvs49/d4TCYf2NvLdM/6Tm5+8hlmLZsSsXU3kaJtXiiEi4FrQozDyoIJduAwvX81740gmor4wEJ5+r5MWb+Q7byutRoqXTOn3nozcdHzfm8M/JgR5bWyA6sy+hassx8msGbs5HBg4KoZREXsn7Lamdq5dcgsv/t9rMW9bEz5avFIR1WOZFNweVWyuLjKkAYfR/+wow8wiwx3d18VSisaLx/W5AEzPSSfw/So2Z4W3o3fGCUEO+8M7l+kr8zJtyUQczujif/VFwB/k51/5LQ/e9jgj8ZTKUECLVyrS5dt1BH/oLGOU/KnlInxW3zOdAmcRK9eV0dQRvb/T5rbDjL7ilOPK07PTsH5Qxcas8O1PnUb4McPqJx+g6eY6Tvp6fHKy/PHHD/OrK/+HYKD3DQtN/NDilYpYvXm/B0OZrqNgS0ffLhKFzmLeWlvGvubB+zm1zCnA6BZ5wpPpge/PY0MEwgXglsyI+/bPjcxOFwnP/f5lfrzsDtpbOga+WRMztHilIl02L6M8JFhGKQS2hzJdO2fRe1j/vvmPwt4PZxc5R/Hm+yXUtcTGQXNNZwPt/72Iiivn48lwY159MutzIhMugHarfyN9bzia4rs3teqF9/nemf/JoX2RR6fVRIcWrxREsm8G5xwQd0iwrH2g7B+0f13oWgSMNT5kckYJHy8oYVFeOYJQ5Crhn2tGUd8aWcyxgWjwdVK3sAjjhlP5IELhEhSLpqSHZajvyaEZsQ1P3Rvb1+7kOwuvZ091bdz70uiQOCmL5V0D7feDt7eYVhJyqQjWgRXeD6lDZZEmIQ/y7YH5XPLiXBraBy9cE/Py8Tic7Gk+TLvfjz/Cc4ILJqSTmwFev5BR+BEdwbaIHWoBlFKUvz6WwEaLtX+Kb0rPrPxMbnn6WmYs6H+HVRMeOiTOMMNwz0FybgGjtxRkKpTqzNoHjimhCKoD0CVcABnKGJRwTc4vYF5ZOXmeNLY1NvBh/QGavN6IhQsgJx06MtaSXrCNRv+hqIQLQi4me8/aTd03apj6TAWTF40fuFKUtDS0cs3im3nzyZVx60OjxSulESMbyb6xnzssCFSHPO6dc8NuN8+5jxxXdDPyqrJytjQcYtXeWho7B2fAXjw9g8y8AwRUIKqlYl80FzXTeutB5t00M2Zt9sTX6ecnF/+cx+96Nm59jHS0eKU67nORzKv7v0c1g/+9UMz7MFjVcBJNvsgD2Vw4O526jlgk9FV8ep6HrIJd7PfFzjv+GBywb/Eesouy4tM+oaXqvd+/n99cdR/BoHaliDVavFIcEUEyL0dyfw2GfWDYrAylSHOf3eNuH0jekVcKg017z6ctcOwxF78V+dcizWnQ7F7DnBP2YgwigFem2+CzZ7RRZ67hoL9u4AqDwHJYVJ4VuxhgffG3/36BWz93F96O+LlrjES0wX4YoZQVimNvFNPlLqEOfxO8Lx+9yawIHScKbGDHwXO47MZxpLkVJYXgcoLTocgrzOURV3Mo/EIYLJ2ZTk6mn71qHQBG4zz+WR1dXLDF0zPwZicuBHPO/lw2fXKQ2cfDZPqCKdzy1A/JLojfbG84og32IwARAzFLQn9FQo+sqwGT+sAp3L7hSzz40Rm8v20iH9ZexBW3VQLQ4RV21ArVO4UPtxls2mpxWpjHjT41N422jNVHhAugtDg6+1RBhoPMgsjySA6WppLDcTXed2fjW9V857Qb2PdR/N02RgI6qsQwRxyVrO24hk8/03UW0MW8rRXU7u3b0fPgoVZa3/ZSsiCd/Vbf3vcXVXmod7x3XLklfsBJ+M6yCqcpnDnNQX2cl4q94RmVuGSze6r38u1Tr+OWZ65l6smDy/g90tEzrxGA33XOMa+3TO6ksrKg3zqdXj9T92dSZKT1ej0kXGt6vbbPt4vlCxyku3r/emW7TXLSDM6bmc7n5jv5xOkHOOfUGhqdH4TxbmKLp8VDzYrIwggNlsP1zfzgrJt4+5nhZ7pIJFq8RgDuHgltm5SXjePaKC3pP5TOlk0HyHjTy+LOcnKNo7OTi+b1LVxd1FrrubBKGJsfSgpSku1gwYR0PndGO3PnbGLuSdW0ZqxmL+s47G+gJdBMQMXWmz8cOrM6Sc/rXaDjibfDx02fupNn7o0sSKPmKHrZOAJYs//4mcVhy8vOacLssRVsXNn72UYAnz/IxpU1FKQ5mTunnNz5h6gz+xeuLmqt9YybYnKys4wOq4XmQBN7ffHdIHIZbjxGKA+mdFu2CgIiCIJHMjCVBxBQgv8MqNmQ2NkX2BmKvv4/1O85yJduvSQUq00TNlq8RgB1ba29lh+yOgmGmS2to8PPpn/VsLA8HSLIbRFUwfj5avVCgTWVR9+K7CB57niTUS4HAV/iZ34Af7n9SeprDvG9//kaTpczKWNIRfSycQSwqrbvmdVWZxPTZpWF3da/Hm2ndFf43vqJJ/LZS4HfwOVJrmi88qc3uP7jt9PWFJ2LyUhE+3kNU96trSHd6aTZ6+ULT/61/5uVIsfwMGGzm/37w8vyc/rn09k7+vidxkRQ6Czm7yuLMMXA5QDD6MqGpGj3WbRGEar6vNVBtvw5cf5lfTFu5hhue+46iir631AZSWg/rxGEPxjkh6+8xJeeepwrnv3bwBVEaFJeciaF7zy54qF2yvYkbwbW5lM0e4McbAtS1xKgrsVPXUsgKuECeGW2SdG4ohiPMnJ2rN/NVQuuZ8f6xPq7pSJavIYhDsPgmoWnc6ijg3Z/+PYfiXASvuKhdspq4i9gppjkeediNlbhOFzFgX1jYt6H34TcGaUxbzca6msO8Z3Tf8yaf6xP9lCGNFq8hiEigi8QufH5XUcdbndkezgrHmynfG/8BMzEgIbZPP1+B69Xt/Pa5nbe2hZ5Zu9weOXsdPLK8ga+MQG0N3dw3Xm38cqf30j2UIYsWryGKU4z8mw57ZafPfMtpi2I7LDyG39qZ/TB2RH3NxCmmOR7F7JmZ2IiMgREkVUafRq5WBMMBLnj0v/mL7c/qTMU9YIWr2HKwtFjcUUoYJZAAMWrjhomTIospf3B6th+lVyGi0D9bJ54/xC5Hk9M2+4TEfZeNIbRJ43B4Ro6XkT3Xf8Qd3/rDzqsTg+0eA1TcjwephT0FmV1YJQI75U3Ujku/PoS5Tcpx5FLvrOQfGchBc4iKhxTkYYqtldP5017eZjj9pDmSIyYVGf4ePPSMoonRCbe8ebp377EzZ/+f3S0RRdJdjgydP570cScsqws1tdFF8GgXQVYO7aJkyhk547eUq0dS7TO4R9uqmTnoZ6JOI61aa2vO8DM4lFRv5dICRjgWjwOc+uBIZWP8e2nV3P12Tdxy9PXkjcqN9nDSTqDmnmJyM9EZLOIfCAiT4pIbrdrPxKRbSJSLSJLupXPFZH19rVfi30mQkTcIvKIXb5SRCq71blMRLbaj8u6lY+z791q1w3TX3xkUJE9OPtNuwrwwdhmKsoHMmIr0nKjU6+hasp5u9IiLTtBy9UIqF61nW8v0BmKYPDLxpeBE5RSJwJbgB8BiMh0YDkwA1gK/FZEugww9wBXAJPsx1K7/CtAo1JqInAXcIfdVj5wI3AKcDJwo8iRcKB3AHcppSYBjXYbGpsvnzSXyVEuHbtoVX6yS9IZNSqbKTNKmHpaBYUFoaSvY8bkUzE6l6rr6jg4NTqnXzNMzTMSfO5vRqOT1oah6e2+f0cdVy24ng/f3JTsoSSVQYmXUurvSh0JBfAO0LVNtQx4WCnlVUrtALYBJ4tIKZCtlHpbhbZPHgA+2a3O/fbzx4CP2bOyJcDLSqkGpVQjIcFcal87274Xu25XWxqgNCuL288+h8L09EG1symnmT3T/bxWsJ9XzVraZjnIzvJQU9NIyxSDDhW960I4h5GrysrZ3RSe53+s2J4TGFJG+560NLZxzTm38MZjbyd7KEkjlgb7LwNdSQTLge6ncWvssnL7ec/yY+rYgtgEFPTTVgFwuJt4dm9LYzO7tIw3v3QF3zp5Pg4juo+7NtjKQavziGFre7CJjnlusjI9FPvTCDSccEwEh0gIxwXgYFvboDMRRUqD06LyzPASliQLv9fPrZ+7iyd++Vyyh5IUBvw2i8grIvJhL49l3e65HggAD3YV9dKU6qc8mjr9tdXb+7hCRFaLyOr6+lhkuEkdXKbJd+cv5GeLl8Rs+bUr2MyWkzp5i30YAqrvf/q+x2W4aA0nRE6SQsW8vjSbzPyMpPQdLkop7vneH7nnu38cca4UA4qXUmqxUuqEXh5PQciYDlwA/Js6+t9oDTC6WzMVwF67vKKX8mPqiIgDyAEa+mnrIJBr39uzrd7ex++VUlVKqaqiouSfYUsGy6ZO5+oFp8WsvaCAH0VRYd8hpfvCY3ho3juTupaBjy/5g8kJVeNSxpC1e/XkiV89x23L78LX2XPndvgy2N3GpcAPgQuVOsbw8TSw3N5BHEfIMP+uUmof0CIi822b1aXAU93qdO0kXgy8aovhS8C5IpJnG+rPBV6yr71m34tdt6stTR98ZXYVpZmZMW3zo5pcSlyjB77RJt1Mp273dFbtDG8p2NCR2CVjFy1mkFETRyWl72hY8fhKfnjuLTQ3tAx88zBgsDavu4Es4GURWSsi9wIopTYAjwIbgReBbyiluua0VwL/S8iIv52jdrI/AAUisg34HnCt3VYDcAuwyn78xC6DkHB+z65TYLeh6QeHYXD96Yti2ua7O9pZ+WEBmY6Bo1JkObLZvX0q62rCd7ZMWoRREXLmhS/KQ4EP39zMVQuuHxEZinQ8rxFI0LK4419v8PimDTR2xs5jOy/dZOncTmr8W3u9nuvM58ONY9lxKLLkq5kuF62+5CyHZja5CNz+Nr7OyKKzJpvcouxhk6FIx/PSHME0DK47fRH3XrBs4JsjoLE9yF9WOCnwzQ1Fg+hGgbOQNR+MiVi4ks36HB9ls1Jr9gWhDEVXn30z7zybnICRiUCL1whmdkkZ2e7Y5yz825oO3l49DVdTFSYGRc4S3lpbRm1TdLOnoJXc1UHgtNT0wOls93LjJ+8YthmKtHiNYByGwdzS8OPXR4I3oPjHpnaKrdls2FRJXUv0O4bRuGHEkrq81M3q05Wh6PdXP4BlRRdldqgydF2INQnhm/Pms6m+nv19ZBgaLI++20GaI0Cm00WrP7qZV7Klo6zOYmeSxzAQrjQX7jQXrnQXzgw3zjQnpseJmeZETc/nmdwmqp97jv867zzcCYrQEW+Gx7vQRM3s0jJ+c/4nuOSJR/HFycnRZRoEgtH/r5/sLSXXob43NQzTwHSYiISeO9wOTKcD02FguhyYTjP01+3A9DgQh4nhMhH7gdMAp4FymiingeUQLKdB0CEETQg6hIAZClMdMMBnQsBQeA1Fp6loNyzaxQKjP4m3AB8bdmxh71Pt/O6CZWS7h96h80jRu40aAN7bW8sv3vmXbV/qOsAQWrB1fUUUCst+oZTCUqEyhUKpo9e7nqPAUgoLKMnIZH9rC91bh9CBa2WXGAIGcsQ1QimFiOA0DAKWdaTckNBhJLGTyIrY7XWVd3tOt9d06/foK9XtL/b7DY1Z2e8lfU8brv0d7CiCBmcQvyh8orBso8vsklLe7yWx71Blcn4B9y27iLKs7GQPJSz62m3UMy8NAHPLysl2u3lp+7a4tP9RYwNjsnPY3ZzYA9YxwQX0k/Mj2nOdyWJLwyE+/ehf+N9PfJIZxanjhNsTbbDXHCHayKvhklo/8fDZ2dSY7CFEzIG2VpY//gj/3Lkj2UOJGi1emiN8repkijPidxB5uBooKrK8xzDoAAAb3klEQVSGTtKOSGjz+7n82b/x+KYNyR5KVGjx0hzB43Dy6he/zK+XfpyzK8fHvP1kuzzEC7cj8kxNQ4WAZXH1yy/yq5VvpVyGIi1emmNId7m4YPJUfrn044OOwtqTXE8ac0pKOSGF7SzDlV+tfJtrXnkJfwqF1dHipemVTJeLH5+xKKZtrq87wJr9+xKWCUgTGY9v2sDlz/wtaedII0WLl6ZPFlSM4ZITTkz2MIY8qbXY6p83du9k+WMPc6A1Pk7LsUSLl6ZPRIRbzlrMBZOnxLTdVXtrObkssqzcQ5lUsxUNxMaD9Vz06ENsPji0Iw5r8dL0iyHCnYuX8H8XXsS/zZwVs93Id/fWMLuklDxPGlmu2B8OTyRJizcWR/a1tvDZxx7mrT27kz2UPtHipRkQj8PJmZXjuOWsxfznGWfFrN339++jsbOD/LS0mLWZDIbbzKuLVp+PLz31OH/bPDRTrGnx0kTErJJS3GZsDe6p7kKR7JA98cRvWXzv789z7+p3h5xIa/HSRMy5EybGtL39ra1MyMtneoomRrHU8Ao10xt3vrWCH7/+DwJDKKyOFi9NRPx9+zae2bI5pm36gkG2NzbgNlLThcIaYjOSePHQ+nV87dmnaPcPjZDYWrw0EfFcjIWrOxaKk8srOLm8nHG5uXHrJ9YERsDMq4tXd37E5594lPr25KeE0+KliYjvnroQZ5SZtwdi3YH9vFtbw7u1teSnpcelj3hgDWObV298cGA/Fz/6Fz5qbBj45jiixUsTEQtHj+XFL/w7Z4ypZOHoMXE7yG2KwcziUZxQVByX9mNJYIQsG7uzp7mJi//6F1btrUnaGHQwQk1UBC0LSykOd3Zy8z9f5fltW+LSz7TCIjYNcWfJsTm57GqKPGv4cMBlmvzi3PM4f1JsHZm7o1OfaWKKaRg4TZOijAwuO2l22PVmlHpYNicNCdM9wmWaOIa4E+hwdFINF18wyHdfep5gEnYhtXhpBk2G0xXWfWdPTWdU5XoaXO9x3mm1nHtCOmPyXfR3OvCDA/sJKMW80qGbfixopU4khngQVApvEqJRpObetGZIUdfW986Tw4ApJWnkpAu5xTXU+0Lb7C2BZshczdjJsMA1npq9+by5rf24+l2yNpQnN0PJ9ykZWEoRsIKAM6H9avHSDJoP6/YjKCYUezhhtBAMCm9u8dHYHuQzC33U+DcCUN9HpJVa30e4ivawvHgqq7Y42HHISypt4HkDI3vmBbC14RBzEzw71uKlGTSB7HUsWbiHtmArhwCccMrsTHyHprK62k1JGEFZ/cpPrVpP2UQYPdGgyFVG/YESXq8OzcaGspb5R/jMC+Bzjz3CQxd9lpPLExctRNu8NIPCUhYfdWyhLXhs/Ke2YCv+3NXMqoy8zSAW+301BPNW87lTnWS6DTI8FqPzneSkDb2vrD8YfTbw4YKlFH9evzahfWpXCc2g2dKylTs3/xy/iu+xkTJPGUFLcATy6ex08bc17QyFnEQGobSuGvjGvFP49smn4jRjF9df523UxI3JWZModBeyrzO+iVdF4ICvFqgFF3x63hweX9VXNmtFWQY4BNymwmmCxwEeEywF2U6FaUKaqUhzhB4ZTgu3aeEyFB6HIs0M4jaDuE2LNDOA07BwGEFchoXL9OOQIKZY/PzDM3lmW11c33uq8JtVK3lv714evOgzcXch0eKlGTTtgXb2d+5PQE/H/hianRu54LRj09YrFK6OibR1CjdPe44iIz7Os9052H5K3PtIJd6p3cN7+/ZSVRZfA/7QMyBoUo6trdsSEpOrZ2bqTquTpsDhYx7NgSYOOt+jI2s1z7fNifuYIDSj0xzLH9euiXsfWrw0g2Z69jTK08ri3o8R4TLk7aZ62lVWnEZzFIcx8uzGA/GvPbtp6Djeby+WaPHSDBqn4STLEX+RiBSv5eMt36K49+NO3ZyzcaPJ28m3XngurtFXtXhpBk2Lv4UtLVuTPYxeefWwSbw31F2G3mvsjbdrdtPQ0RG39rV4aQbNuw2rsIaos8A+7yE2BhfEtQ+nXjb2idM04zb70uKlGTQKGOUeunG3XmkZE9f2R2cO/QStyWLhfb/jyuefpjMQex/AmIiXiPxARJSIFHYr+5GIbBORahFZ0q18roist6/9WmxnEBFxi8gjdvlKEansVucyEdlqPy7rVj7OvnerXTe88AaamLJ41NncOet2rpnyfQpc+XHsKTq/oXUte6kPjo3xWI6S7ezL10zT5vfz9+3b+MRf/sym+tj6wg1avERkNHAOsLtb2XRgOTADWAr8VkS6zJr3AFcAk+zHUrv8K0CjUmoicBdwh91WPnAjcApwMnCjiOTZde4A7lJKTQIa7TY0SWJGznQ+P+aSuLUfrcujQvFqZ/x8sdzG0EhIMZTZ3tjAp//6F7794rMxS1gSCw+Vu4BrgKe6lS0DHlZKeYEdIrINOFlEdgLZSqm3AUTkAeCTwAt2nZvs+o8Bd9uzsiXAy0qpBrvOy8BSEXkYOBv4vF3nfrv+PTF4T5ooqcqfw2mFC8h2ZJPrymVs+hgyHZl4TDeGmBgYbG7ZjFKK6pYtvF7/Rtg+Yj39vCLhjcOHWZaegUdinzgix9UK5MS83eFGZyDAs1uqmV1SxpdOGrwP3qDES0QuBGqVUut6HAUoB97p9rrGLvPbz3uWd9XZA6CUCohIE1DQvbxHnQLgsFIq0EtbmiRy+fj+J8DzC0KzoFML5/P5sZfwet0/qW6ppjytnA3NG6lpr+Fjoz5GjjObem89r9a9TlAFaQ40Rz2m9mAHb/sWcZb7uajb6ItC9yH0Vy98apuj/xy7M6B4icgrQEkvl64HrgPO7a1aL2Wqn/Jo6vTX1vEDErmC0HKVMWPia8DVhI/LcHJuyWLOLVkMwEV8Er/lx2kcDWy3pOQc1jSu5bGaJwbV12tNbhYVxT6wYZHrQGwbHMZccsKJXH/6mTFpa0Cbl1JqsVLqhJ4P4CNgHLDOXg5WAGtEpITQLGh0t2YqgL12eUUv5XSvIyIOQvPwhn7aOgjk2vf2bKu39/F7pVSVUqqqKEUzM48UugsXQJG7iCUl5/Cf068nz5nXR62B2dNZz9bgccEJBk2hqzbmbQ5HphYWcdOZZ8fswHbUBnul1HqlVLFSqlIpVUlIZOYopfYDTwPL7R3EcYQM8+8qpfYBLSIy37ZnXcpRW9nTQNdO4sXAqyrkIPIScK6I5NmG+nOBl+xrr9n3YtftbnfTDDNGp1fwi5PuZFFR9P9zv9E+MYYjCqGUEfHRpZHGTWeezWOfuSSmoXLicqRUKbVBRB4FNgIB4BtKqa5YuVcCfwTSCBnqX7DL/wD8yTbuNxDarUQp1SAitwCr7Pt+0mW8B34IPCwitwLv221ohjGGGPx75Rc5vWghd27+OV7LG1H99jj40m5tOzFmO2jDjamFRdy86GzmlcU+wmrMxMuefXV/fRtwWy/3rQZO6KW8E/hMH23fB9zXS/lHhNwnNCMIEWFi5gTGpI9ma+u2iOp+LfvJmI9nQ3PsZ3OpiMMwyHQ5yXA6SXM5WDi6ghvPWDJwxWj7i1vLGk2cWVJyDtu2bR/Q1aLIlUeG6cSM09JufUMxEN8ICokiz+OhJCsNt8Mg3S24HAqHqTBNC8MMYBgBMAIgPizDiyWdBKWTgLQTwHvMZsjkkt728mKHFi9NyjInbzYXlJ7Ps/ue71XABOGCwmKWpT2BKfE5e6kUvL1v+Ni75ozJwJe/4sjroP0Ih57/CpMyJ8VqWL2ixUuTsphicvHoi5iRM53n9r3ArrbdR3zBshwZfLW4mRmOx+I6hq1ts9jdHHvH14FwmSbZbhfpTgdO08BpGrgdBk5TcDnA6QjFGTPNIIZhIUYQwwiC4QfxowwfQfGh8BIULwE6CYoPX4x0eGHBAubknRSbxvpAi5cm5liWhc/yUdNRy/jMcRjS96a2L+Djr7VPUOwpoip3DiDkuXOPXPcGfbjN/o+sTsueyrTsqQRVkDWN7/PKgVf5bPa7jDM/iNVb6pNX9h977Ghifi5up4HTEBymsHZvw4BJacfl5TB9jBfEAgmABFBGEEUAJUEUfizxEcRHEC9B8Q3oq2YBfaTJ7JNYzR9n5pzA5eO/rGPYa1KPmuYaXjv8Bttbt7Ok5BwWFoZC0ljKorplC37LT74rj4r0ClwOF6cWnMLNG2/lz7seQhBOzJnJ58cup8HXwM+rf8nXxl/OvIIqvEEvppgIgmkcv+Vuism8/Crm5VdhBQ8jwZ2ojr9CYDuS9lkwMsAxEXCCuFGtd0PHI1G/z7u3fo0Hq93A0YPZ4yZuodM4eOT1aeVugsqPEovswCTESgeCYARR4kPhJ2AcoNk4FHa/Q32Ruql5Mw2+Rgrc8Tykr8VLEwfG5I7hstwvHHkdVEHu23E/1S3V1HtDP+zzS5eyyDiTUZ5iMh2ZOMWBXwVQKNY1fUDz9hZKPMV8qnwZs+3lR0AFUUrhcXh67bc7hpkL5kngnAX46TXgSPbNKILQEfnSsiWQy2/WCX7rqHCdX+WnpZtwAQTFCxISnBbn0AzYGGtchpOWQEvcxUvnbdQkhCZ/Ez/64Abagkd35WbmnMAPpnwXgMO+Jra2bMXCIseZw5SsyXFfdgCo4CHUwbNBhRHx0zkbVDugQPl4es8pXPdOIZ2BAPNGF+IpfRN0VFUE4Xdzf4PbdMemPZ23UZNMcpw5fGX8l7h/55+pTB/LmcWnMzfvaGSBXFcO8wpif3RnIMQsQKVdBO0P9n+jUQ6Balu8QlxYtpOp587ixrXnkVn2HoE47WimGgrF5pYtzMqdGdd+tHhpEsbcvDnMzJmJq8fZxWQj6V9A9Sde5hSw9h0jXF1MzlzHr+Yf5o76mTTosF5H2N66Pe7ipcNAaxLKUBMuAMzxoUefWKD6DuNS7NjFt4p34ZQh+N6SRKQnH6JBi5dG43sTgru7FTg58tNwzITgwIb2SnM9XykxyHUOvRRwyWD56F5P+sUUvWzUaBAwx0Dwo9DupPtsxMhDyRjoeBHYRCi+QP+c4nyFUcUzubk2d8B7hzs+K/5raC1emhGPuE+DwhfA9xYqWIO4z0TMEgRQnvmoJi90hnegu9yo5uqyOfyrvZK3Do/cOF8F7oK496HFS6MhFKkC98LjHEBFDMi5FYUXOp8fsB2n+JjueIdpWe/QFvwc61r6jI85bBGEJn8T+a7oA0eGg7Z5aTQDIOJEcu5Csn8Ckh5Wnb3WFD5s3R/nkQ1NzitdwriMyrj3o8VLowkDEUHSlyOFLyCZPwDJ6PNeS8EDh08kqEae39f4jHF8tuLigW+MAVq8NJoIELMUybwCyX8k5HHfCyv957KlbfjPupQCR8BNWmce2a2l5DeO48zMxQk5GQHa5qXRRIU4J0P+/aiGL4J/3THXKt0BbhjtxCEWe31p/M/+FtRQPE5tCY6gG2cwDbc3CzPgwgg4Mfwu8DvA60B5HVg+IRgQAh2Ct93C22HR2RGkvd3P4abQsaqPLZzKheecyMyysoQNX4uXZlhR11lPsaf/7FDKaoLAVnCMR4zoDw+LeCD3v1GHrwb/e0AAjBJKjYOgQuF4xrrg5MoSdgRn8FCdwQ5vZ/+NHh0l6YZBpmmSZph4TCHNMMg0DdINId2ADBPSDEWaYeGSUDjG6j1lrHyvlKBPCPog4AO/T+HrtPB5LXy+IJ2dATo7/fj8fYUZ9BFpQJ29dYeZOzOxKQW1eGmGDUopcp1hZK6WLFTHkxDYivJ8AklbhhjZUfUpZglS8CeUfz2q8SqwasA6dsloWvuZKPu5vrSEIOkY+FEYCEGCeDDxIgQQFUTwgvIihCtyx3Kg6dOsfLX/+GdxIQnxHbR4aYYNIoJrgMCFofsMJOc2lPKjGq9EtdyG8nwcI/fn0fftnInKvhmavgeqqdd7TGs/PaOQxfoH6HFFlk0pVsyaXkFzayfZmQOHK4oV2mCvGbGIOMEsBCzofAbV9sdBtWd4TkcKXwBn8hJaZXpaktLvw0+v5umX1w18YwzR4qUZ0YjnE+CsAtcCVOuvUP7BhY4WsxDJ/187CGLiyUrvfdaXCGr3H05of3rZqBny+Cx/3KJRiHsh4l4IgAruA2Pwx1qOGPIPXggqsT/oTHdjQvvrjtc38PnPWKJnXpohzYamjXz9vW9x/84/cdgX31mFmKW9h4uOqq0SJOd2Ev0Ty/QkViy7M3vG6IT2p8VLM6SZnj2Nr0/8Kj7Lx4bmjckeTmS4z4aM/0holx5XcmxeAFMnlCS0Py1emiGNiDAnbzaXj/8KCwtPTfZwIkJEkMzvgOv0hPVpGhYu5/GZlRLBL//wKk0tIadVy4q/74S2eWk0cUTEAZnfRjWsGPjmGLC7fnrCjuf0ZN2mGpZ/8w9MGFvI2g01nH/2CVz3jaVx60/PvDSaeOM8MWHuExt2z0y44bw7La2drN1QA8A/39nCqnU749aXFi+NJs6ICOI5OyF9pbmj88yPB23tPvbsi9/upxYvzYjDCtRjtfyahOYsTfsMifi5eZzJ8bDvizdXbY9b21q8NCMOw1EUygbk/UfC+hQjCxxT496PxxVG8twE4nbGz6yuxUszIpHMb6Caf4IKJjDOvPOkuHfhcQ6dZWPZqByuvPSMuLWvxUszIhEjD9znog59BuX/MDF9Oirj3ofHeXxi3GRx8flzGFMWfcihgdDipRmxSPbVgIlq+CLK+3pc+1IqiOp4LK59ALiSOPMqyMugMD+T7EwP5581g/lzxsW1P+3npRmxiLih4BHU4e+j2u5HGRUYzolx6stEOWeC1QDWwbj0AeBxtcat7Z7kZqdRNiqX8pJcKkpyOWP+JCZVFqOUSoivmRYvzYhGzDJU7v8hhjv+P7i0S8C3HoifeLkcsTPYOx0mpcU5lI3qeuRSNiqH8pJcSotzSE/r/RyojmGv0SQIw0xMAD0JbEE5RoO4IBAfO5tEGNI0NzuN0lE5lI/KtWdRObZg5VJckIVhDMHY+zaDFi8R+RbwTUL50J9TSl1jl/8I+AoQBL6tlHrJLp8L/BFIA54HrlJKKRFxAw8Ac4FDwOeUUjvtOpcBN9hd3qqUut8uHwc8DOQDa4AvKqUiC76t0SQKczTiqgLVifKXgPeVmHfR82C2aRqMKsyivCSXkqKcIzOnipLQci8j3R3zMSSKQYmXiJwFLANOVEp5RaTYLp8OLAdmAGXAKyIyWSkVBO4BrgDeISReS4EXCAldo1JqoogsB+4APici+cCNQBWhSNnvicjTSqlG+567lFIPi8i9dhv3DOY9aTRxw1UVeigv4luBipV4STaYo8EcjWGO5pqvnXNkiVdcmI3DHJ77coOdeV0J/FQp5QVQStXZ5cuAh+3yHSKyDThZRHYC2UqptwFE5AHgk4TEaxlwk13/MeBuCS2elwAvK6Ua7DovA0tF5GHgbODzdp377fpavDRDEhE72oOko8wx4JgCgeowahpgloFZEZq9maPBHAOO0F8xjk06cuE5sR/7UGSw4jUZOF1EbgM6gR8opVYB5YRmVl3U2GV++3nPcuy/ewCUUgERaQIKupf3qFMAHFZKBXpp6zhE5ApCMz7GjElsiiaNpieq8xkIbDtaIJn27GkMOMbYAlVhl5WF4u1rjmFA8RKRV4Deooxdb9fPA+YD84BHRWQ89JphU/VTThR1+mvr+AtK/R74PUBVVVUSEjVpNEeRtIvAcYI9e6oAyU1aKJtUZUDxUkot7uuaiFwJPKFCJ1zfFRELKCQ0C+oeE7YC2GuXV/RSTrc6NSLiAHKABrt8UY86rxPab84VEYc9++relkYzpBHHRHDEx6dspDBYS97fCNmdEJHJgIuQqDwNLBcRt70jOAl4Vym1D2gRkfm2PetS4Cm7raeBy+znFwOv2qL4EnCuiOSJSB5wLvCSfe01+17sul1taTSaYc5gbV73AfeJyIeE8oNfZovKBhF5FNhIyIXiG/ZOI4SM/H8k5Crxgv0A+APwJ9u430BotxKlVIOI3AKssu/7SZfxHvgh8LCI3Aq8b7eh0WhGAJLQmEZDhKqqKrV69epkD0Oj0YSBiLynlKrqWT48HUA0Gs2wR4uXRqNJSbR4aTSalESLl0ajSUm0eGk0mpREi5dGo0lJtHhpNJqURIuXRqNJSUakk6qI1AO7ehQXEs/4vPEjVccNeuzJItXGPlYpVdSzcESKV2+IyOrevHiHOqk6btBjTxapPPbu6GWjRqNJSbR4aTSalESL11F+n+wBREmqjhv02JNFKo/9CNrmpdFoUhI989JoNCnJsBMvEfmBiCgRKexW9iMR2SYi1SKypFv5XBFZb1/7tR3dFTsC7CN2+UoRqexW5zIR2Wo/LutWPs6+d6tdt/d0wr2P+WcisllEPhCRJ0UkN1XGHi0istR+T9tE5Np499et39Ei8pqIbBKRDSJylV2eLyIv2/8GL9tRe7vqxP0ziGD8poi8LyLPptK444JSatg8CMXAf4mQD1ehXTYdWAe4gXHAdsC0r70LnEoomccLwHl2+deBe+3ny4FH7Of5wEf23zz7eZ597VFguf38XuDKCMZ9LuCwn98B3JEqY4/yczLt9zKeUOjwdcD0BH1HSoE59vMsYIv973wncK1dfm2iP4MIxv894CHgWft1Sow7Lp9lsgcQ4y/mY8AsYCdHxetHwI+63fOS/cGVApu7lV8C/K77PfZzByGHPul+j33td3aZ2Pd0CdCphOLsR/MePgU8mIpjj+A9HtNHz/eZ4O/MU8A5QDVQapeVAtWJ+gwiGGsF8A9CeSO6xGvIjztej2GzbBSRC4FapdS6Hpf6yvtYTpg5JIGY5pAcgC9zNK5/qo09XPoaS0Kxl0WzgZXAKBVKEIP9t9i+LRGfQbj8ErgGsLqVpcK448JgE3AkFOk/h+R1hJZfx1XrpSzhOST7G7tS6in7nusJJSx5MMpxRFMnovyXMSIZfR47AJFM4HHgO0qpZuk7Z2IiPoMBEZELgDql1HsisiicKlGMYSh9RwYkpcRL9ZFDUkRmElrXr7O/hBXAGhE5mSGSQ7KvsXd7D5cBFwAfU/bcfKiMPQ709b4SgoTSTz9OaHn+hF18QERKlVL7RKQUqBtgrLH8DMJhIXChiJwPeIBsEflzCow7fiR73RqPB8favGZwrOHyI44aLlcRyvbdZbg83y7/BscaLh+1n+cDOwgZLfPs5/n2tb9yrNH76xGMdymhNHFFPcqH/Nij/Hwc9nsZx1GD/YwEfTcEeAD4ZY/yn3Gs4fvORH4GEb6HRRy1eaXMuGP+WSZ7AHH6gu7EFi/79fWEdluqsXdW7PIq4EP72t0cddr12D/obYR2ZsZ3q/Nlu3wb8KVu5ePte7fZdd0RjHcbIZvCWvtxb6qMfRCf0fmEdvq2E1o6J+q7cRqhJc8H3f69zydk2/kHsNX+m9+tTtw/gwjfwyKOilfKjDvWD+1hr9FoUpJhs9uo0WhGFlq8NBpNSqLFS6PRpCRavDQaTUqixUuj0aQkWrw0Gk1KosVLo9GkJFq8NBpNSvL/Ac6CgpDxoVs8AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_27_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot population density - mile^2\n", + "fig, ax = plt.subplots(figsize = (10,5)) \n", + "counties.plot(column='POP12_SQMI', \n", + " scheme=\"quantiles\",\n", + " legend=True,\n", + " ax=ax\n", + " )\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### User Defined Classification Schemes\n", + "\n", + "You may get pretty close to your final map without being completely satisfied. In this case you can manually define a classification scheme.\n", + "\n", + "Let's customize our map with a `user-defined` classification scheme where we manually set the breaks for the bins using the `classification_kwds` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Population Density per Sq Mile')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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fpsx09BYzOrMJncmYkNVVCkW8GTx4cIeiDEVkLF68ON4u+FFBL4q0VdZ0e93Z3NLrptqeyJo4irovvorELT9Cr0dnNiH0OnQGTxDVmYyegGg0IPR6hEEPUiJdbqTbDVJ62hgNCIMBhMCYloIhLQVDeip6bzDVmYwYUqwYs9KP309LRW81e4KuyegPvgarxfMBIC0FQ4pVZcIKhSJqqKAXRfRmk+Z9um12zfqSLheullbN+tMKndmE3mrBkGLxBMUU6/FgaTaRNnww9pp6dGYjepMJt8MBOh16kxG91eIPynqrBWNWOobUFPRmI0KvB53On90KnTfLFQJhMKAzeO77gr9/75F33VvodKATCJ3O86X3/IsQSJfLk2U7XZ4+vXbS7bHNHDMCa/8+MX8tFQpFR1TQiyJtR6s17S+nZBw1GxNvYV1r3DY7bpsdR11Dt/fttQ3Ubt4ZY68iY/qLjzH8uu/E240TFtnahHPramRDNSIjF0PxWQhr4m5VUMQPFfSiSOPu/ZF3IgRISf6Mqdiq6yLvL8nRGQ007T0YbzdCR62dRg37+4txfPhPsB8XE7W/9keMF1yJaebVcfRMkYioxZMoYauupf7LvRH3k3/6JNIKh1K5agMNO/do4FlyI0xGHPWN8XZDkSDY31+M498vdgh4nhttOP79Ivb3wy+gaG1t5Vvf+hYulytom+uuu45hw4b5ZXt8lZ9SSu644w4KCwuZMGECmzdv7tZ+//79TJs2jaKiIubNm4fdbg/JPhgCSR71JA3Uk1+dCdV+6dKlFBYWxrQiVQU9Dehur+ORd1f514LCRWc20XqsmqbSAxH1cyKh0+vj7YIiQZCtTZ4MLwCOD/+JbG0Oq/8XXniB73znO+hD/J174okn2Lp1K1u3bvXr8b377rvs2bOHPXv28Nxzz3H77d1vml+4cCELFixgz549ZGdn8/zzz4dkHww+yaOrrrqqw3WfNNC6detYv349Dz/8MLW1tQH9itR+3rx5HfT8YoEKehrgK3iQUuJ2uwHY9+LrEfVpys7AnJdN0x4V8Noj9OpXVuHBuXV11wyvM/Y2nFtXhdX/4sWL+fa3vx2WbWdWrFjBtddeixCC6dOnU1dX1+E4MPB8eP7444/9e9nmz5/P8uXLg7YPlp4kj3qSBgrkl5b2sUK9g2iEvyJQCI7+dz1HP14bUX/22gZSCwZq4dqJRZJuZ5DeD0MK7ZANwRWKyYbutw4Fwm63s2/fPgoKCkK2ve+++5gwYQILFizAZrMBwcn2VFdXk5WVhcFg6NImFrI9PY0RyC8t7WNFcr6DJChCCIQQ5Jw6nsLbvqdJf4qO6AxJOr15Eh73F21ERm6Q7UI//7GqqoqsrKyQ7R577DG++uorNmzYQE1NDY8//jgQnGxPoDaxkO2JVDIoWSSHVNCLAkarlal/eoiLd7xN/1kz4u3OCYVI0jU93349hXYYis8CUy+K4iYLhuLQ/watVittbb1MnXZD//79EUJgNpu5/vrrWb9+PRCcbE9eXh51dXU4nc4ubWIh29PTGIH80tI+VqigFyWEEGSNLWLiIwvC7sPewz61kxlnUwv6VGu83QgdlelpjrCmYbzgyoBtjBdcibCmhtx3dnY2Lpcr5MDnW2eTUrJ8+XLGjRsHeOR4Fi1ahJSStWvXkpmZSf/+/TvYCiE455xz/BWVL7/8sn9NMZD9eeedp8l0YU/SQIH80tI+VqigF2VSh4b/icbVatPQkxMDR0MTeos53m6EjFrTiw6mmVdjvOT6rhmfyYLxkusj2qd34YUXsmbNmpBsrr76asaPH8/48eOpqqri/vvvB+Diiy9m+PDhFBYWcvPNN/OnP/3Jb3PxxRdz5MgRAB5//HGeeuopCgsLqa6u5sYbbwxo73a7KS0tDUnCZ8OGDQwaNIh//etf3HrrrYwdOxboKA00derUDtJAPfm1ceNGbrrpprDt44GSFooybpeLf6VNwtUWegDLnzGVylUbouBVcmPMyujxtJZE5dS//JLCW+bF242kIVRpIdnajHPrKmRDDSIjB0PxjLAyvPZs2bKFp556ildeeSWifqLJjh07eOGFF3jqqafi7UrYfPLJJzz55JO8/fbbYdmHKi2kMr0oo9PrKfj+7LBsq9dv09ibEwMZwmbhhCFJq06TBWFNxXjaLE/md9qsiAMewKRJkzjnnHNC2pwea8aNG5fUAW/p0qX84Ac/IDs7O2ZjavaXKITQCyG2CCHe9n6fI4T4UAixx/tvdru29wohSoUQu4UQM9tdnyKE2O6997TwlvgIIcxCiKXe6+uEEAXtbOZ7x9gjhJiv1fPRkv4XnhGyTVrhUIypKVHwJvlJxqlCVYibnNxwww0hb05XBM+8efPYtWtXTLNpLT9+/j/gy3bf3wOslFIWASu93yOEGAN8DxgLXAT8SQjh+616FrgFKPJ+XeS9fiNQK6UsBH4LPO7tKwd4EJgGnAo82D64Jgr9Lj0bnckYmpEQ2Kpro+NQkqMqIRUKRbhoEvSEEIOAS4D258l8G/AdvvYycHm760uklDYp5X6gFDhVCNEfyJBSfi49C42LOtn4+loGnOfNAmcCH0opa6SUtcCHHA+UCYPBZApZI07oBLokLNiIBe7W0EvJ445K9RSKhECrTO93wN1A+3mnvlLKCgDvvz4xsYFAe0nicu+1gd7Hna93sJFSOoF6IDdAX10QQtwihNgohNhYWVkZ6vOLiHDW9VytbZpq5ynii3Qm7rrQiYB025CN25F1n3v+davKZ0X3RCwtJIS4FDgmpdwkhDg7GJNurskA18O16XhRyueA58BTvdm7m9qSNnxw743a0XqkkrzTiqndthtXU3gq6yckXqklhcKHrF8L9etAOo5frP0YmTkNkTk9fo4pEhItMr0zgNlCiDJgCXCuEOLvwFHvlCXef49525cD7SPAIOCI9/qgbq53sBFCGIBMoCZAXwmHdUBoqtnS6aTqsy2khGh3ohPqNHHCkKx+Jziyfi3UrekY8MDzfd0az/0wCVZa6L777mPw4MGkpXUUrbXZbMybN4/CwkKmTZtGWVmZ/15PEjxa2gfDhx9+yJQpUxg/fjxTpkzh448/9t/btGkT48ePp7CwkDvuuMN/nFggv9oTqv3evXspLi7u8jpqTcR/iVLKe6WUg6SUBXgKVD6WUn4feBPwVVPOB1Z4H78JfM9bkTkMT8HKeu8UaKMQYrp3ve7aTja+vuZ6x5DA+8CFQohsbwHLhd5rCUdOybiw7Mx5CVeXE1eSVWVB6NSantZIt82T4QWifl3YU53BSgtddtll/uPG2vP888+TnZ1NaWkpCxYsYOHChUBgCR4t7YMhLy+Pt956i+3bt/Pyyy9zzTXX+O/dfvvtPPfcc35Jo/feey+gX50J1X7EiBF+/cFoEs13kF8DFwgh9gAXeL9HSrkTeBXYBbwH/FBK6fsodTueYphSYC/wrvf680CuEKIUuBNvJaiUsgb4FbDB+/VL77WEQ28xY8rJxNI/n9ShA0krHErG6BFkTxlL3umTyD9zCvlnlWDMyuhg52ppQxiUwL2PkKtgEwVVyKI9zV93zfA6Ix3Q8nVY3QcrLTR9+vQuR4qBRw5o/nzPZ/W5c+eycuVKpJQ9SvBobR8MkyZN8p+DOXbsWNra2rDZbFRUVNDQ0MBpp52GEIJrr722g8xRd361J1L7aKLpu6mU8hPgE+/jauC8Hto9AjzSzfWNQJeUSErZBvxPD329ALwQrs+xwtncgr2mvtd25r55Hb5vOXI0OTdjR4lkPXA6Wf1OaFxNQbYLXUQ2EmkhH+2ldgwGA5mZmVRXVwctExSpfai89tprTJo0CbPZzOHDhxk06PhqU08yR+39yss7/t4VqX00USlEjGg+ENxSo7v9cWVCYLBasSVm8hoXknZ6UyV62qMPcu1HH/rpLOFKC7UnmaR6du7cycKFC/nggw8Cjt3bPR/xlkkKRHK+gyQhjUEqoOvNRvRpKQiDgczxp9BScax3o5MJFT0UPlJPAdHLdLcwQsopIXcdrrRQe9pL7TidTurr68nJyQlaJihS+2ApLy9nzpw5LFq0iBEjRvjHLi8v79CmO5mj9n519j0S+2iigl6MqNm4I6h2bcdqcDW1IJ1O6rd/jU6n67AWmDNlXJd1v5MJV0sSbkwHFayjgNCZIXNa4EaZ0zztQiRcaaH2zJ49219ZuWzZMs4991yEED1K8Ghhf++99/LGG28E7WNdXR2XXHIJjz32GGeccfy4xP79+5Oens7atWuRUrJo0aIOMkfd+dWeSO2jiQp6MaLqsy2hG0mJq82GvaaetopKmg8cpmbTjphXAloH9IUEWZMSyaqcrogKInM6ZJ3ZNeMTRsg6M6J9esFKC919990MGjSIlpYWBg0axEMPPQTAjTfeSHV1NYWFhTz11FP8+te/BgJL8PziF7/gzTffDNt++/bt9OvXL+jn+Mwzz1BaWsqvfvUriouLKS4u5tgxz+zSs88+y0033URhYSEjRoxg1qxZAf0CKC4u9j8Oxz4WKGmhGNBacYw3BpylWX8ZY4to2LlHs/56I72oAEu/PJoPHKHlYHy3QSajrBDAaa/8H8O+Hz/hzGQjZGkht81Tpelq9qzhpZwSVobXnmSQFurMzJkzef/9hNy1FTRpaWk0NQVZpETo0kKqkCUGVK3TViLImBYb9YX8s0qQbje1m3fRuKcMc142KUMGxDXwqf1uiu4QOjOkjde0z/bSQsmitJDMAW/v3r1897vfpW/fvlEdRwW9GHBMYyFYZ1OL54SPKErsmPNyqN9Vir26zn/NmJVBU2lwBTnRImmnN9WaXlJyww03xNuFk4YTYXO6wsuxT7qe1hAJ9Tv3YMqMzlE9how08s+YjNvp7BDwACx9c6MyZigk6zFksVyoVygUPaMyvSjTdqyaui++0rTPlMH9cTZrfwi1KTeLrLFFPWemCSDeqjZ5KxSKSEjOj81JxIEl/9Zc6bvlUAWZY4s07dPcNw+33RFwKtbeEPrJFlqTrJmeIrq4pI1623aq2z6n3rYdl1TSQoruUZlelNn38vKo9Fu7ZRe6VCvu5taw7FOGDMDSNwed0YitqhZHYzPOxsBBTZ8I514maSHLyVglHSuq29ZS07YOyfFzOI+1fkyOZRq5FiUtpOiI+tgcRb544HfUbt4Zlb5zJo8la1wRuacVY84P7TSD3FMnYK+tp2bDDqo+20Lj12W0VfQurOtsbqHv+afTZ8ZU8meUkH9Wiedf3+Mzp5B3+iRypxeTO20CWRNHkTm2iIxRw0kvGkraiMGkDB2AdWBfLH3zMOVmYcxIQ59iQWcyBpXF6dT0pqId1W1rqW5b0yHgAUgcVLetobotutJCLS0tXHLJJYwaNYqxY8dyzz33+O8lg7TQ+vXr/fvzJk6c2GFj+4kqLaQyvShSu3lX1Pp22ezUeLdCpI0YTMao4VSuDm7voau1rdesrjsavy6j5fBRrP37RLeKU69HZ9Aj9HqEXofQ69GbjQiDAZfDQcqQ/gihA70gvXAozWXHD9sVOh3odJ6tDcL3r0DodJ5iEt/3wvMvwqtE7Cs08d3zZWbtrneL8P3vuKax7zvPY4nEo7Kh0BaXtFHTFlhaqKZtHVnmSehF6K9/sNJCd911F+eccw52u53zzjuPd999l1mzZnWQ0FmyZAkLFy5k6dKlfmmgjRs3IoRgypQpzJ49m+zsjjJikdoHw7hx49i4cSMGg4GKigomTpzIZZddhsFg8EsDTZ8+nYsvvpj33nsv4PPqTKj2vupNFfSSGH2KJWp922uPKzY07T1Ec9kRsqeMpXZT4MzSkJEW9Dmg3eFqbkVnNoVtH9wgLtydPl33JCCTMqAvjV+XRdcfDSi69XvxduGEo8n+dZcMrzMSB032r8k0h76Hb/HixfzjH/8I2CYlJYVzzjkHAJPJxOTJk/1nTq5YscJ/OsvcuXP50Y9+1EUaCPBLA1155ZUd+o7UPhhSUo7v+W1ra/NXGbeXBgL80kCzZs3q0a/2FcqR2kcTNb0ZRayDu2psaUH+jKld3uily4V0OHu1TR8xBFdbBIv8QmD7pip8e61Ra2UnLU4Z3KkdThkbaaG6ujreeustzjvPo0KReDUAACAASURBVKiWLNJC69atY+zYsYwfP54///nPGAyGsKSBOvseiX00UZleFJn0+F3YvqnkwD//rWm/7jYbqUMH0nyg4y963bbdZE0Yic5swu1wgtuNMBo6ZH+NpQfIO30S9TtLcdQ3Bj1mytABpA4dSEv5NzTvO9S7QYxIlpCnClm0xyCCmwYziOhLCzmdTq688kruuOMOhg8fDiSPtNC0adPYuXMnX375JfPnz2fWrFlKWkgRHnqjkYKrZ2veb/X6baQM7V5KpG7bbmo2bKdu65fUbdtN3Re7yZ02EWH0fL5xNjZT9dkWsiaODGnM1MH9qVy1IaECHqAyvZOYNNMpCAJXFAuMpJmiLy10yy23UFRUxE9+8hP/tWSRFvIxevRoUlNT2bFjh5IWUoRP9bovotKvsym4KRvpdFK97osu+/rajoY2naD1XkOFIlL0wkyOJbC0UI5lWlhFLKFIC91///3U19fzu9/9rsP1ZJAW2r9/P06nZ1nkwIED7N69m4KCghNaWkhNb0aZb1Z+rn2nQmCvDU1pQLpcZIwpxNnQhCEjFXNeDo279wdlq7da0BkS9FdFZXonNb59eJ336QmMEe/T80kLnX/++T22KS8v55FHHmHUqFFMnjwZgB/96EfcdNNN3HjjjVxzzTUUFhaSk5PDkiVLgI7SQEAXaaGSkhJmz54dlv327duZPTv42aU1a9bw61//GqPRiE6n409/+hN5eXmARxrouuuuo7W1lVmzZnWQBurOL/BIC/nOzwzHPhYoaaEoYquu5fU+p2ueJeWfMZnKTzdr2mdPpAzpj7V/PtUaK0VoRe70YqrXRv+Q2kg57e9PMCwKU90nKqFKC7mkjSb71zhlMwaRSprplLAyvPYoaaH4oKSFkpgj762OKOAJvR5L31zSCoeC203lp5sx5WTistk19LIXHwyGhA14AEg17arwTHWGsy0hEEpaKLYoaaETgAP/eDsie+lyYczKoNJ7Hmbu9Im4bQ5qNu7Qwr3eEYK2owm0PaE73EkyU3ESzqicCChpodgRK2khFfSiRNO+Q5pMQZrzjpdNV6+NTlFMT5iy0nE0aa/moCVJE0uSxlGF4sRGVW9GiaP/Xc9lpR8wY/kfMeeFfjyQj+aywxizMjT0LAR0uoSX8tEZE9s/HyrmKRSJgQp6UaDh6/2g02HJyyH/rJKIju1qOVhB1oTQ9tRphaVvLu5ITm+JAYkelP2oqBdVWp0trDmymn/vf5s1R1bT6kzsGQpF/FDTm1Hg0LL3OfbpJg4v/4jKNZuwVdVG1mGAU96jRf5ZJUEfYB1XkkWRXAW9qPFO2du8d+BdbK7jH9Be3bOEi4bO4uKCS+PomSIRUZmexhz9ZB1NZYepeGcV5cs/ijzgQVzW1VoPH435mCcyanN/dHin7G1W7FveIeAB2Fw2Vuxbzjtl4ReTBSMtBHDRRRcxceJExo4dy2233eZvnwzSQj4OHjxIWloaTz75pP/aiSotpIKehki3G53ZhLNZ2yBlq6zRtL8TiiRJ9FSmpz2tzhbeO/BuwDbvHXiXVmd4QsvBSgu9+uqrfPHFF+zYsYPKykr+9a9/AR2lgRYsWMDChQsB/NJA69atY/369Tz88MPU1nb9cBypfSgsWLDAv3nch08aaM+ePezZs4f33nsvoF+dCdU+VtWbKuhpSMuhCpyNzRFvVeiMo6HJf3ZmtBF6PfkzSmhKtDM2e0DFvJOXTcc2dcnwOmNz2dh8bFNY/S9evNh/dFYgMjI8hWZOpxO73e4/UmvFihXMnz8f8EjorFy5sos0UHZ2tl8aqDOR2gfL8uXLGT58OGPHjvVfay8NJITwSwMF8qs9kdpHExX0NKL1m0q++egzj7qBxjibWjBmRDfl95F/xiQqVyXBWp4XmSxhT0U9zam31ffeCKi314Xcd6jSQjNnzqRPnz6kp6czd+5cIDmkhZqbm3n88cd58MEHu4x9okoLqaCnEVsXPsm6m+5n3U33R6V/Z0vwJ75HQtIEES/J4q1a09OeTHNmcO1MwUsE+QhVWuj999+noqICm83Gxx9/DCSHtNCDDz7IggULuqyjKWkhRUDqd5VStvgtANq+qYzKGO7W6Ae9nKnjqd+5J+rjaEmy5E/RmAE42ZnSZwpmfeDzNc16M5P7TAm571ClhQAsFguzZ89mxYoVQHJIC61bt467776bgoICfve73/Hoo4/yzDPPKGkhRWC23P0EMorbCnQxWs/TW8zYq0OfClL0TjR/P05WrIYULho6K2Cbi4bOwmqwhtx3sNJCTU1NVFRUAJ438HfeeYdRo0YBySEttHr1asrKyigrK+MnP/kJP//5z/nRj36kpIUUPVO79UuO/PuTqI6hs5hjkiloXXUaG5Ik11NrelHBtw+v8z49s94c8T69YKSFmpubmT17NjabDZfLxbnnnsttt90G9Cyhk0jSQoFQ0kInEFpKC3127d2UvbJCk756wtI3LyYHP+tMRrInjqJ6w/aoj6UVybKJfvJT9zJqwXXxdiNpCFVaqNXZyuZjm6i315FpymJynylhZXjtUdJC8UFJCyUwrUerOLjknaiPozMboz4GgNvuQG+NTIMs1iTLh7Zk8TNZsRqsnDHgTE37VNJCsUVJCyUBXz7xPG6Ho/eGEaIzhX92ZyikFw1FJotUj49kqYpUQS8pUdJCsUNJCyU4UkpKn1sak7F0puj+mIROR96ZU2jYvZ/GPQeiOpbWJEsGpbYsKBSJgQp6YdJ84DDOxuaYjKUzRnd6M2PMCL9QbdKRJEEvaTJSheIERwW9MCl97tWYjSUM0VlPMPfJwdovn+aDFVHpPyYkSdBLumnjJKO+tYllmz+mor6K/pl5zJ18LpnW2JxipEguVNALAyllTCscdVEIehljRoCEum27Ne87lrgdSbL/LUmCczLyyLsv8tj7i2i2HT9Y+v/967fcO/Na7pt1fRw9UyQianN6GAghOOX2K2M3nsaVY9mTRiN0Ohq+3Ktpv/HAXtcQbxcUceSRd1/k/jf/0iHgATTbWrn/zb/wyLsvht13sNJCPmbPns24ceP83yeStNCqVauYPHkyBoOBZcuW+a//5z//obi42P9lsVj8B0PfeOONTJw4kQkTJjB37lz/NoLa2lrmzJnDhAkTOPXUU9mxY4e/v9/+9reMHTuWcePGceWVV3a7uX/FihVMmDCB4uJiSkpKWLNmjf9eQUEB48eP99/z8bOf/Yx+/fp1kD4KF5Xphcng71zIoMvPp3z5R1EfS+i0+WySMXoE5rzsxNjXptORWjAQpEQIQdO+Q2SMGo6lTy5tVbU07CoFIKVgAIYUK267E6ETICXS7cbtdKE3mUCvw5ST6b0ukRKk07ORX0qJ2+6IiwivIvrUtzbx2PuLArZ57P1F/PjsK8iwpobcf7DSQgCvv/56l/Mr20voLFmyhIULF7J06VK/NNDGjRsRQjBlyhRmz55Ndna2pvbtGTJkCC+99FKXoHHOOef4KyZramooLCzkwgsvBDwBzKcgceedd/LMM89wzz338Oijj1JcXMwbb7zBV199xQ9/+ENWrlzJ4cOHefrpp9m1axdWq5UrrriCJUuWcN1113UY87zzzmP27NkIIdi2bRtXXHEFX331lf/+f/7zH/Ly8jrYPPHEE6Smhv4z7A6V6UVA1sRRsRlIF/kRPaacTBq+3BvTgGfOyyFz/Ejyz5pC/plTyJ8xlexJY+gzo4SUwf1o3neI5v3lNO07hCEthaa9Bzm2agONX5dh7pND1sRRtB46SsOuvTibW3A7nDTuOUDT3kO0HDhC454yGr/ah72mHnttA476RpwNjbhaWnG1tOJubSP/tOKYPd+AJIvCexKxbPPHXTK8zjTbWlm25eOw+g9WWqipqYmnnnqK++/veNh8IkkLFRQUMGHCBHQBPkAvW7aMWbNmkZKSAhyXTJJS0tra6j8qbNeuXZx33nkAjBo1irKyMo4e9YhOO51OWltbcTqdtLS0dHsmaFpamr+v5ubmmB5BBirTi4iUAX1iNFLkvxSpQwdirwlOiqUz6YVDsQzoQ2tFJU2lB7pdnzLlZqEzGUEI0guH4Gq1Ubf1S2xVwQngOtupw0unE9uxGmzHjtu2VVSSfkpB6M6rYHPCUlEf3ClFwbZrTyjSQg888AA//elP/cHCRzJIC7VnyZIl3HnnnR2uXX/99bzzzjuMGTOG3/zmNwBMnDiR119/nTPPPJP169dz4MABysvLmTJlCnfddRdDhgzBarVy4YUX+rPGzrzxxhvce++9HDt2jH//+9/+60IILrzwQoQQ3Hrrrdxyyy0RP6/OqEwvArInj4m3C71izEgj7/RJ1G7Z1eG6MBlBpyO9cCj5Z0wm74zJZI4t6tDGOrAveadPoqnsMJWrNtC0pwxLvzws/fLImjCSnJJx5J46gbTCobhtdtoqKnHb7FSt2UzNhu2anxeqM8XmZBpFctA/M6/3RiG0a0+w0kJbt26ltLSUOXPmdLmXDNJCPioqKti+fXuXg69ffPFFjhw5wujRo1m61LMv+Z577qG2tpbi4mL+8Ic/MGnSJAwGA7W1taxYsYL9+/dz5MgRmpub+fvf/97teHPmzOGrr75i+fLlPPDAA/7rn376KZs3b+bdd9/lj3/8I6tWrYroeXWHCnoRcPQ/62I0UviVf2lFQ6n6bEuX61njitAZ9DSWHqDy081UfbqZht37ySkZR+bYIvJnTMXR0ETVZ1v8a2TgybjavqmibttuajbuoHr9NppKD/gzNXt1XdQ2YptyMrH0z49K34rkY+7kc0k1Bz5fM9VsZe6kc0PuO1hpoc8//5xNmzZRUFDAmWeeyddff83ZZ58NJIe0kI9XX32VOXPmYOxmT7Ber2fevHm89tprgGfa88UXX2Tr1q0sWrSIyspKhg0bxkcffcSwYcPIz8/HaDTyne98h88++yzguDNmzGDv3r1UVXmycd/z6NOnD3PmzGH9+vURPa/uUEEvAmo27YzNQBHs8dIZDFgG9PGvp+WdVkzu9GJqN+/yFHm0Qzqd1GzcQf3OPVSu2hCzzffBUrlqI20VlaQM7kefGVPj7Y4izmRa07h35rUB29w789qwiliClRa6/fbbOXLkCGVlZaxZs4ZTTjmFTz75BEgOaSEf//znP7nyyuMV6VJKSktL/Y/feustv2RSXV0ddrsdgL/97W/MmDGDjIwMhgwZwtq1a2lpaUFKycqVK7s9NLy0tNSfrW7evBm73U5ubi7Nzc00NjYCnrW+Dz74oEM1rFaoNb0IKH7sTup37Im68GokmZOjvhFnY3PynrjSDS2HvqHtmypySsZTs7GX/ZJBzPoY0lPJmTQGW209rpY27LX1Ya9/KmKLbx9e5316qWZrxPv0gpEWCkQiSQtt2LCBOXPmUFtby1tvvcWDDz7Izp2eD+1lZWUcOnSIb33rW/72Ukrmz59PQ0MDUkomTpzIs88+C3hUDa699lr0ej1jxozh+eefB2DatGnMnTvXvzVi0qRJ/jW5P//5zwDcdtttvPbaayxatAij0YjVamXp0qUIITh69Kh/mtjpdHLVVVdx0UUXhfXaB0JJC0VI3Y6v+WjG97HXRu9NMnd6MdVrwz+INf2UAiz98qj6bGuHqcpkJmPMCBr3HED2sm6YP2NqwIAvjAYMqSk42u33s/TLI234YNoqa2naU6aJvxMfvZOx996qSV8nA6FKCzW0NrNsS7sTWSadG1aG1x4lLZRYPPTQQ6SlpXHXXXd1uB6qtJCa3oyQrHGncP7qxZ49Z9Eiwn1mjV+XUblqI7nTJmjkUPxp2LWX9MKhvTeUsscKztRhg0grGNgh4AG0fVNF1WdbaDlwmMzxp2jhriLKZFhTueH0y7hv1vXccPplEQc86CgtlCycqAHvZz/7GX//+9812aungp4GZI0tYub6ZfSfNSMq/budGv3RnUCHHmeMHBbUiTKVqzeSUzIOQ0bHjcPpI4eDIKCqhNvuoH7716SPHIYpOyMyh9XWiaTkhhtuSBotvROZJ554gtLSUm6//faI+1JBTyMs+Tmc9dofwttL1gtaafYJw4mzhGvumxt025oN20kbPpj8M6dgyvWUoVv65tC8rzwo+8bd+7H0i6xqVMW80DkZl14UoRHO74gKehpisFo46/Vn6HvudE377VxlGS61X3zVe6MEJ39GCZnjR1K5KrQ12bqtX1K5ZhOmrAzyz5xC6+GjIdk3fLWPzPGnoDOHJ+ir9fmpJzoWi4Xq6moV+BQ9IqWkuroai8USkt2J89E/QcgaW8R5K1/m2JqNrPr2D7HX1EXcp8tbHhwRQpBRVEDNph29t01gHPVN1G8PXxmiae9BmvYeDN1QSuq3f+0pjFmzKfSpYpXqhcSgQYMoLy+nsrIy3q4oEhiLxcKgQYNCslFBL0r0ObOESU/ezbobfh5xX+42DTI9KdGnhvaJKBGZcNd0pPs0Vs8P//T8SKhctYH8GVOp+nxLr5WjivAxGo0MGzYs3m4oTkDU9GYUGXrFLE36cbXZNOknkk3u8Sa7uIizXr6GftMM5E7IIX1ErM497Urlqg1kF48mfeQw9ClWdBZzrzZaKWUoFIrIUJleFJEuN3qrBVdr78cZBSJSex9arQ3GitELLmHY7H5IlySjQIfO4JkitJoaGPvTC1n7g+7P9YsFNe1EhPUWc+97KdXspkKREKiPn1HEmJHGd6vXMeCSsyPqx23TYE0Pz0b6ZCJnTDaZw3RkFer9Ac+HOTtxpmpdbTaa9h6kz4xu98IqFIoEQmV6UcZgtTD9hUd5Z9Ll2GvqMVjN6CxmdGYjOqMJncmIzqBHZzQiDHqEXofQCc9p6giETsexT7Q52FpvteBq0SZrjDYF3/sWg8/rPrC11GSw7o6/xtijwNgqa3AXBdgsrwpZFIqEQAW9GGDpk8tZ//q957iyENfndFYL+WdOQbrdSKcLt8uFdLqQTiduuxO33YHb4cBls3set9l6lPRxNjSRNnwwTfsOdXs/kSi+q8ijlN4JiWDXHzfSVtnQjVV8afy6LN4uKBSKXlBBL0bknz6ZoVddStkrK0Kyc7e2eUrkQ0RnNqEzm9AbjQiTwZtZGkkZ3D9hg57Q6dBZzKQXDsSS01MrI3sXrYmlW0Fjr22gz4ypHOvurE+130yhSAhU0Ish4+67jcrVG2kui1zluDfcNjtum53OOZ8hNaXb9onAtD98j2GXpCLdstssD0BgZ8icEspejZWWYfBIl4u6HV9jyEjH2dDY8aaa3lQoEgJVyBJDMkYOp/jXd/XeMIq4WloT8jiy4deeS8HFnoDcU8ADkFgpf+eLWLkVMvaaejJOGQqdTmBRWxYUisRA/SXGmAGzZqAPYl9XNLAO6OuR40lAeaEBZw9EBJUNuUgd3OPcZ0JQs3EH2cWjOlwTevWnplAkAuovMcYYM9KY8eazmPOyYz62KTcr9mtLQmDpm4sxKx19irXLGZSm7AwGXTadviXBfRBwtFix1bVEw1NNaSo9iCkn0/+9OntToUgMIp7nEkIMBhYB/QA38JyU8vdCiBxgKVAAlAFXSClrvTb3AjcCLuAOKeX73utTgJcAK/AO8P+klFIIYfaOMQWoBuZJKcu8NvOB+73u/K+U8uVIn1O06X/BGVy0+Q1WDDk7puOastNjOh7A4MtP58zfjulwTbqlZ7O29BwaqwshCzKl1JN/6nAOrdissafa4mqzYco+HvR0BhX0FIpEQItMzwn8VEo5GpgO/FAIMQa4B1gppSwCVnq/x3vve8BY4CLgT0II3zvCs8AtQJH3y6cVfyNQK6UsBH4LPO7tKwd4EJgGnAo8KISIfQoVBimD+mHpE7w8jha0Hj4W0/EQgjG3jux62bsPUehESAHPxxm/n8YZz8/XwsOoYc7Nou2b44clC6Mxjt4oFAofEWd6UsoKoML7uFEI8SUwEPg2cLa32cvAJ8BC7/UlUkobsF8IUQqcKoQoAzKklJ8DCCEWAZcD73ptHvL2tQx4RngWgGYCH0opa7w2H+IJlP+M9HlFGyEEKUP603asOmZjth6tImfqeDwplgAp/fv+3A6HZ5+fzYHLZsNlsyMdzshOg5ESe4ML0DbLETgZfJ6Fod+dyoHXutkekICoNT2FIjHQtIxPCFEATALWAX29AREpZYUQwndC8EBgbTuzcu81h/dx5+s+m0PevpxCiHogt/31bmw6+3YLniySIUOGhPX8kh1XUwtNpQew14a2sVtnNKCzmBF6PXqzEZ3JhM5o8JwgY9Cj0+vRmUztTpTReUr0BRx87yiGlAHkjdM68LmZ/uTpVG7YT8vBKk37jgZqTU+hSAw0C3pCiDTgNeAnUsqGAJV43d2QAa6Ha9PxopTPAc8BlJSUJMRO4YzRI6jZGFt9O0u//JCDntvh9J/yEuqR1ZWrYMCMa9A62wPQiRYu/c+1LJ/6HPaaJs37jwSdqeN0ZqBtGAqFInZoMucihDDiCXiLpZSvey8fFUL0997vD/gWlMqBwe3MBwFHvNcHdXO9g40QwgBkAjUB+koKJj91D9b++TEd0x2H7Qqf3rqUirXaHJrdGb2+kVkf3oKlf1ZQ7YXBQP5ZJaSPHIYhI3qFPZZ+HX+uap+eQpEYRPyX6F1bex74Ukr5VLtbbwK+aoP5wIp2178nhDALIYbhKVhZ750KbRRCTPf2eW0nG19fc4GPpZQSeB+4UAiR7S1gudB7LSmw5OVQcM23Yzqmo7aRzHFFMR3TbbPz+Y/fpHRZdM7LTMlt5KJ3bsSUk9Zr27zTi6lcvZHG3fsx52WSNkL7qW5znxwcnU5kUdObCkVioMX05hnANcB2IYRPUOznwK+BV4UQNwIHgf8BkFLuFEK8CuzCU/n5Qymly2t3O8e3LLzr/QJPUH3FW/RSg6f6EylljRDiV4CvmuGXvqKWZCG9qCCm49mqasgYPTymYwLYKmvZ8dTHFM69PCr9W7MbOOO5q/jP3OcCtmuvKdi8r5ys4tHkzygBt6Rmyy5cza0YMtLQG41I6Ua6pWeLBaA3GdFbTYDwHADucoPb5e9PuiQSScboETTuOeDP9kxZ6ehMiXcKjkJxMqJF9eYaepbIPK8Hm0eAR7q5vhEY1831NrxBs5t7LwAvBOtvoqE3x6GUPQ6HH+dMGcn4O6dFdYx+JZLihy9n64PLe2zTdrRjtWzd1i/9jzNGDcfe0IQpI42Gr/Z1sQ12Yrjyvx0rStu+qUQ6XT20VigUsUQtNMQZ6XLH24WYYO2bSb9TTVEfZ9T8QQz73vRu7+XPmErz/vJu7wE0fLUP27GabgOeQqE4MVBBL84YszJiP2gciioGnDcMnTH6FYwCGyW/OqPDtZwpI8g/q4TK7iR/OpGI55IqFArtUEEvzvS94PSYBz6dUU/K0AFYB/bFlJ2BMSMt6oUWekvsCjkMxgb+p/Sn5J46gokPXMaMF+ZQuXpjzMbvFiUtpFAkBGp1Pc64GppwNsR2j5nb4aLlQMedHTkl46K3Z1AI+p8eWx0/g6GFc1/9PgaOUH8w1N2F2qO2LCgUiYEKenHm4GsfIN2xWdfTmU2YsjJwtbZ1uRfNQgtjeir6WKopCSuYcjHYPOt3DaX1MRxcoVAkMiroxRFbdS1fPv7XqPVvSEsha+IonM2tGCxm3C4XNRu203a067Fdbkf0siFHQxN7ltbgtruw1bYhdIL+3+pL/2lRKmwxZoM34NUfSuWzW/8QnXFCQU1vKhQJgQp6ceTox2tpKf8mav1njjuFqk+Dk+DRRVnY9ouHX/c/NqSlMP6HV2nTsWkASCfoTOBqAX0KOI4HdemWuEPIYrPGj2DQrLE0H2lE6AQHX1+Ls0kD/T4V9BSKhEAFvThyaPlHUe0/lJP9Y7nm5GppQ69VkuduAWfd8e+dHc8myBraQkZhPxr29P7hou85xZzx+xLMGQLoC0BucR823LkkYjeVyoJCkRiov8R44o7uJvHu1u56wl5d13sjrdDpkFosY5oHdgx4PZA2tHfdwoyRQzn1kSnegHecEZenYe2fF7aLPlQhi0KRGKhML45UR1thIcgpNWEy0rTvUO8NNcKcm4lOi0zPHpworqVfD4dRC4ExPZUR82cw6roBWHO7vl5CJ5j+9KVse2I9eI8lA6/6u5THD7dxu3E7XJ7jydxuj2Sh241PbcSQEv2N+QqFondU0IsTbocDW2WUjwkNsipU68rN3NOK0el0oNMhBLja7FSv3+a/3+fM0QSQngoOYfVssnf1XoBj7ZOG3mrBOjAPS34W+dOG0u/0fLKKDBitoLcE9qXfVCP9Xj0jYJte6dsvMnuFQqEJKujFCVerLTJV8iCo2/Y1lj45tB3rObhaBvQhvXAIbUcqQa/zBECdQOj06Iz644Kx+uNBDIT3v+PBQvr+L8Fe10j99t3+e9mTx5I1YSTS5cmC+p4xmIgx5YDtcOA2ulQQOvJPL2TUdYMxZcSzmERNbyoUiYAKenHCmJFG0Q+v5qvfRO+sbOlykTpscMCg13bkGOmFQ2ksPRA1P2o37+zwvSn9tMg7dQaQKdJZwZDpnf50078Eej4TPUYIFfQUikRA/SXGEeuAPlEfQ7oCT11a+uZStWZT1P1oT9+plgisDWDMB1djgCaZYP8GODkO81YoFMGjgl4cSR3SP+pjOBubA9532RwYMnoXX9WKrIlFmLOCyLp03mPLhBkMOWAe5AlmSHBUdm9jzG8X8BIN9aemUCQCanozjgy8/Hws/fNpq+jhTVwDWo4cI33kcBp3dy+X46hrIP+MydRu291rgNSCIZeOA32Gt7JUx/Fpx3aBUG/1BDZ9Krgdnr13ziCKfoSx54AYBE69hYbUPNp0TpzYvT71vK1EIJB0njjtPqD31QtieRKbQqHoHhX04ojeYCD31AkcXrEyamM4G5ux9M3tMegBVH66mczxI2k9cjSq+/XSTxlC0bxcCes+nAAAIABJREFUcAVYjwMgB9w2wBZcx8ZcT0ZoP9J72x5oSBtApb4aN+WB4lz4qANZFIqEQM25xBlTlGWFUocNouqzLb22q9++m9ShAwHQGaPzWahw/qnBVVDqQ1Rk0FnDDngSqE0fyFF9Be5gg2xYqKinUCQCKujFmQkP/xidyRi1/k05mUELo9Zu3kl64VCE0Uje6ZPJnjSG/DOnaOZL9ZajwTV01nmmKoNCgKM2LH9cwsSxrIFU6Q4TnfROoVAkGiroxZnUoQMpuHq25v1a+uaRP2MqtZt29t64HY2lB3C1tFL12WZqt+yics0mzQJfw+4gszEJQZ9TJvTgDn0tss2UxaFMMw2yl71+CoXihEIFvQRgxO1Xat6ndWAfKldt0KSvyk83Y+kX+fmTfc8qCq6huwl0wW5rCH3a0Km3UJ1ixiEDbHvQGKnJYaMKhSJSVNBLAPJKxpE6bJCmfRqskeyF60jmuCLc9sj19g69vZXG8iCnEfWpQfYa/K+wxFOwsj/NTosMcqpVM1TQUygSARX0EgAhBDmTxmjap1tDNXZzdib2msjVx/UmI662IP0K9kTqEM7wrEsfxFH9EeIRgKRaM1QoEgIV9BKEPmefqml/tirtth7YG5o06aex9BCbfrkJKYMJAMEGieB+hRtT+1OlKw+yz2igMj2FIhFQQS9BSBms7ekstqNVmHJ7kNQJEb1Zu+rSyjXbafkGHC29BDVXsFqAwWV6Dcb4Bh21pqdQJAYq6CUIbUerNO3P0dBEasFATfqq/3K/ZhWc0u3mzdP+xuc/2x64YVAHNAtkENObdmM6rbI6OAc1IEVm0Nc1sMOXPsqCwQqFIjhU0EsApJTse+E1zfu119ZjyEiPuB9nQ6Nn68KMqZhyMjXwDCo+2BS4qEXoA1jrcJr7UpGRRUNKFg5Dx6KXNnOO/7EbwdFUM5Lg9ipGilmm0r9VT0bT4Q5fJre2moUKhSI81DFkccYT8JZ1EFnViuZ95ehTLOSfVULl6o0R91f12RaMmdocTu12OCn/qIZR1+YgdN1ka9IF6IGOwULqzNSlZlOl8xwq3SRqIVWQLgZgdgma9Q5aZSV5pkGkttXRbMmkrbe9eL7Y20vSqJN6CtpykEKPW6dDogPh0RA0uBzoXM3gtiFkN/sGg1rHVCgU0UYFvTgjhGD/Kyui1r+rpQ1nc4s2nQltjyhzO9xIVw8zmW4b/oBnzPNkftJFkzmFKt3BTo0ljfIIjTr8AaxKV05VCkDPRThZ7jyy7Qb0jjrchnQOWupx48Qtul9/y5J56O1hbnWwfwMpI8KzVSgUmqGmNxOAETf9T1T7b9i9H0N6sPveekY6nFj65GHU4LzQnJJRjL05H52xh/TK1ew5isyYB44qsB8FRxVChj9NqJN6DNIEErJkPnmtdgy2bxDuNvT2SgoaXGSQ272xhOy2CPYq1n+OrF0VZOWqQqGIFirTSwCGff/bNOzez87/fTYq/buaWzWb4hx582TMWWdhq2nG0dSGq82Bs82J3qT3ZG5OF642Jy6bE5fDhbvNgaOxDWeLHWerHWdTG44mG8b03oR2XGDsA/aKiH32kUomfRubQFhANCNcHTNggYu8pgasKYNo0DXQLLxqEBKG2gegc4Sv4gBAw3pwNiDzLkII9aenUMQD9ZeXIEx4+A7stQ3s+ePiqPRfve4LLP3ysPTNo+6Lr8Lqo8+ZIxl+mS9jTPV+hYsOzAPAVkHndTs/9uPaeE5zP74xN+HgWEijmKSVDJlFZpsdncNbwRmofsZtI62pnDQEDWkDcQm31zbCgOej5SscZTbIviDqChsKhaIranozQRA6HSPvuAZzXrY2HWZakelm//u72+6g7ZsqGnaVkj+jBH2qNeQuj63ZTUuNVm/UbjzRJ9B05fGKS7dOT6towilCm2IcaMsgu7HieMALGklG0+EwbQNj1O1n288fovmgRoFUoVAEjcr0EoiMU4ZxwZp/8PH519NS/k1Yfbh+fgGto7PZM7iJBlcjRmHEpDNhESaMUo9B6tnvFqTaTiH/B+8jy0J7Q2855iQl1wTSQcRyPM4mMA3wVLIEyvjCHEkndejtsdufFwqO2mren3YF57z7V7KLR8fbHYXipEGcjAvrJSUlcuPGyNe3osXR/65n3Y330bS3c5UiiKI+tN55BsLuRjg9PzvhdCNsLnQtDg5dkEMZwa2DZejTmfTofvjgy6B9yxw9gFnvXYGQdjCkeKos3cGenhIAQyY4/z979x0eR3Uufvx7ZrZq1ZutYlnuDfeCO5iAbXro/dJyCSm0kOQXuOnkphdSSUhCLmlAQhJCNzYQbIN7x8ZGcpPlql5X2jLn98fKRrLaandWsyudz/PoQZqdOfPKSPvqzClv9/t7tiYVUmbv2zZiaUY2uQ3mLvo3Q11ZEm9d9wdaTtZjS05i4fM/I3/ZIqvDUpQBQwixRUo5q6vXVE8vDg05bw5LXv8db150B01XjiE4xINW5UU0+vGPyWDD2J7eyMOf+FEfbGDLo8OZsfIDRJh/+xQsnwzSB/hDScpZCK0m7GkpepvY0rc/zmzSSU5jfeTxxIjExab/9wotJ0OxBRqbeeeye5nz5DcZdec1FkenKAOfSnpxKmX0cIpWPsZvyv4POF33TQDmbSQN0BhsIvi15di+/nqv59qSXUx5oBhBu3G1CKuWdxJsAlsWBKoIfZ8dk5zsY928XCMbEYcFYg++VEXFutIOx2QgwIa7HqW57DjnfPUziD5UjlAUpW/URJY4Nn3UAj475X5cunm18bry/vka5Pe+OfX5z9yJOLtagNEEWvRrADGaQHeHlinY0sHRcQPuvvTzhBS4W0145Gqy5upUdn33tW5f3/X1n7PhE/+D4Y++dqGiKF1TSS+OCSGYnD2FR2Z9GZfe99mW4aoLNhC8s+fSRgUXTyV7cjeVAuzmVHPAXx16ZBqowUB0SHT1ujfsZpJINX3GZbRqD7r594wf0Xy0557xgaf+wTuX34u/wZxyToqidKQmsiSIFYdf55/7n49Z+8l6MiNq0sh6fAvynZKPXhAw7LLpnPujC7A7uhkj0zyhnppJJIJRv36TQ7XVAOR4PCS7Uxiams7Q1BSGpCaTm+oiN9VBdrKN7FSN7BSJu63ubKG/AHdz/DzaPLFJ8PZ1T/bpmsyZkzjvpV/jzsuNUVSKMnCpiSwDwNKiZWyr2MrB+gMxab8x2Miu1EYmPziTcS2SUbfNIXfuUJKyfQhagR4mhRhN5k1oAZ7c4z2T8AAqmpqoaGriYGXPyzhSXEnkp2Uyt3Aov106EZs0ac/RCEjhQTbZKX35EFu++Pc+X1+9ZTdvLvkvFv7z56RPHBODCBVlcFKPNxOEEIKrRl0d8/tUDw1w4XNLGHGZB092Q1vCC0OwAbQkU2LQI5zI0dDSzL6T5Ty9ZTObLV6pEDzgpeXXz0WU8E5zZKaxcsHNnPzPBhMjU5TBTSW9BDIuYzxzhpwb03uM9uREdmGgDjSHKTHcPcHN95ZdxNShkVeT//aajabEEqnA+x8SblX3ruhJLhpKy/DX1vP20rs59MzL5gWnKIOYSnoJ5voxNzI6LXaPu6a4e1sv1xMZmn0ZJYHBF6an8cVF8yJu4+W9e/EJc2r/hU04kMEMghUejA9Lez+/B86cTForQo94Db+f925+mD3fe1JVaVCUKKmkl2BSHCmcX7gEvcfK4pFriOY9NVDXVvzVHBcUZfKvm69nSHJkyWuvuUsaeyFo/uM6vI8/Q+sf/3H6UMTsqZ2/5+1f+hGbPvU1jED/VIFXlIFIJb0ENHvIHD479X6SbOaMobX3WmUZ+wJRtBuoBpPK5gyx13JFEQxLi2xJxNM7PzQljt5pBCuT4FRF76eGqW7Xh+QsmNHpeOlvnmPtdQ8Q8MbfOkRFSQQq6SWoiZmTuHfyp3Hq0TyO7Oykt4KfHN7KFn+kC84l2EyqFAEEhYvNRyObFfqLdRswRDcL+4UDGUxHkhLGFmg9EHaCxx20Pv2PyNvoRmtV113V8hdW8daFd9ByKr7WIipKIlBLFhLYuIzxfGvet/ln6T9Yf2IdMtqqB+38rmwracXTGa33fdp/EAcINxp+hAw9itta4+GtQ8fZevwkG8vLeWj+uZxXNIR6X4D/HDpOitPOfZM7J9qjXnvE34PfCPJBvZ1JKad7RQIQyGAqvjd3Edy1O3RYE+jTpuJYMg5B95teh5qwYTR6CJaeAH+A4OFyjEOHI46xJ/X7DpIxfSI12/Z0eq3yvW2smHs957/6JGnjR8Xk/ooyEKmkl+BSHWncMfEuZg+Zw692/YKAYc54j0RyxA+jIxg6/MXWUh565RU+c+65PH7BWH63u4ZPvdhxYf19L7/a4Wu7pnPf5Ks6HNvXmMrFf/5X3wNo55ltJXx9RBGytoHgoSMIj4fgrhUQaDf2aEiCW7fj3bodx7WXoRc7EbIJsCFlMhiC4KEaAlt3Yxw7DmGOqfmJchedXiatNB0sZ+WCmznvxV+Rs2BmdPdSlEFCJb0BYlLWOXx+xhd5fNtPaAmGv2VXT1ZWHmRY3og+9/bWlYfGtn65YQO/3BDeGjMDA4nosPnY0CSByx7dhJ3yg8fxbdoe9vm+59uWBjidEAyGleD8OKgzcjn4vheHx4Fm1/BWezm5oXMPra/q9pSSMraYhg8PdR1vdS1vXXgn8/70fYquXR71/RRloFNJbwAZkTqSuUPn8p+jb5vSXnVrDT8+XMeXhk+lKMy9L1sC8MoHu/p8r6AhOXtb6TStjj13n8en3yrj1xsjW3cXccGC1s6L8gM4KD01lJQ0DU2T+HyCk6X11B86SVPZus73tkX/6+XITDuzdKE7wZZW1l73ADMef5TxD9we9T0VZSBTSW+AuWb0dbx7fC1+w5yd+pPtyaSJ8B+ZVvsEzf6+zyzUNdHlDP8T/nS2H498obkezbqBNl5SKT2cQv2xWio3vxv2dTIYJGPmJPz1jTSWRDbu587PpWbL7rDO3frgt2k6fIzpP/gimh6bJS2KkujU7M0BxqE7uLT4ctPaq/fV8/c6X9iLovOTJPfNP6/P90l2dD2DstkvWX+kcwX5cNmjqE3XIjwcrC9mw8omDry4nsrNe/vWgBBodhu2JDfZXSw/aC99yjiy5k5D2Dv+HWpz962s1L6f/B/vXv+gWtKgKN1QSW8AWpi/yNT2Nlfvo1aGP63/u/OyGZbRt+3Mkuxdb2GW4Yyup2YTkf2IH24qZsObPnY+vYa6vYf6dG3y6OHkLJpF8ohCqtbvoHbHXirf3UrWuVOxnbXoPGP6RJJHDaN25z6q1m8nbVJotx13fi7Z86fTVH68z7Ef+ecbvHnB7bRU9vxYVFEGI5X0BiC/4acoZbipbT5xqhK/EV4Cctngv2f23LM5m1Pv+kn7toroKiVEunl12eYT1O7ue0WLrHOn0nz4KBVrNtO4v2MPtWrDDuzJSaRNGoOnuICMmZOo2baHxv1Hzpwj9NCvZNDbQs32vTQfOhZR/FXrt7Ny/k00HjKn8oWiDBQq6Q1AUkoW5S9mQsZEcpxD8fgKyJDDyBbDIm6zrOkYb7SE/6htUUFKn9r3ODuux5PCwa/3eLnyr9HVELRFMKZ3MlBE9a6+752puZw0HjiC4e9+DNR77BT1H+yn5VRVl2N1raeqSBlbTNrksQSbo5uF21ByiJXzb6R6a3hjgooyGKiJLANQljuLRfmLmZ+3gCU/+RRrSz+aTXn/5RfR4ur7IzOA3Y2VXJrU9U4tN7x6mHVlB3Ha7Nh1nWafr09t13q9rKt049Q1HLrGd9Zu5ZmdOyKKs71we3qGFNSSx94N9VRuXd/rGrmuZM06h4q1W3o9TxoGweaux9yaj5zAluLBCJizh6n3eAUrF93Con/8jPzli01pU1ESmUp6A5QQApuw8Ysbvsj0b//XmYkoP3tpJXdfeD62jKqIdnDxSQ2HMDoc8wclKU4n5bWRF7E7Wl/Pgt/9CYARWfkcrIrssd5pDt3OuSMm4bB1PxYppcTARqPIZv3zB2k5EXndupwFM8JKeOEINDQRbAmzjmEYgs1e3rn8U8x58puMuvMa09pVlESkHm8OcB6Hi4L0jpNKfr/qP9Qc86D3cWPoAw1H2BXovMvIkSbBu4cPRRNmB75g5+UWSY7wHq2OG1LE/JGTcdhsrCndzvf3bUImdX7UGsDB9tI8Xv7lYf7z8820nIh8H8v0qeOp3LAz4uvPZkvxkDp2hGntAchAgA13Pcr2L/0QaRi9X6AoA5RKegNcaUU5j11+DxOGFnc4/rd313OwRPZ5w+r/K99JQHZ8ZDgiRVKckRltqGfYz5rUMilvJP5ggPkjJ3c4npGUwqLRU1k8ZhqLR09jSsFoSk6V896BXTS2hsbD/NLg0JDCDte14mbrjmTKXt8U0WPM9jSXk0BjM9LEcj+BhiZaK2tMa6+9Pd/7Le/e9DlTe5KKkkhU0hvglk2cyx/Xv8aDF9zY6bXXt+3ghZWHcHnzSLOHV77Hb/iplR2XFwghqGhqNCVegHT3R9P6daFR01yPPxjgvQO7GJqaRXFWHkWZQ6hpbmBN6Q5Wl2xndel2dh4txZCdezFP1TZSaRRQ1lxMaU0xm9dpHF8d/XghQNacKZ1maZrBnurBmWPeHxLtlf3tNd5edje+2vqYtK8o8UyN6Q1wQggWjZ7G3pOHGDdkOPtOdtwZ5EhVFT97eSUAl8yYTlaKh12Hyrlk4Ugqfac6tVeQNJQ0Ok5S2V4l2Fre9+n9XZlaOKbDSOP8UVNYU/rR3pkn6vv+GPLXpz5k7rOtBGobTIjwI57iAirf3Wpqm6e58nJoiHAXl3CcWr2JN+bfyJLXfotneEHM7qMo8Ub19AaBFFcSP3v77zx4wQ3YtO63p3p16zb+9M5ath8+xJqtx0i2dxwLK0oqxu1N5R8H/fzrUJDXjhisOippDEgmDS2KOs7Fo6exo7yEHeUlZ47VNEffG/ESxHfu2KjbOZsjMw0ZNK9SfHtV67aTOXsytpRI6xr2rv6D/bwx7wZqdvZxpxlFSWCqpzcIvLl3E0EjyJ82vMYNsy7kLxtX9HrNmg/20ugdwezZoTfddEc6S4cvZcf+4zz44q851WDumFOqy8Pq0o7VEBaOmsra/eY8htw0XOdcU1oKyVk8m4rVm0xssSPDH6B60y5yFs+i7v0SHJnpNJaa3/PzHq9g1aJbWPzCLxmyZK7p7StKvFE9vQGuoqGGN/dtxqbpvHdgF3XeRhaMmhLWtdsOHSTHP4Evz/4qX5/7GLPz5vCJhVdS+s3nuXn2UlPjnFIwusPXi0dPMy3hAfxZL0NzRF6Qtj1nTibVG82brdmTitWbMXx+pBEke/70mNzDX9/I28s+wf6notsIQFESgUp6A9yfN77OvJGTGZIamhTx8q53WX8w/B06Vr9fwrCUIty2j5YqpLg8/PGOr4WdPHujCY2G1mYm549iRHYeC0ZOwdnNXpyRqpataLPHRd2O5naRVJTXr7MfA43NNB0op+nQ0dC+notnkzRsqKn3MPx+Ntz9P+z4yuNhby6uKIloQCQ9IcRyIcQ+IUSpEOJLVscTL6SU/GbNCyydMIdkZ9KZ40Ej/HGoqYWjuzyuazrfvvJeFo2eyvRhY/E4Oq7fy0/LZljGEGYPn8CsogkdXvvYuFmMHzKcoswhzBw2jjS3hx3lJTS2ejlYeZx3D+zE6zM/qbw2zYWIsuROxtRxYZf6MZv32Cmay45RsXoTvtoGchfPNv0eu7/1BBvufhTDxCUYihJPEn5MTwihA78ELgLKgU1CiBellNGXrU5wq0u2YddtzB4+kSVjZ/LSrrU8sfqf1HnDX15wwbhZ3b6W7Exi7f6dSCnJTk4nze0h3Z1CssvNlrJ9BI0gR2pOAjCtcAypLg+S0NrBw9UnACirDr0+f+TkDj3QloD5SW9VWi0Xfeki+N/XI7o+qSif+r0HTY6qbwxfaOF+oKGJU6s3kbNoFhVrNpt6jwN/+Cctp6pZ+NxPsHmSer9AURJIwic9YA5QKqU8ACCEeBa4EhjUSc8wDF7atZavXHInSyeGpnDMHzWF+5dcz3W/fZR394c3JvX67vUsGTuT9LZdTfZXlDMkJZO3P9zCfc/96MyjsMrGWgCO1XW9Fdn2djMyITRm19jqJdnppr6lifcOdKy2Xudtwq7b8AfN63FomuCd8xu55MP5NP39vT5f7x6aTXNZdNujma1q4070JHfUm1Of7dgr/+HNC+/kvH//CldulqltK4qVBsLjzQLgSLuvy9uOdSCEuEcIsVkIsbmioqLfgrOKpmn88Jr7uX7mhR2O56Vl8+KnfsCMYeGNbz259gW++ervz3y9/uD7ZH5+KVc88YUzvbVIrC7dztYj+1hdur1TQgQYkpJpasID0DUNhGDFJ2wkf6wPk0I0jez5M6jd9aGp8ZjBaPWhu/u2q064qtZvZ8Xc66n7YH9M2lcUKwyEpNfVNvqdRuKllE9KKWdJKWfl5PStwOlAk+lJ43+vvDescx+84EZ+cNV97CwvYf2B93nw74+bnoy6omnRFY/timj7UQnaJG98IYXk+ZN6j8PlJHPmRCrf20owDquRJxXl4auqjVn7TQfLeWP+jZyK4fIMRelPAyHplQPtC8UVAvH1DCoOLZ80j6dv/yrp7hQK0nNw2jrPlnTY7CwYNQVd18lJyWD5Lx488xgz1uqam84kKbPo+kc/7n675J1Hc0ka2/Oies3hQAbjd4Nm3RmbXl57/tp63rroTsr+/lrM76UosTYQkt4mYIwQYoQQwgHcCLxocUwJ4b/mXsLXLr2boGHw+HUPop+1W4sv4Of13esAGJqaxbN3P8aEocUdZoLGys5jpbgd5r6h28+audnoDrD12+NwDu1+zMozPJ+GDw+ZGoeZGkoOkbN4dkx3boHQBJq1NzzE3p8+HdP7KEqsiYGwJkcIcQnwOKADT0kp/7en82fNmiU3bzZ3xlsiq2qsIys5jafXvcIdf3zszPHROYVccs58dE3jh1ffj6Zp1DY30Oxr4d6/fo8jNSepaqo/M0PTTJPyRrD7uLkzJacNL2bOnOROxyccTGbIPSs6ldxxZKTiysuhfk/8j2llzp5MsLmFut2dx0fNNv7hu5j+/S8gtIHwN7MyEAkhtkgpu5x6PiB+aqWUr0opx0opR/WW8JTOspLTALh93qUc/NY/GZkdmgdU3VzPRRPmMDFvJP/esRqA9KQU0pNSqG6uZ3t5CfUtTYzJHdZt25HHFF7Vh74wuima+8GIRoJf6bjDTNKwoXiKCyxNeELTyJx1DmmTx+LISO3x3OpNu6jbXULO4u6XmJhl74+eUuWJlIQ1IJKeYp7irHz2fPUZZhaNp7qpnu+8/jR3zbuMhaOnnjknyeHisskLAajzNuJxuHrcyDoSvoCPyQVdL4yPlL+HBddrFjdhf+giAJLHFaM5HNRs+8DU+/dV8ugiqje/T92uD3EX5pE2eSw5i2f1uBuL4e+fReVlf3uNt5beha+mrl/upyhmUUlP6cRpd/Dyp3/En+74Gj+65gE0TSPVlUQwGOSNPRuoaarnnQ8/KqmzvbyEc0dM6lT8NRrrD+4mo4uK59EwenmUv+pSL0m3LYagjEmNvHB5hheQOXsyTWXHzxyr27WPul0fUrF6M4HGZlInjOryWs1uzv6i4ahYs5k3FtxE0+Gj/XZPRYmWSnoDiJnjs0PTsrj0nAX8besqih69ktwvXsLv3nuRBaOm8Py2t6lvaaIoY8iZ89/dv5OC9FwWj5nOglFTmD5sLJqI7MdrSsFoFo2eyrayfWZ9OwD4A71svyYEb9xqwLhcU+6Xs2gWWfOm9XnrM3taMtWbdmF08/jQV1NP/Qf7yV4wo/OLRv/ONK3/YD8r5t5A9VZrtmZTlL5SSW8AEUIQNLG+2w9W/pmfvPksR2pOUt/SxMoPNuJxupk/cjKjcgrJ9KRxzfQlZ84/VHWM1SXbeHf/TrYd+ZDJBaNIdrp7uENIbkoGC0dNZc7wiaQnJbPzaClrSnfQ2GruLiPhrC8M2gSvPuDCc828iO6ROmEUmtNB2uSxVG7cSdW67dhSPOQsnAmi5yUYnpGF5Cyahb8uvG3iKt/bRs6CGWjOj5abWDG5pOVEBasW38qx197p93srSl+ppDfA6G29CjN6fbkpmR2+Xl2ynaZWL5PyR/L07V/lt7c+gl3TmT9ycpfX7ygvYWhq5+UAQggm5Y1k8ZjpjBsynFMNNazdv4ONh/dQ2/zRG77sZuJJX9n62NOSOrx6r07SrYvCvkZzOrCnJmMEAgi7jYaSw8jWUIV5f209FWu3kHbOWFxDsgFIHTeCpOH5CE0jaXg+OQtm0HSgnIo1m8N/XCgl/poahl0Y2l0md/FsTpm8D2e4Ak3NvHP5p9j/+79bcn9FCddA2HtT6UYgEMBmi/x/cXbbrM4z7RlB3inZxiXnzEcIwcS8Eawu3UF1D9XNs5PTaPa14A8GGJM7DENK9p48xO7jB3q8t67pfaoG0d6wrCwykpOZN24kKR4nwt1EY42DY1W1QJgzDjXBa3fCMtcSWn73drenCV3HXZCLLclN/d4D+Ou776XV7Qo9rrWlJlO/7yDCbsOZnU7z4WM0H+77fgrpE0cw70I7PlpoOncyp1ZvQug6tpSkUG+xn5cjyWCQDZ/4Mk2HjzH5G/cjeunZKooVBsQ6vb4aDOv0gkYQr6+VZFfkC8l3lpdw9ZOPYNN0bJrOJxZcwQMX3NDhzWxneQnPbVnFD1f9FV/A32U7brsTr79v09udNjut3bTXk/suvYjUdKjwHe/95HBIydLnHbT++q1OL+UsmoX3RAVNh48hfX2PNRpp44qZt9SBk9AjYK9Io7wqHZtNUJxykKCwUx3MY92v+r6xthlG3H4Vc578JrrD3LqIihKOntbpqZ7eAKVrelQJD6AwI5f9FeWXBEb9AAAgAElEQVRnvj5aW9Hpr/cphWOYUjgGl93JV196sst2+prwAGyaTivhJ5KLpkxm+tgC6sRRKnwm/iEnBG9c62NZcAktv+3Y45OGQWPJYfPuFabUsUXMW+rCSdOZY25Zx5jM08sHBDYC5OpHmHrnInb8YU2/x3jw6X/hPXqSRf/4OfbUzhsCKIpV1Jie0q0kh6vD1/sry7s5Ex5dfjuZnp4XUPfF2Vui9WTRhPGMGieoFeWmjQN2IAQrbvCjf34pCEHO4lnkLJxJY2kZwhX7vS/bSxlVyLzlSR0SXk+Kkw+Rf960GEfVtROr3mPloptpLo+8GoeimE0lPaVbNc0NHb5uaGmmuqnrxci6pjM5v+u1Y5E4e5/M7ty+ZBETzrFhEOOp+kLw1sVe/N+6GH9DExVrt5AybgTS33+PNVNGFrDg0hRcMryEd1pWsbnrHfuiduc+Vsy9npqdey2LQVHaU0lP6daLOzs+Ftt0+IMzlc67kp9mXsmmcHp6d33sPFzZ/VP14bTdizUyHwntRlOxehP0UwWG5OJ85l+ahlOGX/X+tBEphxgyt/cySrHiPXqSlQtv5uR/NlgWg6KcppKe0q3HXn2qw9d13kaueOILXP6rhzlYeQzjrIXQeWnmVdjWe1lv9sllF2DLrIrN48wuDHXncFvhTL5VmMecOYKcc83dIq0nnqI85l+egYuG3k/ughDgyoxtFYbeBBqaeHvZ3Rz8iyqAolhLJT2lW1metE7HjtSc5OVd7zLyK1eT96VLKa85dea1exdfFfEuLGfTRfc9vc9eeiEy9VS3r5tpeHIB9xbN5Kt5mSxwNmEXIJDM+u4l/XJ/T9FQFnw8EzfdLwvplSaY9c1FjLgpsgX3ZjF8ftbd+gXe/98nTN09SFH6QiU9pUtSSg5W9bx27FRDDZMfu4Un3vkHAGNyi7h2xpIerwlXwAh2WUT2oSuX4UuK/cSIIk8+nyyayZdyk5lmb0I7a9Zq2ggfaRPyYx7HnCsLcMsoEh7guucGHNn1zP3OOVxX8jCXrn2QjCnmV8YI184vP86W+7+FYeLuQYoSLpX0lC4JIbj//OsZN2R4j+fVehv48ku/YW3pdqoa65hSMBq3PfoZjSfqq5haOJoFo6acOfbwx5fR5Ijt5sajU4v4TNFMHhmSwnR7U7cLrAVBzv3xVTGNBcAuoivf47rnBjTPR+OeNns9qYVNLHn2BpIKM3u4MrY+/MWfeffGhwg0NVsWgzI4qcXpSrcMw6A14GPkV67hRH1Vj+emuJIIGgaHHvsXTX4vn37mB6zYswFDdhz30zUdKWWn4z21Oyq7kAtnj6Te1v2SiWiNTyvmsoxMRuvhvwkbJPFc8U9jFhPA0vsmRfxo0/Xf16Oldn+tz5vKS/OewFdrXeJJnzKO81/9LUkFQ3o/WVHCpBanKxHRNA23w8WCUVP4x7but+KC0HKGJIeLdQd3ccXUxdwx71KGpGZS19yIXbex4oMNjM0toqKxlkNVx9CEhk3TcdhsOG0OkhwuBOCw2bFpOrqmowmBrmlctmAcpwxzq6gDaEJjRsYYLkxNolj3An1789doZt6vb2XdvX82PTaAJffNxCkr6OIpb6+cd13XY8IDcLjrOe+vd5Jc5KJiYxVr7/q/yAIVAmG3IXQdzW5D0zWEzYaw29AcNjS7HU3XQdfRdB3NYUfYdISuIYBNn/k60779MGkT+29ykDJ4qaSn9KqrCS3t2TSdqYVj2HV0P9f/7svMKZ5IdnIab+3bQlOrl6A08DhcbDq858w1hjTwBQ18QT+NrV6quln/B1BYmkz+SNO+HQSCOVnjuDjFxVC9BYi8msPw5elsy0un5bh5SydsHjdjrphJEsfQRN+fxDj/6xr0jPBmemZP9AE+8s9zUHjlImp3l4FhIIMG0jCQwWDoc2mAIZHBIEGfHxloOx4IgJRInx+JHyPCf8qKNVs576UnyJnfRbkkRTGRSnpKr+6cdxlPrn2h29fHDR3OlrKPFh+vKd3e6ZxoygT1tnwh7HaEzoLs8Vzk0cnRfUBL1G0KWsk9dxRlL2yJPsA2No+bsdmRbW/mvPUq9Jy+LV4HkH4DGfDRWNr/26oB+KpreevCO1nw3E8ovPwCS2JQBgeV9JRezSmeyIShxXxw4lCXrztMrJjelZU73udTY2bS4I9sbMsmbCzKmcBFHkGm5gPMnTXoHhrd9muOjFQmXzsVXTNAiNB/OdLndpw3fxx9SGR/XJS/7eXoK9YuHg96W1jz8c8w+4mvM/qeGyyNRRm41OxNpVeapnHPwo93+3o4hWKj0exr5Ud/fY9kf0FE15+TPoLrkn1tCc98Ux6egeaMLPE7MlJZcOsYCl2HyHOUkWc/TK4eQcK76Ur0vMh7rnZP32oOxoo0DDZ+8qtsf+RHakmDEhMq6SlhyUruflwvaPTPVlxJnshmGpc29r1WXV/Y7PUs+sPtfb7Onp7C/FvHkkp0C+0dV12Mnh/d0oa0UQ4cWelRtWGmPd99krXX3k/AG/0jaEVpTyU9JSxHayu6fe1YbWWnigyx0NIc2Y+rJnQaZGzruuXNd5E6Li/s8+2pHhbcNp40ut/LNCyZGdhG2aNrA/A3SzxFudg8se2190X5C6t4e9nd+Gq6n+SkKH2lkp4SlqO13fdGDlUfZ1rhmJjHEDT63tNLc6Tyhfxi0rTYVkMQ+Jj7s2vCOteW4mH+7RNJI/qdZeyzpoGM/rFtxhiNpX9bzMUrb2bU7fEzkaRizWZWLrqFpiMmFQVWBj2V9JSw7Dq6v8fXS06VMywjNguMdU3jkVsuoNnRt8eU6Y40Hi4Y0TZTM/ayxrUy7Mqep9zbkpOYf/sk0k1IeADCbV49P80uSM7XmPbwSOxp1pUjOlvd7hLemHs91dv29H6yovRCJT2lV4ZhsLms53podl2nqdVLcWb4j/jCFTQM6vzVfaqokOnM4HMFxeRq0Y119VX6+O6/f5vHzbw7JpMhzOu1yEbzd1NxpArG3H2e6e1Gw3vsFKsW3cKx196xOhQlwamkp/RK0zTmj5zc6bhAkOZOxm134rI5qG6uJ9nlJi812/QYUmX4yTTLmcHD+cP6PeEBTPrUCM75YucKDLrbxbw7p5ApTJ5U4zP/sa2UkgN/XWd6u9EKNDXzzuWfouSJv1odipLAVNJTwjKraEKnY2OHFFHnbcTrb+VAW0WG948dwOtvYdbw8abe/7t/f40sR+9FarNdmTycP4ysGC1P6I2glcmfLuDaDx8+k/x0l5N5d081P+EBWrH5PWsAd555tRHNJINBNn36G2x/9MeqPJESEZX0lLA8uvx2vnbp3QAsHDUVu25j38mud++o9TZyrLbS/CD8oRmiqfbQ8omza+7lurN5OK8wZuvx+sLuqOecTw9j9J2LOffuGWTFIOFh09HSzRvTO00IwbBLJ5rerpn2fOc3rL/jSxj+2E5QUgYelfSUsCS7knhgyQ0MzxyKpmn4g4Eezz9WV8mc4old1sSLVJM3gLsln/97eRdaQy67draS68hHQ2OIO4fP5eWRYVnCEyDsIGxI4UGSilHhZtJ0nRzN5OoQWaFemPOGKxCOGnPbbuPOiZ+lC905+McXeOfye/E39n3bNWXwUqWFlD55cs0LfGfF05RVnwyrPFBxVh5p7mR2lJdEfW+Py4VN06hr7jh547Ebr+SzRUkxX5bQFaMhjWDpMQJ7SpAnToCmg2HiTiIOO1pBAXpRAf4du7FNGIt9QSG0OhGOOiA2GwP4GiRbv7sf3alT9u/N+KrM21DbbBkzJnH+K7/BPbT3x9/K4NBTaSGV9JQ+8Qf8NPlauObJR3hrX/j/hmNzi8jypLH7+AHqW8z9y/zKSTP41+UmlmHokUC2piOlxP/OToLvx3YavTZuLM7Lz0EQxL+5CvusTFPW5fVFzT6DD54q4eirWwg0xGevyjO8gPNf/y1p40dZHYoSB1TSO4tKetHbe+IQU751a6+POc/msjuYUzyJ1SXbTIlDExqv3XoVFxWY9xi1K5I0jOMt+Dfuwig9ENN7nc1x/RXYhvlAOPo94bVXuTPAjh9u5dTqHZbF0BNHZjrnvfgrchbMtDoUxWI9JT01pqdEZPzQYr5w0S19vq7F72N1yTYWj56GQ49++yxDGnzr3T14/bH7481ozcD70+dp/eu/+zXhaQX5kJwcSnhgacIDyJ5iY9EvZjP1a1fjidGs0Wj4qmt582N3UPb316wORYljKukpEXvs8k8yY9i4iK5dXbqdFJeb3JSMqOO4duJI7DEqEmC0ZtDyq79BoJ93/Hc4cV57HkmfXNy/9+2FI1Uw8c4shizuvIQlHhitPtZe/yC7v/MbtaRB6ZJKekrENE1jxf0/ZeGoqRFdX9VUT1GUW5elu5O5dbQbmxaDx5uOPIyjteZOTAmDbd4c3PdejbA3EauJKtEae2ux1SH0aMejP2bzZ76BEejb43dl4FNJT4lKdnI6b9z/U66aFtm2VcfqKnvtLfb0GNQXDNAYjEHCs+eC7wS2UTbsyz9mfvvdcN55LfZ5IxH2GssfZ/YkfYyGI7P7clPxoOSJZ1h91WcJNEdWWFcZmFTSU6Lmdrj45ye/x+9ufZTirNBYT7IzidvOvZiphWPY+ujTvPKZH5OX1nl7smN1lZyor+o28X1RTuDxNQ4e0rteLK1rGo0xW6kgQfqxTcoGd+xLJ7nuug49sxEh4nd5wGlCCEbdttDqMHp17OW3WXX+bbScqrI6FCVOqNmbiunKa06R5vaQ4vJ0OP7+0f2c+/27afZ1Lgzq0O2MGzqcg5VHaWwN/WU+Mn0o3/zuB2eWt/vTkjBcdgy7hrTrSE0DKcmbM55Lv2vStmf2HBA2EBq0Hj1z2L+xDv+aGO5H6XCSdP+yuO7dna21VvLyBX/HVx3/9e6SRxWx5PXfkTJ6uNWhKP1Azd5U+lVhRm6nhAdwTsEoHr7w5g7HPA43OcnpCCE4UFHOkJRMZgwbR2FqNo/+9VSH/Vzsdc04T9bhLq8h6WAlnv2n8ByoYOqyYnMCd+SDvwJ8xzskPADb7OGhhecxYpszHWRibanlTBcUfXy21WGEpXF/GW/Mv5HKjTutDkWxmEp6Sr968IIbSHMnA3DVOTOpeuASDn3yY+z/9JX8dMwiPrFLZ3FjCg9vELjLwnsktf6hF3jrzk3RBeYsAF/3+2MK0YjIjc2OH/r0qTjm5kAfSifFi1HXF1kdQthaK6p58/zbOPLCKqtDUSxkszoAZXDJ9KTx2fOuZZynhhtG2rDrAnRw2yWL/Rqb/r2Don/3rU1/bQMn395O2ZvnUPSxCPaMdBZ06tl1ImzIhoa+tx0G++zxIOP/EWFXbEmx3RTAbEFvC2uu/iwzfvII4x+43epwFAuonp7S7x644HquKHaFEl47xZckkz458m2kNn3hJQLePvaWwkl4ALIFx4WLIgvsbG4X2rix6LOmo8+ajpaSmAkPwDNUgEisxIeUbH3w22x56NtIIz6XhCixo3p6Sr/LSclEekeCt+Mm1DaXYNkL57Pp60Uc+NPbfW7XV13H9h+XMeW+IhypYbwRh5vwTsc3WsD1l+P720t9C0zTsc2ejshKRx+WhZbSipQtbeOVgkR8rAmhYrOrbloDCToZbt/jT+M9epJ5f/w+usv8Ek1KfFKzNxVLyLr1ULu2y9eMgGTNZ3ZwbEVk43T29BQmfPZjjLslG5u7m+TnKABf+AnvIwLpT8f7s2e6flnT0QoLsM2fjl7ggqAAKRC22JQAstqepyrZ8c1/WR1GVHIWzmTxv3+FMzPd6lAUk6jZm0r88XZdgBZAswkWPD6V7LmTImraX9vAzm+9wIvn/ZN9f60h2HrWH3bOSBMegEQ4GnFcdzn67Olga5vRmZmB/fyFuD55Ha7rJmMraEVQh9BrB2zCA7AnO6wOIWoVa7ewcsFNNB4yue6hEpdUT0/pd1JKOPJTkD1vEeWrl1Tu9LHm7mcxWiNfv+YZnsekh85jxGXJaJ7CPj3S7I000kEK0GRCLCo3hwA0EIK6/QavL38Kw5dYyy264hqaw/mvPknm9PiuGq/0TpUWOotKetaSRisc+XnY5x94qZEN93XzOLEPRt91AbO+OhqRoGNoZjAa0giWVXQcRjz7PeD05A4pIWhAMEiwqgZjb0mX+5BWGgW8+8v1sQu6H9mSk1j0j5+TtzT+d5tRutdT0lMTWZT+F2wM+9TGcoPtj60w5balT71F2phMxtyUOWgTX/BIJf7X3zK1zWS9PjSDcwD8AR1obOY/l36Sc3/7GCPvuNrqcJQYUGN6Sv9r6X4R+NmSCzVG3DjXtM2NtzzyPCXPVQ/SlAcY5n7nUko+PJAyIBLeaTIQYP2dj7DrsV+q8kQDkEp6Sv9r2t2n06c9lM/Vm6+j8PK5pIyNfgeQLf/vefb/o37gJD7hRJKGlGlIUpEiud1HUrvPPeDtvO9pVLcWgsnFR0kuzje13Xiw66s/Y+M9X8HwJ/54pfIRNaanxJys3wzukQh7JtJ7CE49H3lbUrL2/t2UvxT95s9zfnIjI69KIcGWVndiNKfT8sSzlsZQ1lzMtt+vsTSGWMlbvoiFf/8p9uTO+8kq8UktWVAsI1uPQ906OPEssnE3VL4SVXtCCGY8Ogl7anLUsW186FkO/jv88cW4FQdZe5j7IGOumZ94u7OE4fjra1i1+Fa8x09ZHYpiApX0lNiq3wJGKxjNUPUaGNEX9HRmgL/enGS14YFn2P/PAfSo0yJCCHKGSIQeu0oUVqrZtoc35t1I3Z5Sq0NRoqSSnhJbGYshdY6pTdbs6zxtPhobP/ccB19qMrXNmBB2AmV2AgcEgYMagUM6gcM6gZ2HrI6sjUB3dl/lPtE1HT7KGwtu4uQ7G60ORYmCGtNTYkpKCc0lUPmieW0akt2/rWTXd14wrU2Aub+6lRGXRFCloT8IO61vlhHctsPqSHrkFWm88bP3rQ4jpjSHnblPf4/iGy+1OhSlG2pMT7FQEOrMXbgsNEHKCPMnFaz/9J85/Ear6e1GTdhpfftI3Cc8ALeso2hZl+81A4bh8/PeTZ/jgx89pZY0JCCV9JSYEsIGmvn7M+bOcHPpf+7m4lV3odnN22PhvXv+yOGVcZT4hI3Wd44S3LLd6kjC5muMo3+/GNr2+e+x+b7HMILmPm5XYkslPSX2ksaa3qQ7W5BarJE+WmfsvUtNbfu9//4j3tpUU9uMiHAgtTQcC3IRuQVWRxO2sdPd2FIGx/T+kl/+hXevf5Bgy+BI9AOBSnpK7LmGxbT5aZ/LZ+iFM2N6j36nuUBPQgSrEDoJtRQgQxyncPEkhK6jJ7msDifmjvzzDd5edje+2nqrQ1HCoJKeEnv2bBCxK0EjhGDhT6eTPe8c89q05DdDB8cQcOSF/r0CiVu1YcqIY1z6qWHMvXOa1aH0i1OrN7Fy4c00HjxidShKL1TSU2JOCBGTR5zt2T2C8387l+xzTSoLE4uOleZqS2pD233khT6chUAQfCfBdxyCHXsN+qjh6JOmxiCo2BBCoIsgR0p7Lh81kNTtLuGNeTdStXmX1aEoPVBJT+kf0oj5LezJgvN/P5+sOXFaD82W2pbUTrT7OB76aO25gKljXgqOi4b2U6DmmTCumdQx0e+XmihaTlay6rzbOPry21aHonRDJT2lf8R4XO+004kvc9b4frlf30TbfUyccb3TXLKBORdnkFQ4xOpQ+k2w2cvqKz9NyRN/tToUpQsq6Sn9pP/WMzlSBEueWkjquOGRXZ+ehO40OShTJO6asNbKxB2fjIQ0DDZ9+hts++L3kUbsn3Io4VNJT+kfSaNC41n9xJEqyJjWLukJwYSHLsXmOWvHFSEYsiQ02aLwsunccOBBrt5+B3ZHDGbiRbuQOUF/Ww8cdg/aKf0f/OD3vHfL5wm2+qwORWkT1a+REOIHQoi9QoidQoh/CSHS2732iBCiVAixTwixrN3xmUKIXW2v/UyI0FxsIYRTCPFc2/ENQojidtfcLoQoafu4vd3xEW3nlrRdG7spgkpUhO6BITeCs//qrrmyk0AI5j95C+M/s5xpD+Rz1eZbWPD7W9GcbT8qUuIpzGDer25m9G2z0LQmBPFZP00IAbp5C/H7S0rmwN2PMxyHn30ltKShps7qUBSi/9txJXCOlHIK8CHwCIAQYiJwIzAJWA78Sghxevv1J4B7gDFtH8vbjt8N1EgpRwM/Ab7X1lYm8DXgXGAO8DUhREbbNd8DfiKlHAPUtLWhxCmh2SF1Hoj+eeOecFchV6y7m+FLk5j++UIAbG5B0cfcTHxgOUIL/fgf+PN/yJzkJnde/BZCbX3tON4/74Jg4s2GTE82t3BtIjr1zkZWLryZpsNHrQ5l0Isq6Ukp35BSnv4tXA8Utn1+JfCslLJVSnkQKAXmCCHygFQp5ToZ2rTuj8DH213zdNvnzwMfa+sFLgNWSimrpZQ1hBLt8rbXLmg7l7ZrT7elxCmRNALy/xvSFwOxLUPjzhJ4hnY9+WPyp4cw7bGrQ4u+paT8zWp00Qi29C7Pt5pRW4M8mZhvmGnyOPb0FKvDsFzdnlLemHcDNds/sDqUQc3MUYK7gNfaPi8A2q/SLG87VtD2+dnHO1zTlkjrgKwe2soCatsl3fZtdSKEuEcIsVkIsbmioqLP35xiHmHzINLmQN5t/fq482zjb8lg2jeuxpacRPOJptDaOMMPIg5nsVizWt4UQghm3DSl83jqIOQ9XsHKRTdzbMXArDKfCHr9TRJCrBJCvN/Fx5XtzvkfIAD85fShLpqSPRyP5Jqe2ur8gpRPSilnSSln5eTkdHea0o+EIxtyrwW7df8/xt+WwTU7bmL659rWkukekDGadJF4Kw5MM9RWxrirBnb1hXAFGpt559JPsv+p53s/WTFdr4MrUsoLe3q9bWLJZcDH5Ed1NsqB9guzCoFjbccLuzje/ppyIYQNSAOq246ff9Y1/wEqgXQhhK2tt9e+LSVBCM2BzLkBKp4Df//3wIUQCF2EnrRq7lCF91jQkiAY+diWECKBFyyEJOAcnJiRwSAb7v4fmsqOM/lrn0Uk0N6qiS7a2ZvLgf8HXCGlbP9u8SJwY9uMzBGEJqxslFIeBxqEEHPbxuT+C/h3u2tOz8y8FnirLYmuAJYKITLaJrAsBVa0vfZ227m0XXu6LSWBCLsLci6L6f6cYTF8ocebZtNTAK3T1mLhkoZEemOUjPtRY3V8zoq10vvf+AUbPvE/GH71b9Nfoh0o+AWQAqwUQmwXQvwaQEq5G/gbsAd4HfiMlPJ00alPAb8jNLllPx+NA/4eyBJClAKfA77U1lY18Biwqe3jm23HIJRwP9d2TVZbG0oCEvYsSLX68VcQHCY/arWlgQyA0Rh5GwbIqpPmxWSRpLTBvXShOwee+gfvXPEp/A1R/IwoYRODsfLvrFmz5ObNm60OQzmLDNTBsT+EkoRldLBnAgL8p6JrypYBQS/I6Kbsy6DE+/iK6GKJA4YUvPZ0FYGGJqtDiUsZ0ydy/iu/wZ2Xa3UoCU8IsUVK2eVf0Yk7JUwZcIQtDTIusDiKYGhsMdpq7/YsCDRFnfCAATMBRhOSlOGJt2l2f6nZtocVc2+gdneJ1aEMaCrpKfHFMz5UgsdqreWhOoBaBMsX7LngrwVM2npqgCQ9gLypeVaHENeay46xcsFNnHhzndWhDFgq6SlxRWgOyLsdMpeGxsOs5K8Mzeh0FhB25nEMbZuFGuz11HANpJl9+VkNpI4dPKWGIuGva+Dt5Z/g4F9etDqUAUklPSXuCFsKImUK5F5tdSih6uWtR9sSXy87yDjyQzXyEn5xQex4ZDWjFkZW/WIwkYEA6279Anu+/1sG47yLWFJJT4lbwp5l6cL1DlrLQzM7nYVtMZ31q+MsAF9slokOtDe9xjrzesED3fb/90M23/cYRlD9m5lFJT0lvrkKez+nv/hOhJKfvwKc7camnAWh3mCsDLD3uwl5ZRQttXp5SuIo+eVfWHP1Zwk0Jf5azXigkp4S35z9U3G9z1qPhqpFOAtjm/CAATWThbbdZQZY7zXWjr74FisX34r3eJTLaBSV9JQ45+x2D3HraR6Q/tDyhJgaeAnCnR4HM3QTTM3W3aw493pq3//Q6lASmtoNT4lrwuZBZi6HmlUWL1rvQrAu9CHsoS3UpKqOHY5mkc7RDfutDiOu6W4XtmQ3No8Lm8dN3sfGM/62fGxusCWvQLa4Ea44fQoS51TSU+KeSDkHac+Ak89YHUrXbGmh5Q0xM7AebwaljaayE1aH0SdC09DdTjSXE93lQHc70J2h/2p2G7rbju6woTvtaC4butOG7tDRnDZsbhu624Y9yYbu1rG5dXSXhi1Jx+bWsLnA5hLYkgQ2N+iu3paptMLJ55HZlyA84/rt32CgUElPSQjCVYBMmw9171kdSmfBplDx2UBtbNofWDkP2cOoirC1JQ6HHaFrCF1Hs+sImx6qdK8JBAJh17G1JRxh19FsoXN0lx3NrqM7bWgOHf10AnLZ0F06mk2gu3R0h47u1NDdOrpdoDk1dIdAdwlsToHuFGgO0B2gO0Gzx9t6ySBUvoQMNiJSZ1odTEJRSU9JGCJ9PoZMIrhtVVspIBGqraqLUGIQoRJBwkboa022/ReEkKERbK3tNSFBk+a8kRleMGJUgw9CsUZ1vQgVoRUCNO2jz4UGunamQK3QtI7nnj5f09v+KxCaDklJCLcrNNRoBEHKtnbbrmlLVtjaPnStrXyTBhLcjZJzPAUEW4OkjU6jcEkSuh2EDkLr5v+HszA0c1bpqOZtZKAeMs6Ps6Qcv1TSUxKKljENX/lrBDe/aU6DQoBma3tTb/fGLUJfi9Nv+KcTALRLIKItIQiQEm3IUJxLR59u+KwbyXYfWudjZ2Yznj7Wlq2RoAVx3XMpZya0aOJMIhSCj5L4mf+2vd52LN7eDNOAyfOHWB3GwLWJhjcAACAASURBVNGwBYINyKyLEZqqZNEblfSUhKNPWWBe0pMSgv5u18L1pY8lbXYIZJoSVnsCECl9ueJ01PGV7JQYav4Qgo3InI8j9CSro4lrasmCknC0tGyrQ1D6lR7bx8cDResxOPFXpL/G6kjimkp6SsIR+SPRho+3Ogyl37QNxAr1YKpXgdpQ4mtNrNmx/UklPSXhaE4X9ov/y+owlH7jb6t4oR7bhcXwwslnkd4DVkcSl1TSUxKSNnY6zi/+Gtvci8GdbHU4Sqw58iBYb3UUiUMG4NS/kA07rI4k7qikpyQkzWbHVjga580P4370d7ju//FHsyutEmezJJXBTkL1SmTNarXXaTsq6SkJT0vLRh89Bfsld1gah6ytxrfZG/rY1EzwlPr1Mo36eyJy9Ruh8hVkvG3jZxE1MqwMGI6lN2Ps30Xwg02W3F/WVRN4550zX4uLL0TPVb9iplBv2NFp3gsnG5G5H0dog3uzb/WnqDKg2JfdYnUI7ajuiWnUkoXotZbDiWdDO7gMYirpKQOKNmIStvmXWB1GiMp55lFjUubwV4aWNPgGb10+lfSUAUUIgeO6+9EnzbU6FPyrN2JU61aHMUAYVgcwcAQbQz0+70GrI7GESnrKgCN0G847v4I2drq1gTTW0fqv9RhVKvFFTaqkZyrpg1P/RDbstDqSfqeSnjIgCYcT191fQ59xPiLHuurrsraKlufXElSJLzqa+vczn4TqN5C1awfVkgY1tUwZsIQ7GdcdX0YaBsF9W8DbSOtffgj+fp4U0VhHcG8NjMngdAUFLdlAJA2eN5qoGaqnFzN16yFQj8xahhAD/48LlfSUAU9oGrYJswEwjh/Cv+Iv/R5DYP17BNZ/9LV9+YXYJ6lfv/CppBdTTXsg2ITMuQKhOa2OJqbU401lULHNWGJ1CEokDL/VEQx8LYfbljQ0Wh1JTKmkpwwqIjkNPKlWh6H0mVqc3i/8FXDiL0hfpdWRxIxKesrg4nBhv+B6y/fpDH54BN+6Bvw7fJbGkTjU+Ge/CTbAyWeQLWVWRxITKukpg4px7CCytsLyiRHGgX0E3nuXwM69lsaRWNTbVb8xWuHk88jGPVZHYjr1U6QMKoGtbxNY82+rw/iI6sCEbxDMLIwvBlS9iqzbMKCWNKikpwwasrmBwLrXrA5DiZRKetaoXQPVq5ADZIMANWdaGTRkQy34WqwOowNZeYLm3zSBEAihgQBt5EicF2RbHVr8UUnPOo07INiAzL4MoTmsjiYqqqenDBrakGHYL/+E1WF0JA1orIOGWmR9NbKuGtnktTqqOKXerizlPQAnn0MGm6yOJCrqp0gZVOwX3oD98rvB7bE6lO6pCuxdE+rtynK+k3D8L0h/ldWRREz9FCmDihACx0U34X709+gzliDyR2A7dxkia6jVoX3E6yVYLgiUgv/9AMHj6tc0RP0xEBeC9XDiGWRLudWRREQMpFk54Zo1a5bcvHmz1WEoFpOGgaw+gZadj2ysw/fKHwhsWAGB+Nr9w7b4PByz3VaHYT1bJgSqrY5COUOH7EsQnnFWB9KJEGKLlHJWV6+pPyGVQUtoGlp2fujz5DScNzyI/fxrLI6qM+FU880A9Xgz7gSh8iVk/RarA+kT9VOkKO3o42ea0o79vAU4b7zClLaEU81aBEAGrY5A6Urd+t7PiSPqT0hFaS/afTk9Hlx3XIHmqgF8uB+8keChOnwvRL4+MLDrAIFSNyLJhWN+NsI5+IYkANXTi1daYj16V0lPUdqLcjzP/YkrELaaM18LvRbbKNAfvJHAnpNIr5fAus0QCH8DZeNw6ZnPg8OWYxsdVYgJTCW9uBSoRkojtM40AaikpyjtyNoIdpd3OqG1Fa24CGFv7HJrMaHXYp/sBJzYp1+HUdOK/811GMeO9zHAUBHaQUkt5YhfMpgwPXGV9BSlHeNkmDvL23Sc112G8DgR6QFAIGQLyN57isJeg54L2k2L8f7pLThVEV3Qg0ZivKkOOsIOInFSSeJEqij9wDjwfugThxM8STiXL0ZkuqElSPBIJf433g69rmloeW6EqI1402hBHUm3zcJoSMW/bjfBXe+HEeAg7umppBefpB+8JZA01upIwqKSnqK0kTKAfeEoHBeNAlsAQSPQGvpwgZbuxDbxJlpf3ohRup/Wf63HdfX4aO+KllKHc2khctkEIABBJ62vbMH4sCT6b2ogGay5PhFUvIhMnorIusjqSHql/nRSlNNaytFSahC2GgQNdNWFE3oNrisnYD9/IcbBQ8hghmm3F7IBIb0IrRbX5aNwP3wD2ohi09pPfCrrxbXGHQmxPZlKeopymj/csbUA9pnJ2BfPBz38WZh9JajDec0M7IvmnTk2CDdQakclvbiXAEVn1eNNRTnNcw60noDmfWGdbp+dBjTENCQhm7HPScM268bQAZuf/91cwW82buSh+XN4YLIbKUNDfTYttLeoolimfjPSNQzhLrY6km6ppKcobYTuRmZeAP4q8IezdKH/ul1Cqw19YkCNt4Xy2koefvVVHn71o3N23HMtY1LBpX6rFcsE4dTzyIwLIGUqIg5rIKpfD0VpR+gepDM/zKRnDa2bztzUJ59nxW3XcFGB9b09Q0r8QWg1BC1BaAkKfAZtn4M3IPEGJK2GJGBIfEFJS1DSEjBoDRo0B4L4g5KAYRAwJJ+dNY48u6ozmDBq3gJvKTLn43FXdFYlPUU5WxwnPOj5Eea64w20BJMRhPqhgtDAvWz7MKTknAydkW27rf1+r4/NJ2owJAQNSdAIEjAkfsPAHwziCwaRgE3TaGhtxR8MEpSSoGHQEvDTGgjQEvDhCwRoCfjx+lvxBQIY0jD1e75vZjH4a01tU4mxljJoLoHkSVZH0oFKeorSjjT80HrS6jB61F1PD+Drq97o9fprp8zivKKhBKTkQJ2P36xfY2J0seE3BvUMnsTV9IFKeooS17z7gfjezV9EOYvx+Z2beX5n6HObppOdnE5lY3z3onzx/b9E6U7LIaTvFMKRa3UkZ6glC4rSXpyNP3TFqZv3axswgkzMG2Fae7ESUD29xFW30eoIOlBJT1HaEe6R4JlodRg90k3e2HfficNocb5ZsEp6CSzYiDR5jDca8f2TrihWcMd3z0eavFTiZEM1s4dHu51abLUE4+dNU+mj1nKo+DcyTnZWUElPUc7WUm51BP2utQ/1/azQqpJeYvPuB+8Bq6MAVNJTlA6kNMBb2vuJForFKrzt5R9SnJUfg5bN4VNJL/E1xccWZSrpKUp7LWUQbLI6ih4ZMdoJpjAjJybtmkH19AaAOJkkppKeorTX9IHVEfRKi9HGy1vL9pHiTIpJ29HyBdWahcQXH1uSqaSnKG2kYUCgxuowLNPsa2HasPgsBPr+qWqrQ1Ci1bwXefwvyMrXkEHrtpRTSU9R2ghNg4zzIW0BJE+zOpxu6T1tyRKlsuoTMWs7GpuPHrc6BCVaRgv4jkPTbjjxF6TRakkYKukpSjvCmY9In4fIuhDy7wJX/C1fiGX1oMPVJ5gxbFzsbhChulZr3iCVGAnUwpGfI+u39vutVdJTlG4IeybkXAGa2+pQOtBiXDMvHmvy1be0WB2CEgs1byGrViKb9/fbOj5Tkp4Q4vNCCCmEyG537BEhRKkQYp8QYlm74zOFELvaXvuZaPsNE0I4hRDPtR3fIIQobnfN7UKIkraP29sdH9F2bknbtfExPUgZMIRmDz3y7NtVsQiln1qHLWV7KcoYEuO79E1tiyorNGA17oCKf4Uee/aDqDecFkIMAy4CytodmwjcCEwC8oFVQoixUsog8ARwD7AeeBVYDvz/9u48TIrqXPz49+3u2fcNGGaBGQZcUFEYBBQUF1SMEeMS0Rs1mptE4jUao0aDNyYmJj81LjHeYPRqXKIxiZq4XUMg7msEEUVE2WXfZmCYrbc6vz+qgBmYfbq7errfz/P0Mz2nTlW9dWj6nao6dc7LwLeAemNMjYjMBG4DzheRQuBmoBZ7dpSFIvK8MabeqXO3MeYpEbnf2cac/h6TUu1kHQJ188CTDilF9pmfLxvSyuzfrQB4MwGPfe3Rkwn+DdC6FiQVTMB+798YkXCifaYHMKyolC/r42e2idX1dRgEieHEvSrG6l/HpA6K+uDUkZhl4W7geuC5NmUzgKeMMX5gtYisAI4WkTVArjHmXQAReQw4CzvpzQB+6qz/NHCfcxZ4KjDPGFPnrDMPOE1EngJOBC501nnUWV+TnoooEQ+mfBbiSev5Sunl9muvYzHNK+zk17wCwrv7HM/mpuhf6vvwy2Vkp2XQ6I+PM6ywZbAkDa/Ry5wJy2qBpmUQz0lPRM4ENhhjFu93H6AM+0xuj/VOWdB5v3/5nnXWARhjQiKyCyhqW77fOkXATmNMqINtdRTrd7DPMKmsrOz5QSoFvUt4nW0jswYyazD5k2HnOxBuhMyRgNj/2VtW2WeK+VPAlwu+PAjtgsAW2PU+OGc5q+p29TuW7jQFWplSM4Y3VyyO+r56KmBSyUCTXsLyZIKkRH033SY9EZkPDOlg0Wzgx8ApHa3WQZnporwv63S1rQMXGPMA8ABAbW2tXiNRrhFPGhSe0L4w6yBMuAlMGPHl7itPHQSZIzEZNdC4mP9btoR/fhGb4Zw27IyvGeSbwh4y4uP5ZhVxXig5A0mP/glJtx1ZjDEnG2MO2/8FrAKqgMXOZcty4EMRGYJ91lXRZjPlwEanvLyDctquIyI+IA+o62Jb24F8p+7+21JqwBFvVvuE13ZZ2hCk6FSmTbiSr487KSbxrNq+gSPKamKyr57Y6dehyBJW0bSYJDzoR+9NY8wnxphBxpjhxpjh2MlprDFmM/A8MNPpkVkFjAT+bYzZBOwWkYnO/bqL2Xcv8HlgT8/Mc4FXjN2HdS5wiogUiEgB9pnlXGfZq05dnHXb3ldUKuGk+lJ5+OKb+N3M62IyB156Svx0iN7WHHQ7BBUNGSOR7MNitruo/K8xxnwK/AVYCvwDuMLpuQkwC/hfYAWwErsTC8BDQJHT6eUa4AZnW3XAz4EPnNctezq1AD8CrnHWKXK2oVTCm3X8Obxy9X2cO/bEqO5nwdplDM4tjOo+empHS8DtEFQkpQ6CvIlQfHpMdxuJ3psAOGd7bX+/Fbi1g3oLgAPSujGmFTivk20/DDzcQfkq4Oi+RazUwHb8qLEcO+IIfB4vTy2YF5V9WMZi1KAKtjS4P/ZlVX4m0PderyqOeDKg5GzElx37Xcd8j0qpiPF5fTx52S18+pM/ccKocVHZh0T9cfieqczWr6uEkD8ZSi9xJeGBJj2lBjwR4dDSKq45+YKIb3tKzZFs2rUj4tvtLQGyPfE9z6HqRu4EKL0IyZvoWsKDCF7eVEq5a8Lw0aR4fQTDoe4r91DICrN827ruK0bZaaMOQkzkjktFlxEfRlKwJIUQXkLeHLLzJ8fFuK6a9JRKECU5Bcysncbj77/cfeUecv8rynbayGq3Q0h4BgFJwYgPS3yE8RLCQ9AIAYSAZQgYQ7MVpsUK0xIO0xwOsjsUoCHUyq5gKzuDzdQFmwiZ9o+XHFZ0GFeWxsenSZOeUgnkumn/wT8+fY9tjb2fDLc8fxBVxaUYYzDG7sSyrn5rFKLsvWlVpUCD22EMeNs8xXze0kxTOERzKEB9qJWNLbvYGWymMRy96Zs2N8XPPI2a9JRKIIeX1TDnguv44TP3srYXE8KOH3YIn2/5Mq6GHWurOsfqYrwl1VOrW/38cX1s57BL9aQyfXhsH0voiiY9pRLMOWNP5KSDx/PNx37Oc4vf6LKuz+NlUvXhvLnioxhF13tfOeggUk2j22G4zsC+y4/4sMRDGC9hhKARgghBY1+CDFiGgGXRYoVptcK0hEO0WiGWNq6KedxDsko5qmRszPfbGU16SiWg/Mwc/vbd2/jjv//Bl3Wb+deyBby2/MN2E3UOzSsmLyM7rhMewDWTaoGBM3O6nZx8gBcjXizxEsJHAC8hAyFj1wkYCBiD37JoMWFaw2GawyEaQ352hwM0hwM0OvfLGkOtNIcH3sP5xenFfO+I/yIrxb3emvvTpKdUL5lgPfjy+9QTzRgDuxfac/OlDoaU4qj1aBMRLpowHYDZ0y+loaWJHz83h6cXvUJ9025agn427oqvQaU7cvSQNGKV9ILeXFaHfIQsQ8hYBIyF37JotUIELIugFSZgWfitEEETJmgsmkMBmsJ+msP2z5awDpcGMDhzCD+dcEtMhsvrDU16SvWAsSxoXg7BrdC0FPKnYDJrEE8vx6Y0IXv6lLr59vuMaoykgAlDhj31kHjT7arGQPMyCOyArMOR1Lx+HUNuRhb3zbyWu8+7mkfefYm3Vy5m2ea1rNi2nh1NHU9XNGH4aCxjyE7LYHBuIU3+FuZ+9j6BUPS/2IfnFzIkJ5vMDjqwWJ4MHti6HX84RKsVpCUcYHfIT8AKMWvY0eR6vYQBC0PYQIXPIi28r3OP3VPRt/dSoRHBwsPaoIe7Vr8T9WNLBv6wn9UNqxiRFz+DloMmPaV6xr8bwg3gzbB/b3gPgtug4PgDqprmleDNBqsZmlcgRdP2LhNPCuSMwVh+2PmGPYfeHi0roKEAU3KmPZ9euBm2v+RsNLh3OiLLsruDezwemvwtZKVl9OpQUrw+vj15Bt+ePGNv2fOL3+DCP9zMxROm861jzuSgwZUEwyHyM3MOOBNdumk1p/72KtZHsWdnVmoq/7z4LGoyO++x+dGujp8fvHfNuweUZXvT8ImHViuE3wpqn5gY2OmvZ93udZr0lBqIJCMPMsYDYHLGIm0u2RjLDzv+Ad5cCNW3T2RpFRhjDryEGexkLMtQPWx6lH1PyHkhrRQKpuyt0hRo4bPNa6jMH0x+Vk4Ejg7OHHMcC294hBElZfi8XX8tHFpaxQuzfs2xv/4O/lCQsBXusn5f/OYrp3Wa8Oo9xby0Y1OvthfN7viqc69veJWp5Sd0XzGGpO2N7WRRW1trFixY4HYYKoGY1nWw5WmggwSQNtS+f5c/Ze/lUOPfbJ/FhfZ7nk58kH0EZB9mv/dmIp706B9AH7y85B2+++RtrKvfEvFtXzBmDH+cPrLDh+Pv2lzHF03x8fyg6trPJ97KoMzBMd+viCw0xtR2tEzP9JSKAEmvwOQeBQ0d/DHl32i/Mg+CdHsOZUkbghl6GQTrwWoEKwSeNPDlIr7InL1F22mjJzGscEi/kt6UmiNZW7eJQCiERwR/KECjv4U/f/wJ1QUF3DJpKGLsXouWJx2/ZBIw2yJ1CCrKVu5a4UrS64omPaUiJe9YaFxq38trK2cc5I6zp1NpQ0QgtRCIj/nqektEuP6Ub/DWnL4/0C4IX9Z1nDRvfe013lpTxYvnn0CWNFBPDrO/eLPP+1Kxt60l/noHx1dfUqUGMPGkQOlFkDUafIV2J5chFyGFJyC+XHt5gjnj8MkcNHhYn9Y9ZMhwFq37vMs6r69ZzZC7/8iindm8FQezPajeeXvjm4Ss+BooXJOeUhEkvhykeDoMvRjJHY+kxdelnUgTEa6c2uHcz12aMmIMG3dtZ7e/udu6TYEA4+5/hIU7O36sQsWvnYGd1LXG1x8rmvSUigKR5Llz8PVxJ5Gdltnj+qNLq3lr1cfsaund0GJ3/vVVilOH9DY85bLtrfF1iVOTnlKqX0pyCqgpKe9x/YLMHPrSa7wlEOBP8z4my8UJSFXvfbpjidshtKNJTynVLwvXLuvxRLNHlNXw1sq+d3xZvXUriz9pxJtEZ9ID2YnlJ3NuzdfdDqMdTXpKqT6zLItzHriBJn9Lu/LCrFyOrT6C4UWl5KZnISKk+lIoyOz/4xivLFnC8s9CFJiKfm9LRdfuQAMmzsa/0T+XlFJ95vF4OOPwyTzw1t8Jhu1eeh7xcPaRU6koGExuehbHjTySioLBzPv0fX417zFSfSn9Hrtz/idLePXTpXzrnEMjcRgqSsImHHcDTsdXNEqpAeee866m/s55/PXbv2R0aTWWsTiqfBQTqkZz5QnnUVU0lJKcAi6ceBpvX/sgtZUHR2S/YcuixFtBSdoQUhLwcZBEYBnL7RAOoMOQKaUixrIs3lu9hAlVo/F6vB3W2dpQx8Tb/5PVOzZGbL+TDz6YCWML2R3sfIBqFXvfPORSJpUeG/P9djUMmZ7pKaUixuPxcMyIIzpNeACDcguZ+/17qCyM3OMHby1bxpMvf0ZeSn7Etqn6751N8TdNkyY9pVTMjRxUyTvXPcBxI4+K2DYPHjqUhqA+wB4vslKyOX/UTLfDOIAmPaWUK8ryB/Hq1f/DnAuuJzO1fzNJ+LxeJhw5KO56CiYrQZgy9DjKs+Ovh6323lRKucbj8XD5cWczftih/HnhPJ5aML9PszbMOu0E6gKboxCh6ospZcfxtRFnux1Gh/RMTynlunHDDub2s6/k0588yRXHn9urdQ8eOhQrO77Gd0xm6d4Mvlp1ptthdEqTnlIqbuSkZ3HfzGt56KLZjOjh0GbnTh1D0Orfc38qMrwhoSalktzUPLdD6ZRe3lRKxZ3LjvkqXz18Msfc8W1WbFvfZV1LAu1+t5/CMvZ8har3jCE94CWnJZWcJi+ZTR7SmyGtxZDSZJHSHCZlSyvyZT2msRVrdwvhxhZyairxpqRyyPXj3T6CLmnSU0rFpZKcAuZe+RuO+uXFNLQ2dVrvpdfX8mXdFgKhIP5QkJAV5rvTj8PK2pE0iU/CkBH0kh7wkhHwkub3kN7qIXM3pDcafK0WvlYLT0sYb3MIT3MQmoKwuxXT0IrV2EK4oYlQYwsmEMRYHT9UboCA82pr5BX/wdi7bsCbmhrtQ+03fThdqSRhrDAQRjy9/2La8z0hIhgT27OoFz5+kzPnXNfhsqKsPNJTUtmwc9sBy8ryS5h00EiKB4OV5u5D68YSPPjwWD6wvBjLB5aA2O3qCwlHrAqR0mpIaTX4Wgze1jAefxhPSxhpDiKNAWgKYFr8mOYAVrOfcGMzoYYmTNDdiVrF4+Gs9a+TUTrI1Tj26OrhdD3TUypZBLaANxP6kPQArnv2t7zw8VvUDCrniUt/Rn4EBo/uiTMOn8zKnz/Dym3r+e4Tt7F6x0ZGDaqkIDOHZVvWdpjwADbs3MbT79vLvlY7kSHlgCUYy35wXjwWxrK7NYgvhAl7wQjGEvsnYMKCZUE4DKGwwQpbhMKGYNgiFAoTDIcJtvkZCIVoDQZoDQZoDvhpCfhpCrTuHZe0M8Mkm0lz9s1UEXZeA4WxLDbPf4eqi85yO5Ru6ZmeUqpHmgOtfP3B2Xy47nOG5hVz8OBh3PLV71BdUhazGHY07uKEe75HQUYOb6z4KGb7jbZcUplzf+8f1YgXntQUpi/6O3mH1rgdCqDDkCmlIiAzNZ0Xr7iTDb96gfsv/BGvLf+QET85h+Gzz+KWlx5ie+POqMdQlJ3HS9+7C0ise3UNxu92CP0yevblcZPwuqNJTynVKyJC7bBDmHbI0QCsrdvMzS8+yLOLXovJ/isKB3P72Vdw4kEd/iE/MIngzcpwO4o+++Lex3nvWz+mcU3XPW3jgSY9pVSfbNrV/oHwHz5zL69/8WFM9j2h6jDmX/Vb7jr3qribr62vvPmxuUcaDf4dO1n1h2dpXLmu+8ouS4xPi1Iq5oYXlbb7vdHfzIUP38zSTatjsn8R4QcnXcATl/4sIR5NkJxMt0PoH2NoXh//Q8Fp702lVJ/cfe5VDC8qpa5pF8XZ+SzZuIrH33+Z/337Oe469+qYxTFz/DRWbl/PTc//Pmb7jAaTM3Avb+7RsqnjnrTxRJOeUqpPMlLTueHUi9uV/fDkC6ksGBzzWG489RLeXbWEl5a8HfN9R0ooO/4f7O5OYEe92yF0Sy9vKhXnlmxYyfXP/pYP1ix1O5RujSkfSUFWbsz36/F4eOSS/2ZoXknM9x0pwfQUt0Pot4zyyE0MHC2a9JSKc/e88hR3zHuCY+74NlPvmsV7q5a4HVJcKs7O54nLfoqvi1nb41kgbWDGvYc3M4Nh55/udhjd0qSnVJy7+7yr+eHJF1KSU8Dryxdx8r1X0uRvcTusuDR11DhunXG522H0iT9lYH8dlxw7lowh8X+mrff0lIpzOelZ/Pqc7/PLGbNYtX0DKV4fWWkDv9NDtFw37Ru8s+oTnlv8htuh9Io/ZWD3QG1as4H6jz6j4MhD3A6lSwP7TwulkkiqL4WDhwzv8TxzyUpEeOgbszm0tMrtUHql1Tewh4TcvXwNC3/wS6xQiHge3lLP9JRSCacoO49fzpjFhQ//hOZAq9vh9EjrwL6lB8C2Nxfy3jdvICU/l+EXfIUVD/6VUFMzQ06cyMhZF7odHqADTiulEpQxhpE3n8fKbiahjQdVksMdr3vxL43Ng/1uOHPlfLKrK2KyLx1wWimVdESEH5w4k7L8+O9c4YGETngAa558gaa1G9wOQ5OeUipxnX7YMZ3OtxdP6g+YizzxfPzfv6Fu0Wduh6FJTymVuKqKhw6I2RjqrYFx37G/io4+3O0QNOkppbpnjOHRd1/iX8s+cDuUXrv+lG+4HUK3jAie9DS3w4i6j370a7dD0KSnlOqeiFCUncflT97GloYd3a8QR46rOZJUX/wP8eXNSPykZ8KW2yFo0lNK9cwZh08mOy2TO+c/6XYovZKRms4RZfE/q7ckQdLzpLj/lJwmPaVUj/30jP/k1/Of5M8L5rkdSq9MHTnW7RC6lehJz5uexshZF7gdhiY9pVTPzRhzHFNHjmXWn+7giy1fuh1Ojx02tNrtELqXMfCnFuqMLyeLUxc8Q/HEI90ORZOeUqp3vjnpK9Q3N3D0bZfx949edzucHhk9AJKeSU/cpFd96dnkjx7pdhiAJj2lVC9dNGE6F02Yzq6WRs5/6CYef/9lt0PqVkvA73YI3TLexP06Du7c7XYIeyVuKyulokJEuPXMyxERAqEgFz/yM2556aG4HWR4864dacI+OAAAClhJREFUTP+fH7gdRresVPc7efRX7kFVpDvTC3nSUikcN5rjnp/D+N/d7HJk+wz8VlZKxVxF4WDeufZBLn70Zyzfuo6bX3yQL7Z+yWOX3IzHE19/Sxdm5XLokCo+WBvfM8+H0wfu17EvJ4vs4WVMeux2Co48BCscJrizgbSiArdDO8DAbWWllKsmVh/G29c+wDVP/4YTDhrHlBFj2NG0i5Kc+PqiS/Wl8OA3buTOeU+yZNNKFq37wu2QOhRK9cb1F3JGaQnZIyrJqione3gZ2SMqyR5RQU7NMNIHFyOybz5Aj9cblwkPNOkppfqhJKeAP1x0Ez5ffH+VjCguY1BuAVu/qHc7lE6FfB5Xv5C9GelkV5WTVV1BdnU5OSMqya6uILu6gqyqcnwZ6S5GFznx/UlVSsW9eE94AK2hAHfOf5I0X/z2kPTGYLSSjNIS+0ytqtw5U6t03leQUTqo3dlaoor/T6tSSvVTIBTk8LIRXD7la+SkZ3HpY78gbIXdDqudYIqH/g6W5klLtZOYc4aWPaJi3/vqCnyZGRGJdSDTpKeUSnhFWXmcfPB45rzxNyxj8buZ13HbPx9n1Xb353fbw5/qIbMH9VIL8uyztT2XIEdUkjNyGNkjKsksG4zEWUeieKNJTymV8NJSUvnamKncec5VNPqb+WTDSv5w8U0cf9cst0PbK+xcWRSfj6zKUvvSY/W+s7acmmFkVZWTmpfjbqADXL+TnohcCfwXEAJeMsZc75TfCHwLCAPfN8bMdcrHAY8AGcD/AVcZY4yIpAGPAeOAHcD5xpg1zjqXADc5u/yFMeZRp7wKeAooBD4ELjLGJP5sjEqpXpsy0h4CKyc9i4LMHEJWmLyMbHa1NMY8lvyMHKqLh1JdUsaI4jKqi8uYmFVG9a9+QWb5EDwD4D7pQNWvlhWRE4AZwBHGGL+IDHLKDwVmAqOBocB8ERlljAkDc4DvAO9hJ73TgJexE2S9MaZGRGYCtwHni0ghcDNQCxhgoYg8b4ypd+rcbYx5SkTud7Yxpz/HpJRKfIeUVvHBmqX88OQL+ckLD0R8+x7xUFk4mOriMqqLhzKipNxOcsV2kivIyo34PlXP9PfPiVnA/zPG+AGMMVud8hnAU075ahFZARwtImuAXGPMuwAi8hhwFnbSmwH81Fn/aeA+sbsSnQrMM8bUOevMA04TkaeAE4ELnXUeddbXpKeU6tYbKxb1qzNLbnrW3qRWXVzGiJKyvb8PKyolxatna/Gov/8qo4ApInIr0Apca4z5ACjDPpPbY71TFnTe71+O83MdgDEmJCK7gKK25futUwTsNMaEOtjWAUTkO9hnmFRWVvb6QJVSiWXVto387o1nOl3uEQ/lBYOoKSlnREkZVUVD9ya4quKhFGXlJUUX/0TTbdITkfnAkA4WzXbWLwAmAuOBv4hINdDRJ8F0UU4f1ulqWwcuMOYB4AGA2tra+BwkUCkVM1NqjuSJD+buS2R7klqJc7ZWWDogZlxXvdNt0jPGnNzZMhGZBTxr7JFm/y0iFlCMfdZV0aZqObDRKS/voJw266wXER+QB9Q55VP3W+c1YDuQLyI+52yv7baUUqpLXx93EjPHT3M7DBVj/X2g4+/Y99UQkVFAKnYyeh6YKSJpTg/LkcC/jTGbgN0iMtG5X3cx8JyzreeBS5z35wKvOMl0LnCKiBSISAFwCjDXWfaqUxdn3T3bUkqpLsXbwNgqNvp7T+9h4GERWQIEgEucZPSpiPwFWIr9KMMVTs9NsDu/PIL9yMLLzgvgIeBxp9NLHXbvT4wxdSLyc+ADp94tezq1AD8CnhKRXwCLnG0opZRSHZJ4nQMrmmpra82CBQvcDkMppVQUiMhCY0xtR8v0/F4ppVTS0KSnlFIqaWjSU0oplTQ06SmllEoamvSUUkolDU16SimlkoYmPaWUUklDk55SSqmkoUlPKaVU0tCkp5RSKmlo0lNKKZU0NOkppZRKGpr0lFJKJQ1NekoppZKGJj2llFJJQ5OeUkqppJGUk8iKyDZgbSeLi4HtMQxnINO26jltq57Ttuo5bauODTPGlHS0ICmTXldEZEFnM+6q9rStek7bque0rXpO26r39PKmUkqppKFJTymlVNLQpHegB9wOYADRtuo5baue07bqOW2rXtJ7ekoppZKGnukppZRKGgmd9ETkWhExIlLcpuxGEVkhIp+LyKltyseJyCfOsntFRJzyNBH5s1P+vogMb7POJSKy3Hld0qa8yqm73Fk3NTZH3HsicoeILBORj0XkbyKS32aZtlUEiMhpThuuEJEb3I4nWkSkQkReFZHPRORTEbnKKS8UkXnOv/E8ESlos07UP2PxTES8IrJIRF50fte2ijZjTEK+gApgLvbzeMVO2aHAYiANqAJWAl5n2b+BSYAALwPTnfLvAfc772cCf3beFwKrnJ8FzvsCZ9lfgJnO+/uBWW63RxftdArgc97fBtymbRXR9vU6bVcNpDpteqjbcUXpWEuBsc77HOAL53N0O3CDU35DrD9j8fwCrgGeBF50fte2inabux1AFD9MTwNjgDXsS3o3Aje2qTPX+bCUAsvalF8A/L5tHee9D/tBUGlbx1n2e6dMnDp7EskkYK7b7dHDNvsa8IS2VUTbtN0x7d+uifwCngOmAZ8DpU5ZKfB5rD5jbrdBN+1TDvwLOJF9SU/bKsqvhLy8KSJnAhuMMYv3W1QGrGvz+3qnrMx5v395u3WMMSFgF1DUxbaKgJ1O3f23Fe8uw/5LEbStIqWzY09ozqW0o4D3gcHGmE0Azs9BTrVYfMbi2T3A9YDVpkzbKsp8bgfQVyIyHxjSwaLZwI+xL9sdsFoHZaaL8r6s09W2XNFVWxljnnPqzAZCwBN7VuugfsK3VRQkwzG2IyLZwDPA1caYBucWU4dVOyiL9GcsLonIGcBWY8xCEZnak1U6KEuKtoq0AZv0jDEnd1QuIodjX/Ne7PxnKwc+FJGjsf+iqWhTvRzY6JSXd1BOm3XWi4gPyAPqnPKp+63zGvYlhHwR8Tl/XbXdlis6a6s9nBvZZwAnGed6B0naVlHQWTsmJBFJwU54TxhjnnWKt4hIqTFmk4iUAlud8lh8xuLVscCZInI6kA7kisgf0baKPrevr0b7Rft7eqNpfzN4FftuBn8ATGTfzeDTnfIraH8z+C/O+0JgNfaN4ALnfaGz7K+075zxPbfboYv2OQ1YCpTsV65tFZn29TltV8W+jiyj3Y4rSscqwGPAPfuV30H7zhm3x/IzFu8v7AS0556etlW029vtAGLwgVqDk/Sc32dj93z6HKeXk1NeCyxxlt3Hvgf3050v5hXYvaSq26xzmVO+Ari0TXm1U3eFs26a2+3QRfuswL6+/5Hzul/bKuJtfDp2T8aV2JeUXY8pSsc5Gfsy2cdtPk+nY99H+hew3PlZ2GadqH/G4v1F+6SnbRXll47IopRSKmkkZO9NpZRSqiOa9JRSSiUNTXpKKaWShiY9pZRSSUOTnlJKqaShSU8ppVTS0KSnlFIqaWjSU0oplTT+P/RI3VysqtXtAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_29_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "counties.plot(column='POP12_SQMI',\n", + " legend=True, \n", + " cmap=\"RdYlGn\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[50,100,200,300,400]},\n", + " ax=ax)\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we are customizing our plot, we can also edit our legend to specify and format the text so that it's easier to read.\n", + "\n", + "- We'll use `legend_labels_list` to customize the labels for group in the legend." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Population Density per Sq Mile')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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o/fkVOJc8jOs/f8e55GHf1288F1G9Q4cOZfz48UyaNIny8vKO47W1tZx11lmUlZVx1llnUVdX16NsRUUFzz//fMjXPPfcc8nKymLevHldju/Zs4cZM2ZQVlbG5ZdfjtPpG7WKdRojFfSixFFTR8O2ryKuJ/+UyaSVllD13ic0frFTh5YlN2E24WpoinczFEU3zjeew/WfxeDslgHcacf1n8URB77Vq1ezefNm1q9f33HswQcf5IwzzmDnzp2cccYZPPjggz3KhRv0fvzjH/Pss8/2OH7XXXdx5513snPnTrKzs3nqqaeArmmMFi1axC236LsusTsV9HQQaHPWgyve67gXFC7NYqbtaA3Nu/b2f/IJQvPvJKEoxwPZ1ozrzSV9nuN6cwmyrUXX6y5btoz58+cDMH/+fJYuXdrjnAULFvD+++8zadIkHn30Uex2O9dddx3jx49n8uTJrF69OmDdZ5xxBunpXTO+SylZtWpVR96+zteMdRojFfR00D7hQUqJ1+sFYPfif0ZUpzk7A0teNs07VcDrTBjUP1nl+OHe/H7PHl53Tjvuze+FVb8QgrPPPpupU6eyaNGxvH1Hjhzp2EuzqKiIo0eP9ij74IMPMmvWLDZv3sydd97ZsUPLli1bWLJkCfPnz8du76ftfjU1NWRlZWE0+qaRdE5VFOs0RuodRCcdMwKF4Mi76ziy6uOI6nPWNZI6dJAeTTu+JOlyBun/MKQoncnG4Ca7ycbAy5/688EHH7Bx40ZWrFjB448/znvvhRc8AdasWcPVV18NwKhRoygpKek3GW27vlIVxTqNUXK+gyQoIQRCCHKmj6f05it0qU/pSjMm6fBmhEPdyvFJZOQGeV5OWPW3pwMqKCjgG9/4RkfqoMLCwo4hxEOHDlFQ0P9ymkDBKVh5eXnU19fjdruBrqmKYp3GSAW9KDDZbEz706+Y+/nrFJ03O97NOa6IJL2n175eT1E6M06aBWZr3yeZrRgnhf4+0tLSQlNTU8fj//73v11SBz399NMAPP3001x00UU9yqenp3eUB5g9ezbPPeebVLNjxw727dvHyJEjg2qLEII5c+bwyiuv9LhmrNMYqaAXJUIIssaWMfG+O8Ouw9nLOrUTmbu5FUOqLd7NCJ3q6SkBCFsaprO+3ec5prO+jbClhlz3kSNHOO2005g4cSLTp0/n/PPP59xzfXn5FixYwJtvvklZWRlvvvkmCxYs6FF+woQJGI1GJk6cyKOPPsqtt96Kx+Nh/PjxXH755fz973/HYrH0KDdr1iwuvfRS3n77bYqLi3njjTcAeOihh3jkkUcoLS2lpqaGG264AfClMRo+fDilpaV897vf5U9/+lPI32soRCRd1mRVXl4uO0/fjSZHbT2v5s4Iq2zaiCHJuSYtysy5WThr6uPdjJBMe+Jeyr4X+ZC3kjy2bdvG6NGjgzrX+cZzvlmcnSe1mK2Yzvo25nOujFILjw+BXmchxAYpZXmg89WOLFFm8m/w7LGHvrDcNqhQBb0ApCf5JoWo+7NKX8znXIlp9sW4N7+HbKxFZORgnDQ7rB6e0jcV9KJMMxgYetWFfPXkyyGXrVn3WRRalPykxxPvJoQuSWedKrEjbKmYTj4v3s047un2myiEMAghNgkhXvd/nSOEeFMIsdP/d3anc+8WQuwSQmwXQpzT6fhUIcQW/3N/EP6Px0IIixDiRf/xtUKIoZ3KzPdfY6cQYr5e34+eis4+NeQyaaUlmFJTotCa5JeM0/9VR09REoOeHz+/D2zr9PUC4G0pZRnwtv9rhBBjgCuAscC5wJ+EEO1T8v4M3ASU+f+c6z9+A1AnpSwFHgUe8teVA/wSmAFMB37ZObgmigHzTkczm0IrJASOmp774SlqJqSiKOHTJegJIYqB84HOGVMvAp72P34auLjT8ReklA4p5R5gFzBdCFEEZEgpP5K+2TXPdCvTXtcrwBn+XuA5wJtSylopZR3wJscCZcIwms0h54gTmkCz9pwZpYC3LbhdIBKK6uopSkLQq6f3GPAToPO4U6GU8hCA/+/21Y+DgP2dzqv0Hxvkf9z9eJcyUko30ADk9lFXD0KIm4QQ64UQ66uqqkL9/iLSfl8vFJ42u66585T4ku4kvA+pxJT0OpBNW5D1H/n+9qqsKtEQcdATQswDjkopNwRbJMAx2cfxcMt0PSjlIilluZSyPD8/P6iG6ilt+OD+T+qk7WAVeSdPwpCm7ut1oXpMynFINnwMlU9A7RvQ8IHv78onfMcjcP3111NQUNCxKL1dX6mFHnjgAUpLSxk5cmTHGrvuHnvsMVpbW0Nqy5VXXsnIkSMZN24c119/PS6XC+g7tdDKlSsZOXIkpaWlATNBhEOPnt6pwIVCiArgBeDrQoh/AEf8Q5b4/27f0bQS6BwBioGD/uPFAY53KSOEMAKZQG0fdSUc28DQsmZLt5vqDzeREmK5412ow8QJI1nbrUSdbPgY6teAdHV7wgX1ayIKfNdeey0rV67scby31EJbt27lhRde4IsvvmDlypUdC9K7Czfoffnll2zZsoW2tjaefNJ3N6y31EIej4fbbruNFStWsHXrVpYsWcLWrVtDfQl6iPg3UUp5t5SyWEo5FN8ElVVSyquA14D22ZTzgWX+x68BV/hnZA7DN2FlnX8ItEkIMdN/v+6abmXa67rEfw0JvAGcLYTI9k9gOdt/LOHklI/r/6QALHkJNy8nrpI1y4LQVA9V6Ul6HdCwtu+TGtaGPdQ5e/ZscnJ67tvZW2qhZcuWccUVV2CxWBg2bBilpaUd+3W2+8Mf/sDBgweZM2cOc+bMAWDJkiWMHz+ecePGcddddwVsy9y5czv2J54+fTqVlZUd1wyUWmjdunWUlpYyfPhwzGYzV1xxBcuWLQtYdyiiuU7vQeAlIcQNwD7gUgAp5RdCiJeArYAbuE1K2f5R4hbg74ANWOH/A/AU8KwQYhe+Ht4V/rpqhRD/C3ziP+/XUsrwtiOPMoPVgjknE81ixmA2I0xGNJMRQ4oVg8U/0UUI6rfswNVp+zFPqx1hNCL9G7We6DSzCa/T1f+JiUYNyyqBtOzo2cPrTrqgdQekjdftsr2lFjpw4AAzZ87sOC9Qmp877riDRx55hNWrV5OXl8fBgwe566672LBhA9nZ2Zx99tksXbqUiy++mEBcLhfPPvssv//97zuuGSi1UKDja9f28wEhCLoGPSnlO8A7/sc1wBm9nHcfcF+A4+uBHl0iKaUdf9AM8NzfgL+F2+ZYcbe04qxt6Pc8S2Fel69bDx5JzsXYUZKsG04na7uVKPM0B3mevklkexNOmp9PPvmE008/nfa5EldeeSXvvfder0Hv1ltvZfbs2cyaNavPa0Yr5VByjhUloZa9wd1q9HberkwIjDab2qy4k6Qd3lQdPSUQQ1qQ5+m7HVlvqYXCSfMTyv7N9957L1VVVTzyyCMdx3q7ZrRSDiXnO0gSagoyA7rBYsKQloIwGskcfxKth3pmND6hqeihHE9STwLRz8YVwgQpJ+l62d5SC1144YW88MILOBwO9uzZw86dO5k+fXqP8p3TDs2YMYN3332X6upqPB4PS5Ys4Wtf+1qPMk8++SRvvPEGS5YsQes0sau31ELTpk1j586d7NmzB6fTyQsvvMCFF4a29CsQtfdmjNSu/zyo8+xHj92SbNiyA4PFjKHTvUBLXjZNX+3rct/vROJpTcKF6aCCtRKQ0CzIzBm+2Zu9yZyB0MLbqOLb3/4277zzDtXV1RQXF3Pvvfdyww03sGDBAi677DKeeuophgwZwssv+/YGHjt2LJdddhljxozBaDTy+OOPYwgwNH/TTTdx3nnnUVRUxOrVq3nggQeYM2cOUkrmzp0bMD/fzTffTElJCSeffDIA3/zmN/nFL37B3LlzWb58OaWlpaSkpLB48WIAjEYjCxcu5JxzzsHj8XD99dczduzYsF6HzlRqoRhZNvTrtOw90P+JQTDnZAZ1f1AvtoGFtB2phgS4t2hMT8XdFJv7G3o6+dnfMOyqnm8EyvErlNRCsuFj3yzOzpNahMkX8DJn9l5QUamFElHboaO6BTwAa1FBTIOeMdVG/imTadl7kNZ98V0GqSaEKMcjkTkTmT7ZN0vT0+K7h5dyUtg9PKV3KujFQPVafVMEmWK0S0v+rHKk10vdxq007azAkpdNypCBcQ18ar2bcrwSmkXXZQlKYCroxcDR9z7p/6QQuJtbfTt8RDHFjiUvh4atu7pkKDdlZdC8K7gJOdEijEna01P39BQlIajZmzFw9J11/Z8UgoYvdmLODHKqc4iMGWnknzoFr9vdJeABWAtzo3LNUCTrNmQqc7qiJAbV04sy+9Ea6j/9Utc6UwYX4W4Jbd+7YJhzs8gaW9Z7zzQBkreqe3qKokQiOT82J5G9L/xH90zfrfsPkTm2TNc6LYV5eJ2uPodinY3xnzWZrD09RemPRzpocGyhxv4RDY4teKRKLRQN6h0kynY/vTQq9dZt2oqWagu7fMqQgeRMG0feKZNJP2koQhP9LgUwhJr9PRqSdCLLibg0SAlejf1jdjc8wZG2N6ixf8CRtjfY3fAENfbwMyzs37+fOXPmMHr0aMaOHdux1yXEJ7XQDTfcwMSJE5kwYQKXXHIJzc2+LdhinVpIDW9G0ac/f4y6jV9Epe6cKWNxOxwITaN51z4cVcHvs507fQIN274KeRamu6WVwjNPQTpdSGTXjIYSkBLp9SK9EqQXj92JdHuQHv8frxev2+M75vbgdbuRLrfv7/bj/fSKNTW8qRxnauwfU2PvuThd4uo4nmsNfa2e0Wjk4YcfZsqUKTQ1NTF16lTOOussxowZ05FaaMGCBTz44IM8+OCDPPTQQ11SCx08eJAzzzyTHTt29Fig/thjj3HVVVeRkhL8TPJHH32UjIwMAH7wgx+wcOFCFixY0CW10Nq1a7nllltYu3ZtR2qhN998k+LiYqZNm8aFF17ImDFjQn4turwuEZVW+lS3MfLcT73xOJzU+pdCpI0YTMao4VS9H9yCe0+bPawF3k07Kmg9cARbUUF0Z3EaDGhGA8JgQBg0hMGAwWJCGI14XC5ShhQhhAYGQXppCS0Vx9ZACk0DTfMtbRDtfwuEpvkmk7R/LXx/I/xxu32iSftz7T2zTscDEu3/O/YJoP0r32OJxJdlQ1G680gHtfa+MwfU2teSZZmMQYT2b6ioqKgjk0J6ejqjR4/mwIEDjBkzhmXLlvHOO+8AvtRCp59+Og899FCvqYXad1GBrqmF8vLyWL16NUuWLOH+++9HSsn555/PQw891KM97QFPSklbW1vH5K7eUgtVVFR0pBYCOlILqaCXwAwp1qjV7aw7tji9+av9tFQcJHvqWOo29N2zNGakBb0PaCCeljY0izns8sFdxIO32+4vvSVfSRlYSNOOiui2Rwdl37si3k1QElCzcwey13/dPhIXzc4dZFrCX8NXUVHBpk2bmDFjBhC/1ELXXXcdy5cvZ8yYMTz88MMd14xlaiF1Ty+KbIOLolJv/uxpPd7opceDdPWfcy99xBA89ghukAuB43B1+OX1pu6VKUnMLYNLLeSW4U8ia25u5lvf+haPPfZYR2+rN5GmFjIajR2phQJZvHgxBw8eZPTo0bz44ot9XlOlFkpCkx/6ESXfPl/3er12B6klg3ocr/9sO1kTRpIzbTxZk0aTNWEk2VO7btDatGsveadMxpSZHtI1U0oGkj97GqnDinHU1PVfIEaSJeSpiSxKIEYR3HpbowgvtZDL5eJb3/oWV155Jd/85jc7jscrtRCAwWDg8ssv59VXX+3zmiq1UBIymEwMvTLyVBjd1az7jJSSwD/8+s+2U/vJFuo3b6P+s+3Uf7qd3BkTESbfSLa7qYXqDzeRNXFkSNdMHVxE1Xuf0LJ7f/8nx5IKJkoSSzOfhKDvWdECE2nm0FMLSSm54YYbGD16ND/4wQ+6PBfr1EJSSnbt2tXx+N///jejRo3quKZKLXQcqVn7aVTqdTcHN9wh3W5q1n5K1qTR1G/e1nHcfqQmpOvpvdZQURQwCAs51hkBZ2+2y7HOCHkSC8AHH3zAs88+y/jx45k0aRIA999/P3Pnzo15aiEpJfPnz6exsREpJVa2kpMAACAASURBVBMnTuTPf/4zgEotFAuxTC3031OvoPrDTfpWKgSpQwfRsqcy6CKZ409Cery4G5sxZqRiycuhKsg9QQ02K7nTxuu+h6ge8k6eRPVHm+PdjH6d/I/fMiwKvX4lcYWSWqjG/jG19rVdJrUITORYZ4S1XOFEolILJRBHTR01H+vf08s/ZTJVH2zs/8ROGrbs6Hbkq6DKpQwpwlaUn5ABD9TopnJ8yLXOJMsymWbnDtyyBaNIJc18Ulg9PKVvKuhF0cGV70c0LCgMBqyFuaSVloDXS9UHGzHnZOJxOHVsZT9tMBqp0Tk1kq6kGnZVjg8GYYloWYISHBX0omjv869HVF56PJiyMjqGIXNnTsTrcFG7/nM9mtc/IbAfSaDlCYF4k6Srp7qkipIQVNCLkubd+0MeggzEkpfV8TgaQ6V9MWel42rWP5uDnpImliRNQxXl+KaWLETJkXfXccGu/zJ76eNY8rLDrqel4gCmrL4XlEaNpiV8Kh/NlNjta6dinqIkBhX0oqBxxx7QNKx5OeTPKo9o267WfYfImhDamjq9WAtz8Uaye0sMJHpQ7qCintKPNncraw6+z3/2vM6ag+/T5k7sUZZkpYJeFOx/5Q32vbSc975xG6+PPJe2A0ciq7DbPpSxkD+rnMatwc3wjKtkyUiugp7Sh+UVr3PXBz/m2S+f5rU9S3n2y6e564Mfs7wi/HkBdrud6dOnM3HiRMaOHcsvf/nLjufikVqo3e23305a2rGdaGKdWkgFPZ0deWctzRUHOLT8PSqXvoWjOvItu+JxXy3iQK10oRb3K71ZXvE6y3YvxeHpOqri8DhYtntp2IHPYrGwatUqPv30UzZv3szKlSv5+GNffr721EI7d+7kjDPO6AgonVMLrVy5kltvvRVPgA/d4Qa99evXU19f3+VY59RCixYt4pZbbgHoSC20YsUKtm7dypIlS9i6NfLMNSro6Uh6vWgWM+4WfYNUKLnyTjhJ0tFTPT0lkDZ3Kyv3rujznJV7V9Dmbgu5biFER4/K5XLhcrm6pPOZP38+4EsttHTp0o7jgVILddY5tdCcOXMAWLJkCePHj2fcuHHcddddAdvj8Xj48Y9/zG9+85sux3tLLbRu3bqO1EJms7kjtVCkVNDTUev+Q7ibWiJeqtCdq7G5Y+/MaBMGA/mzy2lOtD02e6FinpLMNhzd0KOH153D42Dj0Q1h1e/xeJg0aRIFBQWcddZZQaUWCpTmp7M77riDgQMHsnr1alavXt2RWmjVqlVs3ryZTz75pCOIdrZw4UIuvPDCjuu2CyW1UPe2hEMFPZ20Ha7i8Fsf4g0ivU+o3M2tmDKC2409UvmnTqbqvdhs0aYHmSxhT0U9JYAGR0P/JwENzvr+TwrAYDCwefNmKisrWbduHZ9/3vca32ilFjp48CAvv/wyt99+e9DXjFZqIbVOTyeb7/ode55ZinVAflTqd7fao1Jvd0kTRPySpbXqnp4SSKYlM7jzzFn9n9SHrKwsTj/9dFauXMm4ceM6UgsVFRXFJLXQpk2b2LVrF6WlpQC0trZSWlrKrl27er2m0+lUqYUSVcPWXVQ8928A7IeronINb1v0g17OtPE0fLEz6tfRU7L0n6IxAqAkv6kFU7EY+t5f02KwMKVgash1V1VVdUwaaWtr46233uqSzieWqYXOP/98Dh8+TEVFBRUVFaSkpHSkGlKphZLQpp/8FhnFZQWayRiTN02D1YKzJrxhFKVv0fz3oSQvmzGFc0vOY9nunvfA2p1bch42oy3kug8dOsT8+fPxeDx4vV4uu+wy5s2bBxDz1EJ9UamFYkDP1EJ1m7exYvLFutTVG2N6Ku6m4PLnRSJ7yhjqNkY+JTiW8meXJ8U9yCmP3s2o/3dtvJuhxFAoqYWWV7zOyr0rukxqsRgsnFtyHnOHzotWE48LKrVQjG17ZHHUr2FMscUk6DV8vpPcaeOp+WRL1K+lm2T5zJYs7VTiYu7QecwpPoONRzfQ4Kwn05zFlIKpYfXwlL6poBeBtiPV7HthedSvo1lMUb8GgNfpwmBLrvxdyTJSkSztVOLHZrRx6sDT4t2M456ayBKBbb99Cq/L1f+JEdLM4e/dGYr0shJksqTqaZcssyJV0FOUhKB6emGSUrJr0YsxuZZmju6PSWgaeadNpXH7Hpp27o3qtfSWLD0otWRBURKDCnphatl7ICb32QA0U3SHNzPGjOhIVJt0kiToJU2PVFGOcyrohWnXopdidi1hjE76HEtBDrYB+bTsOxSV+mMiSYJe0g0bKzHX0NbMKxtXcaihmqLMPC6Z8nUybbHZielEou7phUFKGdMZjloUgl7GmBFYcrOp/2w7rvpG3euPFa8rSda/JUlwVuLjvhWLGXT3Bdz4j/v5+b8XceM/7mfQ3Rdw34rozw6H+Kf7iSUV9MIghOCkW74du+vpnCg1e/JohKbRuC0J8uX1w5nEAVtRwBfwfvbaX2hxdM2k0OJo42ev/SWiwNc5T15vEiHdTyyp4c0wDf7m2RRffCaVS9+K+rWEps9nk4zRI7DkZVP1fgIs5tY0UocOAikRQtC8ez8Zo4ZjLcjFXl1H41bfFkUpQwdiTLHhdboRmgApkV4vXrcHg9kMBg1zTqb/uERKkG7f7jVSSrxOV1yS8CpKMBramnngjWf6POeBN57h9tMvI8OWGnL95eXlzJgxgxtvvJE5c+b02LC5Pd3P888/z7/+9a+O472l+6moqOhI9wN0pPsZM2ZMyG2LFxX0IpA1cVRMgh5a5Nsqm3MyY96zs+TlYC3Kx5yV5lucrWm4m1owpafQvPcgLZ3SFxnTUmj+ah+NX+5GGI2++41FBTR8vhPp8WAtysdgtdCypzKkNuSfNpWqNeGlZdFVsmR4V2LqlY2revTwumtxtPHKplVcf8oFIde/Y8cOVqxYwcKFC7ntttu4+uqrufbaazs2btYj3c/atWtDblc8qaAXgZSBBTG6UuRvmKklg3DWBpfGpLv00hKsAwtoO1RF8669Ae9PmXOz0MwmEIL00iF42hzUb96Gozq4BLjuTtnhpduN42gtjqPHytoPVZF+0tDQG6+CjZLADjVU63pedwaDgXnz5jFv3jyqqqq4++67GTJkCB9++CHFxcW8/PLLvPPOOz3KxTrdTyypoBeB7CmJ36U3ZaSROa6M6g83dTkuzCak20P68MFYC3ORgKu+qUuWBdugQlJLBlKzbgtNu3zr96xF+SAl1oJcNLMJoWk4ahuwH67CWVOPOTeL6jUbo7IuTTPHZmcaRYmVosw8Xc8LpKGhgRdffJHFixdjMpl46qmnmDBhAm+//XbCpPuJJRX0InBkday69eHP/EsrK+kR8ACyxpXR8PlOmnbt7Qhowmgkp3wcnjYH5tws6jZt7VHWfsiXOsl+OPAnz2hmaTDnZGItyu9og6Iku0umfJ3vv/xon0OcqRYbl0z+elj1X3XVVXz00UdceumlPPPMM5SVlXU8157up11aWlqXdD8LFy7kiiuuYO3atR3pfvLz8zvS/QwaNIgXXniB559/Pqy2xYsKehGo3fBFbC4UwRovzWjEOrCA9NIS3E0tGKxmpISajzf3OFe63dSu7zuzcjy1Z1NIGTyAtGGDOZqsC+oVxS/Tlsbd51zDz177S6/n3H3ONWFNYgG47LLL+Pvf/47RGNpbfazT/cSSCnoRmPTAD2j4fGfUE69GMlToamjC3dSSvDuuBNC6/zD2w9XklI+ndn0/6yWDuN1gTE8lZ/IYHHUNeFrtOOsawr7/qSihuue86wDfLM3OPb5Ui427z7mm4/lwhJJ0tbm5ueOxEILHH3884Hlz585l7ty5Ybcp3lTQi0DasMGc+sIjvDX7Kpx10XuTjGQ3j8Yvd5N+0lCsk0dT/eHmjun8yS6trIS6T7cFcWbfUU+YjAiDoUuv0Togj7xTJmOvqqN5Z0VkDVWUINxz3nXcfvplvLKp044sk78edg9P6Z0KehHKGncSZ77/HO/O+x4tFQeic5EI15k17aigaUcFeadOofqDjf0XSAKNW78iY/SI/pdhSOmbwRlg1lnqsGI0o6HHJtv2w9XYD1ejmU1kjj+Jhi079Gy6ogSUYUsNa1mCEhq1I4sOssaWcc66Vyg6b3ZU6ve6dVpcfRxtepwxclhQ6w6r3l9PTvk4jBld9zBMHzkcBH1mlfA6XTRs2UH6yGGYszMia3CSTetWlOOVCno6sebnMOvVP4a3lqwfeuXsEyHezE5klsLcoM+t/WQLacMHk3/aVMy5WQBYC3No2R3cQvem7XuwDsgPq53tVMw7MSVL6qtkFc7rq4Kejow2K7P+uZDCr8/UtV6vU5+gV/fpl7rUE0/5s8vJHD+yYyZnsOo3b6NqzQbMWRnknzaVtgNHQirf+OVuMsefhGYJL6Gv3vunKonParVSU1OjAl+USCmpqanBarWGVO74+eifILLGlnHG209zdM163rvoNpy1ka9b8zidkTdMCDLKhlK7IXGXJATD1dBMw5btYZdv/mofzV/tC72glDRs2UH+7Gm+bc1CHSpWXb0TTnFxMZWVlVRVqXWl0WK1WikuLg6pjAp6UVJwWjmTf/cT1l7/04jr8tp16OlJiSE1tE9EiWjCj2YivSfz/vzYpFzpruq9T8ifPY3qjzYhXcfHTFglOkwmE8OGDYt3M5Ru1PBmFJVcdp4u9XjsDl3qiWSRe7xlTypj1tNXM2CGkdwJOaSPiNW+pz1VvfcJ2ZNGkz5yGIYUG5rV0m8ZvTJlKIoSGdXTiyLp8WKwWfG02SOqJ9Ly7fS6Nxgro+88n2EXDkB6JBlDNTSjb4jQZm5k7A/P5uNb/xG3ttV2SiJssFrInTkp4C43HdTopqIkBPXxM4pMGWl8q2YtA88/PaJ6vA4d7ukB9Z8n13qznDHZZA7TyCo1dAS8dpbsxBmq9dgdNH+1j4LZ5fFuiqIo/VA9vSgz2qzM/Nv9LJ98Mc7aBow2C5rVgmYxoZnMaGYTmtGAZjIhjAaEQUNowpfGA4HQNI6+o8/G1gabFU+rPr3GaBt6xdcYfEbgwNZam8HaO/4a4xb1zVFVi7espPcT1EQWRUkIKujFgLUgl1kv/963XVmI9+c0m5X806YivV6k24PX40G6PUi3G6/Tjdfpwuty4XE4fY/tDry9TLBwNzaTNnwwzZ2StyaqST8q82VK70Yi2Pr4euxVjXFoVd+adlTEuwmKovRDBb0YyT9lCiXfmUfFs8tCKudts4eV+VuzmNEsZgwmE8Js9PcsTaQMLkrYoCc0Dc1qIb10ENac3s4y8dUza2LZrKA56xopmD0tcPYHtVZLURKCCnoxNO6em6l6f3309ujsxOtw4nU46d7nM6amRP3a4ZrxxysYdn4q0isD9vIABE6GfKOcipdilcsweNLjof7zHRgz0nE3NnV9Ug1vKkpCUBNZYihj5HAmPfijuLbB09qWkNuRDb/m6wyd6wvIvQU8AImNyuWfxqpZIXPWNpBxUgl024FFLVlQlMSgfhNjbOB5szEEsa4rGmwDC2nauTch0wsNPH0QIqjekIfUwb2OfSaE2vWfkz1pVJdjwqB+1RQlEajfxBgzZaQx+7U/Y8nLjvm1zblZsb+3JATWwlxMWekYUmw99qA0Z2dQfMFMCsuD+yDgarXhqG+NRkt11bxrH+aczI6v1d6bipIYIh7nEkIMBp4BBgBeYJGU8vdCiBzgRWAoUAFcJqWs85e5G7gB8AB3SCnf8B+fCvwdsAHLge9LKaUQwuK/xlSgBrhcSlnhLzMf+Jm/Of8npXw60u8p2orOOpVzN/6LZUNOj+l1zdnpMb0ewOCLT+G0R8d0OSa90rdYW/o2jdVC6AWZUxrInz6c/csSOy+gx+7AnH0s6GlGFfQUJRHo0dNzAz+UUo4GZgK3CSHGAAuAt6WUZcDb/q/xP3cFMBY4F/iTEKL9HeHPwE1Amf/Puf7jNwB1UspS4FHgIX9dOcAvgRnAdOCXQojYd6HCkFI8AGtB8Olx9NB24GhMr4cQjPneyJ6H/esQhSZCCnjtTv39DE59ar4eLYwaS24W9sPHNhoWJlMcW6MoSruIe3pSykPAIf/jJiHENmAQcBFwuv+0p4F3gLv8x1+QUjqAPUKIXcB0IUQFkCGl/AhACPEMcDGwwl/mV/66XgEWCt8NoHOAN6WUtf4yb+ILlEsi/b6iTQhBypAi7EdrYnbNtiPV5Ewbj6+L5csm3r7uz+ty+db5OVx4HA48DifS5Y5sNxgpcTZ6AH17OQI3g8+wUvKtaex9NcDygASk7ukpSmLQdRqfEGIoMBlYCxT6AyJSykNCiPYdggcBH3cqVuk/5vI/7n68vcx+f11uIUQDkNv5eIAy3dt2E75eJEOGDAnr+0t2nuZWmnftxVkX2sJuzWREs1oQBgMGiwnNbEYzGX07yBgNaAYDmtncaUcZzTdFX8C+lUcwpgwkb5zegc/LzN+dQtUne2jdV61r3dGg7ukpSmLQLegJIdKAV4H/J6Vs7GMmXqAnZB/Hwy3T9aCUi4BFAOXl5QmxUjhj9Ahq18c2v511QH7IQc/rcnfs8hLqltVV78HA2Vejd28PQBOtzFt9DUunLcJZ26x7/ZHQzF2HM/tahqEoSuzoMuYihDDhC3jPSSn/6T98RAhR5H++CGi/oVQJDO5UvBg46D9eHOB4lzJCCCOQCdT2UVdSmPLIAmxF+TG9pjcOyxU++N6LHPpYn02zuzMYmjjvzZuwFmUFdb4wGsmfVU76yGEYM6I3scc6oOvPVa3TU5TEEPFvov/e2lPANinlI52eeg1on20wH1jW6fgVQgiLEGIYvgkr6/xDoU1CiJn+Oq/pVqa9rkuAVVJKCbwBnC2EyPZPYDnbfywpWPNyGHr1RTG9pquuicxxZTG9ptfh5KPbX2PXK9HZLzMlt4lzl9+AOSet33PzTplE1fvradq+B0teJmkj9B/qthTk4Oq2I4sa3lSUxKDH8OapwNXAFiFEe0KxnwIPAi8JIW4A9gGXAkgpvxBCvARsxTfz8zYppcdf7haOLVlY4f8DvqD6rH/SSy2+2Z9IKWuFEP8LtM9m+HX7pJZkkV42NKbXc1TXkjF6eEyvCeCoquPzR1ZResnFUanflt3IqYu+w+pLFvV5Xuecgi27K8maNJr82eXgldRu2oqnpQ1jRhoGkwkpvUiv9C2xAAxmEwabGRC+DcA9XvB6OuqTHolEkjF6BE0793b09sxZ6WjmxNsFR1FORHrM3lxD7ykyz+ilzH3AfQGOrwfGBThuxx80Azz3N+BvwbY30RgscZjKHofNj3OmjmT8D2ZE9RoDyiWT7r2Yzb9c2us59iNdZ8vWb97W8Thj1HCcjc2YM9Jo/HJ3j7LBDgxXvdt1Rqn9cBXS7enlbEVRYkndaIgz6fHGuwkxYSvMZMB0c9SvM2p+McOumBnwufzZ02jZUxnwOYDGL3fjOFobMOApinJ8UEEvzkxZGbG/aBwmVQw8YxiaKfozGAUOyv/31C7HcqaOIH9WOVWBUv50k4j7kiqKoh8V9OKs8KxTYh74NJOBlJKB2AYVYs7OwJSRFvWJFgZr7CZyGE2NXLrrh+ROH8HEn1/A7L99g6r318fs+gGp1EKKkhDU3fU48zQ2426M7Rozr8tD696uKztyysdFb82gEBSdEts8fkZjK19/6SqMHKRhX6irC/WnliwoSmJQQS/O9r36X6Q3Nvf1NIsZc1YGnjZ7j+eiOdHClJ6KIZbZlIQNzLkYHb77d427GmJ4cUVREpkKenHkqKlj20N/jVr9xrQUsiaOwt3ShtFqwevxUPvJFuxHem7b5XVFrzfkamxm54u1eJ0eHHV2hCYo+lohRTOiNLHFlA3+gNewP5UPv/fH6FwnFGp4U1ESggp6cXRk1ce0Vh6OWv2Z406i+oPgUvBoUU5s++m9/+x4bExLYfxt39GnYvNAkG7QzOBpBUMKuI4FdemVeEPoxWaNH0HxeWNpOdiE0AT7/vkx7mYd8vepoKcoCUEFvTjav/StqNYfys7+sbzn5Gm1Y9Crk+dtBXf9sa/dXfcmyCppJaN0AI07+/9wUThnEqf+vhxLhgAKAcidVMAnP3gh4maqLAuKkhjUb2I8eaO7SDzQvbveOGvq+z9JL5qG1OM2pmVQ14DXi7SS/vMWZowsYfp9U/0B75gRF6dhK8oLu4nt1EQWRUkMqqcXRzXRzrAQ5JCaMJto3r2//xN1YsnNRNOjp+cMLimudUAvm1ELgSk9lRHzZzPq2oHYcnu+XkITzPzDPD777Trwb0sG/uzvUh7b3Mbrxevy+LYn83p9KQu9XtqzjRhTor8wX1GU/qmgFydelwtHVZS3CQ1yVqjeMzdzT56EpmmgaQgBHruTmnWfdTxfcNpo+kg9FRxh8y2y9/Q/AcdWkIbBZsU2KA9rfhb5M0oYcEo+WWVGTDYwWPtuy4BpJga8dGqf5/SrcEBk5RVF0YUKenHiaXNElpU8CPWf7cBakIP9aO/B1TqwgPTSIdgPVoFB8wVATSA0A5rJcCxhrOFYEAPh/+9YsJDt/5fgrG+iYcv2jueyp4wla8JIpMfXCyo8dTARM+eA40Df52ipIDTyTyll1LWDMWfEczKJGt5UlESggl6cmDLSKLvtSr58OHp7ZUuPh9Rhg/sMevaDR0kvLaFp196otaNu4xddvjannxx5pe4+0hRpNjBm+oc/vRSVQ+97oseIUEFPURKB+k2MI9vAgqhfQ3r6Hrq0FuZSvWZD1NvRWeE0awSljWDKB09TH6dkgvMwcGJs5q0oSvBU0Iuj1CFFUb+Gu6mlz+c9DhfGjP6Tr+ola2IZlqwgel2af9syYQFjDliKfcEMCa6qwGVM+Z0CXqJRv2qKkgjU8GYcDbr4TKxF+dgP9fImroPWg0dJHzmcpu2B0+W46hvJP3UKdZ9t7zdA6mHIvHFgyPDPLNU4NuzYKRAabL7AZkgFr8u39s4dxKQfYeo9IAbBbbDSmJqHXXPjxulvU+/LSgQCSfeB08ABvdAgiOVObIqiBKaCXhwZjEZyp0/gwLK3o3YNd1ML1sLcXoMeQNUHG8kcP5K2g0eiul4v/aQhlF2eC54+7scBkANeB+AIrmJTrq9H6DzY/7m9aEwbSJWhBi+VfcW58KkNWRQlIagxlzgzRzmtUOqwYqo/3NTveQ1btpNaMggAzRSdz0Kl86cHN4PSEGJGBs0WdsCTQF36II4YDuENNsiGRUU9RUkEKujF2YR7b0czm6JWvzknM+jEqHUbvyC9tARhMpF3yhSyJ48h/7SpurWlZtOR4E501/uGKoMiwFUXVns8wszRrEFUaweITvdOUZREo4JenKWWDGLolRfqXq+1MI/82dOo2/BF/yd30rRrL57WNqo/3Ejdpq1UrdmgW+Br3B5kb0xC0PuUCQN4Q78XaTdnsT/TQqPsZ62foijHFRX0EsCIW76te522QQVUvfeJLnVVfbAR64DI958snFUW3IneZtCCXdYQ+rCh22ClJsWCS/ax7EFnUpfNRhVFiZQKegkgr3wcqcOKda3TaItkLVxXmePK8Dojz7e3//XNNFUGOYxoSA2y1uD/CUt8E1b2pDlplUEOtepGBT1FSQQq6CUAIQQ5k8foWqdXx2zsluxMnLWRZx83mE147EG2K9gdqUPYw7M+vZgjhoPEIwBJdc9QURKCCnoJouD06brW56jWb+mBs7FZl3qadu1nw683IGUwASDYIBHcP+Gm1CKqtcog64wG1dNTlESggl6CSBms7+4sjiPVmHN7SakTIoNFv9mlVWu20HoYXK39BDVPsLkAg+vpNZriG3TUPT1FSQwq6CUI+5FqXetzNTaTOnSQLnU1bNuj2wxO6fXy2slP8tGPt/R9YlAbNAtkEMObTlM6bbImuAbqIEVmUOgZ1OWPIcoJgxVFCY4KeglASsnuv72qe73OugaMGekR1+NubPItXZg9DXNOpg4tg0P/3dD3pBZh6KO0httSyKGMLBpTsnAZu056sVtyOh57ERxJtSAJbq1ipCwylaI2AxnNB7r8MXv1zVmoKEp41DZkceYLeK90SbKql5bdlRhSrOTPKqfq/fUR11f94SZMmfpsTu11ual8q5ZR1+QgtAC9NekBDEDXYCE1C/Wp2VRrvk2lm0UdpArSxUAsHkGLwUWbrCLPXEyqvZ4Wayb2/tbitcfefjqNmjQw1J6DFAa8moZEA+HLIWj0uNA8LeB1IGSAdYNB3cdUFCXaVNCLMyEEe55dFrX6Pa123C2t+lQm9N2izOvyIj29jGR6HXQEPFOer+cnPTRbUqjW9nU7WdIkD9Kk0RHAqrVKqlMAep+Ek+XNI9tpxOCqx2tMZ5+1AS9uvCLw/bcsmYfBGeZSB+dhSBkRXllFUXSjhjcTwIgbL41q/Y3b92BMD3bdW++ky421IA+TDvuF5pSPYux389FMvXSvPC2+rchMeeCqBucRcFUjZPjDhJo0YJRmkJAl88lrc2J0HEZ47RicVQxt9JBBbuDCErLtEaxVbPgIWfdekDNXFUWJFtXTSwDDrrqIxu17+OL//hyV+j0tbboNcY787hQsWbNw1Lbgarbjsbtw290YzAZfz83twWN343G48bg8eO0uXE123K1O3G1O3M12XM0OTOn9JdrxgKkAnIcibnO7VDIpbGoGYQXRgvB07QELPOQ1N2JLKaZRa6RF+LNBSChxDkRzhZ/FAYDGdeBuROadixDqV09R4kH95iWICffegbOukZ2PPxeV+mvWfop1QB7WwjzqP/0yrDoKThvJ8Avae4yp/j/h0sAyEByH6H7froPzWG48t2UAhy3NuDga0lXM0kaGzCLT7kRz+Wdw9jV/xusgrbmSNASNaYPwCK+/bIQBr13rl7gqHJB9VtQzbCiK0pMa3kwQQtMYecfVWPKy9akw04ZMt3S8v3udLuyHm9bC/gAAIABJREFUq2ncuov82eUYUm0hV3l0zXZaa/V6o/biiz59DVcem3Hp1Qy0iWbcIrQhxkGODLKbDh0LeEGTZDQfCLNs30zaHj776a9o2adTIFUUJWiqp5dAMk4axllrnmfVmdfRWnk4rDo8Pz2LttHZ7BzcTKOnCZMwYdbMWIUZkzRglAb2eAWpjpPIv/UNZEVob+itR92k5JpBuog4HY+7GcwDfTNZ+urxhXklTWoYnLFbnxcKV10Nb8y4jDkr/kr2pNHxbo6inDDEiXhjvby8XK5fH/n9rWg58u461t5wD81fdZ+lCKKsgLYfnIpwehFu389OuL0Ihwet1cX+s3KoILj7YBmGdCbfvwf+uy3otmWOHsh5Ky9DSCcYU3yzLL3B7p7SB2MmuHvf39ORUsw+U2jbiGV68yho0nfRvx4a9qWw6tLF2I80YkxL4bRX/sDAc2bFu1mKctwQQmyQUpYHek719BJQ4demM2flk7x91rW0XFSGpzAVraYN0ezCVZbN2pP6eiMPfuJHo6eJDT8tYcqb2xBBfvYZdO54kE7A5QtSlmJw6LCnpehvYktoH86M0kJ+c2P47YkSiZVP7voP9iO+trmbW3l33s1MX/RrRlz3rTi3TlGOfyroJaj00hKGvPm//GXf34H2vG8C0G8jaYBmTwueX56L8Vcr+z3XmGZlwveHIuh0Xy3MrOU9eFrAmAvuGnzfZ9cgJ0PMm1fgzUMkYILYPf+uoeqjXV2OSbebtdf/lNZ9hxj3i9sQIWSOUBQlNGoiSwKbPOJU/mfCHVgN+uXGC+Tz0zUY2P/m1KcvuQ7RPVuAtwW0yNcA4m0Bg823TMGYBeauG3CH0s8TUmBz6DDkqrPW2gy2PLii1+e3/OqPrL3xHryuyHMXKooSmAp6CUwIwfi8Cdxd/jOshtBnWwarwdOE57q+UxsNOm8ieeN7yRRg0iebA65a35Cpuw4vokugazS0BV1NChm6z7iMVP0eG8umPEzrgb57xrv/9irvXnAzriZ90jkpitKVmsiSJN7Yu5J/fvVK1OpPM6QxrC6T3Mc2IN/deewJAYPnTWbGw1/HZO7lHpmW6uup6UQiGPHE21TU1wKQn5pKmi2dARlZDMhIpzAjjYIMKwUZZvLSjORlaOSlS2z+vLPFrkHYWhNnaPPwJ4LVly4KqUzO1LF87d9PYCsqiFKrFOX4pSayHAfOHnIOm6o2sqdxd1Tqb/Y0syWjmfH/byoj7ZIRV0+nYOYAUvKcCBxAH5NCvC36TWgBFm1t6wh4AFUtLVS1tLCnuu9lHOnWFAZm5jCzeAB/PXsMRqnTnqNhkCIV2WJi1+sVbPjJyyGXr93wBW/PuYbT/vlHssaURaGFinJiUsObSUIIwTdGfDPq16kd4ObMF+cwbF4qqXlN/oAXBE8TaCm6tMEQ5kSOJnsr249U8vSG9ayP80oFz+427E+8GFbAa2fOyeTNU7/DkXfW6tgyRTmxqaCXREZmj2J64YyoXqM0NT+8gu4G0My6tOGG0TYeOucsJg4IP5v8/e+v06Ut4XJ/voNgs7oHYkix0rRrH676RlaffQMVS17Xr3GKcgJTQS/JXFZ2BaWZ0RvummDrb71cX6Rv9mWEBF5+PDmTn8w6Oew6Xv/yS5xCn9x/QRNmpCcbT1Uq3h27+j+/D5b8HBxVviFer8vFh9/5IVsfWqSyNChKhFTQSzLp5nROL56Doc/M4uFriuQ91d3gT/6qj68PyeFf37mMwrTwgteX+i5p7Ieg9ZmPaHtsCY5nXm0/FDZTRs/vefOCh/nkll/idccmC7yiHI9U0EtC0wqn8z8T7yDFqM89tM5WVO9juzuCet21oFPanEJTPRcOgcGZ4S2JePqzHbq0o38anuoUOFrV/6lBatiyg/xTp/Q4vusvL7Lm0u/jbku8dYiKkgxU0EtSY3LGcvP4W7EYIhmO7OlIWxWP7t3IBle4C84lGHXKFAF4hJX1B8KbFbrwo7V4RS8L+4UZ6clCkh7EFmh9ECY8h8w4nn41/Dp64agJ3FWtXPoWq868FvvRxFqLqCjJQC1ZSGIjs0fxfyffzz93/X/27js8jupc/Pj3zGzVqjdbxbLcG+4Fd7oxPVSbFlouCUnoSX6Bm056g+QmkJCEe0kCIQlJKKG4AME2uHdsbCQ3Wa7qdaUtc35/7NqWrLbSzmpW0vk8jx6k2Zkzr4y0r86c8v6DdcfXIqOtetDC70u2kFI4lZF696f9B3GAcKPhR8jQo7gtVR7ePXiMLcdOsKG0lIfnnst5BYOo9QX4z8FjJDnt3D+xbaI94rX3+HvwG0E+rrUzIelUr0gAAhlMxvfOToI7d4UOawJ9ymQcF4xB0PGm16EmbBj1HoLFx8EfIHioFOPgoR7H2JnavQdImzqeqq2727xW/uFWls2+ifPffJaUsSNicn9F6Y9U0uvjkh0p3Dn+bmYOmsXTO39FwDBnvEciOeyHkT0YOvzVlmIefuMNvnDuuTx14Wh+v6uK+15rvbD+/n+/2epru6Zz/8RrWx3bW5/MZX/+V/cDaOEvW4v41rACZHUdwYOHER4PwZ3LINBi7NGQBLdsw7tlG44brkQvdCJkA2BDykQwBMGDVQS27MI4egwiHFPzE+UuOl1MWmk4UMqKebdw3mtPkzVvenT3UpQBQiW9fmJCxjl8adpXeGrrkzQFI9+yqzMryg8wJGdYt3t7a0tDY1u/Xr+eX6+PbI2ZgYFEtNp8bHCCwGWPbsJO6YFj+DZui/h838vhpQFOJwSDESU4Pw5qjGwOfOTF4XGg2TW8lV5OrG/bQ+uumt3FJI0upO6Tg+3HW1nNuxffxZw//ZiCGxZHfT9F6e9U0utHhiUPZ/bg2fznyHumtFfZXMXPD9Xw1aGTKYhw78umALzx8c5u3ytoSM7eVjpFq2H3Pefx+XdL+M2Gnq2763HBgua2i/IDOCg+OZikFA1Nk/h8ghPFtdQePEFDydq297ZF/+vlSE85vXShI8GmZtbc+CDTnnqcsQ/eEfU9FaU/U0mvn7l+5I18cGwNfsOcnfoT7YmkiMgfmVb6BI3+7s8s1DXR7gz/4/5Uth3r+UJzPZp1A2Fekik+lETt0WrKN30Q8XUyGCRt+gT8tfXUF/Vs3M+dm03V5l0Rnbvloe/TcOgoU3/yFTQ9NktaFKWvU7M3+xmH7uCKwqtMa6/WV8vfa3wRL4rOTZDcP/e8bt8n0dH+DMpGv2Td4bYV5CNlj6I2XZPwcKC2kPUrGtj/2jrKN+3pXgNCoNlt2BLcZLaz/KCl1EljyJg9BWFv/Xeozd29slJ7n/w/PrjpIbWkQVE6oJJePzQ/d4Gp7W2q3Eu1jHxa/w/nZDIkrXvbmSXY29/CLM0ZXU/NJnr2I36ooZD17/jY8fxqavYc7Na1iSOHkrVgBonD8qlYt53q7Xso/2ALGedOxnbWovO0qeNJHDGE6h17qVi3jZQJod123LnZZM6dSkPpsW7Hfvify3nnwjtoKu/8saiiDEQq6fVDfsNPQdJQU9t85mQ5fiOyBOSywX9N77xnczan3v6T9q1l0VVK6Onm1SWbjlO9q/sVLTLOnUzjoSOUrd5E/b7WPdSK9duxJyaQMmEUnsI80qZPoGrrbur3HT59jtBDv5JBbxNV2/bQePBoj+KvWLeNFXNvpv6gOZUvFKW/UEmvH5JSsiB3IePSxpPlHIzHl0eaHEKmGNLjNksajrK8KfJHbQvykrrVvsfZej2eFA5+s9vLNS9GV0PQ1oMxvROBAip3dn/vTM3lpH7/YQx/x2Og3qMnqf14H00nK9odq2s+WUHS6EJSJo4m2BjdLNy6ooOsmLuUyi2RjQkqykCgJrL0QxnuDBbkLmRuzjwuePI+1hSfmU35wFWX0OTq/iMzgF315VyR0P5OLUvePMTakgM4bXbsuk6jz9ettqu9XtaWu3HqGg5d4wdrtvCXHdt7FGdLkfb0DCmoJoc962sp37KuyzVy7cmYcQ5lazZ3eZ40DIKN7Y+5NR4+ji3JgxEwZw9T77EyViy4lQX/+CW5ixea0qai9GUq6fVTQghswsavlnyFqd//9OmJKL98fQX3XHw+trSKHu3g4pMaDmG0OuYPSpKcTkqre17E7khtLfN+/ycAhmXkcqCiZ4/1TnHods4dNgGHreOxSCklBjbqRSbrXj5A0/Ge163LmjctooQXiUBdA8GmCOsYRiDY6OX9q+5j1rPfYcRd15vWrqL0RerxZj/ncbjIS209qeQPK/9D1VEPejc3ht5fd5idgba7jBxuEHxw6GA0YbbiC7ZdbpHgiOzR6phBBcwdPhGHzcbq4m38eO9GZELbR60BHGwrzuHfvz7Ef/5nE03He76PZerksZSv39Hj689mS/KQPHqYae0ByECA9Xc/zrav/hRpGF1foCj9lEp6/VxxWSlPXHUv4wYXtjr+tw/WcaBIdnvD6v8r3UFAtn5kOCxJUpiWHm2op9nPmtQyIWc4/mCAucMntjqelpDEgpGTWThqCgtHTmFS3kiKTpby4f6d1DeHxsP80uDgoPxW1zXjZsv2REre3tijx5gtaS4ngfpGpInlfgJ1DTSXV5nWXku7f/Q7Prj5EVN7korSl6ik189dOn42f1z3Fg9duLTNa29v3c4rKw7i8uaQYo+sfI/f8FMtWy8vEEJQ1lBvSrwAqe4z0/p1oVHVWIs/GODD/TsZnJxBYUYOBemDqGqsY3XxdlYVbWNV8TZ2HCnGkG17Mc9V11Nu5FHSWEhxVSGb1mocWxX9eCFAxqxJbWZpmsGe7MGZZd4fEi2V/O0t3rv0HnzVtTFpX1HimRrT6+eEECwYOYU9Jw4yZtBQ9p5ovTPI4YoKfvnvFQBcPm0qGUkedh4s5fL5wyn3nWzTXl7CYFJoPUllW4VgS2n3p/e3Z3L+qFYjjXNHTGJ18Zm9M4/Xdv8x5G9OfsLsl5oJVNeZEOEZnsI8yj/YYmqbp7hysqjr4S4ukTi5aiPL5y7lgrd+h2doXszuoyjxRvX0BoAkVwK/fO/vPHThEmxax9tTvbllK396fw3bDh1k9ZajJNpbj4UVJBTi9ibzjwN+/nUwyFuHDVYekdQHJBMGF0Qd58KRU9heWsT20qLTx6oao++NeAniO3d01O2czZGeggyaVym+pYq120ifORFbUk/rGnat9uN9LJ+zhKod3dxpRlH6MNXTGwDe2bORoBHkT+vfYsmMi3lhw7Iur1n98R7qvcOYOTP0ppvqSGXR0EVs33eMh177DSfrzB1zSnZ5WFXcuhrC/BGTWbPPnMeQG4fqnGtKSyFZC2dStmqjiS22ZvgDVG7cSdbCGdR8VIQjPZX6YvN7ft5jZaxccCsLX/k1gy6YbXr7ihJvVE+vnyurq+KdvZuwaTof7t9JjbeeeSMmRXTt1oMHyPKP42szv8G3Zj/BzJxZfGb+NRR/52VumbnI1Dgn5Y1s9fXCkVNMS3gAf9ZL0Bw9L0jbkjMrncoN5s3W7EzZqk0YPj/SCJI5d2pM7uGvree9Sz/Dvuei2whAUfoClfT6uT9veJs5wycyKDk0KeLfOz9g3YHId+hY9VERQ5IKcNvOLFVIcnn4453fjDh5dkUTGnXNjUzMHcGwzBzmDZ+Es4O9OHuqUjajzRwTdTua20VCQU6vzn4M1DfSsL+UhoNHQvt6LpxJwpDBpt7D8PtZf89/s/3rT0W8ubii9EX9IukJIRYLIfYKIYqFEF+1Op54IaXkt6tfYdG4WSQ6E04fDxqRj0NNzh/Z7nFd0/n+NZ9jwcjJTB0yGo+j9fq93JRMhqQNYubQccwoGNfqtYvGzGDsoKEUpA9i+pAxpLg9bC8tor7Zy4HyY3ywfwden/lJ5a0pLkSUJXfSJo+JuNSP2bxHT9JYcpSyVRvxVdeRvXCm6ffY9d1nWH/P4xgmLsFQlHjS58f0hBA68GvgEqAU2CiEeE1KGX3Z6j5uVdFW7LqNmUPHc8Ho6by+cw3PrPonNd7IlxdcOGZGh68lOhNYs28HUkoyE1NJcXtIdSeR6HKzuWQvQSPI4aoTAEzJH0Wyy4MktHbwUOVxAEoqQ6/PHT6xVQ+0KWB+0luZUs0lX70Evvd2j65PKMilds8Bk6PqHsMXWrgfqGvg5KqNZC2YQdnqTabeY////pOmk5XM/+uT2DwJXV+gKH1In096wCygWEq5H0AI8RJwDTCgk55hGLy+cw1fv/wuFo0PTeGYO2ISD1xwEzf+7nE+2BfZmNTbu9ZxwejppIZ3NdlXVsqgpHTe+2Qz9//1Z6cfhZXXVwNwtKb9rci2tZiRCaExu/pmL4lON7VNDXy4v3W19RpvA3bdhj9oXo9D0wTvn1/P5Z/MpeHvH3b7evfgTBpLotsezWwVG3agJ7ij3pz6bEff+A/vXHwX5736NK7sDFPbVhQr9YfHm3nA4RZfl4aPtSKEuFcIsUkIsamsrKzXgrOKpmn89PoHuGn6xa2O56Rk8tp9P2HakMjGt55d8wrfefMPp79ed+Aj0r+0iKuf+fLp3lpPrCrexpbDe1lVvK1NQgQYlJRuasID0DUNhGDZZ2wkXtSNSSGaRubcaVTv/MTUeMxgNPvQ3d3bVSdSFeu2sWz2TdR8vC8m7SuKFfpD0mtvG/02I/FSymellDOklDOysrpX4LS/Sfek8L1rPhfRuQ9duJSfXHs/O0qLWLf/Ix76+1OmJ6P2aFp0xWPbI8I/KkGbZPmXk0icO6HrOFxO0qePp/zDLQTjsBp5QkEOvorqmLXfcKCU5XOXcjKGyzMUpTf1h6RXCrQsFJcPxNczqDi0eMIcnr/jG6S6k8hLzcJpaztb0mGzM2/EJHRdJyspjcW/euj0Y8xYq2lsOJ2kzKLrZ37c/XbJ+49nkzC680X1msOBDMbvBs26Mza9vJb81bW8e8ldlPz9rZjfS1FirT8kvY3AKCHEMCGEA1gKvGZxTH3Cp2dfzjevuIegYfDUjQ+hn7Vbiy/g5+1dawEYnJzBS/c8wbjBha1mgsbKjqPFuB3mvqHbz5q5We8OsOX7Y3AO7njMyjM0l7pPDpoah5nqig6StXBmTHdugdAEmjVLHmbPL56P6X0UJdZEf1iTI4S4HHgK0IHnpJTf6+z8GTNmyE2bzJ3x1pdV1NeQkZjC82vf4M4/PnH6+MisfC4/Zy66pvHT6x5A0zSqG+to9DXxuRd/xOGqE1Q01J6eoWmmCTnD2HXM3JmSU4YWMmtWYpvj4w4kMujeZW1K7jjSknHlZFG7O/7HtNJnTiTY2ETNrrbjo2Yb++jdTP3xlxFaf/ibWemPhBCbpZTtTj3vFz+1Uso3pZSjpZQjukp4SlsZiSkA3DHnCg58958MzwzNA6psrOWScbMYnzOcV7evAiA1IYnUhCQqG2vZVlpEbVMDo7KHdNh2z2OKrOpDdxgdFM39eFg9wa+33mEmYchgPIV5liY8oWmkzziHlImjcaQld3pu5cad1OwqImthx0tMzLLnZ8+p8kRKn9Uvkp5insKMXHZ/4y9MLxhLZUMtP3j7ee6ecyXzR04+fU6Cw8WVE+cDUOOtx+NwdbqRdU/4Aj4m5rW/ML6n/J0suF69sAH7w5cAkDimEM3hoGrrx6bev7sSRxZQuekjanZ+gjs/h5SJo8laOKPT3VgMf+8sKi/521u8u+hufFU1vXI/RTGLSnpKG067g39//mf86c5v8rPrH0TTNJJdCQSDQZbvXk9VQy3vf3KmpM620iLOHTahTfHXaKw7sIu0diqeR8Po4lH+yiu8JNy+EIIyJjXyIuUZmkf6zIk0lBw7faxm515qdn5C2apNBOobSR43ot1rNbs5+4tGomz1JpbPu5mGQ0d67Z6KEi2V9PoRM8dnB6dkcMU58/jblpUUPH4N2V+5nN9/+BrzRkzi5a3vUdvUQEHaoNPnf7BvB3mp2SwcNZV5IyYxdchoNNGzH69JeSNZMHIyW0v2mvXtAOAPdLH9mhAsv82AMdmm3C9rwQwy5kzp9tZn9pREKjfuxOjg8aGvqpbaj/eROW9a2xeN3p1pWvvxPpbNXkLlFmu2ZlOU7lJJrx8RQhA0sb7bT1b8mSffeYnDVSeobWpgxccb8DjdzB0+kRFZ+aR7Urh+6gWnzz9YcZRVRVv5YN8Oth7+hIl5I0h0uju5Q0h2UhrzR0xm1tDxpCYksuNIMauLt1PfbO4uI5GsLwzaBG8+6MJz/Zwe3SN53Ag0p4OUiaMp37CDirXbsCV5yJo/HUTnSzA8w/PJWjADf01k28SVf7iVrHnT0JxnlptYMbmk6XgZKxfextG33u/1eytKd6mk18/o4V6FGb2+7KT0Vl+vKtpGQ7OXCbnDef6Ob/C72x7DrunMHT6x3eu3lxYxOLntcgAhBBNyhrNw1FTGDBrKyboq1uzbzoZDu6luPPOGLzuYeNJdtm72tKQOb35OJ+G2BRFfozkd2JMTMQIBhN1GXdEhZHOowry/upayNZtJOWc0rkGZACSPGUbC0FyEppEwNJesedNo2F9K2epNkT8ulBJ/VRVDLg7tLpO9cCYnTd6HM1KBhkbev+o+9v3h75bcX1Ei1R/23lQ6EAgEsNl6/r84Mzyr83R7RpD3i7Zy+TlzEUIwPmcYq4q3U9lJdfPMxBQafU34gwFGZQ/BkJI9Jw6y69j+Tu+ta3q3qkG0NCQjg7TEROaMGU6Sx4lwN1Bf5eBoRTUQ4YxDTfDWXXCp6wKafv9eh6cJXcedl40twU3tnv34azvupdXsDD2utSUnUrv3AMJuw5mZSuOhozQe6v5+CqnjhzHnYjs+mmg4dyInV21E6Dq2pIRQb7GXlyPJYJD1n/kaDYeOMvHbDyC66NkqihX6xTq97hoI6/SCRhCvr5lEV88Xku8oLeK6Zx/DpunYNJ3PzLuaBy9c0urNbEdpEX/dvJKfrnwRX8DfbjtuuxOvv3vT2502O80dtNeZ+6+4hORUKPMd6/rkSEjJopcdNP/m3TYvZS2Ygfd4GQ2HjiJ93Y81GiljCpmzyIGT0CNgr0ihtCIVm01QmHSAoLBTGcxh7dPd31jbDMPuuJZZz34H3WFuXURFiURn6/RUT6+f0jU9qoQHkJ+Wzb6y0tNfH6kua/PX+6T8UUzKH4XL7uQbrz/bbjvdTXgANk2nmcgTySWTJjJ1dB414ghlPhP/kBOC5Tf4uDR4AU2/a93jk4ZBfdEh8+4VoeTRBcxZ5MJJw+ljblnDqPRTywcENgJk64eZfNcCtv/v6l6P8cDz/8J75AQL/vE/2JPbbgigKFZRY3pKhxIcrlZf7ysv7eBMeHzxHaR7Ol9A3R1nb4nWmQXjxjJijKBalJo2DtiKECxb4kf/0iIQgqyFM8iaP5364hKEK/Z7X7aUNCKfOYsTWiW8zhQmHiT3vCkxjqp9x1d+yIoFt9BY2vNqHIpiNpX0lA5VNda1+rquqZHKhvYXI+uazsTc9teO9cTZ+2R25I4LFjDuHBsGMZ6qLwTvXubF/93L8Nc1ULZmM0ljhiH9vfdYM2l4HvOuSMIlI0t4p2QUmrvesTuqd+xl2eybqNqxx7IYFKUllfSUDr22o/VjsY2HPj5d6bw9uSnmlWyKpKd390Xn4crsnaoPp+xaqJH+WGg3mrJVG6GXKjAkFuYy94oUnDLyqvenDEs6yKDZXZdRihXvkROsmH8LJ/6z3rIYFOUUlfSUDj3x5nOtvq7x1nP1M1/mqqcf5UD5UYyzFkLnpJhXYVvvYr3ZZy+9EFt6RWweZ7ZjsDuL2/On8938HGbNEmSda+4WaZ3xFOQw96o0XNR1fXI7hABXemyrMHQlUNfAe5few4EXVAEUxVoq6SkdyvCktDl2uOoE/975AcO/fh05X72C0qqTp1/73MJre7wLy9l00XFP74tXXIxMPtnh62YampjH5wqm842cdOY5G7ALEEhm/PDyXrm/p2Aw8z6VjpuOl4V0SRPM+M4Cht3cswX3ZjF8ftbe9mU++t4zpu4epCjdoZKe0i4pJQcqOl87drKuiolP3Moz7/8DgFHZBdww7YJOr4lUwAi2W0T24WsuxZcQ+4kRBZ5cPlswna9mJzLF3oB21qzVlGE+UsblxjyOWdfk4ZZRJDzAde8SHJm1zP7BOdxY9ChXrHmItEnmV8aI1I6vPcXmB76LYeLuQYoSKZX0lHYJIXjg/JsYM2hop+dVe+v42uu/ZU3xNirqa5iUNxK3PfoZjcdrK5icP5J5IyadPvbopy6lwRHbzY1HJhfwhYLpPDYoian2hg4XWAuCnPvza2MaC4BdRFe+x3XvEjTPmXFPm72W5PwGLnhpCQn56Z1cGVuf/OrPfLD0YQINjZbFoAxManG60iHDMGgO+Bj+9es5XlvR6blJrgSChsHBJ/5Fg9/L5//yE5btXo8hW4/76ZqOlLLN8c7aHZGZz8Uzh1Nr63jJRLTGphRyZVo6I/XI34QNEvhr4S9iFhPAovsn9PjRpuu/bkJL7vhanzeZ1+c8g6/ausSTOmkM57/5OxLyBnV9sqJESC1OV3pE0zTcDhfzRkziH1s73ooLQssZEhwu1h7YydWTF3LnnCsYlJxOTWM9dt3Gso/XMzq7gLL6ag5WHEUTGjZNx2Gz4bQ5SHC4EIDDZsem6eiajiYEuqZx5bwxnDTMraIOoAmNaWmjuDg5gULdC3TvzV+jkTm/uY21n/uz6bEBXHD/dJyyjHae8nbJefeNnSY8AIe7lvNevIvEAhdlGypYc/f/9SxQIRB2G0LX0ew2NF1D2GwIuw3NYUOz29F0HXQdTdfRHHaETUfoGgLY+IVvMeX7j5IyvvcmBykDl0p6Spfam9DSkk3TmZw/ip1H9nHT77/GrMLxZCam8O7ezTQ0ewlKA4/DxcZDu09fY0gDX9DAF/RT3+ylooP1fwD5xYnkDjft20EgmJUxhsuSXAzWm4CeV3MYujiVrTmpNB0zb+mEzeOVCQYDAAAgAElEQVRm1NXTSeAomuj+kxjnp69HT4tspmfmeB/gI/c8B/nXLKB6VwkYBjJoIA0DGQyGPpcGGBIZDBL0+ZGB8PFAAKRE+vxI/Bg9/KcsW72F815/hqy57ZRLUhQTqaSndOmuOVfy7JpXOnx9zOChbC45s/h4dfG2NudEUyaoq+ULEbcjdOZljuUSj06W7gOaom5T0Ez2uSMoeWVz9AGG2TxuRmf2bHsz523Xomd1b/E6gPQbyICP+uLe31YNwFdZzbsX38W8vz5J/lUXWhKDMjCopKd0aVbheMYNLuTj4wfbfd1hYsX09qzY/hH3jZpOnb9nY1s2YWNB1jgu8QjSNR9g7qxB9+Dotl9zpCUz8YbJ6JoBQoT+y+Fut+O85VPog3r2x0Xpe16OvGHt4vGgt4nVn/oCM5/5FiPvXWJpLEr/pWZvKl3SNI1753+qw9cjKRQbjUZfMz978UMS/Xk9uv6c1GHcmOgLJzzzTXp0GpqzZ4nfkZbMvNtGke86SI6jhBz7IbL1HiS8m69Bz+l5z9Xu6V7NwViRhsGGz36DbY/9TC1pUGJCJT0lIhmJHY/rBY3e2YorwdOzmcbF9d2vVdcdNnstC/73jm5fZ09NYu5to0kmuoX2jmsvQ8+NbmlDyggHjozUqNow0+4fPsuaGx4g4I3+EbSitKSSnhKRI9VlHb52tLq8TUWGWGhq7NmPqyZ06mRs67rlzHWRPCYn4vPtyR7m3T6WFDreyzQi6WnYRtijawPwN0o8BdnYPLHttXdH6Ssree/Se/BVdTzJSVG6SyU9JSJHqjvujRysPMaU/FExjyFodL+nl+JI5su5haRosa2GIPAx+5fXR3SuLcnD3DvGk0L0O8vYZ0wBGf1j27RRGov+tpDLVtzCiDviZyJJ2epNrFhwKw2HTSoKrAx4KukpEdl5ZF+nrxedLGVIWmwWGOuaxmO3Xkijo3uPKVMdKTyaNyw8UzP2MsY0M+Sazqfc2xITmHvHBFJNSHgAwm1ePT/NLkjM1Zjy6HDsKdaVIzpbza4ils++icqtu7s+WVG6oJKe0iXDMNhU0nk9NLuu09DspTA98kd8kQoaBjX+ym5VVEh3pvFIXiHZWnRjXd2VOrbj79/mcTPnzomkCfN6LbLe/N1UHMmCUfecZ3q70fAePcnKBbdy9K33rQ5F6eNU0lO6pGkac4dPbHNcIEhxJ+K2O3HZHFQ21pLocpOTnGl6DMky8mSa4Uzj0dwhvZ7wACbcN4xzvtK2AoPudjHnrkmkC5Mn1fjMf2wrpWT/i2tNbzdagYZG3r/qPoqeedHqUJQ+TCU9JSIzCsa1OTZ6UAE13nq8/mb2hysyfHR0P15/EzOGjjX1/j/8+1tkOLouUpvpSufR3CFkxGh5QlcEzUz8fB43fPLo6eSnu5zMuWey+QkP0ArN71kDuHPMq41oJhkMsvHz32bb4z9X5YmUHlFJT4nI44vv4JtX3APA/BGTses29p5of/eOam89R6vLzQ/CH5ohmmwPLZ84u+ZetjuTR3PyY7YerzvsjlrO+fwQRt61kHPvmUZGDBIeNh0t1bwxvVOEEAy5Yrzp7Zpp9w9+y7o7v4rhj+0EJaX/UUlPiUiiK4EHL1jC0PTBaJqGPxjo9PyjNeXMKhzfbk28nmrwBnA35fJ//96JVpfNzh3NZDty0dAY5M7ikZwc0ixLeAKEHYQNKTxIkjHK3EyYqpOlmVwdIiPUC3MuuRrhqDK37TB3VvwsXejIgT++wvtXfQ5/ffe3XVMGLlVaSOmWZ1e/wg+WPU9J5YmIygMVZuSQ4k5ke2lR1Pf2uFzYNI2axtaTN55Yeg1fLEiI+bKE9hh1KQSLjxLYXYQ8fhw0HQwTdxJx2NHy8tAL8vBv34Vt3Gjs8/Kh2Ylw1ACx2RjAVyfZ8sN96E6dklc34aswb0Nts6VNm8D5b/wW9+CuH38rA0NnpYVU0lO6xR/w0+Br4vpnH+PdvZH/G47OLiDDk8KuY/upbTL3L/NrJkzjX1eZWIahUwLZnIqUEv/7Owh+FNtp9NqY0TivOgdBEP+mCuwz0k1Zl9cdVXsNPn6uiCNvbiZQF5+9Ks/QPM5/+3ekjB1hdShKHFBJ7ywq6UVvz/GDTPrubV0+5jyby+5gVuEEVhVtNSUOTWi8ddu1XJJn3mPU9khSMI414d+wE6N4f0zvdTbHTVdjG+ID4ej1hNdS+Y4A23+6hZOrtlsWQ2cc6amc99rTZM2bbnUoisU6S3pqTE/pkbGDC/nyJbd2+7omv49VRVtZOHIKDj367bMMafDdD3bj9cfujzejOQ3vL16m+cVXezXhaXm5kJgYSnhgacIDyJxkY8GvZjL5m9fhidGs0Wj4Kqt556I7Kfn7W1aHosQxlfSUHnviqs8ybciYHl27qngbSS432UlpUcdxw/jh2GNUJMBoTqPp6b9BoJd3/Hc4cd5wHgmfXdi79+2CI1kw/q4MBi1su4QlHhjNPtbc9BC7fvBbtaRBaZdKekqPaZrGsgd+wfwRk3t0fUVDLQVRbl2W6k7ktpFubFoMHm86cjCOVJs7MSUCtjmzcH/uOoS9gVhNVInW6NsKrQ6hU9sf/zmbvvBtjED3Hr8r/Z9KekpUMhNTWf7AL7h2Ss+2rTpaU95lb7Gzx6C+YID6YAwSnj0bfMexjbBhX3yR+e13wHnXDdjnDEfYqyx/nNmZ1FEajvSOy03Fg6Jn/sKqa79IoLFnhXWV/kklPSVqboeLf372R/z+tscpzAiN9SQ6E7j93MuYnD+KLY8/zxtf+Dk5KW23JztaU87x2ooOE99X5DieWu3gYb39xdK6plEfs5UKEqQf24RMcMe+dJLr7hvR0+sRIn6XB5wihGDE7fOtDqNLR//9HivPv52mkxVWh6LECTV7UzFdadVJUtweklyeVsc/OrKPc398D42+toVBHbqdMYOHcqD8CPXNob/Mh6cO5js//Pj08nZ/SgKGy45h15B2HalpICU5s8ZyxQ9N2vbMngXCBkKD5iOnD/s31OBfHcP9KB1OEh64NK57d2drrpb8+8K/46uM/3p3iSMKuODt35M0cqjVoSi9QM3eVHpVflp2m4QHcE7eCB69+JZWxzwON1mJqQgh2F9WyqCkdKYNGUN+ciaPv3iy1X4u9ppGnCdqcJdWkXCgHM++k3j2lzH50kJzAnfkgr8MfMdaJTwA28yhoYXnMWKbNRVk39pSy5kqKPjUTKvDiEj9vhKWz11K+YYdVoeiWEwlPaVXPXThElLciQBce850Kh68nIOfvYh9n7+GX4xawGd26iysT+LR9QJ3SWSPpNY9/Arv3rUxusCceeDreH9MIeoR2bHZ8UOfOhnH7CzoRumkeDHipgKrQ4hYc1kl75x/O4dfWWl1KIqFbFYHoAws6Z4UvnjeDYzxVLFkuA27LkAHt12y0K+x8dXtFLzavTb91XWceG8bJe+cQ8FFPdgz0pnXpmfXhrAh6+q633YE7DPHgoz/R4TtsSXEdlMAswW9Tay+7otMe/Ixxj54h9XhKBZQPT2l1z144U1cXegKJbwWCi9PJHViz7eR2vjl1wl4u9lbiiThAcgmHBcv6FlgZ3O70MaMRp8xFX3GVLSkvpnwADyDBYi+lfiQki0PfZ/ND38facTnkhAldlRPT+l1WUnpSO9w8LbehNrmElz6yvls/FYB+//0Xrfb9VXWsO3nJUy6vwBHcgRvxJEmvFPxjRRw01X4/vZ69wLTdGwzpyIyUtGHZKAlNSNlU3i8UtAXH2tCqNjsyptXQx+dDLf3qefxHjnBnD/+GN1lfokmJT6p2ZuKJWTNOqhe0+5rRkCy+gvbObqsZ+N09tQkxn3xIsbcmonN3UHyc+SBL/KEd4ZA+lPx/vIv7b+s6Wj5edjmTkXPc0FQgBQIW2xKAFlt93PlbP/Ov6wOIypZ86ez8NWncaanWh2KYhI1e1OJP972C9ACaDbBvKcmkzl7Qo+a9lfXseO7r/Daef9k74tVBJvP+sPO2dOEByARjnocN16FPnMq2MIzOtPTsJ8/H9dnb8R140Rsec0IahB6db9NeAD2RIfVIUStbM1mVsy7mfqDJtc9VOKS6ukpvU5KCYd/AbLzLaJ8tZLyHT5W3/MSRnPP1695huYw4eHzGHZlIponv1uPNLsijVSQAjTZJxaVm0MAGghBzT6Dtxc/h+HrW8st2uManMX5bz5L+tT4rhqvdE2VFjqLSnrWkkYzHP6fiM/f/3o96+/v4HFiN4y8+0JmfGMkoo+OoZnBqEshWFLWehjx7PeAU5M7pISgAcEgwYoqjD1F7e5DWm7k8cGv18Uu6F5kS0xgwT/+h5xF8b/bjNKxzpKemsii9L5gfcSn1pcabHtimSm3LX7uXVJGpTPq5vQBm/iCh8vxv/2uqW0m6rWhGZz94A/oQH0j/7nis5z7uycYfud1VoejxIAa01N6X1PHi8DPlpivMWzpbNM2N9782MsU/bVygKY8wDD3O5dS8sn+pH6R8E6RgQDr7nqMnU/8WpUn6odU0lN6X8Oubp0+5eFcrtt0I/lXzSZpdPQ7gGz+fy+z7x+1/SfxCSeSFKRMQZKMFIktPhJafO4Bb9t9T6O6tRBMLDxCYmGuqe3Gg53f+CUb7v06hr/vj1cqZ6gxPSXmZO0mcA9H2NOR3oNw8uWetyUlax7YRenr0W/+POvJpQy/Nok+trS6DaMxlaZnXrI0hpLGQrb+YbWlMcRKzuIFzP/7L7Antt1PVolPasmCYhnZfAxq1sLxl5D1u6D8jajaE0Iw7fEJ2JMTo45tw8MvceDVyMcX41YcZO0h7gOMun5u39udJQLH3l7NyoW34T120upQFBOopKfEVu1mMJrBaISKt8CIvqCnMw38teYkq/UP/oV9/+xHjzotIoQga5BE6LGrRGGlqq27WT5nKTW7i60ORYmSSnpKbKUthORZpjZZtbfttPlobHjkrxx4vcHUNmNC2AmU2AnsFwQOaAQO6gQO6QR2HLQ6sjCB7uy4yn1f13DoCMvn3cyJ9zdYHYoSBTWmp8SUlBIai6D8NfPaNCS7flfOzh+8YlqbALOfvo1hl/egSkNvEHaa3ykhuHW71ZF0yitSWP7Lj6wOI6Y0h53Zz/+IwqVXWB2K0gE1pqdYKAg15i5cFpogaZj5kwrWff7PHFrebHq7URN2mt87HPcJD8Atayi4tN33mn7D8Pn58OZH+Phnz6klDX2QSnpKTAlhA838/Rmzp7m54j/3cNnKu9Hs5u2x8OG9f+TQijhKfMJG8/tHCG7eZnUkEfPVx9G/Xwxt/dKP2HT/ExhBcx+3K7Glkp4SewmjTW/SnSlILtRIHakz+nOLTG37w//6I97qZFPb7BHhQGopOOZlI7LzrI4mYqOnurElDYzp/UW/foEPbnqIYNPASPT9gUp6Suy5hsS0+SmP5DL44ukxvUev01ygJyCCFQidPrUUIE0cI3/hBISuoye4rA4n5g7/cznvXXoPvupaq0NRIqCSnhJ79kwQsStBI4Rg/i+mkjnnHPPatOQ3QwfHIHDkhP69An23asOkYUe54r4hzL5ritWh9IqTqzayYv4t1B84bHUoShdU0lNiTggRk0ecLdk9gvN/N5vMc00qCxOLjpXmCie1wS0+ckIfznwgCL4T4DsGwda9Bn3EUPQJk2MQVGwIIdBFkMPFnZeP6k9qdhWxfM5SKjbttDoUpRMq6Sm9Qxoxv4U9UXD+H+aSMStO66HZksNJ7XiLj2Ohj+bOC5g65iThuGRwLwVqnnFjGkkeFf1+qX1F04lyVp53O0f+/Z7VoSgdUElP6R0xHtc75VTiS58xtlfu1z3Rdh/7zrjeKS5Zx6zL0kjIH2R1KL0m2Ohl1TWfp+iZF60ORWmHSnpKL+m99UyOJMEFz80neczQnl2fmoDuNDkoU/TdNWHN5X13fLInpGGw8fPfZutXfow0Yv+UQ4mcSnpK70gYERrP6iWOZEHalBZJTwjGPXwFNs9ZO64IwaALQpMt8q+cypL9D3HdtjuxO2IwEy/ahcx99Ld1/yH3gJ3S//FP/sCHt36JYLPP6lCUsKh+jYQQPxFC7BFC7BBC/EsIkdritceEEMVCiL1CiEtbHJ8uhNgZfu2XQoTmYgshnEKIv4aPrxdCFLa45g4hRFH4444Wx4eFzy0KXxu7KYJKVITugUFLwdl7dddcmQkgBHOfvZWxX1jMlAdzuXbTrcz7w21ozvCPipR48tOY8/QtjLx9BprWgCA+66cJIUA3byF+b0lK77/7cUbi0EtvhJY0VNVYHYpC9H87rgDOkVJOAj4BHgMQQowHlgITgMXA00KIU9uvPwPcC4wKfywOH78HqJJSjgSeBH4Ubisd+CZwLjAL+KYQIi18zY+AJ6WUo4CqcBtKnBKaHZLngOidN+5xd+dz9dp7GLoogalfygfA5hYUXORm/IOLEVrox3//n/9D+gQ32XPitxBq81vH8P55JwT73mzI1ERzC9f2RSff38CK+bfQcOiI1aEMeFElPSnlcinlqd/CdUB++PNrgJeklM1SygNAMTBLCJEDJEsp18rQpnV/BD7V4prnw5+/DFwU7gVeCqyQUlZKKasIJdrF4dcuDJ9L+NpTbSlxSiQMg9z/gtSFQGzL0LgzBJ7B7U/+mPj5QUx54rrQom8pKX2nEl3Ugy213fOtZlRXIU/0zTfMFHkMe2qS1WFYrmZ3McvnLKFq28dWhzKgmTlKcDfwVvjzPKDlKs3S8LG88OdnH291TTiR1gAZnbSVAVS3SLot22pDCHGvEGKTEGJTWVlZt785xTzC5kGkzIKc23v1cefZxt6axpRvX4ctMYHG4w2htXGGH0QczmKxZrW8KYQQTLt5Utvx1AHIe6yMFQtu4eiy/lllvi/o8jdJCLFSCPFROx/XtDjnv4EA8MKpQ+00JTs53pNrOmur7QtSPiulnCGlnJGVldXRaUovEo5MyL4B7Nb9/xh7exrXb7+ZqY+E15LpHpAxmnTR91YcmGawrYQx1/bv6guRCtQ38v4Vn2Xfcy93fbJiui4HV6SUF3f2enhiyZXARfJMnY1SoOXCrHzgaPh4fjvHW15TKoSwASlAZfj4+Wdd8x+gHEgVQtjCvb2WbSl9hNAcyKwlUPZX8Pd+D1wIgdBF6Emr5g5VeI8FLQGCPR/bEkL04QULIX1wDk7MyGCQ9ff8Nw0lx5j4zS8i+tDeqn1dtLM3FwP/D7haStny3eI1YGl4RuYwQhNWNkgpjwF1QojZ4TG5TwOvtrjm1MzMG4B3w0l0GbBICJEWnsCyCFgWfu298LmErz3VltKHCLsLsq6M6f6cETF8ocebZtOTAK3N1mKRkoZEemOUjHtRfWV8zoq10kff/hXrP/PfGH71b9Nboh0o+BWQBKwQQmwTQvwGQEq5C/gbsBt4G/iClPJU0an7gN8TmtyyjzPjgH8AMoQQxcAjwFfDbVUCTwAbwx/fCR+DUMJ9JHxNRrgNpQ8S9gxItvrxVxAcJj9qtaWADIBR3/M2DJAVJ8yLySIJKQN76UJH9j/3D96/+j78dVH8jCgREwOx8u+MGTPkpk2brA5DOYsM1MDR/w0lCcvoYE8HBPhPRteULQ2CXpDRTdmXQYn3qWXRxRIHDCl46/kKAnUNVocSl9Kmjuf8N36LOyfb6lD6PCHEZillu39F990pYUq/I2wpkHahxVEEQ2OL0VZ7t2dAoCHqhAf0mwkwmpAkDe17m2b3lqqtu1k2ewnVu4qsDqVfU0lPiS+esaESPFZrLg3VAdR6sHzBng3+asCkraf6SdIDyJmcY3UIca2x5Cgr5t3M8XfWWh1Kv6WSnhJXhOaAnDsgfVFoPMxK/vLQjE5nHhFnHsfg8CzUYJenRqo/zezLzagjefTAKTXUE/6aOt5b/BkOvPCa1aH0SyrpKXFH2JIQSZMg+zqrQwlVL28+Ek58Xewg48gN1cjr84sLYscjKxkxv2fVLwYSGQiw9rYvs/vHv2MgzruIJZX0lLgl7BmWLlxvpbk0NLPTmR+O6axfHWce+GKzTLS/venV15jXC+7vtv2/n7Lp/icwgurfzCwq6SnxzZXf9Tm9xXc8lPz8ZeBsMTblzAv1BmOln73fjcspoWCR1ctT+o6iX7/A6uu+SKCh76/VjAcq6Snxzdk7Fde7rflIqFqEMz+2CQ/oVzNZCO8u0896r7F25LV3WbHwNrzHolxGo6ikp8Q5Z4d7iFtP84D0h5YnxFT/SxDu1DiYodvHVG3ZxbJzb6L6o0+sDqVPU7vhKXFN2DzI9MVQtdLiRevtCNaEPoQ9tIWaVNWxI9EoUjmyfp/VYcQ13e3ClujG5nFh87jJuWgsY2/PxeYGW+IyZJMb4YrTpyBxTiU9Je6JpHOQ9jQ48RerQ2mfLSW0vCFm+tfjzaC00VBy3OowukVoGrrbieZyorsc6G4HujP0X81uQ3fb0R02dKcdzWVDd9rQHTqa04bNbUN327An2NDdOja3ju7SsCXo2NwaNhfYXAJbgsDmBt3V1TKVZjjxMjLzcoRnTK/9G/QXKukpfYJw5SFT5kLNh1aH0lawIVR8NlAdm/b7V85DdjKqImzhxOGwI3QNoetodh1h00OV7jWBQCDsOrZwwhF2Hc0WOkd32dHsOrrThubQ0U8lIJcN3aWj2QS6S0d36OhODd2to9sFmlNDdwh0l8DmFOhOgeYA3QG6EzR7vK2XDEL568hgPSJ5utXB9Ckq6Sl9hkidiyETCG5dGS4FJEK1VXURSgwiVCJI2Ah9rcnwf0EIGRrB1sKvCQmaNOeNzPCCEaMafBCKNarrRagIrRCgaWc+Fxro2ukCtULTWp976nxND/9XIDQdEhIQbldoqNEIgpThdsPXhJMVtvCHroXLN2kgwV0vOceTR7A5SMrIFPIvSEC3g9BBaB38/3Dmh2bOKq1VvYcM1ELa+XGWlOOXSnpKn6KlTcFX+hbBTe+Y06AQoNnCb+ot3rhF6Gtx6g3/VAKAFglEhBOCACnRBg3GuWjkqYbPupFs8aG1PXZ6NuOpY+FsjQQtiOveKzg9oUUTpxOhEJxJ4qf/G349fCze3gxTgIlzB1kdRv9RtxmCdciMyxCaqmTRFZX0lD5HnzTPvKQnJQT9Ha6F604fS9rsEEg3JayWBCCSunPFqajjK9kpMdT4CQTrkVmfQugJVkcT19SSBaXP0VIyrQ5B6VV6bB8f9xfNR+H4i0h/ldWRxDWV9JQ+R+QORxs61uowlF4THogV6sFUlwLVocTX3Ldmx/YmlfSUPkdzurBf9mmrw1B6jT9c8UI9touI4YUTLyG9+62OJC6ppKf0SdroqTi/8htssy8Dd6LV4Six5siBYK3VUfQdMgAn/4Ws2251JHFHJT2lT9Jsdmz5I3He8ijux3+P64Gfn5ldaZU4myWpDHQSKlcgq1apvU5bUElP6fO0lEz0kZOwX36npXHI6kp8m7yhj42NBE+qXy/TqL8neq52A5S/gYy3bfwsokaGlX7DsegWjH07CX680ZL7y5pKAu+/f/prcdnF6NnqV8wU6g07Oo174EQ9MvtTCG1gb/at/hRV+hX7pbdaHUILqntiGrVkIXrNpXD8pdAOLgOYSnpKv6INm4Bt7uVWhxGicp551JiUOfzloSUNvoFbl08lPaVfEULguPEB9AmzrQ4F/6oNGJW61WH0E4bVAfQfwfpQj897wOpILKGSntLvCN2G866vo42eam0g9TU0/2sdRoVKfFGTKumZSvrg5D+RdTusjqTXqaSn9EvC4cR1zzfRp52PyLKu+rqsrqDp5TUEVeKLjqb+/cwnoXI5snrNgFrSoKaWKf2WcCfiuvNrSMMguHczeOtpfuGn4O/lSRH1NQT3VMGoNE5VUNASDUTCwHmjiZqhenoxU7MOArXIjEsRov//caGSntLvCU3DNm4mAMaxg/iXvdDrMQTWfUhg3Zmv7Ysvxj5B/fpFTiW9mGrYDcEGZNbVCM1pdTQxpR5vKgOKbdoFVoeg9IThtzqC/q/pUHhJQ73VkcSUSnrKgCISU8CTbHUYSrepxem9wl8Gx19A+sqtjiRmVNJTBhaHC/uFN1m+T2fwk8P41tbh3+6zNI6+Q41/9ppgHZz4C7KpxOpIYkIlPWVAMY4eQFaXWT4xwti/l8CHHxDYscfSOPoW9XbVa4xmOPEysn631ZGYTv0UKQNKYMt7BFa/anUYZ6gOTOQGwMzC+GJAxZvImvX9akmDSnrKgCEb6wisfcvqMJSeUknPGtWroXIlsp9sEKDmTCsDhqyrBl+T1WG0IsuP0/jbBhACITQQoA0fjvPCTKtDiz8q6VmnfjsE65CZVyI0h9XRREX19JQBQxs0BPtVn7E6jNakAfU1UFeNrK1E1lQiG7xWRxWn1NuVpbz74cRfkcEGqyOJivopUgYU+8VLsF91D7g9VofSMVWBvX1CvV1ZzncCjr2A9FdYHUmPqZ8iZUARQuC45Gbcj/8BfdoFiNxh2M69FJEx2OrQzvB6CZYKAsXg/yhA8Jj6NQ1RfwzEhWAtHP8LsqnU6kh6RPSnWTmRmjFjhty0aZPVYSgWk4aBrDyOlpmLrK/B98b/Eli/DALxtfuHbeF5OGa6rQ7DerZ0CFRaHYVymg6ZlyM8Y6wOpA0hxGYp5Yz2XlN/QioDltA0tMzc0OeJKTiXPIT9/Ostjqot4VTzzQD1eDPuBKH8dWTtZqsD6Rb1U6QoLehjp5vSjv28eTiXXm1KW8KpZi0CIINWR6C0p2Zd1+fEEfUnpKK0FO2+nB4PrjuvRnNVAT7cDy0leLAG3ys9Xx8Y2LmfQLEbkeDCMTcT4Rx4QxKA6unFK61vPXpXSU9RWopyPM/9masRtqrTXwu9GtsI0B9aSmD3CaTXS2DtJghEvoGycaj49OfBIYuxjYwqxD5MJb24FKhESiO0zrQPUElPUVqQ1T3YXWy/oRcAACAASURBVN7phOZmtMIChL2+3a3FhF6NfaITcGKfeiNGVTP+d9ZiHD3WzQBDRWgHJLWUI37JYJ/piaukpygtGCci3FnepuO88UqEx4lIDQACIZtAdt1TFPYq9GzQbl6I90/vwsmy6IIeMPrGm+qAI+wg+k4q6TuRKkovMPZ/FPrE4QRPAs7FCxHpbmgKEjxcjn/5e6HXNQ0tx40Q1T3eNFpQQ8LtMzDqkvGv3UVw50cRBDiAe3oq6cUn6QdvESSMtjqSiKikpyhhUgawzx+B45IRYAsgqAeaQx8u0FKd2MbfTPO/N2AU76P5X+twXTc22ruiJdXgXJSPvHQcEICgk+Y3NmN8UhT9N9WfDNRc3xeUvYZMnIzIuMTqSLqk/nRSlFOaStGSqhC2KgR1tNeFE3oVrmvGYT9/PsaBg8hgmmm3F7IOIb0IrRrXVSNwP7oEbVihae33fSrrxbX67X1iezKV9BTlFH+kY2sB7NMTsS+cC3rkszC7S1CD8/pp2BfMOX1sAG6g1IJKenGvDxSdVY83FeUUzznQfBwa90Z0un1mClAX05CEbMQ+KwXbjKWhAzY/39tUxm83bODhubN4cKIbKUNDfTYttLeoolimdhPSNQThLrQ6kg6ppKcoYUJ3I9MvBH8F+CNZutB73S6hVYc+MaDK20RpdTmPvvkmj7555pzt997AqGRwqd9qxTJBOPkyMu1CSJqMiMMaiOrXQ1FaELoH6cyNMOlZQ+ugMzf52ZdZdvv1XJJnfW/PkBJ/EJoNQVMQmoICn0H4c/AGJN6ApNmQBAyJLyhpCkqaAgbNQYPGQBB/UBIwDAKG5IszxpBjV3UG+4yqd8FbjMz6VNwVnVVJT1HOFscJDzp/hLn2WB1NwUQEoX6oIDRwL8MfhpSck6YzPLzb2h/2+Nh0vApDQtCQBI0gAUPiNwz8wSC+YBAJ2DSNuuZm/MEgQSkJGgZNAT/NgQBNAR++QICmgB+vvxlfIIAhDVO/5/unF4K/2tQ2lRhrKoHGIkicYHUkraikpygtSMMPzSesDqNTHfX0AL61cnmX198waQbnFQwmICX7a3z8dt1qE6OLDb8xoGfw9F0NH6ukpyhxzbsPiO/d/EWUsxhf3rGJl3eEPrdpOpmJqZTXx3cvyhff/0uUjjQdRPpOIhzZVkdymlqyoCgtxdn4Q3ucunm/tgEjyPicYaa1FysB1dPru2o2WB1BKyrpKUoLwj0cPOOtDqNTuskb++49fggtzjcLVkmvDwvWI00e441GfP+kK4oV3PHd85EmL5U4UVfJzKHRbqcWW03B+HnTVLqpuRTKXkXGyc4KKukpytmaSq2OoNc1d6O+nxWaVdLr27z7wLvf6igAlfQUpRUpDfAWd32ihWKxCm9b6ScUZuTGoGVz+FTS6/sa4mOLMpX0FKWlphIINlgdRaeMGO0Ek5+WFZN2zaB6ev1AnEwSU0lPUVpq+NjqCLqkxWjj5S0le0lyJsSk7Wj5gmrNQt8XH1uSqaSnKGHSMCBQZXUYlmn0NTFlSHwWAv3oZKXVISjRatyDPPYCsvwtZNC6LeVU0lOUMKFpkHY+pMyDxClWh9MhvbMtWaJUUnk8Zm1HY9ORY1aHoETLaALfMWjYBcdfQBrNloShkp6itCCcuYjUOYiMiyH3bnDF3/KFWFYPOlR5nGlDxsTuBj1U02zNG6QSI4FqOPw/yNotvX5rlfQUpQPCng5ZV4PmtjqUVrQY18yLx5p8tU1NVoegxELVu8iKFcjGfb22js+UpCeE+JIQQgohMlsce0wIUSyE2CuEuLTF8elCiJ3h134pwr9hQginEOKv4ePrhRCFLa65QwhRFP64o8XxYeFzi8LXxsf0IKXfEJo99Mize1fFIpReah02l+yhIG1QjO/SPdVNqqxQv1W/Hcr+FXrs2Qui3nBaCDEEuAQoaXFsPLAUmADkAiuFEKOllEHgGeBeYB3wJrAYeAu4B6iSUo4UQiwFfgQsEUKkA98EZhCqjrJZCPGalLIqfM6TUsqXhBC/CbfxTLTfk6K04hkHlStAc4E9I9TzsyWCMy/0teEDPQHQQs8etQRoPgJNh0A4QPpCnzcfNSWcWPf0AIZm5FBSFT/VJg5UVSIRiF4s3Kv0sqr3kY7smG9ObUaVhSeBrwCvtjh2DfCSlLIZOCCEKAZmCSEOAslSyrUAQog/Ap8ilPSuAb4Vvv5l4FfhXuClwAopZWX4mhXAYiHES8CFwC3ha54PX6+SnmIqITRk/n0IzRn5Ra780Mdp85CNxaHk11gMwboex3O8IfaP+raU7CHR6aa+OT56WEFDYggnulSPOfstwwsNeyCek54Q4mrgiJRy+1njAHmEenKnlIaP+cOfn3381DWHAaSUASFEDZDR8vhZ12QA1VLKQDtttRfrvYR6mBQUFET+TSoKdC/hddRGwkhIGIlMnQ/VH0KwHhJGASL0y+7dH+oppi4AWzLYUiBQA74TULMewr2c/ZU1UcfSlQZfEwtGTmZ18faY3ytSPunAjUp6/ZaWAMIe89t0mfSEECuBwe289N/A48Ci9i5r55js5HhPrumsrbYvSPks8CzAjBkz1DMSxTJCc0L6Ba0PesYggw0ggwhb8pnjjmxIGIV0j4T67by55yOWf9I72zkdqY6vCvINQQ13fKxvVkynQ9aVCFfsOyRdTmSRUl4spTzn7A9gPzAM2B5+bJkPbBFCDCbU6xrSopl84Gj4eH47x2l5jRDCBqQAlZ20VQ6khs89uy1F6XOE7mmd8Fq+5hyMyLiUS869n5umX9Qr8ewvP8KkvJG9cq9IVDerrcj6rYxLeiXhQRSzN6WUO6WU2VLKQillIaHkNE1KeRx4DVganpE5DBgFbJBSHgPqhBCzw+N1n+bMWOBrwKmZmTcA78rQHNZlwCIhRJoQIo1Qz3JZ+LX3wucSvrbluKKi9DsOm4PnPv01nl765V6pgeeyx8+E6LJGv9UhKLHgHoVIPKfXbheT3xop5S7gb8Bu4G3gC+GZmwD3Ab8HioF9hCaxAPwByAhPenkE+Gq4rUrgCWBj+OM7pya1AP8PeCR8TUa4DUXp9+4773refehX3DDtwpjeZ9OhPQxKTo/pPSJV4fVZHYJiJkc2pMyGzMt79bZmzN4EINzba/n194DvtXPeJqBNWpdSNgE3dtD2c8Bz7RzfD8zqWcSK0redN3oa80ZMwqbpvLRpRUzuYUiD0dlDOFFr/d6Xw1ITgJ7PelXiiOaGrOsQtsTev3Wv31FRFNPYdBsv3v0ddn3jL1wwenpM7iFivhw+MgWJ6u2qX0idDzl3WJLwQCU9RenzhBCMzxnGIxffbHrbC0ZO4VhNhentdpcAErX4rnOodCH5XMi5HZEy27KEByY+3lQUxVrnFk7ArtvwBwNdnxyhgBGkqOxw1yfG2OLRYxDSvO9L+f/t3XmYFNW5+PHv292zT88+wKwwM6AoIiogoEBwF2Pcjch1ieaaiF6jiUs05MbERPNzNyY3GL0mrlxN1MSFa7iQuG8RxAURZZd9HRhm663O748uYAZmn+mumu738zz9TPep7a1D0W9X1alzYsuIDyMpWJJCGC9hr5/svImu6NdVk55SCaLYn8+0MSfx5Aevdj5zFzn/FRV16rBqp0NIeAYBScGID0t8RPASxkPICEGEoGUIGkOjFaHJitAUidAYCbE7HKQu3MyuUDM7Q43sCDUQNq0fLzms8DCuKXHH0aRJT6kEcuNJ/8bfP3+frfXdHwy3PG8AVUUlGGMwJtqIZW3tlhhE2X0nVZUAdU6H0e9t9RTxZVMjDZEwjeEgteFmNjTtYmeokfpI7IZv2tTgnnEaNekplUBGlg1l1oU3cv3zD7KmGwPCjh18CF9u/tpV3Y61VO23OuhvSXXVquYAT62L7xh2qZ5Upg6J72MJHdGkp1SCOfeo4zlh+Fi+88QvefGTNzuc1+fxMqF6JG8t/zhO0XXfNw8+mFRT73QYjjOw7/IjPizxEMFLBCFkhBBCyEQvQQYtQ9CyaLIiNFsRmiJhmq0wS+pXxj3uQVklHFl8VNy32x5NekoloLxMP3/9/p089a+/8/WOTfxj6QJeX/ZRq4E6S3OLyM3IdnXCA/jRhDFA/xk5PZqcfIAXI14s8RLGRxAvYQNhE50naCBoDAHLoslEaI5EaIyEqQ8H2B0J0hgJUm/fL6sPN9MY6X8P5xelF3HV4f9BVopzrTX3p0lPqW4yoVrw5fWoJZoxBnYvjI7NlzoQUopi1qJNRLh43FQAZk69jLqmBn7y4iyeW/RPaht20xQKsGGXuzqVbsvRg9KIV9ILeXNYFfYRtgxhYxE0FgHLotkKE7QsQlaEoGURsMKETISQsWgMB2mIBGiMRP82RbS7NICBmYP4+bjb4tJdXndo0lOqC4xlQeMyCG2BhiWQNwmTORTxdLNvShOODp+yY370fUY1RlLARCAjOvSQeNOjsxoDjUshuB2yRiKpub3ah5yMLH437QbuP/86HntvDu+s+ISlm9awfOs6tje0PVzRuCEjsIwhOy2DgTkFNASamPvFBwTDsf9iH5JXwCB/NpltNGCxPBk8vGUbgUiYZitEUyTI7nCAoBVmxuCjyfF6iQAWhoiBCp9FWmRf455oS0Xf3kuFRgQLD2tCHu5b9W7M9y0ZBCIBVtWtpCbXPZ2WgyY9pbomsBsideDNiH6uex9CWyH/GwfMahpXgDcbrEZoXI4UnrR3mnhSwD8KYwVg55vRMfT2aFoOdfmY4jOi4+lFGmHbHHulob3DEVlWtDm4x+OhIdBEVlpGt3YlxevjiolncsXEM/eWvfTJm0z/061cMm4q3z3mDA4eWEkoEiYv03/AmeiSjas45bfXsi6GLTuzUlP5v0vOYmhm+y02P97V9vODD65+74CybG8aPvHQbIUJWCFtExMHOwO1rN29VpOeUv2RZORCxlgAjP8opMUlG2MFYPvfwZsD4drWiSytAmPMgZcwQ+30ZRmuhY2Ps+8JOS+klUD+pL2zNASb+GLTairzBpKX5e+DvYMzRk1m4c2PUVNchs/b8dfCoSVVvDzjHo6953sEwiEiVqTD+XviN988td2EV+spYs72jd1aXyyb46v2vbH+NaaUH9f5jHEkLW9sJ4sxY8aYBQsWOB2GSiCmeS1sfg5oIwGklUbv3+VN2ns51AQ2Rc/iwvs9Tyc+yD4csg+LvvdmIp702O9AD7y6+F2+P/tO1tZu7vN1XzhqFE9NHdbmw/H3bdrBVw3ueH5QdeyX429nQObAuG9XRBYaY8a0NU3P9JTqA5Jegck5Eura+DEV2BB9ZR4M6dExlCVtEKb0cgjVglUPVhg8aeDLQXx9c/YWa6eOmMDggkG9SnqThh7Bmh0bCYbDeEQIhIPUB5p49tPPqM7P57YJpYiJtlq0POkEJJOg2dpXu6BibMWu5Y4kvY5o0lOqr+QeC/VLovfyWvKPhpzR0eFUWhARSC0A3DFeXXeJCDedfBFvz+r5A+2C8PWOtpPm7a+/zturq3jlguPIkjpq8TPzq7d6vC0Vf1ub3Nc62F1tSZXqx8STAiUXQ9YI8BVEG7kMuhgpOA7x5USnJ5jTR07k4IGDe7TsIYOGsGjtlx3O88bqVQy6/ykW7czmbReM9qC6550NbxG23NVRuCY9pfqQ+PxI0VQovQTJGYukuevSTl8TEa6Z0ubYzx2aVDOKDbu2sTvQ2Om8DcEgox96jIU7236sQrnXzuBOdjS768eKJj2lYkAkee4cfHv0CWSnZXZ5/hEl1by98lN2NXWva7F7//IaRamDuhuecti2Zndd4tSkp5TqlWJ/PkOLy7s8f36mn560Gm8KBvmfeZ+S5eAApKr7Pt++2OkQWtGkp5TqlYVrlnZ5oNnDy4by9oqeN3xZtWULn3xWjzeJzqT7s+PLT+S8od92OoxWNOkppXrMsizOffhmGgJNrcoLsnI4tvpwhhSWkJOehYiQ6kshP7P3j2P8c/Filn0RJt9U9HpdKrZ2B+swLuv/Rn8uKaV6zOPxcPrIiTz89t8IRaKt9Dzi4ZwjplCRP5Cc9CwmDzuCivyBzPv8A3497wlSfSm97rtz/meLee3zJXz33EP7YjdUjERMxHUdTrsrGqVUv/PA+ddRe+88/nLFHYwoqcYyFkeWH8S4qhFcc9z5VBWWUuzPZ/r4U3nnhkcYUzm8T7YbsSyKvRUUpw0iJQEfB0kElrGcDuEA2g2ZUqrPWJbF+6sWM65qBF6Pt815ttTtYPxd/86q7Rv6bLsThw9n3FEF7A6130G1ir/vHHIZE0qOjft2O+qGTM/0lFJ9xuPxcEzN4e0mPIABOQXM/cEDVBb03eMHby9dyuxXvyA3Ja/P1ql6792N7humSZOeUiruhg2o5N0bH2bysCP7bJ3DS0upC+kD7G6RlZLNBQdNczqMA2jSU0o5oixvAK9d91/MuvAmMlN7N5KEz+tl3BEDXNdSMFkJwqTSyZRnu6+FrbbeVEo5xuPxcOXkcxg7+FCeXTiPZxbM79GoDTNOPY4dwU0xiFD1xKSyyZxdc47TYbRJz/SUUo4bPXg4d51zDZ//bDZXf+O8bi07vLQUK9td/Tsms3RvBt+qOsPpMNqlSU8p5Rr+9Cx+N+0GHr14JjVd7NrsvCmjCFm9e+5P9Q1vWBiaUklOaq7TobRLL28qpVzn8mO+xbdGTuSYu69g+dZ1Hc5rSbDV5+hTWCY6XqHqPmNID3rxN6Xib/CS2eAhvRHSmgwpDRYpjRFSNjcjX9di6puxdjcRqW/CP7QSb0oqh9w01uk96JAmPaWUKxX785l7zW848o5LqGtuaHe+OW+s4esdmwmGQwTCIcJWhO9PnYyVtT1pEp9EICPkJT3oJSPoJS3gIb3ZQ+ZuSK83+JotfM0WnqYI3sYwnsYQNIRgdzOmrhmrvolIXQPh+iZMMISx2n6o3ABB+9XSsKv/jaPuuxlvamqsd7XX9OF0pZKEsSJABPF0/4tpz/eEiGBMfM+iXv70Lc6YdWOb0wqzcklPSWX9zq0HTCvLK2bCwcMoGghWmrMPrRtL8ODDY/nA8mIsH1gCEq1XX1g4fGWYlGZDSrPB12TwNkfwBCJ4miJIYwipD0JDENMUwDQGsRoDROobCdc1YELODtQqHg9nrXuDjJIBjsaxR0cPp+uZnlLJIrgZvJnQg6QHcOMLv+XlT99m6IBynr7sF+T1QefRXXH6yIms+OXzrNi6ju8/fSertm/goAGV5Gf6Wbp5TZsJD2D9zq0890F02tljxjOoHLAEY0UfnBePhbGizRrEF8ZEvGAEY0n0L2AigmVBJALhiMGKWIQjhlDEIhyOEIpECLX4GwyHaQ4FaQ4FaQwGaAoGaAg27+2XtD2DJZsJs/aNVBGxX/2FsSw2zX+XqovPcjqUTumZnlKqSxqDzXz7kZl8tPZLSnOLGD5wMLd963tUF5fFLYbt9bs47oGryM/w8+byj+O23VjLIZVZD3X/UQ238KSmMHXR38g9dKjToQDaDZlSqg9kpqbzytX3sv7XL/PQ9B/z+rKPqPnZuQyZeRa3zXmUbfU7Yx5DYXYuc666D0ise3V1JuB0CL0yYuaVrkl4ndGkp5TqFhFhzOBDOOmQowFYs2MTt77yCC8sej0u268oGMhd51zN8Qe3+UO+fxLBm5XhdBQ99tWDT/L+d39C/eqOW9q6gSY9pVSPbNzV+oHw659/kDe++igu2x5XdRjzr/0t9513revGa+spb1587pHGQmD7Tlb+6QXqV6ztfGaHJcbRopSKuyGFJa0+1wcamf7HW1mycVVcti8i/PCEC3n6sl8kxKMJ4s90OoTeMYbGde7vCk5bbyqleuT+865lSGEJOxp2UZSdx+INK3nyg1f573de5L7zrotbHNPGnsSKbev46Ut/iNs2Y8H4++/lzT2aNrbdktZNNOkppXokIzWdm0+5pFXZ9SdOpzJ/YNxjueWUS3lv5WLmLH4n7tvuK+Fs9z/Y3Zng9lqnQ+iUXt5UyuUWr1/BTS/8lg9XL3E6lE6NKh9GflZO3Lfr8Xh47NL/pDS3OO7b7iuh9BSnQ+i1jPK+Gxg4VjTpKeVyD/zzGe6e9zTH3H0FU+6bwfsrFzsdkisVZefx9OU/x9fBqO1uFkzrn3Hv4c3MYPAFpzkdRqc06Snlcveffx3XnzidYn8+byxbxIkPXkNDoMnpsFxpykGjuf3MK50Oo0cCKf3767j42KPIGOT+M229p6eUy/nTs7jn3B9wx5kzWLltPSleH1lp/b/RQ6zceNJFvLvyM1785E2nQ+mWQEr/boHasHo9tR9/Qf4RhzgdSof6908LpZJIqi+F4YOGdHmcuWQlIjx60UwOLalyOpRuafb17y4hdy9bzcIf3oEVDuPm7i31TE8plXAKs3O548wZTP/jz2gMNjsdTpc09+9begBsfWsh73/nZlLychhy4TdZ/shfCDc0Muj48QybMd3p8ADtcFoplaCMMQy79XxWdDIIrRtUiZ+73/ASWBKfB/udcMaK+WRXV8RlW9rhtFIq6YgIPzx+GmV57m9c4YGETngAq2e/TMOa9U6HoUlPKZW4TjvsmHbH23OT2gPGIk88n/7nb9ix6Aunw9Ckp5RKXFVFpf1iNIZaq3/cd+ytwqNHOh2CJj2lVOeMMTz+3hz+sfRDp0PptptOvsjpEDplRPCkpzkdRsx9/ON7nA5Bk55SqnMiQmF2LlfOvpPNdds7X8BFJg89glSf+7v48mYkftIzEcvpEDTpKaW65vSRE8lOy+Te+bOdDqVbMlLTObzM/aN6SxIkPU+K80/JadJTSnXZz0//d+6ZP5tnF8xzOpRumTLsKKdD6FSiJz1vehrDZlzodBia9JRSXXfmqMlMGXYUM/7nbr7a/LXT4XTZYaXVTofQuYz+P7RQe3z+LE5Z8DxF449wOhRNekqp7vnOhG9S21jH0Xdezt8+fsPpcLpkRD9IeiY9cZNe9WXnkDdimNNhAJr0lFLddPG4qVw8biq7muq54NGf8uQHrzodUqeaggGnQ+iU8Sbu13Fo526nQ9grcWtZKRUTIsLtZ1yJiBAMh7jksV9w25xHXdvJ8KZd25n6Xz90OoxOWanON/LorZyDq0i3hxfypKVSMHoEk1+axdjf3+pwZPv0/1pWSsVdRcFA3r3hES55/Bcs27KWW195hK+2fM0Tl96Kx+Ou39IFWTkcOqiKD9e4e+T5SHr//Tr2+bPIHlLGhCfuIv+IQ7AiEUI760grzHc6tAP031pWSjlqfPVhvHPDw/zoud9w3MGjmVQziu0Nuyj2u+uLLtWXwiMX3cK982azeOMKFq39yumQ2hRO9br6CzmjpJjsmkqyqsrJHlJGdk0l2TUV+IcOJn1gESL7xgP0eL2uTHigSU8p1QvF/nz+dPFP8fnc/VVSU1TGgJx8tnxV63Qo7Qr7PI5+IXsz0smuKieruoLs6nL8NZVkV1eQXV1BVlU5vox0B6PrO+4+UpVSruf2hAfQHA5y7/zZpPnc20LSG4feSjJKiqNnalXl9plapf2+goySAa3O1hKV+49WpZTqpWA4xMiyGq6cdDb+9Cwue+JXRKyI02G1Ekrx0NvO0jxpqdEkZp+hZddU7HtfXYEvM6NPYu3PNOkppRJeYVYuJw4fy6w3/4plLH4/7Ubu/L8nWbnN+fHd9gikesjswnyp+bnRs7U9lyBrKvEPG0x2TSWZZQMRlzUkchtNekqphJeWksrZo6Zw77nXUh9o5LP1K/jTJT/lG/fNcDq0vSL2lUXx+ciqLIleeqzed9bmHzqYrKpyUnP9zgbaz/U66YnINcB/AGFgjjHmJrv8FuC7QAT4gTFmrl0+GngMyAD+F7jWGGNEJA14AhgNbAcuMMastpe5FPipvclfGWMet8urgGeAAuAj4GJjTOKPxqiU6rZJw6JdYPnTs8jP9BO2IuRmZLOrqT7useRl+KkuKqW6uIyaojKqi8oYn1VG9a9/RWb5IDz94D5pf9WrmhWR44AzgcONMQERGWCXHwpMA0YApcB8ETnIGBMBZgHfA94nmvROBV4lmiBrjTFDRWQacCdwgYgUALcCYwADLBSRl4wxtfY89xtjnhGRh+x1zOrNPimlEt8hJVV8uHoJ1584nZ+9/HCfr98jHioLBlJdVEZ1USk1xeXRJFcUTXL5WTl9vk3VNb39OTED+H/GmACAMWaLXX4m8IxdvkpElgNHi8hqIMcY8x6AiDwBnEU06Z0J/Nxe/jngdxJtSnQKMM8Ys8NeZh5wqog8AxwPTLeXedxeXpOeUqpTby5f1KvGLDnpWXuTWnVRGTXFZXs/Dy4sIcWrZ2tu1Nt/lYOASSJyO9AM3GCM+RAoI3omt8c6uyxkv9+/HPvvWgBjTFhEdgGFLcv3W6YQ2GmMCbexrgOIyPeInmFSWVnZ7R1VSiWWlVs38Ps3n293ukc8lOcPYGhxOTXFZVQVlu5NcFVFpRRm5SZFE/9E02nSE5H5wKA2Js20l88HxgNjgT+LSDXQ1pFgOiinB8t0tK4DJxjzMPAwwJgxY9zZSaBSKm4mDT2Cpz+cuy+R7UlqxfbZWkFJvxhxXXVPp0nPGHNie9NEZAbwgon2NPsvEbGAIqJnXRUtZi0HNtjl5W2U02KZdSLiA3KBHXb5lP2WeR3YBuSJiM8+22u5LqWU6tC3R5/AtLEnOR2GirPePtDxN6L31RCRg4BUosnoJWCaiKTZLSyHAf8yxmwEdovIePt+3SXAi/a6XgIutd+fB/zTTqZzgZNFJF9E8oGTgbn2tNfsebGX3bMupZTqkNs6xlbx0dt7en8E/igii4EgcKmdjD4XkT8DS4g+ynC13XIToo1fHiP6yMKr9gvgUeBJu9HLDqKtPzHG7BCRXwIf2vPdtqdRC/Bj4BkR+RWwyF6HUkop1SZx6xhYsTRmzBizYMECp8NQSikVAyKyfmquawAABO1JREFU0Bgzpq1pen6vlFIqaWjSU0oplTQ06SmllEoamvSUUkolDU16SimlkoYmPaWUUklDk55SSqmkoUlPKaVU0tCkp5RSKmlo0lNKKZU0NOkppZRKGpr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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_31_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "counties.plot(column='POP12_SQMI',\n", + " legend=True, \n", + " cmap=\"RdYlGn\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[50,100,200,300,400]},\n", + " ax=ax)\n", + "\n", + "# Create the labels for the legend\n", + "legend_labels_list = ['<50','50 to 100','100 to 200','200 to 300','300 to 400','>400']\n", + "\n", + "# Apply the labels to the plot\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's plot a ratio\n", + "\n", + "If we look at the columns in our dataset, we see we have a number of variables\n", + "from which we can calculate proportions, rates, and the like.\n", + "\n", + "Let's try that out:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FID_NAMESTATE_NAMEPOP2010POP10_SQMIPOP2012POP12_SQMIWHITEBLACKAMERI_ES...AVG_SALE07SQMICountyFIPSNEIGHBORSPopNeighNEIGHBOR_1PopNeigh_1NEIGHBOR_2PopNeigh_2geometry
00KernCalifornia839631102.9851089104.2828704997664892112676...1513.538161.3506103San Bernardino,Tulare,Inyo2495935NoneNoneNoneNonePOLYGON ((193446.035 -244342.585, 194033.795 -...
10KingsCalifornia152982109.9155039111.42742183027110142562...1203.201391.3906089Fresno,Kern,Tulare2212260NoneNoneNoneNonePOLYGON ((12524.028 -179431.328, 12358.142 -17...
20LakeCalifornia6466548.66525349.0823345203312322049...72.311329.4606106None0NoneNoneNoneNoneMULTIPOLYGON (((-240632.150 93056.104, -240669...
30LassenCalifornia348957.4350397.4228562553228341234...120.924720.4206086None0NoneNoneNoneNonePOLYGON ((-45364.032 352060.633, -45248.844 35...
40Los AngelesCalifornia98186052402.399043412423.264150493659985687472828...187.944087.1906073San Bernardino,Kern2874841NoneNoneNoneNoneMULTIPOLYGON (((173874.519 -471855.293, 173852...
\n", + "

5 rows × 59 columns

\n", + "
" + ], + "text/plain": [ + " FID_ NAME STATE_NAME POP2010 POP10_SQMI POP2012 POP12_SQMI \\\n", + "0 0 Kern California 839631 102.9 851089 104.282870 \n", + "1 0 Kings California 152982 109.9 155039 111.427421 \n", + "2 0 Lake California 64665 48.6 65253 49.082334 \n", + "3 0 Lassen California 34895 7.4 35039 7.422856 \n", + "4 0 Los Angeles California 9818605 2402.3 9904341 2423.264150 \n", + "\n", + " WHITE BLACK AMERI_ES ... AVG_SALE07 SQMI CountyFIPS \\\n", + "0 499766 48921 12676 ... 1513.53 8161.35 06103 \n", + "1 83027 11014 2562 ... 1203.20 1391.39 06089 \n", + "2 52033 1232 2049 ... 72.31 1329.46 06106 \n", + "3 25532 2834 1234 ... 120.92 4720.42 06086 \n", + "4 4936599 856874 72828 ... 187.94 4087.19 06073 \n", + "\n", + " NEIGHBORS PopNeigh NEIGHBOR_1 PopNeigh_1 NEIGHBOR_2 \\\n", + "0 San Bernardino,Tulare,Inyo 2495935 None None None \n", + "1 Fresno,Kern,Tulare 2212260 None None None \n", + "2 None 0 None None None \n", + "3 None 0 None None None \n", + "4 San Bernardino,Kern 2874841 None None None \n", + "\n", + " PopNeigh_2 geometry \n", + "0 None POLYGON ((193446.035 -244342.585, 194033.795 -... \n", + "1 None POLYGON ((12524.028 -179431.328, 12358.142 -17... \n", + "2 None MULTIPOLYGON (((-240632.150 93056.104, -240669... \n", + "3 None POLYGON ((-45364.032 352060.633, -45248.844 35... \n", + "4 None MULTIPOLYGON (((173874.519 -471855.293, 173852... \n", + "\n", + "[5 rows x 59 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What new options and operations have we added to our code?\n", + "1. Based on our code, what title would you give this plot to describe what it displays?\n", + "1. How many bins do we specify in the `legend_labels_list` object, and how many bins are in the map legend? Why?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.4 Point maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choropleth maps are great, but mapping using point symbols enables us to visualize our spatial data in another way. \n", + "\n", + "If you know both mapping methods you can expand how much information you can show in one map. \n", + "\n", + "For example, point maps are a great way to map `counts` because the varying sizes of areas are deemphasized.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------\n", + "Let's read in some point data on Alameda County schools." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYSiteAddressCityStateTypeAPIOrg
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933Public
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932Public
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853Public
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927Public
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894Public
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" + ], + "text/plain": [ + " X Y Site Address \\\n", + "0 -122.238761 37.744764 Amelia Earhart Elementary 400 Packet Landing Rd \n", + "1 -122.251856 37.738999 Bay Farm Elementary 200 Aughinbaugh Way \n", + "2 -122.258915 37.762058 Donald D. Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org \n", + "0 Alameda CA ES 933 Public \n", + "1 Alameda CA ES 932 Public \n", + "2 Alameda CA ES 853 Public \n", + "3 Alameda CA ES 927 Public \n", + "4 Alameda CA ES 894 Public " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We got it from a plain CSV file, let's coerce it to a GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can map it." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Alameda County Schools')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_44_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf.plot()\n", + "plt.title('Alameda County Schools')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Proportional Color Maps\n", + "**Proportional color maps** linearly scale the `color` of a point symbol by the data values.\n", + "\n", + "Let's try this by creating a map of `API`. API stands for *Academic Performance Index*, which is a measurement system that looks at the performance of an individual school." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Alameda County, School API scores')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_46_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf.plot(column=\"API\", cmap=\"gist_heat\", edgecolor=\"grey\", figsize=(10,8), legend=True)\n", + "plt.title(\"Alameda County, School API scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you see that continuous color bar in the legend you know that the mapping of data values to colors is not classified.\n", + "\n", + "\n", + "### Graduated Color Maps\n", + "\n", + "We can also create **graduated color maps** by binning data values before associating them with colors. These are just like choropleth maps, except that the term \"choropleth\" is only used with polygon data. \n", + "\n", + "Graduated color maps use the same syntax as the choropleth maps above - you create them by setting a value for `scheme`. \n", + "\n", + "Below, we copy the code we used above to create a choropleth, but we change the name of the geodataframe to use the point gdf. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Alameda County, School API scores')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_48_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (15,6)) \n", + "\n", + "# Plot percent non-white with graduated colors\n", + "schools_gdf.plot(column='API', \n", + " legend=True, \n", + " cmap=\"Blues\",\n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[0,200,400,600,800]},\n", + " edgecolor=\"grey\",\n", + " linewidth=0.5,\n", + " #markersize=60,\n", + " ax=ax)\n", + "\n", + "# Create a custom legend\n", + "legend_labels_list = ['0','0 - 200','200 - 400','400 - 600','600 - 800','>800']\n", + "\n", + "# Apply the legend to the map\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "# Create the plot\n", + "plt.tight_layout()\n", + "plt.title(\"Alameda County, School API scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the syntax for a choropleth and graduated color map is the same,\n", + "although some options only apply to one or the other.\n", + "\n", + "For example, uncomment the `markersize` parameter above to see how you can further customize a graduated color map." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graduated symbol maps\n", + "\n", + "`Graduated symbol maps` are also a great method for mapping points. These are just like graduated color maps but instead of associating symbol color with data values they associate point size. Similarly,graduated symbol maps use `classification schemes` to set the size of point symbols. \n", + "\n", + "> We demonstrate how to make graduated symbol maps along with some other mapping techniques in the `Optional Mapping notebook` which we encourage you to explore on your own. (***Coming Soon***)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.5 Mapping Categorical Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mapping categorical data, also called qualitative data, is a bit more straightforward. There is no need to scale or classify data values. The goal of the color map is to provide a contrasting set of colors so as to clearly delineate different categories. Here's a point-based example:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1_53_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf.plot(column='Org', cmap='bwr',categorical=True, legend=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.6 Recap\n", + "We learned about important data driven mapping strategies and mapping concepts and can leverage what many of us know about `matplotlib`\n", + "- Choropleth Maps\n", + "- Point maps\n", + "- Color schemes \n", + "- Classifications" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise: Data-Driven Mapping\n", + "\n", + "Point and polygons are not the only geometry-types that we can use in data-driven mapping!\n", + "\n", + "Run the next cell to load a dataset containing Berkeley's bicycle boulevards (which we'll be using more in the following notebook).\n", + "\n", + "Then in the following cell, write your own code to:\n", + "1. plot the bike boulevards;\n", + "2. color them by status (find the correct column in the head of the dataframe, displayed below);\n", + "3. color them using a fitting, good-looking qualitative colormap that you choose from [The Matplotlib Colormap Reference](https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html);\n", + "4. set the line width to 5 (check the plot method's documentation to find the right argument for this!);\n", + "4. add the argument `figsize=[20,20]`, to make your map nice and big and visible!\n", + " \n", + "Then answer the questions posed in the last cell.\n", + "\n", + "
\n", + "\n", + "\n", + "To see the solution, double-click the Markdown cell below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0Heinz/RussellRUS7th8thRUS01E/WExistingNo101.1281660.101MULTILINESTRING ((562293.786 4189795.092, 5623...
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" + ], + "text/plain": [ + " BB_STRNAM BB_STRID BB_FRO BB_TO BB_SECID DIR_ Status \\\n", + "0 Heinz/Russell RUS 7th 8th RUS01 E/W Existing \n", + "1 Heinz/Russell RUS 8th 9th RUS02 E/W Ezisting \n", + "2 Heinz/Russell RUS 9th 10th RUS03 E/W Existing \n", + "3 Heinz/Russell RUS 10th San Pablo RUS04 E/W Existing \n", + "4 San Pablo RUS Heinz Russell RUS05 N/S Existing \n", + "\n", + " ALT_bikeCA Shape_len len_km \\\n", + "0 No 101.128166 0.101 \n", + "1 No 100.814072 0.101 \n", + "2 No 100.037396 0.100 \n", + "3 No 106.592878 0.107 \n", + "4 No 89.563478 0.090 \n", + "\n", + " geometry \n", + "0 MULTILINESTRING ((562293.786 4189795.092, 5623... \n", + "1 MULTILINESTRING ((562391.553 4189820.949, 5624... \n", + "2 MULTILINESTRING ((562489.017 4189846.721, 5625... \n", + "3 MULTILINESTRING ((562585.723 4189872.321, 5626... \n", + "4 MULTILINESTRING ((562688.854 4189899.267, 5627... " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What does that map indicate about the status of the Berkeley bike boulevards?\n", + "1. What does that map indicate about the status of your Berkeley bike-boulevard *dataset*?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1.py b/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1.py new file mode 100644 index 0000000..7a895b0 --- /dev/null +++ b/_build/jupyter_execute/ran/05_Data-Driven_Mapping-Copy1.py @@ -0,0 +1,498 @@ +# Lesson 5. Data-driven Mapping + +*Data-driven mapping* refers to the process of using data values to determine the symbology of mapped features. Color, shape, and size are the three most common symbology types used in data-driven mapping. +Data-driven maps are often refered to as thematic maps. + + +- 5.1 Choropleth Maps +- 5.2 Issues with Visualization +- 5.3 Classification Schemes +- 5.4 Point Maps +- 5.5 Mapping Categorical Data +- 5.6 Recap +- **Exercise**: Data-Driven Mapping + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/california_counties/CaliforniaCounties.shp' + - 'notebook_data/alco_schools.csv' + - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson' +- Expected time to complete + - Lecture + Questions: 30 minutes + - Exercises: 15 minutes + + + +### Types of Thematic Maps + +There are two primary types of maps used to convey data values: + +- `Choropleth maps`: set the color of areas (polygons) by data value +- `Point symbol maps`: set the color or size of points by data value + +We will discuss both of these types of maps in more detail in this lesson. But let's take a quick look at choropleth maps. + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +# 5.1 Choropleth Maps +Choropleth maps are the most common type of thematic map. + +Let's take a look at how we can use a geodataframe to make a choropleth map. + +We'll start by reloading our counties dataset from Day 1. + +counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp') + +counties.head() + +counties.columns + +Here's a plain map of our polygons. + +counties.plot() + +Now, for comparison, let's create a choropleth map by setting the color of the county based on the values in the population per square mile (`POP12_SQMI`) column. + +counties.plot(column='POP12_SQMI', figsize=(10,10)) + +That's really the heart of it. To set the color of the features based on the values in a column, set the `column` argument to the column name in the gdf. +> **Protip:** +- You can quickly right-click on the plot and save to a file or open in a new browser window. + +By default map colors are linearly scaled to data values. This is called a `proportional color map`. + +- The great thing about `proportional color maps` is that you can visualize the full range of data values. + + + +We can also add a legend, and even tweak its display. + +counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True) +plt.show() + +counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True, + legend_kwds={'label': "Population Density per m$^2$", + 'orientation': "horizontal"},) +plt.show() + +
+ +
+
+ +#### Question +
+ +Why are we plotting `POP12_SQMI` instead of `POP2012`? + +Your response here: + + + + + + +### Note: Types of Color Maps + +There are a few different types of color maps (or color palettes), each of which has a different purpose: +- *diverging* - a "diverging" set of colors are used so emphasize mid-range values as well as extremes. +- *sequential* - usually with a single color hue to emphasize changes in magnitude, where darker colors typically mean higher values +- *qualitative* - a diverse set of colors to identify categories and avoid implying quantitative significance. + + + +> **Pro-tip**: You can actually see all your color map options if you misspell what you put in `cmap` and try to run-in. Try it out! + +> **Pro-tip**: Sites like [ColorBrewer](https://colorbrewer2.org/#type=sequential&scheme=Blues&n=3) let's you play around with different types of color maps. If you want to create your own, [The Python Graph Gallery](https://python-graph-gallery.com/python-colors/) is a way to see what your Python color options are. + + +# 5.2 Issues with Visualization + +### Types of choropleth data + +There are several types of quantitative data variables that can be used to create a choropleth map. Let's consider these in terms of our ACS data. + +- **Count** + - counts, aggregated by feature + - *e.g. population within a census tract* + +- **Density** + - count, aggregated by feature, normalized by feature area + - *e.g. population per square mile within a census tract* + +- **Proportions / Percentages** + - value in a specific category divided by total value across in all categories + - *e.g. proportion of the tract population that is white compared to the total tract population* + +- **Rates / Ratios** + - value in one category divided by value in another category + - *e.g. homeowner-to-renter ratio would be calculated as the number of homeowners (c_owners/ c_renters)* + +### Interpretability of plotted data +The goal of a choropleth map is to use color to visualize the spatial distribution of a quantitative variable. + +Brighter or richer colors are typically used to signify higher values. + +A big problem with choropleth maps is that our eyes are drawn to the color of larger areas, even if the values being mapped in one or more smaller areas are more important. + + + +We see just this sort of problem in our population-density map. + +***Why does our map not look that interesting?*** Take a look at the histogram below, then consider the following question. + +plt.hist(counties['POP12_SQMI'],bins=40) +plt.title('Population Density per m$^2$') +plt.show() + +
+ +
+
+ +#### Question +
+ +What county does that outlier represent? What problem does that pose? + +Your response here: + + + + + + +# 5.3 Classification schemes + +Let's try to make our map more interpretable! + +The common alternative to a proportionial color map is to use a **classification scheme** to create a **graduated color map**. This is the standard way to create a **choropleth map**. + +A **classification scheme** is a method for binning continuous data values into 4-7 classes (the default is 5) and map those classes to a color palette. + +### The commonly used classifications schemes: + +- **Equal intervals** + - equal-size data ranges (e.g., values within 0-10, 10-20, 20-30, etc.) + - pros: + - best for data spread across entire range of values + - easily understood by map readers + - cons: + - but avoid if you have highly skewed data or a few big outliers + + +- **Quantiles** + - equal number of observations in each bin + - pros: + - looks nice, becuase it best spreads colors across full set of data values + - thus, it's often the default scheme for mapping software + - cons: + - bin ranges based on the number of observations, not on the data values + - thus, different classes can have very similar or very different values. + + +- **Natural breaks** + - minimize within-class variance and maximize between-class differences + - e.g. 'fisher-jenks' + - pros: + - great for exploratory data analysis, because it can identify natural groupings + - cons: + - class breaks are best fit to one dataset, so the same bins can't always be used for multiple years + + +- **Manual** + - classifications are user-defined + - pros: + - especially useful if you want to slightly change the breaks produced by another scheme + - can be used as a fixed set of breaks to compare data over time + - cons: + - more work involved + +### Classification schemes and GeoDataFrames + +Classification schemes can be implemented using the geodataframe `plot` method by setting a value for the **scheme** argument. This requires the [pysal](https://pysal.org/) and [mapclassify](https://pysal.org/mapclassify) libraries to be installed in your Python environment. + +Here is a list of the `classification schemes` names that we will use: +- `equalinterval`, `quantiles`,`fisherjenks`,`naturalbreaks`, and `userdefined`. + +For more information about these classification schemes see the [pysal mapclassifiers web page](https://pysal.org/mapclassify/api.html) or check out the help docs. + +-------------------------- + +### Classification schemes in action + +Let's redo the last map using the `quantile` classification scheme. + +- What is different about the code? About the output map? + +# Plot population density - mile^2 +fig, ax = plt.subplots(figsize = (10,5)) +counties.plot(column='POP12_SQMI', + scheme="quantiles", + legend=True, + ax=ax + ) +ax.set_title("Population Density per Sq Mile") + +### User Defined Classification Schemes + +You may get pretty close to your final map without being completely satisfied. In this case you can manually define a classification scheme. + +Let's customize our map with a `user-defined` classification scheme where we manually set the breaks for the bins using the `classification_kwds` argument. + +fig, ax = plt.subplots(figsize = (14,8)) +counties.plot(column='POP12_SQMI', + legend=True, + cmap="RdYlGn", + scheme='user_defined', + classification_kwds={'bins':[50,100,200,300,400]}, + ax=ax) +ax.set_title("Population Density per Sq Mile") + +Since we are customizing our plot, we can also edit our legend to specify and format the text so that it's easier to read. + +- We'll use `legend_labels_list` to customize the labels for group in the legend. + +fig, ax = plt.subplots(figsize = (14,8)) +counties.plot(column='POP12_SQMI', + legend=True, + cmap="RdYlGn", + scheme='user_defined', + classification_kwds={'bins':[50,100,200,300,400]}, + ax=ax) + +# Create the labels for the legend +legend_labels_list = ['<50','50 to 100','100 to 200','200 to 300','300 to 400','>400'] + +# Apply the labels to the plot +for j in range(0,len(ax.get_legend().get_texts())): + ax.get_legend().get_texts()[j].set_text(legend_labels_list[j]) + +ax.set_title("Population Density per Sq Mile") + +### Let's plot a ratio + +If we look at the columns in our dataset, we see we have a number of variables +from which we can calculate proportions, rates, and the like. + +Let's try that out: + +counties.head() + +fig, ax = plt.subplots(figsize = (15,6)) + +# Plot percent hispanic as choropleth +counties.plot(column=(counties['HISPANIC']/counties['POP2012'] * 100), + legend=True, + cmap="Blues", + scheme='user_defined', + classification_kwds={'bins':[20,40,60,80]}, + edgecolor="grey", + linewidth=0.5, + ax=ax) + +legend_labels_list = ['<20%','20% - 40%','40% - 60%','60% - 80%','80% - 100%'] +for j in range(0,len(ax.get_legend().get_texts())): + ax.get_legend().get_texts()[j].set_text(legend_labels_list[j]) + +ax.set_title("Percent Hispanic Population") +plt.tight_layout() + +
+ +
+
+ +#### Questions +
+ +1. What new options and operations have we added to our code? +1. Based on our code, what title would you give this plot to describe what it displays? +1. How many bins do we specify in the `legend_labels_list` object, and how many bins are in the map legend? Why? + +Your responses here: + + + + + + +# 5.4 Point maps + +Choropleth maps are great, but mapping using point symbols enables us to visualize our spatial data in another way. + +If you know both mapping methods you can expand how much information you can show in one map. + +For example, point maps are a great way to map `counts` because the varying sizes of areas are deemphasized. + + + +----------------------- +Let's read in some point data on Alameda County schools. + +schools_df = pd.read_csv('notebook_data/alco_schools.csv') +schools_df.head() + +We got it from a plain CSV file, let's coerce it to a GeoDataFrame. + +schools_gdf = gpd.GeoDataFrame(schools_df, + geometry=gpd.points_from_xy(schools_df.X, schools_df.Y)) +schools_gdf.crs = "epsg:4326" + +Then we can map it. + +schools_gdf.plot() +plt.title('Alameda County Schools') + +### Proportional Color Maps +**Proportional color maps** linearly scale the `color` of a point symbol by the data values. + +Let's try this by creating a map of `API`. API stands for *Academic Performance Index*, which is a measurement system that looks at the performance of an individual school. + +schools_gdf.plot(column="API", cmap="gist_heat", edgecolor="grey", figsize=(10,8), legend=True) +plt.title("Alameda County, School API scores") + +When you see that continuous color bar in the legend you know that the mapping of data values to colors is not classified. + + +### Graduated Color Maps + +We can also create **graduated color maps** by binning data values before associating them with colors. These are just like choropleth maps, except that the term "choropleth" is only used with polygon data. + +Graduated color maps use the same syntax as the choropleth maps above - you create them by setting a value for `scheme`. + +Below, we copy the code we used above to create a choropleth, but we change the name of the geodataframe to use the point gdf. + +fig, ax = plt.subplots(figsize = (15,6)) + +# Plot percent non-white with graduated colors +schools_gdf.plot(column='API', + legend=True, + cmap="Blues", + scheme='user_defined', + classification_kwds={'bins':[0,200,400,600,800]}, + edgecolor="grey", + linewidth=0.5, + #markersize=60, + ax=ax) + +# Create a custom legend +legend_labels_list = ['0','0 - 200','200 - 400','400 - 600','600 - 800','>800'] + +# Apply the legend to the map +for j in range(0,len(ax.get_legend().get_texts())): + ax.get_legend().get_texts()[j].set_text(legend_labels_list[j]) + +# Create the plot +plt.tight_layout() +plt.title("Alameda County, School API scores") + +As you can see, the syntax for a choropleth and graduated color map is the same, +although some options only apply to one or the other. + +For example, uncomment the `markersize` parameter above to see how you can further customize a graduated color map. + +### Graduated symbol maps + +`Graduated symbol maps` are also a great method for mapping points. These are just like graduated color maps but instead of associating symbol color with data values they associate point size. Similarly,graduated symbol maps use `classification schemes` to set the size of point symbols. + +> We demonstrate how to make graduated symbol maps along with some other mapping techniques in the `Optional Mapping notebook` which we encourage you to explore on your own. (***Coming Soon***) + +## 5.5 Mapping Categorical Data + +Mapping categorical data, also called qualitative data, is a bit more straightforward. There is no need to scale or classify data values. The goal of the color map is to provide a contrasting set of colors so as to clearly delineate different categories. Here's a point-based example: + +schools_gdf.plot(column='Org', cmap='bwr',categorical=True, legend=True) + +## 5.6 Recap +We learned about important data driven mapping strategies and mapping concepts and can leverage what many of us know about `matplotlib` +- Choropleth Maps +- Point maps +- Color schemes +- Classifications + +# Exercise: Data-Driven Mapping + +Point and polygons are not the only geometry-types that we can use in data-driven mapping! + +Run the next cell to load a dataset containing Berkeley's bicycle boulevards (which we'll be using more in the following notebook). + +Then in the following cell, write your own code to: +1. plot the bike boulevards; +2. color them by status (find the correct column in the head of the dataframe, displayed below); +3. color them using a fitting, good-looking qualitative colormap that you choose from [The Matplotlib Colormap Reference](https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html); +4. set the line width to 5 (check the plot method's documentation to find the right argument for this!); +4. add the argument `figsize=[20,20]`, to make your map nice and big and visible! + +Then answer the questions posed in the last cell. + +
+ + +To see the solution, double-click the Markdown cell below. + + +bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson') +bike_blvds.head() + +# YOUR CODE HERE: + + + + + + +## Double-click to see solution! + + + +------------------------------------- + +
+ +
+
+ +#### Questions +
+ +1. What does that map indicate about the status of the Berkeley bike boulevards? +1. What does that map indicate about the status of your Berkeley bike-boulevard *dataset*? + +Your responses here: + + + + + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
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\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip'\n", + " - 'notebook_data/protected_areas/CPAD_2020a_Units.shp'\n", + " - 'notebook_data/berkeley/BerkeleyCityLimits.shp'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + " - 'notebook_data/transportation/bart.csv'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------\n", + "\n", + "We will start by reviewing the most\n", + "fundamental set, which we'll refer to as **spatial queries**.\n", + "These can be divided into:\n", + "\n", + "- Measurement queries\n", + " - What is feature A's **length**?\n", + " - What is feature A's **area**?\n", + " - What is feature A's **perimeter**?\n", + " - What is feature A's **distance** from feature B?\n", + " - etc.\n", + "- Relationship queries\n", + " - Is feature A **within** feature B?\n", + " - Does feature A **intersect** with feature B?\n", + " - Does feature A **cross** feature B?\n", + " - etc.\n", + " \n", + "We'll work through examples of each of those types of queries.\n", + "\n", + "Then we'll see an example of a very common spatial analysis that \n", + "is a conceptual amalgam of those two types: **proximity analysis**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.0 Load and prep some data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read in our census tracts data again." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " STATEFP COUNTYFP TRACTCE AFFGEOID GEOID NAME LSAD \\\n", + "0 06 001 400300 1400000US06001400300 06001400300 4003 CT \n", + "1 06 001 400900 1400000US06001400900 06001400900 4009 CT \n", + "2 06 001 402200 1400000US06001402200 06001402200 4022 CT \n", + "3 06 001 402800 1400000US06001402800 06001402800 4028 CT \n", + "4 06 001 404800 1400000US06001404800 06001404800 4048 CT \n", + "\n", + " ALAND AWATER geometry \n", + "0 1105329 0 POLYGON ((-122.26416 37.84000, -122.26186 37.8... \n", + "1 420877 0 POLYGON ((-122.28558 37.83978, -122.28319 37.8... \n", + "2 712082 0 POLYGON ((-122.30403 37.80739, -122.30239 37.8... \n", + "3 398311 0 POLYGON ((-122.27598 37.80622, -122.27335 37.8... \n", + "4 628405 0 POLYGON ((-122.21825 37.80086, -122.21582 37.8... " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we'll grab just the Alameda Country tracts." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_8_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "census_tracts_ac = census_tracts.loc[census_tracts['COUNTYFP']=='001'].reset_index(drop=True)\n", + "census_tracts_ac.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.1 Measurement Queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll start off with some simple measurement queries.\n", + "\n", + "For example, here's how we can get the areas of each of our census tracts." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " census_tracts_ac.area\n" + ] + }, + { + "data": { + "text/plain": [ + "0 0.000113\n", + "1 0.000045\n", + "2 0.000071\n", + "3 0.000041\n", + "4 0.000063\n", + " ... \n", + "356 0.000098\n", + "357 0.002275\n", + "358 0.000033\n", + "359 0.000139\n", + "360 0.000316\n", + "Length: 361, dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay! \n", + "\n", + "We got... \n", + "\n", + "numbers!\n", + "\n", + "...?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What do those numbers mean?\n", + "1. What are our units?\n", + "1. And if we're not sure, how might be find out?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at our CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83\n", + "Axis Info [ellipsoidal]:\n", + "- Lat[north]: Geodetic latitude (degree)\n", + "- Lon[east]: Geodetic longitude (degree)\n", + "Area of Use:\n", + "- name: North America - NAD83\n", + "- bounds: (167.65, 14.92, -47.74, 86.46)\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ah-hah! We're working in an unprojected CRS, with units of decimal degrees.\n", + "\n", + "**When doing spatial analysis, we will almost always want to work in a projected CRS\n", + "that has natural distance units, such as meters!**\n", + "\n", + "Time to project!\n", + "\n", + "(As previously, we'll use UTM Zone 10N with a NAD83 data.\n", + "This is a good choice for our region of interest.)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10 = census_tracts_ac.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83 / UTM zone 10N\n", + "Axis Info [cartesian]:\n", + "- E[east]: Easting (metre)\n", + "- N[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: North America - 126°W to 120°W and NAD83 by country\n", + "- bounds: (-126.0, 30.54, -119.99, 81.8)\n", + "Coordinate Operation:\n", + "- name: UTM zone 10N\n", + "- method: Transverse Mercator\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's try our area calculation again." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1.105797e+06\n", + "1 4.355184e+05\n", + "2 6.930523e+05\n", + "3 4.003615e+05\n", + "4 6.183936e+05\n", + " ... \n", + "356 9.653980e+05\n", + "357 2.230584e+07\n", + "358 3.197167e+05\n", + "359 1.355161e+06\n", + "360 3.087534e+06\n", + "Length: 361, dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks much more reasonable!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What are our units now?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may have noticed that our census tracts already have an area column in them.\n", + "\n", + "Let's do a sanity check on our results." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1105796.6056938928" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculate the area for the 0th feature\n", + "census_tracts_ac_utm10.area[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1105329" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the area for the 0th feature according to its 'ALAND' attribute\n", + "census_tracts['ALAND'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "356 False\n", + "357 False\n", + "358 False\n", + "359 False\n", + "360 False\n", + "Length: 361, dtype: bool" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check equivalence of the calculated areas and the 'ALAND' column\n", + "census_tracts_ac_utm10['ALAND'].values == census_tracts_ac_utm10.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What explains this disagreement? Are the calculated areas incorrect?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also sum the area for Alameda county by adding `.sum()` to the end of our area calculation." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1948917581.1122904" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.area.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can actually look up how large Alameda County is to check our work.The county is 739 miles2, which is around 1,914,001,213 meters2. I'd say we're pretty close!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As it turns out, we can similarly use another attribute\n", + "to get the features' lengths.\n", + "\n", + "**NOTE**: In this case, given we're\n", + "dealing with polygons, this is equivalent to getting the features' perimeters." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 5357.060239\n", + "1 2756.937555\n", + "2 5395.895162\n", + "3 2681.974829\n", + "4 3710.388859\n", + " ... \n", + "356 4331.600289\n", + "357 32004.773556\n", + "358 2353.624225\n", + "359 4718.701537\n", + "360 8176.643793\n", + "Length: 361, dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.length" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.2 Relationship Queries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GBP2Co-TutCH" + }, + "source": [ + "\n", + "[Spatial relationship queries](https://en.wikipedia.org/wiki/Spatial_relation) consider how two geometries or sets of geometries relate to one another in space. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jgUkeehpCqnS" + }, + "source": [ + "Here is a list of the most commonly used GeoPandas methods to test spatial relationships.\n", + "\n", + "- [within](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.within)\n", + "- [contains](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.contains) (the inverse of `within`)\n", + "- [intersects](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.intersects)\n", + "\n", + "
\n", + "There several other GeoPandas spatial relationship predicates but they are more complex to properly employ. For example the following two operations only work with geometries that are completely aligned.\n", + "\n", + "- [touches](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.touches)\n", + "- [equals](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.equals)\n", + "\n", + "\n", + "All of these methods takes the form:\n", + "\n", + " Geoseries.(geometry)\n", + " \n", + "For example:\n", + "\n", + " Geoseries.contains(geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------------\n", + "\n", + "Let's load a new dataset to demonstrate these queries.\n", + "\n", + "This is a dataset containing all the protected areas (parks and the like) in California." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "pas = gpd.read_file('./notebook_data/protected_areas/CPAD_2020a_Units.shp')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Does this need to be reprojected too?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: NAD83 / California Albers\n", + "Axis Info [cartesian]:\n", + "- X[east]: Easting (metre)\n", + "- Y[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: USA - California\n", + "- bounds: (-124.45, 32.53, -114.12, 42.01)\n", + "Coordinate Operation:\n", + "- name: California Albers\n", + "- method: Albers Equal Area\n", + "Datum: North American Datum 1983\n", + "- Ellipsoid: GRS 1980\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pas.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes it does!\n", + "\n", + "Let's reproject it." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "pas_utm10 = pas.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One common use for spatial queries is for spatial subsetting of data.\n", + "\n", + "In our case, lets use **intersects** to\n", + "find all of the parks that have land in Alameda County." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 POLYGON ((564744.993 4188317.651, 564946.532 4...\n", + "1 POLYGON ((562861.148 4188278.725, 563070.421 4...\n", + "2 POLYGON ((561264.509 4184672.770, 561409.095 4...\n", + "3 POLYGON ((563734.437 4184562.158, 563961.943 4...\n", + "4 POLYGON ((568821.460 4184008.066, 569030.992 4...\n", + " ... \n", + "356 POLYGON ((591097.402 4154398.989, 591400.070 4...\n", + "357 POLYGON ((578528.935 4151915.982, 578732.686 4...\n", + "358 POLYGON ((563141.438 4184274.978, 563293.747 4...\n", + "359 POLYGON ((572695.844 4175004.761, 572801.274 4...\n", + "360 POLYGON ((581072.943 4169465.752, 581136.259 4...\n", + "Name: geometry, Length: 361, dtype: geometry" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census_tracts_ac_utm10.geometry.squeeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "pas_in_ac = pas_utm10.intersects(census_tracts_ac_utm10.geometry.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we scroll the resulting GeoDataFrame to the right we'll see that \n", + "the `COUNTY` column of our resulting subset gives us a good sanity check on our results." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ACCESS_TYPUNIT_IDUNIT_NAMESUID_NMAAGNCY_IDAGNCY_NAMEAGNCY_LEVAGNCY_TYPAGNCY_WEBLAYER...MNG_AG_LEVMNG_AG_TYPPARK_URLCOUNTYACRESLABEL_NAMEYR_ESTDES_TPGAP_STSgeometry
63Open Access185Augustin Bernal Park87321257Pleasanton, City ofCityCity Agencyhttp://www.cityofpleasantonca.gov/City...CityCity Agencyhttp://www.cityofpleasantonca.gov/services/rec...Alameda217.388Augustin Bernal Park0.0Local Park4POLYGON ((595746.574 4165882.573, 595740.013 4...
145Open Access366San Antonio Park248321228Oakland, City ofCityCity Agencyhttp://www2.oaklandnet.com/Government/o/opr/in...City...CityCity AgencyNoneAlameda10.619San Antonio Park0.0Local Park4POLYGON ((566704.422 4182789.292, 566827.750 4...
217Open Access586Quarry Lakes Regional Recreation Area305942032East Bay Regional Park DistrictSpecial DistrictRecreation/Parks Districthttp://www.ebparks.org/Special District...Special DistrictRecreation/Parks DistrictNoneAlameda254.616Quarry Lakes Reg. Rec. Area2001.0Local Recreation Area4MULTIPOLYGON (((588060.979 4158338.499, 587843...
393Open Access1438Tennis & Community Park262431257Pleasanton, City ofCityCity Agencyhttp://www.cityofpleasantonca.gov/City...CityCity AgencyNoneAlameda15.595Tennis & Community Park0.0Local Park4POLYGON ((596761.389 4170334.335, 597109.868 4...
408Open Access48353Sean Diamond Park329171090Dublin, City ofCityCity Agencyhttp://www.ci.dublin.ca.us/index.aspx?nid=1458City...CityCity Agencyhttps://www.dublin.ca.gov/Facilities/Facility/...Alameda4.986Sean Diamond Park2018.0Local Park4POLYGON ((601693.284 4175288.100, 601695.836 4...
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5 rows × 22 columns

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" + ], + "text/plain": [ + " ACCESS_TYP UNIT_ID UNIT_NAME SUID_NMA \\\n", + "63 Open Access 185 Augustin Bernal Park 8732 \n", + "145 Open Access 366 San Antonio Park 24832 \n", + "217 Open Access 586 Quarry Lakes Regional Recreation Area 30594 \n", + "393 Open Access 1438 Tennis & Community Park 26243 \n", + "408 Open Access 48353 Sean Diamond Park 32917 \n", + "\n", + " AGNCY_ID AGNCY_NAME AGNCY_LEV \\\n", + "63 1257 Pleasanton, City of City \n", + "145 1228 Oakland, City of City \n", + "217 2032 East Bay Regional Park District Special District \n", + "393 1257 Pleasanton, City of City \n", + "408 1090 Dublin, City of City \n", + "\n", + " AGNCY_TYP \\\n", + "63 City Agency \n", + "145 City Agency \n", + "217 Recreation/Parks District \n", + "393 City Agency \n", + "408 City Agency \n", + "\n", + " AGNCY_WEB LAYER ... \\\n", + "63 http://www.cityofpleasantonca.gov/ City ... \n", + "145 http://www2.oaklandnet.com/Government/o/opr/in... City ... \n", + "217 http://www.ebparks.org/ Special District ... \n", + "393 http://www.cityofpleasantonca.gov/ City ... \n", + "408 http://www.ci.dublin.ca.us/index.aspx?nid=1458 City ... \n", + "\n", + " MNG_AG_LEV MNG_AG_TYP \\\n", + "63 City City Agency \n", + "145 City City Agency \n", + "217 Special District Recreation/Parks District \n", + "393 City City Agency \n", + "408 City City Agency \n", + "\n", + " PARK_URL COUNTY ACRES \\\n", + "63 http://www.cityofpleasantonca.gov/services/rec... Alameda 217.388 \n", + "145 None Alameda 10.619 \n", + "217 None Alameda 254.616 \n", + "393 None Alameda 15.595 \n", + "408 https://www.dublin.ca.gov/Facilities/Facility/... Alameda 4.986 \n", + "\n", + " LABEL_NAME YR_EST DES_TP GAP_STS \\\n", + "63 Augustin Bernal Park 0.0 Local Park 4 \n", + "145 San Antonio Park 0.0 Local Park 4 \n", + "217 Quarry Lakes Reg. Rec. Area 2001.0 Local Recreation Area 4 \n", + "393 Tennis & Community Park 0.0 Local Park 4 \n", + "408 Sean Diamond Park 2018.0 Local Park 4 \n", + "\n", + " geometry \n", + "63 POLYGON ((595746.574 4165882.573, 595740.013 4... \n", + "145 POLYGON ((566704.422 4182789.292, 566827.750 4... \n", + "217 MULTIPOLYGON (((588060.979 4158338.499, 587843... \n", + "393 POLYGON ((596761.389 4170334.335, 597109.868 4... \n", + "408 POLYGON ((601693.284 4175288.100, 601695.836 4... \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pas_utm10[pas_in_ac].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So does this overlay plot!" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933PublicPOINT (-122.23876 37.74476)
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932PublicPOINT (-122.25186 37.73900)
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853PublicPOINT (-122.25892 37.76206)
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927PublicPOINT (-122.23484 37.76525)
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894PublicPOINT (-122.23808 37.75396)
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Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org geometry \n", + "0 Alameda CA ES 933 Public POINT (-122.23876 37.74476) \n", + "1 Alameda CA ES 932 Public POINT (-122.25186 37.73900) \n", + "2 Alameda CA ES 853 Public POINT (-122.25892 37.76206) \n", + "3 Alameda CA ES 927 Public POINT (-122.23484 37.76525) \n", + "4 Alameda CA ES 894 Public POINT (-122.23808 37.75396) " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the Alameda County schools CSV\n", + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "\n", + "# coerce it to a GeoDataFrame\n", + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "# define its unprojected (EPSG:4326) CRS\n", + "schools_gdf.crs = \"epsg:4326\"\n", + "\n", + "# transform to EPSG:26910\n", + "schools_gdf_utm10 = schools_gdf.to_crs( \"epsg:26910\")\n", + "\n", + "# display\n", + "schools_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6.3 Proximity Analysis\n", + "\n", + "Now that we've seen the basic idea of spatial measurement and relationship queries,\n", + "let's take a look at a common analysis that combines those concepts: **promximity analysis**.\n", + "\n", + "Proximity analysis seeks to identify all features in a focal feature set\n", + "that are within some maximum distance of features in a reference feature set.\n", + "\n", + "A common workflow for this analysis is:\n", + "\n", + "1. Buffer (i.e. add a margin around) the reference dataset, out to the maximum distance.\n", + "1. Run a spatial relationship query to find all focal features that intersect (or are within) the buffer.\n", + "\n", + "---------------------------------\n", + "\n", + "Let's read in our bike boulevard data again.\n", + "\n", + "Then we'll find out which of our Berkeley schools are within a block's distance (200 m) of the boulevards." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_59_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, we need to reproject the boulevards to our projected CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10 = bike_blvds.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create our 200 meter bike boulevard buffers." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_buf = bike_blvds_utm10.buffer(distance=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's overlay everything." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_65_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "berkeley_utm10.plot(color='lightgrey', ax=ax)\n", + "bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "bike_blvds_utm10.plot(ax=ax)\n", + "schools_gdf_utm10.plot(color='purple',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Looks like we're all ready to run our intersection to complete the proximity analysis.\n", + "\n", + "\n", + "**NOTE**: In order to subset with our buffers we need to call the `unary_union` attribute of the buffer object.\n", + "This gives us a single unified polygon, rather than a series of multipolygons representing buffers around each of the points in our multilines." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'berkeley_schools' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschools_near_blvds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mberkeley_schools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwithin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbike_blvds_buf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munary_union\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mblvd_schools\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mberkeley_schools\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mschools_near_blvds\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'berkeley_schools' is not defined" + ] + } + ], + "source": [ + "schools_near_blvds = berkeley_schools.within(bike_blvds_buf.unary_union)\n", + "blvd_schools = berkeley_schools[schools_near_blvds]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's overlay again, to see if the schools we subsetted make sense." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "berkeley_utm10.plot(color='lightgrey', ax=ax)\n", + "bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "bike_blvds_utm10.plot(ax=ax)\n", + "berkeley_schools.plot(color='purple',ax=ax)\n", + "blvd_schools.plot(color='yellow', markersize=50, ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to find the shortest distance from one school to the bike boulevards, we can use the `distance` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley_schools.distance(bike_blvds_utm10.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise: Proximity Analysis\n", + "\n", + "Now it's your turn to try out a proximity analysis!\n", + "\n", + "Run the next cell to load our BART-system data, reproject it to EPSG: 26910, and subset it to Berkeley.\n", + "\n", + "Then in the following cell, write your own code to find all schools within walking distance (1 km) of a BART station.\n", + "\n", + "As a reminder, let's break this into steps:\n", + "1. buffer your Berkeley BART stations to 1 km (**HINT**: remember your units!)\n", + "2. use the schools' `within` attribute to check whether or not they're within the buffers (**HINT**: don't forget the `unary_union`!)\n", + "3. subset the Berkeley schools using the object returned by your spatial relationship query\n", + "\n", + "4. as always, plot your results for a good visual check!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the BART stations from CSV\n", + "bart_stations = pd.read_csv('notebook_data/transportation/bart.csv')\n", + "# coerce to a GeoDataFrame\n", + "bart_stations_gdf = gpd.GeoDataFrame(bart_stations, \n", + " geometry=gpd.points_from_xy(bart_stations.lon, bart_stations.lat))\n", + "# define its unprojected (EPSG:4326) CRS\n", + "bart_stations_gdf.crs = \"epsg:4326\"\n", + "# transform to UTM Zone 10 N (EPSG:26910)\n", + "bart_stations_gdf_utm10 = bart_stations_gdf.to_crs( \"epsg:26910\")\n", + "# subset to Berkeley\n", + "berkeley_bart = bart_stations_gdf_utm10[bart_stations_gdf_utm10.within(berkeley_utm10.unary_union)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "----------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.4 Recap\n", + "Leveraging what we've learned in our earlier lessons, we got to work with map overlays and start answering questions related to proximity. Key concepts include:\n", + "- Measuring area and length\n", + "\t- `.area`, \n", + "\t- `.length`\n", + "- Relationship Queries\n", + "\t- `.intersects()`\n", + "\t- `.within()`\n", + "- Buffer analysis\n", + "\t- `.buffer()`\n", + "\t- `.distance()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1.py b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1.py new file mode 100644 index 0000000..123e87b --- /dev/null +++ b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1.py @@ -0,0 +1,502 @@ +# Lesson 6. Spatial Queries + +In spatial analysis, our goal is not just to make nice maps, +but to actually run analyses that leverage the explicitly spatial +nature of our data. The process of doing this is known as +**spatial analysis**. + +To construct spatial analyses, we string together series of spatial +operations in such a way that the end result answers our question of interest. +There are many such spatial operations. These are known as **spatial queries**. + + +- 6.0 Load and prep some data +- 6.1 Measurement Queries +- 6.2 Relationship Queries +- **Exercise**: Spatial Relationship Query +- 6.3 Proximity Analysis +- **Exercise**: Proximity Analysis +- 6.4 Recap + + + + + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip' + - 'notebook_data/protected_areas/CPAD_2020a_Units.shp' + - 'notebook_data/berkeley/BerkeleyCityLimits.shp' + - 'notebook_data/alco_schools.csv' + - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson' + - 'notebook_data/transportation/bart.csv' + +- Expected time to complete + - Lecture + Questions: 45 minutes + - Exercises: 20 minutes + + +------------------- + +We will start by reviewing the most +fundamental set, which we'll refer to as **spatial queries**. +These can be divided into: + +- Measurement queries + - What is feature A's **length**? + - What is feature A's **area**? + - What is feature A's **perimeter**? + - What is feature A's **distance** from feature B? + - etc. +- Relationship queries + - Is feature A **within** feature B? + - Does feature A **intersect** with feature B? + - Does feature A **cross** feature B? + - etc. + +We'll work through examples of each of those types of queries. + +Then we'll see an example of a very common spatial analysis that +is a conceptual amalgam of those two types: **proximity analysis**. + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +# 6.0 Load and prep some data + +Let's read in our census tracts data again. + +census_tracts = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip") +census_tracts.plot() + +census_tracts.head() + +Then we'll grab just the Alameda Country tracts. + +census_tracts_ac = census_tracts.loc[census_tracts['COUNTYFP']=='001'].reset_index(drop=True) +census_tracts_ac.plot() + +# 6.1 Measurement Queries + +We'll start off with some simple measurement queries. + +For example, here's how we can get the areas of each of our census tracts. + +census_tracts_ac.area + +Okay! + +We got... + +numbers! + +...? + +
+ +
+
+ +#### Questions +
+ +1. What do those numbers mean? +1. What are our units? +1. And if we're not sure, how might be find out? + +Your responses here: + + + + + + + +Let's take a look at our CRS. + +census_tracts_ac.crs + +Ah-hah! We're working in an unprojected CRS, with units of decimal degrees. + +**When doing spatial analysis, we will almost always want to work in a projected CRS +that has natural distance units, such as meters!** + +Time to project! + +(As previously, we'll use UTM Zone 10N with a NAD83 data. +This is a good choice for our region of interest.) + +census_tracts_ac_utm10 = census_tracts_ac.to_crs( "epsg:26910") + +census_tracts_ac_utm10.crs + +Now let's try our area calculation again. + +census_tracts_ac_utm10.area + +That looks much more reasonable! + +
+ +
+
+ +#### Question +
+ +What are our units now? + + +Your response here: + + + + + + +You may have noticed that our census tracts already have an area column in them. + +Let's do a sanity check on our results. + +# calculate the area for the 0th feature +census_tracts_ac_utm10.area[0] + +# get the area for the 0th feature according to its 'ALAND' attribute +census_tracts['ALAND'][0] + +# check equivalence of the calculated areas and the 'ALAND' column +census_tracts_ac_utm10['ALAND'].values == census_tracts_ac_utm10.area + +
+ +
+
+ +#### Question +
+ +What explains this disagreement? Are the calculated areas incorrect? + + +Your response here: + + + + + + + +We can also sum the area for Alameda county by adding `.sum()` to the end of our area calculation. + +census_tracts_ac_utm10.area.sum() + +We can actually look up how large Alameda County is to check our work.The county is 739 miles2, which is around 1,914,001,213 meters2. I'd say we're pretty close! + +As it turns out, we can similarly use another attribute +to get the features' lengths. + +**NOTE**: In this case, given we're +dealing with polygons, this is equivalent to getting the features' perimeters. + +census_tracts_ac_utm10.length + +# 6.2 Relationship Queries + + +[Spatial relationship queries](https://en.wikipedia.org/wiki/Spatial_relation) consider how two geometries or sets of geometries relate to one another in space. + + + + +Here is a list of the most commonly used GeoPandas methods to test spatial relationships. + +- [within](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.within) +- [contains](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.contains) (the inverse of `within`) +- [intersects](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.intersects) + +
+There several other GeoPandas spatial relationship predicates but they are more complex to properly employ. For example the following two operations only work with geometries that are completely aligned. + +- [touches](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.touches) +- [equals](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.equals) + + +All of these methods takes the form: + + Geoseries.(geometry) + +For example: + + Geoseries.contains(geometry) + +-------------------------------- + +Let's load a new dataset to demonstrate these queries. + +This is a dataset containing all the protected areas (parks and the like) in California. + +pas = gpd.read_file('./notebook_data/protected_areas/CPAD_2020a_Units.shp') + +Does this need to be reprojected too? + +pas.crs + +Yes it does! + +Let's reproject it. + +pas_utm10 = pas.to_crs("epsg:26910") + +One common use for spatial queries is for spatial subsetting of data. + +In our case, lets use **intersects** to +find all of the parks that have land in Alameda County. + +census_tracts_ac_utm10.geometry.squeeze() + +pas_in_ac = pas_utm10.intersects(census_tracts_ac_utm10.geometry.unary_union) + +If we scroll the resulting GeoDataFrame to the right we'll see that +the `COUNTY` column of our resulting subset gives us a good sanity check on our results. + +pas_utm10[pas_in_ac].head() + +So does this overlay plot! + +ax = census_tracts_ac_utm10.plot(color='gray', figsize=[12,16]) +pas_utm10[pas_in_ac].plot(ax=ax, column='ACRES', cmap='summer', legend=True, + edgecolor='black', linewidth=0.4, alpha=0.8, + legend_kwds={'label': "acres", + 'orientation': "horizontal"}) +ax.set_title('Protected areas in Alameda County, colored by area', size=18); + +# color by county? + +# Exercise: Spatial Relationship Query + +Let's use a spatial relationship query to create a new dataset containing Berkeley schools! + +Run the next two cells to load datasets containing Berkeley's city boundary and Alameda County's +schools and to reproject them to EPSG: 26910. + +Then in the following cell, write your own code to: +1. subset the schools for only those `within` Berkeley +2. plot the Berkeley boundary and then the schools as an overlay map + +To see the solution, double-click the Markdown cell below. + +# load the Berkeley boundary +berkeley = gpd.read_file("notebook_data/berkeley/BerkeleyCityLimits.shp") + +# transform to EPSG:26910 +berkeley_utm10 = berkeley.to_crs("epsg:26910") + +# display +berkeley_utm10.head() + +# load the Alameda County schools CSV +schools_df = pd.read_csv('notebook_data/alco_schools.csv') + +# coerce it to a GeoDataFrame +schools_gdf = gpd.GeoDataFrame(schools_df, + geometry=gpd.points_from_xy(schools_df.X, schools_df.Y)) +# define its unprojected (EPSG:4326) CRS +schools_gdf.crs = "epsg:4326" + +# transform to EPSG:26910 +schools_gdf_utm10 = schools_gdf.to_crs( "epsg:26910") + +# display +schools_df.head() + +# YOUR CODE HERE: + + + + + + +## Double-click to see solution! + + + +------------------------------- + +# 6.3 Proximity Analysis + +Now that we've seen the basic idea of spatial measurement and relationship queries, +let's take a look at a common analysis that combines those concepts: **promximity analysis**. + +Proximity analysis seeks to identify all features in a focal feature set +that are within some maximum distance of features in a reference feature set. + +A common workflow for this analysis is: + +1. Buffer (i.e. add a margin around) the reference dataset, out to the maximum distance. +1. Run a spatial relationship query to find all focal features that intersect (or are within) the buffer. + +--------------------------------- + +Let's read in our bike boulevard data again. + +Then we'll find out which of our Berkeley schools are within a block's distance (200 m) of the boulevards. + +bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson') +bike_blvds.plot() + +Of course, we need to reproject the boulevards to our projected CRS. + +bike_blvds_utm10 = bike_blvds.to_crs( "epsg:26910") + +Now we can create our 200 meter bike boulevard buffers. + +bike_blvds_buf = bike_blvds_utm10.buffer(distance=200) + +Now let's overlay everything. + +fig, ax = plt.subplots(figsize=(10,10)) +berkeley_utm10.plot(color='lightgrey', ax=ax) +bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5) +bike_blvds_utm10.plot(ax=ax) +schools_gdf_utm10.plot(color='purple',ax=ax) + +Great! Looks like we're all ready to run our intersection to complete the proximity analysis. + + +**NOTE**: In order to subset with our buffers we need to call the `unary_union` attribute of the buffer object. +This gives us a single unified polygon, rather than a series of multipolygons representing buffers around each of the points in our multilines. + +schools_near_blvds = berkeley_schools.within(bike_blvds_buf.unary_union) +blvd_schools = berkeley_schools[schools_near_blvds] + +Now let's overlay again, to see if the schools we subsetted make sense. + +fig, ax = plt.subplots(figsize=(10,10)) +berkeley_utm10.plot(color='lightgrey', ax=ax) +bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5) +bike_blvds_utm10.plot(ax=ax) +berkeley_schools.plot(color='purple',ax=ax) +blvd_schools.plot(color='yellow', markersize=50, ax=ax) + +If we want to find the shortest distance from one school to the bike boulevards, we can use the `distance` function. + +berkeley_schools.distance(bike_blvds_utm10.unary_union) + +# Exercise: Proximity Analysis + +Now it's your turn to try out a proximity analysis! + +Run the next cell to load our BART-system data, reproject it to EPSG: 26910, and subset it to Berkeley. + +Then in the following cell, write your own code to find all schools within walking distance (1 km) of a BART station. + +As a reminder, let's break this into steps: +1. buffer your Berkeley BART stations to 1 km (**HINT**: remember your units!) +2. use the schools' `within` attribute to check whether or not they're within the buffers (**HINT**: don't forget the `unary_union`!) +3. subset the Berkeley schools using the object returned by your spatial relationship query + +4. as always, plot your results for a good visual check! + +To see the solution, double-click the Markdown cell below. + +# load the BART stations from CSV +bart_stations = pd.read_csv('notebook_data/transportation/bart.csv') +# coerce to a GeoDataFrame +bart_stations_gdf = gpd.GeoDataFrame(bart_stations, + geometry=gpd.points_from_xy(bart_stations.lon, bart_stations.lat)) +# define its unprojected (EPSG:4326) CRS +bart_stations_gdf.crs = "epsg:4326" +# transform to UTM Zone 10 N (EPSG:26910) +bart_stations_gdf_utm10 = bart_stations_gdf.to_crs( "epsg:26910") +# subset to Berkeley +berkeley_bart = bart_stations_gdf_utm10[bart_stations_gdf_utm10.within(berkeley_utm10.unary_union)] + +# YOUR CODE HERE: + + + + + + + +## Double-click to see solution! + + + +---------------------------------- + +## 6.4 Recap +Leveraging what we've learned in our earlier lessons, we got to work with map overlays and start answering questions related to proximity. Key concepts include: +- Measuring area and length + - `.area`, + - `.length` +- Relationship Queries + - `.intersects()` + - `.within()` +- Buffer analysis + - `.buffer()` + - `.distance()` + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + diff --git a/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_51_0.png b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_51_0.png new file mode 100644 index 0000000..7f8cae9 Binary files /dev/null and b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_51_0.png differ diff --git a/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_59_1.png b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_59_1.png new file mode 100644 index 0000000..39f126b Binary files /dev/null and b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_59_1.png differ diff --git a/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_5_1.png b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_5_1.png new file mode 100644 index 0000000..e599ec1 Binary files /dev/null and b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_5_1.png differ diff --git a/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_65_1.png b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_65_1.png new file mode 100644 index 0000000..db3ab37 Binary files /dev/null and b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_65_1.png differ diff --git a/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_8_1.png b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_8_1.png new file mode 100644 index 0000000..2ce46a8 Binary files /dev/null and b/_build/jupyter_execute/ran/06_Spatial_Queries-Copy1_8_1.png differ diff --git a/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1.ipynb b/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1.ipynb new file mode 100644 index 0000000..bd8f530 --- /dev/null +++ b/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1.ipynb @@ -0,0 +1,2404 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 7. Attribute and Spatial Joins\n", + "\n", + "Now that we understand the logic of spatial relationship queries,\n", + "let's take a look at another fundamental spatial operation that relies on them.\n", + "\n", + "This operation, called a **spatial join**, is the process by which we can\n", + "leverage the spatial relationships between distinct datasets to merge\n", + "their information into a new, synthetic dataset.\n", + "\n", + "This operation can be thought as the spatial equivalent of an\n", + "**attribute join**, in which multiple tabular datasets can be merged by\n", + "aligning matching values in a common column that they both contain.\n", + "Thus, we'll start by developing an understanding of this operation first!\n", + "\n", + "- 7.0 Data Input and Prep\n", + "- 7.1 Attribute Joins\n", + "- **Exercise**: Choropleth Map\n", + "- 7.2 Spatial Joins\n", + "- 7.3 Aggregation\n", + "- **Exercise**: Aggregation\n", + "- 7.4 Recap\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/census/ACS5yr/census_variables_CA.csv'\n", + " - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip'\n", + " - 'notebook_data/alco_schools.csv'\n", + " \n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7.0 Data Input and Prep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read in a table of data from the US Census' 5-year American Community Survey (ACS5)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NAMEc_racec_whitec_blackc_asianc_latinxc_race_moec_white_moec_black_moec_asian_moe...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0Census Tract 4012, Alameda County, California24561287476259283213191116124...0.8149510.1033500.0584150.0102120.0130720.5513700.0643840.1890410.0835620.058219
1Census Tract 4013, Alameda County, California39838451348827796680186411283...0.6118650.2800400.0633480.0226240.0221220.3411530.1089930.3914960.0180840.104594
2Census Tract 4014, Alameda County, California43407131902593981644314440198...0.8076830.1637390.0178030.0063250.0044510.4708460.0213170.2557990.1166140.102194
3Census Tract 4015, Alameda County, California20805631064215190369222283116...0.8413460.1014420.0538460.0033650.0000000.5020370.0906310.2301430.0478620.017312
4Census Tract 4016, Alameda County, California1889324960247274400135376164...0.8306450.0795700.0822580.0021510.0053760.5704810.1227200.1774460.0630180.000000
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" + ], + "text/plain": [ + " NAME c_race c_white c_black \\\n", + "0 Census Tract 4012, Alameda County, California 2456 1287 476 \n", + "1 Census Tract 4013, Alameda County, California 3983 845 1348 \n", + "2 Census Tract 4014, Alameda County, California 4340 713 1902 \n", + "3 Census Tract 4015, Alameda County, California 2080 563 1064 \n", + "4 Census Tract 4016, Alameda County, California 1889 324 960 \n", + "\n", + " c_asian c_latinx c_race_moe c_white_moe c_black_moe c_asian_moe ... \\\n", + "0 259 283 213 191 116 124 ... \n", + "1 827 796 680 186 411 283 ... \n", + "2 593 981 644 314 440 198 ... \n", + "3 215 190 369 222 283 116 ... \n", + "4 247 274 400 135 376 164 ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.814951 0.103350 0.058415 0.010212 0.013072 0.551370 \n", + "1 0.611865 0.280040 0.063348 0.022624 0.022122 0.341153 \n", + "2 0.807683 0.163739 0.017803 0.006325 0.004451 0.470846 \n", + "3 0.841346 0.101442 0.053846 0.003365 0.000000 0.502037 \n", + "4 0.830645 0.079570 0.082258 0.002151 0.005376 0.570481 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.064384 0.189041 0.083562 0.058219 \n", + "1 0.108993 0.391496 0.018084 0.104594 \n", + "2 0.021317 0.255799 0.116614 0.102194 \n", + "3 0.090631 0.230143 0.047862 0.017312 \n", + "4 0.122720 0.177446 0.063018 0.000000 \n", + "\n", + "[5 rows x 66 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read in the ACS5 data for CA into a pandas DataFrame.\n", + "# Note: We force the FIPS_11_digit to be read in as a string to preserve any leading zeroes.\n", + "acs5_df = pd.read_csv(\"notebook_data/census/ACS5yr/census_variables_CA.csv\", dtype={'FIPS_11_digit':str})\n", + "acs5_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Brief summary of the data**:\n", + "\n", + "Below is a table of the variables in this table. They were combined from \n", + "different ACS 5 year tables.\n", + "\n", + "NOTE:\n", + "- variables that start with `c_` are counts\n", + "- variables that start with `med_` are medians\n", + "- variables that end in `_moe` are margin of error estimates\n", + "- variables that start with `_p` are proportions calcuated from the counts divided by the table denominator (the total count for whom that variable was assessed)\n", + "\n", + "\n", + "| Variable | Description |\n", + "|-----------------|-------------------------------------------------|\n", + "|`c_race` |Total population \n", + "|`c_white` |Total white non-Latinx\n", + "| `c_black` | Total black and African American non-Latinx\n", + "| `c_asian` | Total Asian non-Latinx\n", + "| `c_latinx` | Total Latinx\n", + "| `state_fips` | State level FIPS code\n", + "| `county_fips` | County level FIPS code\n", + "| `tract_fips` |Tracts level FIPS code\n", + "| `med_rent` |Median rent\n", + "| `med_hhinc` |Median household income\n", + "| `c_tenants` |Total tenants\n", + "| `c_owners` |Total owners\n", + "| `c_renters` |Total renters\n", + "| `c_movers` |Total number of people who moved\n", + "| `c_stay` |Total number of people who stayed\n", + "| `c_movelocal` |Number of people who moved locally\n", + "| `c_movecounty` |Number of people who moved counties\n", + "| `c_movestate` | Number of people who moved states\n", + "| `c_moveabroad` |Number of people who moved abroad\n", + "| `c_commute` |Total number of commuters\n", + "| `c_car` | Number of commuters who use a car\n", + "| `c_carpool` | Number of commuters who carpool\n", + "| `c_transit` |Number of commuters who use public transit\n", + "| `c_bike` |Number of commuters who bike\n", + "| `c_walk` |Number of commuters who bike\n", + "| `year` | ACS data year\n", + "| `FIPS_11_digit` | 11-digit FIPS code\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're going to drop all of our `moe` columns by identifying all of those that end with `_moe`. We can do that in two steps, first by using `filter` to identify columns that contain the string `_moe`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['c_race_moe', 'c_white_moe', 'c_black_moe', 'c_asian_moe',\n", + " 'c_latinx_moe', 'med_rent_moe', 'med_hhinc_moe', 'c_tenants_moe',\n", + " 'c_owners_moe', 'c_renters_moe', 'c_movers_moe', 'c_stay_moe',\n", + " 'c_movelocal_moe', 'c_movecounty_moe', 'c_movestate_moe',\n", + " 'c_moveabroad_moe', 'c_commute_moe', 'c_car_moe', 'c_carpool_moe',\n", + " 'c_transit_moe', 'c_bike_moe', 'c_walk_moe'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "moe_cols = acs5_df.filter(like='_moe',axis=1).columns\n", + "moe_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df.drop(moe_cols, axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And lastly, let's grab only the rows for year 2018 and county FIPS code 1 (i.e. Alameda County)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df_ac = acs5_df[(acs5_df['year']==2018) & (acs5_df['county_fips']==1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------------------\n", + "Now let's also read in our census tracts again!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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zhaJaYVOZRN3f7tUSrqdGwuzv8HJ2LMJMPMuhLi+nRm4mY1s81kXJX6OydGmljo6OzlJsGAM/FU3zyacv8/T5Ke0xp0UduRdJ5WteE0rlmY1nCMSyGBXBzhY3ZqNCOqfKDfgdZqSUPHclQF+Pn+NDIfZ3eBmYSVQ9j1gQCAsms/jsJsKpPKdGIhzo8JLMFRgLpxmo0el6ZnRxNY6Ojo7OSqx7A58rlPj7V4f4i+evafHyeRI1dGEq6a0Ip3T67Zpn3ew2s7XRic1kwG0zacZ9T6ubgelY1XMc6fEzE8tUPTYWStPosrCp3sGNuSTXZ+O4rCaQkkxh8bCQFr16RkdH5zZY10nWQrHEX37/Gl85OUa2huFcDp/dhEER7G33YDUqNLnVGHiT28J0LMe1QIKxcJqzY1GOD4Vo8VhxWIxsa3KVSx/BYlS4FkgwHEwtev5APMtEJM2BDi+xTLFckll7jSeGQvzgSkDTwNHR0dFZDevagzcaFD7xw9v5xA9vJ1socmIozIf//tiq5qH21Ds4Vm5yMhmEJjHQ5rUxE1O1Zua7TA91+XCaDbxYbnx645Z6csUSZqOyqBmqkmyhxJmxCEd6/LwyGORAh4czCxqeAKZjGT76DyfY3+HlS7/wqDY7diFjoRTtPhtC6DF7HZ3VcnU6zr+cGuNd+9vWXVPhujbwlViMBt6wuY4tDU6uBRLLnvtIm5tCUXK428fQXJK5RI6L5aTpvPHs8ts4OxZhT5ubQCxD1mbCZBDki5LrswkmoxmEUO8EHmnzEMsUGAwkFoWFDnf7tI1kJJTCZTVq4wEXcnYswi984QSPb6lnR7ObN29vQAhBPJPnz569xudfH+Yde5r5Hz++b8lNQEdHB6LpPP92bpKvnBzjXDn0en02yd9/5PB9XtnasmEMPMD/+4PBZY27y2okXyzhtZl5eXCOQ11e5hI5Ht9SR76gdq5eKFfD+B0WMoUSLquR6+VwzeFuH4FYlpFQCqtJYUuDk+FgkpFgkpFQGkXAjmYnPoeZsWCaiUia67M3Rc1CyTyHunycGlm6G/aVwSC5ouRT373KnjY3P7q3lb97ZUgb4P2t81McvRHkg0e6+PW3bdW9eR2dMqWS5PUbQb58cozvXpxeFLZdj8Vq69bAh5M5zo5F6PDbaPfZef1GkD97bmDJ83u7fAwE4hzq9BHNqFU186GcVLbImbEIfd0+Ovx2Wjw2soUi25tcvHRtjsPdPq5Mq52vbV4bsUyerjo7Z8vhlkRW7XAtSbg6nWB7s4vxSJrHN/lBCI4Ph8iXSzBPjYTp6/Yxm8gxPJekVjRpfvjIxYmYdmdRyVwix/98/hp1TjM/81j3bf4EdXTWBxORNF85OcZXTo5XjdLcCKxLA39iOMQnvnyOQqlEq8fGyRFVDfJwtx8pJdlyMnNoLkE8W8TvMHNlKkaTx8qJ4SCKoiAEWE0KO5ocXJtRSxezhRLXZ5O4bSZi6TzDc0l2tbixmw14bCZcFiMWo2BHs4tcQdLgtDBboQ3f7LbgtpkYmInT2+UjkS1yZSZGo8vKRCRNT72DXKHEieEw+9o9NLotNLutKEIgBMTSeQZnk9hMq8uN/8kzA/z4oXbs5nX5a9bRWZJsociz/TM8dWKMVwbnVpV3ewBlue6YFT/5Qggr8BJgKZ//L1LK3xFCPAVsL5/mBSJSyv01rv8N4OcBCVwAPiqlzCw8by0olSSffvE6f/rsAMXyKL14ukCz28pYOFU1C3WenS0u/A4zwYQ6qWlLo5v+qRhbG528dj3EzmYX7X47HpuJY0MhOnxq7H1Hs4uihP6pGN6oiUgqz55WN69eV+PpioC+bj9bG50UpSSdL3J5Kka900KrV910+rr9ZAsSs1HhsU1+To+GNdGxgZk4DW6rFh8E2NzgoLvOTnB+fOAKRNN5Pvn0ZT75rj0o6/H+U0dnAVemYzx1YoxvnJkgvER/y1KsQ/u+Kg8+C7xVSpkQQpiAV4QQ35FS/uT8CUKIPwEWlX8IIdqAXwN2SSnTQogvAz8F/MOarH4BJ4ZD/I/vXa16LJ4t1JyDOs/lqTi7WlzakI4tjQ7avDZ8DnUcn8tq4vhwiP0dHna1uGhwWRgLp3FUeMWRVJ5Gl0WtZS9TknB0KITLaqTLb2c6lmFbk4uLkzdDKoWSeidhUAQnR8JamAYglS/R5rEyFkpR3qu4Ppvk8c11Wh6hr8fPeCiFRDIVXTxFCuCfjo2SzBb4kd3NeO0m/A4zdQ4LPrsJo2FdV8nqbBBiGTVh+uUTY1UO0a3yICrr3ikrGnipvuv5zKSp/KX9JISaxXs/8NZlXsMmhMgDdmDyTha8HH09fn7rR7YvMvIrcS2QoK/HTy5fwmwUmFxqiOZwt49IOqfF09u9Ni5PxznY6eXcAq13n93M6zeCuCwGGt1Wmj1WUtkC07EMM7EM25ucmnevnm/i/HgEv8NMJJWrMu4AFqNgOp6lt9uvadIDvHo9SKPLQqffTr5QYjKaYV+Hh3AyX7NJCuCbZyf55tnqH7ta4WMuG3wz9U4L9c7yvy4LfoeZeqcZv8NCndOMy2LUE7Y6DwxSSo4PhXjq5BjfvjBFZokeko3OqoKzQggDcArYAvyVlPJYxeE3AjNSymsLr5NSTggh/hgYBdLAM1LKZ+582Uuuk195yxY2Nzj442cGGFyhHHKefFFqRlQI2NmshmkAXBYDh3vqmIykafJYGI+ksRgVDnT6uDIVQ6IO2RZAX4+PYklNlPodZs6MRfHYjHjtZl69rg7wrnOYmYlnmItneWxzPcWS5LXrwar1tHlttPtsHBsKcWM2uUhnPhDP4rIaafGoM17PjUU50uPn1EiI1fZCSakma0PJHIOrON9sUPA7yhuCU90U6pyLN4L68r92s0HfEHTWnJlYhq+eHucrJ8cZmkuufMEtsP7891UaeCllEdgvhPACXxdC7JFSXiwf/gDwpVrXCSF8wLuAHiACfEUI8SEp5RdrnPtx4OMAnZ2dt/xGKnn7nhbSuSJ/8f1BGl0WIqkcA4HEkkmUVo+VNp+NXLHEjdmkZtwBfA4L378SwG83YVQUWjxWzo5FSeeLKAI1cQucHg2zrcnFXDyL32Hm9GiEPa0u/A4LV6fj2EwKV6bj7Gh2MRhI0uA0c3I4TDpfpK/HT6kkuTwVw2M3kcwVuDgR5VCXj4Fydc4Tm+t4/UaQeUffoAiGK+bGHhsKcbjbx4lVDBy5HXLFEtOxDNOx1aVPLEaFeqdF2wx2trj5jR/ahkkPC+ncAolsgeNDQV4dDPLq4NyiecdryTqM0NxaFY2UMiKEeAF4O3BRCGEE3gscWuKStwFDUspZACHE14A3AIsMvJTyM8BnQJ3odCvrqsV7DrbzzXOTvHB1FgC3zciWBieg6tM4LAaKJVWEbCKSYTJa23C5reqPKJTKc2woRF+Pn6mo6u3bTAZuzCWZLWu7v3Rtjl0tbkCSK5Soc1p4cUDtZN3d6mIkmOJquSKn0W1ldjJGq9dK/2SMRFZNBhdKUhM/mwiniGcLFEqSkVCSzY1OpFQ15msZ8pMjYRpdFrrr7By/S4Z+tWQLJSYiaa0s7QdXZ7kwEeWvP3iwKleho1NJtlDk9EiE167P8ergHOfGo1rBhM6ts5oqmgYgXzbuNlSj/any4bcBV6SU40tcPgo8KoSwo4Zo/h1wd2bx1UCpCBHE0gVOV6gyWo0KzR4rDS4L7T47JSlJZosUSiXcVlWHJpMvYlQUjvT4GZ5Lki4UNd13gJ0tbk2M7JXBOZ7cWs9rN4IoQrCjyclr14NsaXQyGEhweSrOm7c1cGMuyXAwhcNs5FCXl2S2yGRE3Vxm4hme2FJPNJVnU4ODQFkSoVAqMZfI8UibnRMjoSU9DSnV8E2z28rhbl/5fee5OrO6UNXd5uVrc/zE37zOP3y0j+ZyeElnY1MsSS5ORHn1+hyvXw9yYjh03+Lp83Ik64nVePAtwOfLcXgF+LKU8lvlYz/FgvCMEKIV+KyU8p1SymNCiH8BTgMF4AxlL/1esJw4V6ZQUkXAgkueUoXHZmRfu1fTlmnzWqvKLuerZna1uEnni5wrNyBZTQqHu32Ml+e9pvNFjvT4uR5IEM/k2depGmK/w0yz28rL1+Z4dJOfY0OqIffYTDitRqxGhdOjIRqcFgLx2hUz85yv2IT2d3hX9wbvEVem47znr1/lcx89zI5m9/1ejs59YHguyYsDs7w6OMfRG0FiS0hz3GvW443CaqpozgMHljj2kRqPTQLvrPj+d4Dfuf0l3j61Ru/dLtubXbx8bY4dzS68NhPnJqKkF8gPZwslLk/HOVA2qkd6VENtMgh6u3ycGYuQL0qCyRxz5Vr240Mh3rK9gbNjES32f/RGiL3tHoyK4PpsgomKWa8dfvuKBr6SSCqHIh6sP96paIaf+PTrfPpDh3hia/39Xo7OXUZKyYWJKN+7NM0zl2ZW1IK6X5TWYRB+Xbc4tnltnGBtYtHzv/sr03EMimB/u5dgMstwMKUZdLNR4dx4hGNDIfa2ebhaTgjta/fy+g21ScpsVKhzmBlETZT2dvkIxLNV4aRGlwW31cjATIJoutq7yd3ipmUyKA+cgQe1P+HDf3+MH93bym+8bSubyvkRnfVBoah2ZH/v0jTP9s88FBIBD9pnZC1Y1wb+V9+6lX89N7nmv7hiSXJqVN04GlwW8qWSpgnT5LJgMZY4PxFlS4OTXa0ujg+FMCkCr93MhYkoNpMqbdDstmjVLy6rkVyhyPZmF7FMgVcGgxgUVfbAZb1Zg24331oVyrVAgr5u331PutZCSvi3c5N8+8IUH+jr4L/96C4sRl0F82Elky/y6uAc37s0zXOXA5pm0sPChmx0epjZ0ujkPQfa+erppXLAd85sPMtsPKuVKM7Esxzs9HJ6NILRIDh2I8Shbh9SwonhMNubXFyaivHYpjpeux7kcLc6NUpKONDh5eRIhHqnmUNdXhQhalbL7OvwMDSbXHXs8vhwmEOdPm1TetAoliRfPDrK0FySP3v/fhrdegL2YSGeyfODq7N879I0L1wJVA2Qf9jQQzQPIZ963yN89PFujg+FeP1GkGf7Z1a8pq/Hr8bvpRpGMSgC0wpaLhcnY3hsRqLpgiYBMBhIYDEqHB8Ks7fdg8tqxGk1IqV67PHNdbxabnKq9LLbvDYujEfJFSUHOr3aTNZtTU6cFvX6Hc0ukrkilyYXq0nWXl+U7jp7zelSDwqvDgY58gfP87HHe/hvP7rrfi9HZwnmElme7Z/he5emeW0wSK64PrpI16F9X/8G3mhQ2NPmYU+bh4890cP/9+J1/uA7V1a4SjIWSpPI5MmVO4t6u3zLXuG3m5mKqnHGRNmzLpQku1rdnB+Pcn48is9u0mLtgXgWh8WAw2ygxWvTNOC76tTZr/N/a+fHIuzv8BJJ5QjEs9pA721NTgZmEhzo8DISSq14O5wtlJiNZ+mpd6x5B+BaIiX83StD7G338K79bfd7OTplxkIpLUm6XKnuw4zuwa8DfvHJzfz0kU6uTsfpn4rx+vUg37k4XXWOQBBK5jAbFfZ3eDAZBG6rOlx7npKUlEoSi9FAtlAkni1osf7+qRiHu32cHo1grZD2DafynBhWJQvS+SJDcykOdXqJpPNah+pIMEVfj4/jQ6rBL0pIZPNE0zm2Nrq0kM3ATEILBTkthkVyBrVI5op47Q9Hk9Fvf+0Cu1vdbGl03e+lbEiklFydifO9i6qnXtndvV7Rk6zrBJfVRG+3n95uPztb3BwbClV5wPO/51yhxNkxNTwyb0xrcaDDS3xBtcuJ4TC7W91V1THzXJmOowh4YnMd45EMoWSWvh4/AjAaBPliiTdsqmMmnsEgBFOxDPFMAUUIrfQS0CSAE9kiJ4fDvGlrPdF0nmuBBKmcmrD1WE0MBOJad+z58Sj1TjNziQc7AZbKFfmlL57mm7/6uK5nf48olSTnxyN8++I0z1yafqDDeXcDPcm6Djnc7efkf3kbFyejvDQwy4sDsyysU+mqs3NjNsGhTi+nykbeqAgK5S3/zFgEswHesLmOCxNRbabqTCxDNGXgcLc6hq/SQ2jx2LgaSFAsSbY2ujSxs33tHk3y9JE2N9cCCa2zL5bJc3lKnbkx0PQAACAASURBVDo1FU0v2lReGZzjjVsbaHBa8NpNWqinw2ejyWXFYzdRLJZwWIy8tMww8AeFa4EEv//0ZT757j26cNldIl8scfRGkO9eVMsZA/Es25ucjIcf/LLGtUb34NcpiiLY2+5lb7uXX33rVmKZPO/+q1e5UZ6XeqjTxzfOTnB+PMrBTi/T0QzhVB4pi1poZV+Hj9euB3FZjPT1+Dk/FqHVY+P8RJTxSJquOjsCGA6m6PLbiWfzhJKqVx1K5tje7AKJNieyt8vH6dGbm0Kl5z4RSVPnNC/q1N3c4GQukaVQklW62GMLPqyeigHhDyptXhttPhvfODPBjhY3H360634vad0QTed5cWCWZ/tneOFqYNGQ96szCfa2eao6ojcCegx+g+C2mpj3Fw90evmjH9/LTx/p5COfO6GWPyrQ2+1nIpym0WWlfzJCsSTZ0uhgMJDk+FCII5v8FCoM6EgwhdNiZE+rm9FQalGJ49Vy2Ka3y89jm/y8fkM15kZFsL/Dqxn3TfUOiqUSlyZjWI2KljTd0ugkXyxxLZBgf4d32caSaDpPb5dP09F5ULCbDexudXNlKlYlVPZ7/9bPgQ4ve9o893mFDy/j4RTP9c/w3OUAR28EtbvPWjgthlWrhq4ndA9+A/GPP/8o1wJxeuodGA0Kvd1+fuUtW/jUd69QKKlyAvNedafPRipXZDCQ5ECnl2SmQP9kjHimwJ5WN4F4lkA8SyJbwGExLlm/LoSgJCUD0wl6u3xcmoyytdGlGeIDHV4uT8docFlwmA0kc0VKpRIHO72MBFPaKL+zYxFts1mKW5E7uNuYDIKDnT76p2I16/5zxRL/5esX+MavPK6HalaJlJJLkzGe6Z/huf6ZW0qS7mhxr5iwX4/oMfgNRLPHukjx8GNPdDMaSvG10+PkiiUtTjkaTrO3XfUuz4xGONDpZbvdxcnhMBcnYzgtRnY0q2MBK736fe0erkzHtbDM3naPZsxPjoTZ2ugkUR43WBmiGQul2dPmJpbK47CYMBmURXNazYblO0JHQyl2t7pXXUd/t3BZjLR6rdp7q8WOZhd/8YEDunFfgVxBjac/2z/Dc5dnmFpCAntF1p+dWxV6iGaDYzEa+IP3PsJv/ch2vnF2gr954ToA25tcnC/HvCurbQ50eLk0pWq9z8Qy9NQ7yJfnsPb1qKP4Khuc0rliVWz8WiCBxSB4y/aGKo/KYlTw2c1cmYpRCKepK8+PraR/KoZJEeSXue98EHS2d7a4OT68tHF/9/5W/uC9e7GZdQmDWkRTeX5wNcCzl2d48eqs5hDcCWdGw+zr8HBubIPF4NdHv1YVuoG/DfwOMx97vIcPHeni2xemeKZ/mqszagy9srTszFiEbU1OZmJZwqk8uUKJeqeb3m6fVjVzvFxOeWkyxpXpeFVsvNFlwWk18oOrs3jtJg51+cgVSszGM7wyOMemegfXZ5MEkzk2N6j/n6en3s5YKM2RHj+hZI6pSJqeBgc2s5HhuSSBeJYr0/G7OgVqNUzFaucKjIrgv/77nfzsG7p1z30BY6EUz/bP8Gz/DMeHQ2u+URclWDbg5C3dg9epwmxUePeBNt61v5UTw2GePj/JF46OVJ0zMJOgzWvFYzPR4rFiVNASqPNMRzO0+Wy0eW0YBBgVKJSg02/XjH0yW8CoCAxmA9PlQSCGCvmEeqeFSCpPMJmju85Oi8dGviCrQh8XyoJoLqtRky0YmEngshiIZ++9hogQMB6qbeB/+507+cjjPfd4RQ8m83K7Lw3M8q3zU3d1bN08G3FTtRjX36amG/g1QAhBX4+fvh4/P/fEJv7oe1f41vkp7fhEeWLTaCjFE1vqFmXrg8kcJNF033u7fKRzBU6OhHFZjWxvdnF1Oq4Z6+1NLq7OxBmYSeA0G/DYzZgUQTCZ43C3j0y+uGiQdyXxTAGbyYDfYSKUzK+52uTuVjfXZuKazMNSmA0KJSlrlmtuqnes2XoeRrKFIq9fvxlPn4llOdDhvSfGHeDkcIgWj/X24/gPIVbT+gsD6gZ+DZFSMh5JMRXNIMRN8SKPzUSjy4LLamQ8vPIH5uRImL5uH3taXYxHMpwaDmt5rzavlTqnGREAq9HAvk4v4VSOV64H2dro5MRwGL/DhEHAcvY1EM/S2+UjlAyrkgpGhcwyE7BWi9WolO9I7Ctq3uzr8GiSDAtxWTfen2Y0lef7V2d4rj/AiwOL4+n3Un63KKHdZ9tQBt6+DvM8G+9TdBeQUvL85QB//vyApgsPYDYIDnb5OHojRDSdp91n04Z4r8Tx4TBHNvlRRJbNjU48ViMGg8KZ0TATkQxPbqunfzLOq4Oqp36gw6tV44SSefZ3eDWZhaU4PRqmzWej3WsjmS1wcQ0qarrrHVyZjtNVZ2dopZOX2YBSD7Hs7K2w2nh6vdPMSOjeSgecGA6zp83NYEU39XpG9+B1FnF5KsYnn+7XDG0luaLUNGBAbSDpn1r9LfaxGyEe21TH6zcWP/fZsQiFskyr1ahgNRnon4pqQmWGFeSNQW3smItnmAinObSCWuZqiWXU9xuueN9LsVwA59f++Qz/+PNH2N26vpqb5uPp80Z9tSEXi8mg5WbuJRcnYsvqMK0n1mGOVTfwd8JLA7N85HPHl+yAc1kMVR9gKW89cXVsKMjedo9WhgnQ4rFQ77TgtBhJ54tcnorz+o1gRemln/MTkVVVyGQL6uJXsyGshploBqtJYSqaxm5SSC3j+S03FD2SyvPTf3uMf/z5Iw99B2utePqtMhFOs6/dw4WJ6D3vuMze613lPpHOr7+7Rt3A3wEOi3HZD1uDy0o8ezMOfTtlWCWpqk/uaHZyZTqh1dZPRbMYFEGb16p9AM+PR2hyWRgJJunr9nNmLHzPva+ihB0NTi5Nxujr9i9b477S9J9oOs8HP3uML/7cER5pf7iMfDSd5/nLqkF/8ersmkw6Ojce1Rrm7iWXJmP47KZV3ZXpPFjoBv4O2N/h5aePdHB2NApIZuM5ZhM3vTO/w8yNikSjvM0WwVyhxEQ4wxNb6nn1+px2K1ksSbx2M6OhNB6bke1NbsxGwSuDQWbKUgSXJqN0+u2MrhC/LdzGVB6LUWh3AJU4LOqf1UobWiy9ssGIpvP89N8e5U3bGtjU4ODJbQ30dvtXvO5+EIhleP5KgGcuTfPqXZp0ZDHd+1K+JpdF+3vSebjQDfwdYFAEv/euR/jEl8/yjbOTgJoMa/FYsZoMixKFd9Ip1+63cWUqSoPTQiCeRQhV5fLGXJI3b2vg+HBI85Z3t7q4NKl6edmCpN5pXtHAC24tRLO33QOSmoqD2YL6vufj8TVfT6jzbFdDPFvg6Qtq2ennXxvm+U+8mQaX5ZbWezeQUnItkODZ/hmevzyDyaAsK7mwFtyPoeSmdVgfXov1WPqvG/g7xKAI/vB9exmcTXBxIsZcIrfkMA3zbX5QmtwWsvkS0XSBTQ0O0rkCmUKJ4WCSUDJH/1SsyluOZ4oIbiYxA+WY7+FuHwLB6dFQVbJuV4ubi5Ora0vfVO/AZFAwCMGZ8dqhn3BZBnlgJkGn38ZojWYmo3J7csWxTIHf/tp5/vQn9+O23vvpVPliiRNDIZ69PMPzlwOLNs5KzaBa2MwG9rZ5iGcKhJI5CqUSbV4byVyRwUBixdcv3Qd5CcM6NHwbBd3ArwFWk4FPf/AQ//4vXl5SKRJuz8AbFQESbswltaTp9iZnOTSjGpdAPFtlWEZDKQ52eFEMN/3yzjobr10PsaXRic10s3P1jVvqOTESWjGR5rWb2NLg5NRoGCnRxNVqMT+bFqDJba1p4Pe2e7U5tLfKc5cDPPlHP+BX37qV9/e247rLhj6azvPC1QDPXw7wgxr66ZUcGwpxqMvLqZGbm58iVEfAZjLgsZuqNgCH2cC58ShdflvVEJmlUNYoGX4rKMoG8eDv9wLuArqBXyM6/Hb+5P37+fgXTq6pGJ/fYaazzk4oldMah66Wh21XNqGcHg3T6LIQTuXIFyV2i4FXKko3D3R6ARgMJHBajDyxpZ5csciV6RgOs2HZOufD3T4uT8Wr9OOXauvu8NsYqzDok5HajTJ3WrUTTuX5vW/184ffuUxfj5/uOgePbqrjx/a13tHzgpqPODMW4QdXArx0bRazQbmlRPWpkQjNbqumqa4IwYEOH8eHQ4scgPnk60goza4WF5F0fsmfGUDxPihibUBZmnWDbuDXkAaXhR3NLsbCKRILtF3avDbCq+xEdFqMbG1yYlQEJ4fDdPntvHFLPd+/OguoXZ4mg+DRTX4CsQxFKYlnimxrcnL0RhABHL0RxGU1at6mqeJTmsgWmIqm8TvMxLMFHu3x8+r1YFXIpM5hps1nw2YykCsWF3VV1po1C9DqqTbwMzXExISAsdDyXa6rJV+UvDoY5OxohGNDakPZh25j+lM4leOFqwFeGwzy7OWZqv6FymHrq6XBZdEMfKEkOT4cWlGeuX8qjkkRy55nEPfe2m5EXZr1gm7g14hiSfJ//st5rs7E2VTvwGrKa7F4m0nBoIgVE53zGBTB5cmYJh1wYiSM325kX7uHUCpHvlDStGMqQzMnhsNabL1QUjVr5r3u1AIDfX02qalPBuJZOn12ilIyHExhVAT1TktV7f3C8rzkErK0CxOn9U6LJo42zyNt1XX9d4rZIOjw27kyHee/ffMiLquRd+1vW/G6iUia71yY4t/OT3FjNoFBEWxtdFYZd7i9QRC12t7n1UIT2cKSpY75kmRkLqnpBC3iPtja9dgAVIv1uJHpN19rhEERfO6jh+mpd3BjLolAaIJZO5rdqzbuoMZ8d7W6qYxihFIFkrkC09GMZjBNiiCVK/DoJtXDXBhHT+WK9Hb52NXiZjAQZ2eLq+br9U/F8TvNjIdSHOnxc6DTy9WZagO00GO/OBnjyALPtsVjrSoL3d7sWmTcYe2aqtR1qa8zbzClhE98+RzfvzKz6FwpJRcnovzhd67w8S+c5PE//D6ffPoy58YixDMFtjW5ODEcZt+C/MJSdyvLUWtTKJYkJ0fCeGzL5wwSuSJbGmr/ru7HNI71OOloo6B78GtIq9fGl3/xMT7yueNcmlQHfdxuo9Hp0Qj72j30T8a0oR2DgSS9XT4C8QwNTiuXp2OaBPCBDi9nKrRnLEaBy2rg2kyCUNkjzS4TZz8xHKZvmQqQy1MxFFE9t/LYUIgntzUQSuYwKIJCsVSVF/Askfy8NrN2jToHOn2LkrWFkuSXvniaz3+sj73tHl6/HuSFq7P84GpAm8K1cHOCm70Aw8EkdQ6zNiXrdhrUlrrCYlQYXUV4aimhtlstZ10LbmeDexhZj+9SN/BrTIPLwlO/+Bi/9MVTvHxt7ra7SOdlCPZ2VFebnBwJ15QguDAR1erf/Q4zHpuRY0Phqm7SG3NJdrW4q+Zz7m/3MpvIMhFJq8PClzDyEjXcsjAEE07luFCjFh6gWMMwWk3KshIFt8K87k4tsoWSKiNRombD0cIwDNysUImm1Vm68wb+tgZqSDVMs6vFjVLe/HLFEhcnYnT6HUxFl+8BmE1ksRiVRXdld0M2QBHqRgkwMB3HaTVqYRmJxGlZfyJcGwU9RHMXcFqM/N3PHuY9B1aOA9diV4uLbU1OBmYSNUsJTw6H2dPqrnqsUJIMz6V4fHMdVqPC0JwaEjo+HOJw900hsYWlmpPRNKaKQudjQ+r5buviD7XXfmvliLUiMXvbvSvqxK8GVXdn+TLLTL60ZDfpYCCObYF6oKHCU71Yllq4XSSSVK7IyZEwx4dCnB6NcD2QoK/bt+oNo1Yo58p0DPsadrPu6/DQ6LJwaiTM6ZFwOQGfYTqmfs3EshtCSRLWZ6OTbuDvEmajwp/8xD7+45ObV31Ns9tKX7eP/qn4sp6/BIbnkvTU27XHHmnz0OCyqN75gj/UVK7I4W4fJkVwdizCnrabm0Ozx8pwMMWeVje9Xb5yMxQkskX2tXvorrv5Gg7z4hs+wzKfilohobWI5x7q8mojD2+Hvm4/e9o9bG10Vj2+sAb99GiIXUvkLVaiVsIunVeT4+eWaBBbSJN7cbduJl9i1xopbBoUwVTkZk5HUju0tB6TjxsFPURzF1EUwX96xw6a3RZ+91v9S1YjbKp38MTWegTw+ddHap+0gESuiIxleWyTn3AqXxUmsZoUrQpjT6ubqzNx8kXJjmYXE+EUlydj9PX4OTkc0vaCSi34vm4/JamKW5kM6rSqazMxXBYjPfUODIogkckzG88uG5+dSy4OQ9xpiGFvm4ezdzAMujIE1dtdLZG8cLMqlGA0mGLngrul1bDURua0GHCYjavSdjEvIUuwFoO1QQ3PnRpdudnsdjWUHj7W30amG/h7wEce76HRbeXX//nsopDB/g4vn/3ZXi2+/YWjI6suS9vZ4larPjqqPbqpaJaeegedfgeXp2JafbvZqLC1ycWF8SjJbIH9HR4uTi5OeFYmFfNFyfGhEPs7PLw8OFd1nhBUhXcqMRlU73AhwSVkHFbD/KjCOxkyXfmzvTFbncgcDiaxmRTSFXceiVyRq9NxtjY6ubZASqCv24cQQjWA81LQQi0hVRQFp8VIk9uC22rCbFSQUs2V5EtyxYlbsLREwFoN2Y6kV/5dmA0Cn82EzWRYl3K6650VDbwQwgq8BFjK5/+LlPJ3hBBPAdvLp3mBiJRy/4JrtwNPVTy0Cfi/pJR/vhaLf5h45yMt1DnM/PI/ntaSd6Am4uqd6q14g8tCb9fKGu7zCKGGFU6NRBYlR4fKCdUDHV5KqIqU58cjOCxGHt3k5+hQiHxRsqXRgZRoNfEAc4nF3uXZsWhV4xSoxnKp1vo2r43hYHVp6CNtniUTsivR5bcxGU3f8R1ApsJIhZI52nw2bRZuIJ7VNPUriWUK5Itperu8RDMFTIrAbjYyHkkv2XXa1+0nkS2QmF3sbafzRZrclpV14Zcw8M41GGdoMSr4Heaq33slioBDXT6G5pK8diO0qtkCDzvrsRx0NTH4LPBWKeU+YD/wdiHEo1LKn5RS7i8b9a8CX1t4oZTyasU5h4AU8PU1XP9DxZFNdXzr155gf4dXe2zhnM0PP9a96ue7MB6huRynPTYUorfLV5XY7J+KkSsWOTEc4uxYhJJUB24fGw6zq0UNOwwGktjNhqqyweFgit4aE55qDcI2KoJ97R4Odfro61YHj/d2+Wj33YzdmwyCBpeF/Crlc40K+O0m2n02tjY62dvmps5lqTLOt8tQsNqgNTqr49xL3aSn80XOjUdxmIz0l2UblpMUWOluf3X6OUvfHd0JO5pdOMzGmgbbIFRZixaPjRPDYa1ZbzycWpdJyEpW0gF6GFnRFZDqtjZ/b2oqf2k/CaFmYN4PvHWFp/p3wHUp5eqCzOuUFo9aK//Jp/v5Qo14+4/tbeGLR0eWTSIK4GCXj/FwijafXUuSnRwJs7vVzdBsQpukdHo0usj7yhVKXJiI0uW3E0nnCcSyzMRj7GlzMxpMEcsUqhqW5nFYjBzo9GJUBEKoNdlWs4GjS6z1yW0NnBoOkcgVmY1nmY1nMQh17VPRjFaTPv+eDvf4uTYTJ5zKEyp/VWI1Kexp9RDP5hkM3J7UQTxToNVrZTKSYXuTc5E3fH02wcFOD0aDASmlpsrpsBiJpfMoiuBIj1/7AMzbPFn+v6EsGKYINd5fKEmKJYmUaiRbSomUqsaQySCQUr0TKkqpHZs/z2pU6PDbKEmQJUlJqnrw8VQBn91EUUpKJUk2X9J6JRbiLW+UDrORdK7IdCzDYCCOEAK31YjdbMRiUjAbFcwGhelomjM1EvxT0eyq5vw+zKw/877KGLwQwgCcArYAfyWlPFZx+I3AjJTy2gpP81PAl5Z5jY8DHwfo7OxczbIeWsxGhf/+rj0c7PQtSmAJIfi/f2w3P/qXL9ecFrWv3UM4ldfKJ2OZAg6zQROtujQZo6feTjxT0LyvE8PhRSGc3i61Pv5Ap1f7QF+ciNFdZydXLBFK5qqu6evx8dr1xbNhD3cvPcv18lSMRIUmvtNioNFl5cRwGEWo116fVSWP97Z7GAkml50alMmXtGauXS0uXFYT6XyRmWhm2aSl22bEajSUNyW1WslnNxNKZmnxWNna6MTnMJPIFLg2EyfrtnJ6tHrTMhsVGl2Wqk2pFnta3asaXr6n1c3lFebzLgxxLYUQahdxvdNCncNMJJUnkSsQiGeIpPI1a/5Bki8WqsTP+nr8BGvJI5S5H1LFOnfGqgy8lLII7BdCeIGvCyH2SCkvlg9/gGUMN4AQwgz8B+C3l3mNzwCfAejt7d0Qf0nvXqJOflermw8e6eJ/HR3BZBA4LUYaXRatsqWSdLkEstJDH5pL0eCy0OW3M1KWSJgP4ZwcqW5+OjOqxu9Pjaga8ZORNI9trufFgVnOjkVwWVRp4cQSErlLhS03Nziq4rsmRdDus2uSAiWpbjyWsuEMJXOrHgACLBpebjMZaPFY8dhNmBRBrqjq5wdiGWLpAjFurr9SPkFKoYmCzVNLRyZXKK3KwC+lsrmQTGHtEpZSwlQ0w1Q0w8FOL2dXWYZZicdm4tIK+ZHZGrkZnQebW8rWSCkjQogXgLcDF4UQRuC9qPH15XgHcFpKuVggRKcmn/jhbYyHU5wcDlMoSa7OLD0MIpldbCxm41kcZgM7W1yap3hqJMzedg/BBeWL58ejbGtyEUzk8DrMvDgwy74OD+fGomxucDMwE2NoLoki1OEgVpOBRLaAlJDKFejr9nFpMqbdRbT7bLitJi0p2+m30e6z17wDyBZKBOJZ6hxmDnf7KZXDFIoiSGbzZAsSk0Gs6O2m88WaYaWVmI5l6K6zV3nLI0t4zqtxYA01tNMFN5u+ZPl5jHdJYz2ayd+yPEad04zTbGBkgW6/32FmU71Du/MRgiopCp0Hn9VU0TQA+bJxtwFvAz5VPvw24IqUcnyFp1nRy9epxms385/esZMP/O3RRYnYhSw02PMkc0WG5pLsbXNzfiKGBK5MxdjZsrALtkT/VByDIrRQx41AkqZy41Rfj5+JcAqPzbRk+MFhNvDEljqkVMNMrwzO4bQYcVuNjIbSK46aCyZzBJeI5Tc4LYsqeNaSOqelysAH4lk2lUXjKllNkrHynEaXhQ6/ndOj4UUlkblCaZG2z51gNynsbvNwdixCvijZ1+5heoXQFaibca5Qwu+0LDLwsXS+agbAwu5pnQef1XjwLcDny3F4BfiylPJb5WOL4upCiFbgs1LKd5a/twM/BPzimq16g7C92cWXfuFRvnV+kqvTcZ7pr30DJKX64csXJbliCZ/dxERELSmMpfP0T8W1ME6uKDEbFfq6/RRKJeYSWWbjOfLFYlV9dTxbYHOjk3a/HSElTW4rElXUrDJZ6LGZCJQTqPMDRuYrcuYbcmwmA8PB29d/n01k2d7kwt1sJJktVmnprAXnxyP47WZCqZsbaYPLclt3BFKqDWU2k4HzE1ECSxjYG3NJLEaF3a3u29YrqqTD76gK0/VPxdjf4aXeZeHKVKxmzX1vl4/LU+qdV0nKKoE1qFFVst7LaNYhq6miOQ8cWOLYR2o8Ngm8s+L7FFB3+0vc2GxvdrG9eTtT0TQvXZutqQsSiGcxGxUtPpx0WaoMS0lKTgyHOdjp5fx4hNl4tspjtZsUdra4sJuMZIslwskcgViGs2MRWjxWjIpgbInY885m1yJp4bEF0sjpfHHFWaUrUfkau1vdWIwKiiI4Mxq548YftR/AqeUlAALxxaGI5cyb325mS6MTq0nRtPpXIlsocXp0cQ/D7WAxCk1moliCgZmYZvC76+xYjAbtZ9juteGxm6q887lEDrvZwOEuL6XyXdiNuWT13eM6rBNf7+haNA8JLR4bP/dEz5LH6yvquZcyeKdHI+xp8xBN59nb7uFIj58jPX72d/pwWowYDILrswlaPFYOdvl409Z6svkSBkXgd5hrPmflS3X6bTyxpY7JGnHaY0MhNjcsrqO/HS5Nxjg9GuFkRT3/nVJYMApvaC5Fk2thjfxiE9/ht7G/w0s0k+f4cIipaGZFvfeFHBsKcbDTu/KJyyEEJ4bDHB8Oc2o0rM3cBbUaZzqW4clt9Ty2qY6pWKbmxKhUrsiJkQiKIjg5EiadUzdmUzmBoDvwDx+6gX+I+I9PbqZuCUN7diyiCWNVyiEYhHor3tftp9Vr5exYVB0nF81wbCjEsSG1CWp4LsXxoRCFYomTI2Ht8XAqx3AwhcUoaPVYF71uCcnWRif1TjPRdIF4psAjbW566u2LNNfrnYvFs+4U4xoND7kwEV006KOrbsGGVPFSO1tc7Gv3MB5Oc3bs5l3E9dkEzW4r9c7av6eluDQZu6MNsJbxVYSq3dPX7SeZzXNxIkZJyhXveC5NRKl3mknnixwbCrGn3UOD06KLjj2E6Ab+IcJlNfH779mzpPGIZwsYBOXKFbWT9ECnWhopkVrn5cBMgpKU9JQ7U1O5Io3ljtjKuayxTIHHt9ShCLXRJVMo0VVWl5yfyuS2mqhzmJlL5Iim85wbj3JhIsbQXIozYzc7bUGVJl5rLk5GcVnuvHU/X5ScG49WzV9NZBfUhEtJb5eP7jo7l6finBuPLopalCTYLQYMQtDqXbwhLkW2UCKeKeC/RUnmeVIVHnun38aRHj8em4nzE1EQkkIJbGYDF1chFZHKl9jccFNp88xohGAye8ty0Tr3H11s7CHj7XtaeHJbI/98YpQ/fWaAeIWyoNdmZjaWpVgo4bMbaXKr5YtOswGjQWA23hy2EUzmSGaL7G3zcH4iyqXJmJaIFQL2tHoASTSdp8Gl6qZEUjm2NDgJJXJsa3bhtBh56dpslZHb3erGbFQIJnI0uCy4LEamY+qw8Egqj9NsqGp+ulPyRcmBTvcdyQdXcmY0jNNiIJEtcnUmUVW9c34iyq4W94oNSAahViP57eZFJZjLEYhn6fDbUBShNamtRdqURAAAIABJREFUlmuBBI9vriMQz3ItkGC0oiJmXuAtly/S0+CokFhQ+28r/fJ5J/36bILGcohKEQJFQL6gx+AfNnQD/xBiMxv46ONqPP53/61fe3xgJk6z24LLaiKUyjMWUuOsBqHGlPe3e7QE4CNt6jjAKzNxHttcx+vXg1yciPHYZj/Dc0lNFKyvx89MTH2e3m4fx8ux9NMjYawmhXafDYMQ1Lss9E/GqmK7o6EUNpNBa7Da1uTk1EgEh9mAz2FesWlotZwZCdPpt9/S3NulyBcle9tdnBpRwy7bGl2apG6+KBkNpXhiSx254k0Zg4WBC5NBvTEOpdTqpPnhLathLJSmyW3B7zDXLI/12k00OtXfsdEgKJRKCAQjwRShZI7xyOKf6fXZJJ1+G6OhNNFMnl0tnqrxjquluUaITufBRjfwDzEfelTtdp2Xvc0WSkgE1wJxshXels1k+P/be+8g2d6zvvPzds5pcp659/5uzmHuTxZC0k9ClrUCYWwsLIxLu0WxrKtMubzlZSnbhRezZUzBlm12C5sFU7bxslqLYBZhQGYFyjfnHCb25Omc07t/nO6ejhN77sz0vJ+qrpnpPuf06XdmnvOe532e75cOu4nXS3HGOu14bEbuTYe4POxhNpjkO29Wy5Ucubws28lpFRmS98d8LERSZQelUoeq0aCjw2bm/myo4Sx1pMOG26JVawx6rQg0wbKJ1TjxYJIrwx7utKBEMFuQGHSaoNlWumGbUZmiLtkOOs0GTve7eDAT4puvVzk34GJyJVF1B1XJgMeKP5Qkms6TWY1vWkmzNIM+1eskndMWuLWuXE0zKJTIMtZhr9JxH/HZWI6lWY6lGfFZ6XVbmQokWKhY7O5ymJkOJEnnJPdmtMqdm5OBLRXGqBT8wUMF+AOMUa/jZz97mn/0+4/LZYyxdJZBr61KJiCT1+rdA4kMR7vs3CqWx1XWX9+eCvLR410sR9NcGHTzYDZMt9PMg5kQuYK2kOq1Gau0Yo51OZrWcPe5LVUdobPBZN2M/aE/TJ/bvKE/6WZ4uxLHoNP0bSKpHJlcvmxbuFWezUfKeu0vFqJcH/PxdC5cVcr4yB8pSyM0uhPpdprwF2fT6ZzkyVy4LN9g1At6XRY8NlPRNlCSzOYJxLMsRpIsRtLrSgnXBlprhbTCVCDJVCDJ+WKjUwm9vnq57cZEgHMDLiZWElswEFER/qChFlkPON97vIvvea8T0PS7s7lCVXDXCbg45C1ryVTOOH02I2OdNq6NeuksShRMrsRIZvOc7HUy0mkvG1G/WophNugY8FoBuDjoXlc+YD6cYsBrxbvOwlw2L+l3W7f/4WvIFTR9mxcL0U3K8TYmlS0wXKygSWbzJDK5qrLDEvPhFPF0jnMD9RZ6teYYp/tdDPts9DjNZPOSmWCSR/4wNycD3JwM8sgfwR9Ksh25e7fVWKeBUxuKG4XmR/4InQ4Tg97W/Q4U+ws1gz/gCCH4/NUhbk0E6gy63+txkM4Wqhp4MrkCTrOebpeFN8txTAY9d6aC5bREIltgKZKi02nBZKC8KAuaSJfbauBUnxOrqbnDT8ksIpMv8CyyvnZJ7cyyFYz4bHVuTVulw25iotjJ2kxm4eyAC5tRTziZ5cNHO/hWhdbOUlTLn/e6LdiMeh77Iwz7rJuy6tuIymXRfo+FGxMBBjwWelyWsiGKrqZ8tNAkF7McTSOAcwMuHvnX7xBu9/l7Oxp+qADfBlwY8tDvsRJN5criw1Jq1RxzNYtuVqOe0/3ucrphIZLiYye6mA8lSWTyxNI5HGYDr5dihOImepzmqqAUTuY41Wfk2XyEM/0unsxF8NmNDPvsvFqMcrLPhckgCMazZeXI9WgWeHZCj9tSVtHcLhMr8XI1zcvFaJXF3sleJ2aDrkrZ88VijEvDHkx6XdnY5ESPg5lgsix5MB1I4rYaCSe1NJfHZmTIayNfKDAbTFZJ965HpcR0qbLFH0rhrzAguTRU3TjVLDhLKYlnCzzya1VUOiGqficlfXrFwUQF+DZACMGA18rXX1V7pnY5TVwf85Vr27OFAg+Kzk6VvFmKVUkRHOnSAtNKPKMpQ1oNRJJrwWc6kGDIZ2cpkuTSsIeHMyHux0MMeCxMrMQIFDXFSxeA9UoFZwOtr40PJTJbKk9sxGo8w9kBF68XY0RSOa6OeMjmJYlMvumFq9Yo42y/qyo/77MZCSbX1jAsRn154bXDbuJkr3NTF8XKhqNsYXM5nWZBuvL5drfkO4yoHHyb8MHJnrrnns5HmQ4minneAPem64M7wEwwWTXjuzcdpK9YEjcbTNLtMFNaxzs/4GYulOKRP0yf26ZpwRSP6Q+lcJgNjHVqzVBP5iJYjDomVxNcGvLUtf6DdgfxftHmr1UpgJeLMfyhJGd2qH742B/hvR6tO/j1UpyZYLLOeLsZl4Y8dcqbR7ocVVUrPRVNYKvxDG+WY5wd2PicK8cp2yRpv1ld+lZ1Aiv2JyrAtwkfPtaBqcE/9cwmZ8jBCiXFgtTK/EAL6EIIjve60AtwVljcLUZTZZ2SEtFUDleFFktJHO3eTIhAIsPVUS/Xx3wM+9YW9mKZHLengox22mgV2bzk1WKUU0X5hu3yyB/m0pCHUDJLp8O0KT9Ul8XQsKSw1pP29WKM0Q5bxeuSlwvRjSULKo7dKMU1PurjxWKUM/1OTvY6Od7jaLqOYNpAxvkw0YYpeJWiaRdsJgMfOtLBX7xc3tb+k6uJsuemEFr998UhN/dn1vLMHyouJJY6XufDKcbHfFVdpMFEFpM+VefoBFoAu11MAxj1gotDHox6TRGyVCd+rNvB603Okjcik5dMLMc50evkxSZSH82IpLS0ysvFGFdGvHWL2ZV86EgHEyuxuvJRi0FXbna6MuxlJphgKZomXpNGyuQloUSWE71OsvkChaIXazqXL5dOGnS6slWiQNTZPsbSWYKJbFVJq89uWtPaEVqLVkl5cnUDvwHFwUUF+DbiE6e6txzg3VZNS0ai3a5fGXazGMlwbzrElZHqhbpSOuDWZJCzAy4e+yM8KtbLl+SJtXp5E5FUFiGaz4ouDHpIZnPohYGRDhuP/GEGPVZWWmwLl8oVmFmNc7zHQSSZq7Pn2wxvluPl/PidqSDjo96GksAOs4HvvF3FbTUyPqp17xak5kX7XrcTk0HH5GqcydX4ukF1NZ5p+PrJXid6nSBfKGw5X14oyDq7R0U1tRfKdkAF+Dbi4ye6gSdb2udYt6M8I51YiXNhyF1ud78zFSpLALitRh5WeH0uhFM4zQai6RzvddvR6wQjHTbuTod4tRTDYtSVW/kbIuDJXPWsulGbfStIZAtMryYwGnRVVSxboTKnfWsyWF5ArmTIZ+XZfJRwMsvNySA9LjP9bivhVLYsDbDumGxAaQH2Q0d8G2yp2A7tmKJROfg2Yshn43iPY+MNK6it/a011/bZtXz68R4HmeJqar/bzKDHyoUhN2cHXEwFEvS4zBQKcHHIQ6/LTDpb4MKguy5n3WE3cXnYw4sWuzJtRKqo1tjntmwqj17LQ3+YgaI6pASmVuPlpq8STnN1c9ViJM29mVBVTX4rYkimkT1TBUpSQFFCBfg2o1E1zXrUaoO/WY5XVXLcnwlzrMuOUa/j0rAHr81It8vKQ3+YcDLHY3+EcDLHbDDJ0/kIgXiGhUgaCdybCXO0y8HZfhcXhtxcHvYw6LVi1OuIZ/K4rUbODrg41edkfNT7Tio6ni9EuTC4dXMNKaGzogools4jpJaWKZHfZMniTnm7HGvqC3B52FNXA6/YHO04g1cpmjbjE6e6+dd/8WbT2zcyf8jUlN51Osx8u6JLc6RDq7R5uRjhvW4Hr5ZirMQyXBv1shxNV5lJN6rr9tqM9LksxDLaBaLEyV6nJtC1S+baJW5PBbk05NmyouKDmTCXhj3lXoLZUJKzAy6ezkUoyGqjld0kmMgy7LOSzRvKzVFGveD8oIc7U0FGfLY685Jm3rCK9kbN4NuMS0OeLRkzNApKLxdjVeWF0XSuYX12OidZjaXLWia3J4PodFqguTbqLeuJ1xJMZPGHU4ST1YH8+UJ0RxZ8TrOe62M+TvU5GdpAX2UulGxYVroR96ZDnBt0U9r1sT/C1ZGSyXjrdO43YjqQ5GSv9jtymA0c7VpbS5kKJHgwG6567KTp67DQjousKsC3GQa9jotbuEWfWIk3DIYlvZPRDhtP5iJEk1lKGRSHWc/pPhfXRr3kpVZ902k3lrXRH86GuD8Twmvbmm1dp8O0KUndWuwmLbAXpKaS+Gw+SiiZxWVtfoO6GE1zcRupGtBm8qf63OUegLtTAT52vLOsXfOuCCWz+OxaFdRmOmAV69OOKRoV4NuQrZg+Z/OSbEHWqT4+nY9woujaBJoM7ZURL1aTnmAiy9P5CNm85vjkshqxmQ04zHreLMe5OuKjz21lJrC1gHeky0FiG25PJ3td3JgIEK/YN5rKcaxr/QXnm5OBKou+rfDIH+Zkn4vxMS89HiuJzLtJz1SSzhXosJt3rLujaF9UgG9DKrtNN8NCOEWnw4zZsLbIeW7ATZfdVNVu/2oxig7K5YH3ZzTTkGA8QyKTZ9Bro9dtRqfTOmFNxq11Sb5ZipXTDpvl6oi3yvyikrvTIY522Rkf8zX1On02F950W38tj/xhbk4E8QeTe3J732E3bVo6QbExbTiBVwG+HdmOFvqrpRhjnQ7ODbo40mnnkT/MN9+sVrXNh5I5ztRonwsEM8EkkWK6IBzP8N23AdK5PKHE5uvNfTYjR7sdzIUSnOhxrHsXcqbfyXvdDpxmA7fX6SoFrSro5kQAr72xzEA0nd+xZg2wrTuPnaAX2gVWoVgPFeDbkK3O4Es8X4iC1My5Lw97ysc63efkaJed4z0OoqkMHzri4+KQh8vDHnQ6uDbmxWEx8u03AU70aheAu9MhTm9SB+ZEj5NcQXJzIkAyW8BhMZDNVQfMbqeZa6Nergx7kVK7IDWzy2vEm+U4x3ucDcfm7nSIId/OTC+ezEUY69xAQ6aF9LotDYXjFDugDcdTlUm2Ia4duBmVFjlLDVP3Z8KM+GxVed5Lw56q2eOpPieBeIYrI17yFU04JfOJKyNedGgm1F6bSTOjkBKdTlCQmnplWdI4L8sdtPlCAX8ohUEHA17rjuVsn8xF6HVZMOoEgZq7i2yuUC753C6VJiG7jadon/iu7xwUBwsV4NuQ7c7gK6n0BPU5TEwFEnhsRkKJLPemQxzvcZTFs4xFV6Z4OsvzhbUA+WY5zve+18k3ijr15wfdVSkVp8XQtOZ9OpDAbtIz4LWSyuTrtNa3y0IkxdVRL4Gai8VCJI1jh+O2eW/TnfNkLorNuCY6VpCa3sxiJMVceOt6O4r2RAX4NmQnM/gS4WS2XGEi0OrrFyMpRofs5AoF7BVGz6XMdu37XhrykMzmy3e+T+cjVaYW0VSuoepkiXgmTzzTen2adLZxxcvboiF2dgMpgGY0szDcLRLZetGxa6NeFeC3STvWwasA34a0YgYPVMkAXxnRAkdl8Lg87OHudIjlogJk5WuXhtw89IfRCS2F82w+SjYvmQ0kqtyW/KEkVqMel8XQEr/SzVCry16iIGHMZ2t6wdmIlWgao06Q3cPk+BN/mH63RQV5BaAWWduSsU57ywWn7s+E6hyZZoNJPDYjc0Uv0Nmg1l15ccjDQ3+EfEGSzUumVxNlY4tYJk80laPXpQl3pbIFktk8PS4Llm2WK24Vu7n5BbDDbt62Jk48k+d0CypydkIiW6gyXFFsHtXopDgQdDjMLRecyhckQx3VjktL0TTnBtw4zWvpmrlQkkf+cJXGTbwY1EsXiNV4Br1OVJVCPvSHy/Z4u816YpI3JwMc67ZjNW3P6Wg/KDk+X4huqZtZ0b6oAN+mfPL01lQlN8ODmRCdjmr5gduTQYwVtm+RVK6hgNlqPIPRoCvLB/hDSbqcJqwVzVCP/OEtyx1vh6fzUY51Ny9pfL4QY9hr21YD1JO5yLoSCe+K7TZvHWbacAKvAny78slTrQ/w2bzkSKcWgPvcFk72OhjpsDHgsXJh0F0X/GuZDSbpcVrKHbOvl+IcLZqFlGjFAvFGxNK5Dd/nxWKU8zWKjJshm5eceEd3IuuxrNQjFagA37a81+1g2Nc6E+sST+bCvD/mI5HJ8XwhxvOFKI/8YZ4vRKo8QBtxddSL02LgeI+zLFz22B/h4tBaIG00+281vW5LWWZ3PW5NBreV6tiOY1SrebsS54JK02yJWvObdkAF+DZFCMEHJ7tbesxel5khr43vTgTqpH7HOh0bBudcvlD2Bb0y4mO0w8awz8pCOM31MR9Wk/6d6Jbrhdi0sffrpShXRrxbOv7LxRi+JoYc75KHM6Fti6kp2oMNk4VCCAvwdcBc3P7LUsqfFUJ8CThR3MwDhKSUFxvs7wF+HTiLlub676SU32nR+SvWoZnrz3bpdlmIpnI4THpiNR2URr2g02FiJZbBbTVwonetmqSUgJkKJMgXJC8XY1wY8pArSISAQa8VKeHaiJevF5uidothn4250ObVF2PpPI/9IU70OnmxBUneo112AusYa78LJFqp67EuO06LkQezISVvcMjYzGpQGvhAShkTQhiBbwoh/ouU8vOlDYQQvww0E/L+l8AfSyn/uhDCBLQ+b6BoSCzTus7KToeJR7NhJJrz0pvlWFVD0CN/hAGPhW6nGY/NWFVDX0s6Vyi/frZfk/otcXHIs2kRrQ67iT6PBSRVqpfr4bEamW5+ak3OV/J2OcZIh42pTRpn5PZRJH1drOvvdpoZ8lkRCB7Mhrbd0NWutONobJiikRql+1lj8VEeCyGEAP4G8Nu1+wohXMD3Ar9RPFZGSqkk8N4R8Ra2zh/pcpR/6c8Xog0VGP2hFKOd9rKEwUbodYJUTfenQSc4sQnJ4LFOOzqd4LE/wuO5COOjvrIBx0bvuR2yeYnPbmKzu5v0+y/7uRRNc2cqxO2pIC6LkfExHxbj/jtPRevY1G9XCKEXQtwHloCvSilvVLz8EWBRSvmqwa5HgGXgN4UQ94QQvy6EaFifJoT4CSHEbSHE7eXl5S1+DEUjWilEVRvX7s+EyzooJXx2E+lsngHP5pQZS4udXY61BqpMrsBcKLmuuqPTYiCWzlVVitycDDDgtTK8zn56oenluK0Gzg+4686zw27i6qgXn71xhc296RBjnfZy09Z67IeF1vWIJLN1v9PDThuusW4uwEsp88X8+iAwLoQ4W/Hy36TB7L2IAbgM/KqU8hIQB/7nJu/xa1LKq1LKq11dXZv+AIrm7HZFyq3JIH/pqI/xMR8DHiuBeIYHs2H6PRaujHg2nIl7bUaSmTz54n+W2SB4vhglmsqRyRYariGUUkCNygAnVxMsRtJcHa1fFD3WZUev1zGxmiCczPHQH6arojO3z23BqBfcngySzmrKko14sxwnm5cbdrs20p7fT2QLkhsTAc4NbL0UVHFw2NL9WTG98ufApwGEEAbgh4AvNdllFpitmPF/GS3gK94BrYzvBSnpdpq5Pubj+pgPW7HT89tvAqSzefyhNVGwu1NBFiJpXixE6XaaGR/zMdZZPeu1GvUk0nmi6RzdTjOXhz2c6HWRKUoML0bTOC2GKlGzPrcmbzATaC5Als4VuD0Z5OqIt9yxqhNaTjyTK1TlnaOptVm23axnoaigGc/kmQ8lGR/1cqzbwfiYj2GfFbNBcLLXwZFOO8d7nPS5LU1TPgdlMnhrMlhl6nKYOSi/s62wmSqaLiArpQwJIazAJ4F/Xnz5k8BzKeVso32llAtCiBkhxAkp5QvgE8DTFp27YgMKLYzwZoOOpWi6XMZ4ZcRLQWoz2UyuQJfdyHJcC5gnep10Oc34g8mqfYa8Vvo9VmaCCTrs5rL2fEldsrakb3I1wZl+Jy8WYvS6LKRyeVZim6tMuT0V5ESvk6VIivd6nA0XfWeDSQY9Vvo8ljpVxlgmz80G+vPPF2I8Z22NQQv62nqE3Wzg0WyYZDbPY3+EQa+V2WDr1TBbiVEvCCb2ttpHsXtspoqmD/h3Qgg92oz//5FS/mHxtR+hJj0jhOgHfl1K+ZniU38X+I/FCpq3wH/bkjNXbEihhUnFVI3E7mwgUVZ/1Am4NuojPB2k22Xh1WKMp/NRTvc5eTq/Vlo4E0wyE0xyZdjDy8X6ksPpBhUqT+aifM+xDp4vRDcd3Eu8WIjS5TTzoElVTjpXYDaUxLJN3RntGLLsUQuaEce5QTcL4WTdmO1HsnmJ3WRgpMPeMs39A0sbJuE3DPBSyofApSavfbHBc3PAZyp+vg9c3f4pKrZLT1GxsRWst2B7bdTHjYkAl4c9xNPZslxuZYA70+/EZjIg0Rqeounq4w14rEQaLEye7nNxfzrEqX7XlgM8bK5l//VSjC6HuSx7vBNCiey6JaL7kdKF9+qIl4mVOKt7XL+vaB2qRqqN+VQLBccqRcEADMUywCOdtrJL093pEF772sLl25U4l4c9jI/6eDIX5dZkkNuTQe7PhBntsFWpUA54rKSyeS4NezhV4eUaTmaJZfLcmgwyPrp7XZm1awSHkdtTQZKZHOcGXAx5d+ZRq9gfqADfxoyP+fDYdibepZluu7gzXZ2PNugERr3AatLjKOqr97otZHLVt7lzoSQ3J+tntJOrCYZ89vJCaDqXJ1uQ3JsO8Ww+yqDXyvUxL4nM2qz+5mSA67vUev94LsLIDo2324FEtsAjf4SZYJKBQxbk2y9BowJ8W2PQ6/jhK4M7OkYyk2dipb5xyaAXXBry8mQuil4nONPvwmM1MhPU8uhWo45Lwx4Meh0fPtpRte+1US/HexykcnkuDXsx6QWvKnLyFwc9dDnNSCnw2MxV8rs3JgKMj/rKhiGtIpHJYzJuPxffjpj1Oo50qgqbg4wK8G3Oj3/kyI66KnMFSUFq8gRXR73l2vQBj7U8Mw/EM9hNep4vRFmOprGb9JwZcHNvOsRCOIVOJ+j3aAF5yGvl7lSQl4sxlsIpwsksH3mvk0QxX++xGXmzHOPedIibkwEmVuJ4bSa8FXciNycDLERSeG1GxjrtnBtwMT7mo7vGcWorXBhyM7myPau+duXtShwhwGUx1KXo2pE2XGNVAb7d6XFZ+Gs7nMWncwWeL0S5PRksd5imc2sLqKMdtqoKjMsjHm4XSwxzBcl8KEkyk+fikAeX1UipFD2WyWMx6vjai2Wttt6o41iXg2iNxMLUagKH2VCn0BhMZJlYifPIH+HmRIB4OrcpmYNazvS7WIlmlDZLA94sx4mkclhN+k3LNCj2DyrAHwJ+8qNHWvbPmc1LjnbZebMcQ7DWsVlpNB2IV1fDvF6OI9B8XWtLCudCKQpSS71YTYamIl0zwSQWo27dWXo8kyebz1dZAW4Gk17HQnh/16vvNYF4pml3b7sg2zALrwL8IWCkw85nz/fXPd/lNHO6z7mlCpJIKsub5TgrsQwXhz1cGvYyWVG/fnnYUxXES4x11QeHYZ+tqiRvNZ7h/kyoqf76XCiFXifoddUH+VKX7XIkw0gDrZhaaYFKSzuTQYfTauT6mA/fDhel2xmbae+tCBVbQwX4Q8L/8LGjVT+f7HUSimd4Oh9lYiXBqV4np/vqFSJrmQkkOd7j4PKwh6mVWFXNd5/bzGIkXSdCBhCIp6vuIsZHfcRSuYZt8nemgk2rZebDKfJS05AHONHj5NKQh5VYmhsTAaLpHA9n14TQnBYDV0a86HXawu+JHgdOs550roDFoKPHaebGRIBQIsuNiQB9mxRKO4zo2jxH0445eHVJPiSc6nNxbdTLrckgVpOeUCJTlVZ5VpILGPU1LGsscWXEw+PZMOmafLVegN1s5PVSjGy+wPUxH4FEBq/ViAQiyRzXx7x8522QYZ+Ne9NBsgWJAMZHvTyZixCvaKa6MRGg32NhLpSqO4flaBqjXnBuwF2WO6jlwUyI0Q4bPruJO1OlEk/Jiwop41SuQKqmEerJXIQelxmrUV91Z6LQHKKuj/mq9PsV+xs1gz9E/Dfn+gDNZKMkrFXL66VqCYEz/a5yTvvKiJc7U6G64A5wddRXtsFbiqZ5MBvi1WKMXEFyazLIi8UooUSOKyMe3FZD+eIi0S4u3S4Ll2o8RMc67Jj0ArtJz1inHYdJj82k58qIl+M9Tp7MhTnapT1fi0En6PdYm14A1mMxkn4n5t8HjZIC5fiod1OSyYq9R83gDxGfuzjA6+UYv/Xd6abbdLvMeGwmTAYdLquRu1MBzg16eD4fxWLUMdZpJ5TIkJeSIa8Vu9lILJWrm9X1e6y8XY7zdiWOw6xntMPOXDjFqNFGvkai5USPs9wNe27AzXI0hd1s4OFsGINeR7YgcVsNeGxOHvvDFTNyrcqj9q6j22nGZtLz7TerG96RNGM/OTLtN24WFSiFaK+0Rjt9lhIqwB8ivHYTP/+D5xgf6+Cnfvtew22eL8TQiWqp4XvTIfQCvvV6tWrbJ8nmHqWe4qw/lMjisxt5PBfBZTEwsRonmy9UKS1WSu4+8odxmvXohCiXS14a8nCviWBYl9OMqEgNnx1wMb2aKCtY3pwM8P4RH999u7kgP+CxMuCxEEpmOT/g5uE27gAOA2+W44yP+Q6c7s5hQ6VoDiHff76PI+togDeavG61RNxQ0VwViGcRAkY77QQTWWLpPAUpcVuNOMx6XtWkhU73u5kLa7l3r83IfIMSRrNBx5VhLxaDjrtFGYVLxQqeSKq6jj6dLXB52FNVOdMMj027GL1cjGHaxPaHmZsTAQY8Fo73tEf5ZBtO4FWAP4wIIfix90d29T3SFV6rJoOOi4MeHs6uzYbnQin6PRZsJkNV3XyP01xluj3otTLSsaZZU+Jkn5M700HmQkkuD3vpcph5sxRreJudzRe4Ox2iz20pm4Y048lcpKycaTcb6NlBd+xhwB9KEWuh96+itagAf0j5ocu2cT35AAAYW0lEQVSDu9p+vlKU3u1ymhnyWhumWJ7NRxmr0TrpdJrLXbJWo56liFb+OOizcaxb2/bKiJcHM9rFIl9skhr2Wetm7iUyxaT/5GqCWCrL2QaG4bU4zAa++3aFxWia84PK1m49oqlcWyy6yjZMwqsAf0hxW4384KWBXTv+XCjFe90OjnbZWWpSsQOavV9Jp+bKiLeqSercgLtsKjIfTpHOauWXteJnJ3sdGPQ6jE3qtCt16aPpPE/mIhuqUp7qc5IuKmM+nA0zqOrjmxJN5fCHkpzo2bpMhGJ3UQH+ELObaZpj3Q4kku++DWDUCy4Pexpuly1IfHYTPrupyuXpeI+jqvrl4pCHmWCSezMhbMa12gC90CQKbkwEONHnbNhOn8hUz+xl8bnLwx6sxsb/AoEa0wuTQVfVCHasy875QbdK4RTJ5iXBREbp1ewzVIA/xJzud3G1iSzATjjZ62Q2lMRp1ippAoksd6dDXBzylM26K3nsj3Cm30W0mGIxG3TEK/K6Ax4rt4vBfsBjJZRcC75XRrxlE26dELxeitV5u9a6UfV7LEyuJLg7HaLDYWbEV59emAlUNzm9XYnzejnGtVEv5wbcvF6OazP7BvseVpai6U11QyveHSrAH3J+7EOtncWf7XcxsRInmcnXVaHcnwlhMei5VJzN++wmTvc5OT/g5sVCpFzlcm3My3x4rYN1wGMtV/YE4mk6HWbOD7o51mUnLyXnBrQcudmgQ6JVd1wYdGMtXkwqA7xRLzDpdeUSzNlgkoVIqupCd27ATaZB2VAmV+DWZLCqeerudLChNs5hxWZWldf7CRXgDzmfPttLp8O08Yab4PygmxeL0fIiaSpb7+MaSGR4NBvSOlDNBvyhFIuRFEvRDBeHPHQ6TNyeCDLSYedIpx2f3cTTubWAerTLweRqgoezYZZjGQLxLI/8YS4Pe9BXFMQ/mA1zps/JiR4nl4c92IupmItDnjoJgnSuwO2pIFdGvIx22pgKbF4XXkrqZIwPM1MHWFN/cjXOf7o901aLrSrAH3LMBj0/cm14x8e5NOxhciVepan+bKG+SgbAZzdj1OuYDiQ40eMsL6TenQ5yrMtOKldgYiXOVCChLXYWq2DODbi4W6E7n80X8NmN2Iw67k6HyObznBvQUgQCCCdzvFiMcnc6hN1i5Pygm1uTwbrzKTETSNDvtBBJbq3sz2RofzOMzbIYTdelyA4KqWyBf/Dlh/zkb90hkytsvMMBQAV4BX/z+vCOFsfGR73cnwlxvKaKIpMrEIhnqgycj/c4yOY1A5GjXXZuVSykjnbYWYpmsBVn2/mC5FuvV+l2WTjb72KxphonkclzZypEKlfgY8e7uDMd5pE/wviol+tjPl4trVXbdDvNTAeai4ddGfGSyOT49kRgy3nkWiniw87DmRBOy8FN1fzJk0V+797sXp9GS1ABXsGAx8onT/Vseb/jPQ7O9Lu4ORlEyvpqFYBwMksym6fPbebSkIeJlTjBRBaDTpAvrFksmPSCVDbP25U4bpupqjvSH0xiNekZ6bA1XKR1W41VpuDzkVSdSbjLaiSUyNbuis9u4vygmztTQWJpLaVU2OItevvc0LeGVK7AqQO+2PoL/+V5nfDeQUQFeAWwucXWsU4742M+zvS7cFkMvFyMVdWtP1+IVhlkl1iJZTja5cAfSpZTOFdGqo1CLhTLIEGreX+9FOP6mA+90CwBb00GuTUZxGE2lBdVS4x22ssVOKB1oB7tcjA+6uP6mPaYXq3PDV8e9pDN5as6bEFT1NyKTMGdqSBnNtE8dZh46g/Tu0HX8H4mmMjyr/7sNclM/TrSQUIFeAUAHz7ayZEG+XIAm1HHxeLs++ZEoKHeC2gaNse7q9M0Rr1gfMzHN1+vYjToGO2wcX3Mx90KRchjXfaymmTlsW5MBBjttDPsXStFXIqmy4uqbquRc0Vz7xLnB93E0zneLse4ORngxoT2qNS+N+oF74/5SGTyGA36OoOSXIGGawfrsRxNl1NLh5XKxeZYJt+w/PQg8QcP5vjr//rbPPaHWYrW+xIcBA73X6SijE4n+OBkd93zJr1guMNepQ+zHvORFBeHPHQ7zVwd8dLlNJcVB/3BJHkpebMcKwdcvdCkeZtlRXx2E7emAuXZfIm70yFsJj32BimbmUCyrsxxxLcWsM8NuPnuRIDnC1EC8QyP/ZE6jRrPFn1dl6JpzgwcTkkDvdAW2bO5QlVq7cZEgAtDB3tMnsxF+OyvfJO/9M/+Pz77K9/gDx7M8Ww+cmAqbQ7uSoii5VhqtGl0Ak72uepSGOvhDybxF1MtS9F6iYKZQBKnxcD4qI9bkwGujjZ3CBrtsHF3OkS+aDQx7LNhNup4VXRlGvRa+e5EgPODbuZDKZZj6aoGqUoWI2uKlLUmIMlsniN2e7n2fsBjLdfQb4WXi7GNN2pDroyuyQYbYxmujXqRUis7rO0IPqjkCpLH/khZZnukw8bP/+BZPvJeFy8WovS6LOSlxGrU841Xy3hsJq6NehFibxfgVYBXlDHqq2/oLg17q8w1WkU0lePmZICxTjvRVP3CZwmH2UC+IrUyHUggBFwb9ZLI5MtpnYezYWwmPeOjXl4sRnGY9PR5rLitRvQ6bfF2JZ7hRI+DF4sxhn023ixX5+SfzUe4OuolX5C8XY6z0uDitBFHu+xVZZyHhUTFRTUQz7RNUF+PqdUEX/zNW/Q4zWVp61p+6oNj/P1PnXjHZ1aNStEoylQuLGqzMEmXw7xrdc0TK3Gezke5POyho6ZZyGsz8nQ+UrePlFoTk17AmYpKjUQmz83JYDGvruPVUozbU0FuTATIS4k/mMRe7LK0N+i2LEi4PRnk3nSIcLL5RWc98ofUBeqw6ubnC7JpcAf4o8cLe57KOZy/GUVDjMUkt8dm5Pl8lMWIlva4ORHgZO/uKQXenQ6RyOa5PuYrK0K+1+1saDwCmsPTQ3+Ex3MRxkerLz7ZvGTIW724Vyj2rDyaDeO2Gnk4G24oSlaJfou17Z0O07b8X9sB3R6nIfYjvS4Lv/qjl/c8RaMCvKJMaSZ2rNvB8R4n/tDa7OTVYrSq2qSk/326z8nZAdeOZ/nJoiJkl8vM+QE34WTj2/whn5U7U2s5+5uTgbogb6nJnz+dj3C0y062IMsXqo1mneYtdqce6XI0vSC1O9lak10Fy7E0q/sgVaVy8IoyRr1WxphI53m5WJ0eyUstPXKy14nTYmB6NUGH3cTTea0ZZMRn5XSfC7NBx0wwgcWoL3uuboW5UIp8QdZ1rZawmwzUdpGXgnxJXvjpXAS7SU+8oobZadGqYqYDcUwGHSa9jo++10kqVyCRyWEzGUjnCpgMOhLpHOnc1uqfI9tM67QDW73bOQzkC5J/8xdvGB/1odvD8VEBXlHGYtTR6TDzfCFSF0QB7k0Hm3qzTgWSwFpAvzzs2VaAB02yoFGAt5v0PF9o3F14czLAtVEvtyaDxNK5KkNok0FHNq+ZhUgpKcgE92ZCnB90N60QGvBsrUlnNbb3s7W94u50SKuKmgo0LXc9jHztxTK/f9/PD10e3LNzUCkaRRmLQc9sMFFu2a9lK8bbO5nVNbu1HelYv/no1mSwnEZ6uxxjfNTLqV4nsiB5MhfhxkSAm5PB8sXj4WyYAW9jp6ZGsgbr0dEiRc6Dys3JAFdGvEqXp4atlBjvBirAK8oY9ToW1rHX2wq1hhlboZHeDIBjEwJWtyaDXB31ohOCm5NBni1Eq7pYa+lv0k6vr3X5rmDIa+XqqJfTfa6yX+tBFtdqFbcng2V3rt0wkjmIfOnWzJ5WV234VymEsABfB8zF7b8spfxZIcSXgFKRpwcISSkvNth/EogCeSAnpbzaonNXtJiTfa2rlNFKEbd3sXg4G8akF3XdqP5NpnxuryMJXMv9mRDDPlud0qRRVz/3OdnrxGTQ8XA2XNbNEYID363ZSkrNbWMb3G0dFhwWA3t5T7OZGXwa+EBKeQG4CHxaCPG+lPLzUsqLxaD+O8DvrnOMjxe3VcF9HzPgsdbVo2+XDsf2XY7Gx3xVuvIlnJbW665n85J0Nl/3ua0mHZ0OE1eGvVwZ9tJhN/F8IVp3yy0lPJuPbrnqpp25Nurl7kzrG+QOIsvRND//lWdMrcb3pNpowxm81Cr1Sz3YxuKj/N8ntELPvwF8sBsnqHh3CCF4/2gHX3k4v+NjZbIFrox4eTATIreFW1SjXjC5Em8owavX6ehxmssGIa1iMZpmrNNOKpsvV95IKViJZVjZxOJpJlcgsk5H7mFELbau8W+/NcG//dYEbquRE71OfDYTv/KFS3Wd47vBphKHQgg9cAc4BvwfUsobFS9/BFiUUr5qsrsE/lQIIYF/I6X8tSbv8RPATwAMD+/cYUixPf7l5y/ykWOd/PJXX2I36fno8S4+eqILi0HPL3/15aalC+7Pai37V0e8zIWTzIU2p8aXzUtsJn35trYUJy4MuXkwE961292JlTin+5y8XIxhMeqZC22tAshqVDP4Ercmg3hsjfX3DzPhZLZc2fX1l8t8YhseDFtFbKWVVgjhAX4P+LtSysfF534VeC2l/OUm+/RLKeeEEN3AV4v7fn2997l69aq8ffv2ps9L0Xpy+QKGBjOMBzMh/v13pvh/H85tydbs4pCHbL7A2+U4yQZerZUIAT0uCzajnonibP7SkId7m1S03AlXRzwkMvlyff9mOdvv4vFcvbTCYaXRuoZijUvDHn7uB85ybnDn6zdCiDvN0t9bCvDFg/0sEJdS/pIQwgD4gStSyg09roQQ/wSISSl/ab3tVIDf/wRiaX73np8/e7ZEtlAgmyvwcjFKMrt+0Bdo9nlmo37dANDlNDPis/FoNoRBryOZzb+TTtFLQx6MBl15prVZ7CY9UkoSG3z+w8J6PQYKDb1O8Pt/58M7DvLrBfgNk0BCiK7izB0hhBX4JPC8+PIngefNgrsQwi6EcJa+Bz4FPN76R1DsN3wOMz/+kSP81o9f5+987CgIQUGyYXmcRMt5mwyioZZ7iRGfjdtTQdJ5STzzboL79TEf92ZC3JwIcHaLDk3xTJ4et7XK9OIwMx1IMD7mQ8nUNCdfkHzxN2/yjVfLu/Yem8ny9wFfE0I8BG4BX5VS/mHxtR8BfrtyYyFEvxDij4o/9gDfFEI8AG4CX5FS/nFrTl2xH9DrBB+c7OGf/dVzpHMFXjTpNK3l9VKc0+sE0dA7bv0/1u3gxkQAp8XA9TEfmXxhy0bkEytxTHod/Vvsgm1HQgkt3zzaYedMv6vONUuhsRrP8JP/4Q6pDdKW22UzVTQPgUtNXvtig+fmgM8Uv38LXNjZKSoOAqf7XVwc8vB2efOmF+tlB322rTkq7ZTXSzFGO2y4LMayAcnVEW+dleBGLERSeG1GxjrtTKzU+8AeNirH4OyAi8d+tU5RS5/HWme20ypU+52iZXxhfJh//J83n4EriTDpdYJLQx5S2TyP5yI4zYZ3sqBaS5fTzFSFEfjUagKTQbelxWTQDJszuQIne50kMnk+fKyTQkGSl5J8ofiQkkJBkivIqtcKUpLLa1+17ajbrvQ1V+wV0PR1QFL8KjULxIKUSLT9pdTSYwWpHVsWxeNK+5Se200e+yO4rcZt6+23Kx873rVrx1YBXtEyPnuhj3/6lafk8oUt6dZcGvaUu0/PD7ixmvRNbfx2k3vTITwVdw5jXXbS2TwPtrFYGM/k6fdY+d9++AKeA5SXr7w4lC4apYtB6eJQkFrtfyqbJ5HJk8zmSWbyJLM5kpmCtiBevFhJqLrYmPSCVLagvQ+ULzylYg9Z+Z7lc1p7rvIcZaPtq45bv13poLLmOFS8l/a6rHuu0ViVv29wjGbHqdw2n5f85bO9jd+gBagAr2gZNpOBn/rgPX7la6+IJBt7o1byeimG12asqm1/uIemGbmC5EiXg5WYdnHJ5yXmbboV6QT8wg+dO1DBHbRmNyFAt6cN9opWocTGFC3lix8eZaTGUakZgXiGvJTcm94/be1P5yLYjNq/hV4Hy9uQAbYa9fzGF6/R7VKLrYq9RQV4RUsx6nX8ve87vuntI8lcQ+35vSKWznF5xMfZfhc3J4NMrMRxWzd/o9vpMPH3v+84Hz/RvYtnqVBsDpWiUbScngM+c325GK3SpD/SaefezOZSR//i85f4nvc6d+vUFIotoQK8QlHDUjTNlWEvd4qpo0iq+XqC02LgC+PDrMQyXBr2qOCu2FeoAK9oOfu5e9Fm0nN11MfXX67fPbgSWxNHe7Mcb1gTb9AJ/vcvXOaju1jmplDsBBXgFS3ndJ+LH70+zH+8Mb3htka9wGLUEy3Oki1GHf1uK71uC71uC31uC71uK30uC9F0ln/8+0+IpTeu0GnGx0508ZlzfRsG+KlAskpP5e1KDIdZTzpX4G+9P8L3X+jndJ9r1xpUFIpWoAK8ouUIIfinnztLMpvnd+/6q14b7bDxP37qBOcH3XjtJpxmA0IIoqks+YLEbTUi1rkFOD/o4f/8+lv+8OH8uoFeCMomHpWa7lOrCT5zto8v/YSZH/uNm2TWMWFIV7SPD3lt/O0PjfKJU914bAer9FFxeNmymuS7QKlJtgdSSv70yQK/c8/PbCDJ+0c6+Om/cqIl7kfJTJ4/fbrAl+/M8q3XKxQkuK1G3j/i42Mnuvn0mV68dhOFguT2VJA/eOAnnMzxo9eHef9IBwBP5sI89of56d951PR9Pnq8i5/+9Ml1dXMUir2kpXLB7wIV4NuLeDrHzYkAHz+5O6WD+YIklsrhsBjQb1EhLJsvsBJL89Ff/PO62fzHTnTxv/zAGUaUv6hiH7NegFcpGsWuYzcbdi24g6Zl496mOJlRryOeznGs20E4mcUfSuKyGPi5z53lcxf7100XKRT7HRXgFYeedK7Al/779/m/bkxzazLAz//gOXrdB7uWX6EAlaJRKBSKA82OHJ0UCoVCcTBRAV6hUCjaFBXgFQqFok1RAV6hUCjaFBXgFQqFok1RAV6hUCj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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1_14_0.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001']\n", + "tracts_gdf_ac.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7.1 Attribute Joins\n", + "\n", + "**Attribute Joins between Geodataframes and Dataframes**\n", + "\n", + "*We just mapped the census tracts. But what makes a map powerful is when you map the data associated with the locations.*\n", + "\n", + "- `tracts_gdf_ac`: These are polygon data in a GeoDataFrame. However, as we saw in the `head` of that dataset, they no attributes of interest!\n", + "\n", + "- `acs5_df_ac`: These are 2018 ACS data from a CSV file ('census_variables_CA.csv'), imported and read in as a `pandas` DataFrame. However, they have no geometries!\n", + "\n", + "In order to map the ACS data we need to associate it with the tracts. Let's do that now, by joining the columns from `acs5_df_ac` to the columns of `tracts_gdf_ac` using a common column as the key for matching rows. This process is called an **attribute join**.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "The image above gives us a nice conceptual summary of the types of joins we could run.\n", + "\n", + "1. In general, why might we choose one type of join over another?\n", + "1. In our case, do we want an inner, left, right, or outer (AKA 'full') join? \n", + "\n", + "(**NOTE**: You can read more about merging in `geopandas` [here](http://geopandas.org/mergingdata.html#attribute-joins).)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, here we go!\n", + "\n", + "Let's take a look at the common column in both our DataFrames.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 06001400300\n", + "1 06001400900\n", + "2 06001402200\n", + "3 06001402800\n", + "4 06001404800\n", + "Name: GEOID, dtype: object" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tracts_gdf_ac['GEOID'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8323 06001441501\n", + "8324 06001404700\n", + "8325 06001442500\n", + "8326 06001450300\n", + "8327 06001450607\n", + "Name: FIPS_11_digit, dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "acs5_df_ac['FIPS_11_digit'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Note that they are **not named the same thing**. \n", + " \n", + " That's okay! We just need to know that they contain the same information.\n", + "\n", + "Also note that they are **not in the same order**. \n", + " \n", + " That's not only okay... That's the point! (If they were in the same order already then we could just join them side by side, without having Python find and line up the matching rows from each!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------------------\n", + "\n", + "Let's do a `left` join to keep all of the census tracts in Alameda County and only the ACS data for those tracts.\n", + "\n", + "**NOTE**: To figure out how to do this we could always take a peek at the documentation by calling\n", + "`?tracts_gdf_ac.merge`, or `help(tracts_gdf_ac)`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STATEFPCOUNTYFPTRACTCEAFFGEOIDGEOIDNAME_xLSADALANDAWATERgeometry...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0060014003001400000US06001400300060014003004003CT11053290POLYGON ((-122.26416 37.84000, -122.26186 37.8......0.8405420.0450690.0584070.0315280.0244540.4208400.0594960.2806720.0678990.057479
1060014009001400000US06001400900060014009004009CT4208770POLYGON ((-122.28558 37.83978, -122.28319 37.8......0.9061610.0656870.0057120.0224400.0000000.5557180.0689150.2133430.0608500.044721
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2 rows × 54 columns

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" + ], + "text/plain": [ + " STATEFP COUNTYFP TRACTCE AFFGEOID GEOID NAME_x LSAD \\\n", + "0 06 001 400300 1400000US06001400300 06001400300 4003 CT \n", + "1 06 001 400900 1400000US06001400900 06001400900 4009 CT \n", + "\n", + " ALAND AWATER geometry ... \\\n", + "0 1105329 0 POLYGON ((-122.26416 37.84000, -122.26186 37.8... ... \n", + "1 420877 0 POLYGON ((-122.28558 37.83978, -122.28319 37.8... ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.840542 0.045069 0.058407 0.031528 0.024454 0.420840 \n", + "1 0.906161 0.065687 0.005712 0.022440 0.000000 0.555718 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.059496 0.280672 0.067899 0.057479 \n", + "1 0.068915 0.213343 0.060850 0.044721 \n", + "\n", + "[2 rows x 54 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Left join keeps all tracts and the acs data for those tracts\n", + "tracts_acs_gdf_ac = tracts_gdf_ac.merge(acs5_df_ac, left_on='GEOID',right_on=\"FIPS_11_digit\", how='left')\n", + "tracts_acs_gdf_ac.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check that we have all the variables we have in our dataset now." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['STATEFP',\n", + " 'COUNTYFP',\n", + " 'TRACTCE',\n", + " 'AFFGEOID',\n", + " 'GEOID',\n", + " 'NAME_x',\n", + " 'LSAD',\n", + " 'ALAND',\n", + " 'AWATER',\n", + " 'geometry',\n", + " 'NAME_y',\n", + " 'c_race',\n", + " 'c_white',\n", + " 'c_black',\n", + " 'c_asian',\n", + " 'c_latinx',\n", + " 'state_fips',\n", + " 'county_fips',\n", + " 'tract_fips',\n", + " 'med_rent',\n", + " 'med_hhinc',\n", + " 'c_tenants',\n", + " 'c_owners',\n", + " 'c_renters',\n", + " 'c_movers',\n", + " 'c_stay',\n", + " 'c_movelocal',\n", + " 'c_movecounty',\n", + " 'c_movestate',\n", + " 'c_moveabroad',\n", + " 'c_commute',\n", + " 'c_car',\n", + " 'c_carpool',\n", + " 'c_transit',\n", + " 'c_bike',\n", + " 'c_walk',\n", + " 'year',\n", + " 'FIPS_11_digit',\n", + " 'p_white',\n", + " 'p_black',\n", + " 'p_asian',\n", + " 'p_latinx',\n", + " 'p_owners',\n", + " 'p_renters',\n", + " 'p_stay',\n", + " 'p_movelocal',\n", + " 'p_movecounty',\n", + " 'p_movestate',\n", + " 'p_moveabroad',\n", + " 'p_car',\n", + " 'p_carpool',\n", + " 'p_transit',\n", + " 'p_bike',\n", + " 'p_walk']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(tracts_acs_gdf_ac.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "It's always important to run sanity checks on our results, at each step of the way!\n", + "\n", + "In this case, how many rows and columns should we have?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows and columns in the Alameda County Census tract gdf:\n", + "\t (361, 10)\n", + "Row and columns in the ACS5 2018 data:\n", + "\t (361, 44)\n", + "Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\n", + "\t (361, 54)\n" + ] + } + ], + "source": [ + "print(\"Rows and columns in the Alameda County Census tract gdf:\\n\\t\", tracts_gdf_ac.shape)\n", + "print(\"Row and columns in the ACS5 2018 data:\\n\\t\", acs5_df_ac.shape)\n", + "print(\"Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\\n\\t\", tracts_acs_gdf_ac.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's save out our merged data so we can use it in the final notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_acs_gdf_ac.to_file('outdata/tracts_acs_gdf_ac.json', driver='GeoJSON')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Choropleth Map\n", + "We can now make choropleth maps using our attribute joined geodataframe. Go ahead and pick one variable to color the map, then map it. You can go back to lesson 5 if you need a refresher on how to make this!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------\n", + "# 7.2 Spatial Joins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! We've wrapped our heads around the concept of an attribute join.\n", + "\n", + "Now let's extend that concept to its spatially explicit equivalent: the **spatial join**!\n", + "\n", + "\n", + "
\n", + "\n", + "To start, we'll read in some other data: The Alameda County schools data.\n", + "\n", + "Then we'll work with that data and our `tracts_acs_gdf_ac` data together." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check if we have to transform the schools to match the`tracts_acs_gdf_ac`'s CRS." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "schools_gdf CRS: epsg:4326\n", + "tracts_acs_gdf_ac CRS: epsg:4269\n" + ] + } + ], + "source": [ + "print('schools_gdf CRS:', schools_gdf.crs)\n", + "print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes we do! Let's do that.\n", + "\n", + "**NOTE**: Explicit syntax aiming at that dataset's CRS leaves less room for human error!" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "schools_gdf CRS: epsg:4269\n", + "tracts_acs_gdf_ac CRS: epsg:4269\n" + ] + } + ], + "source": [ + "schools_gdf = schools_gdf.to_crs(tracts_acs_gdf_ac.crs)\n", + "\n", + "print('schools_gdf CRS:', schools_gdf.crs)\n", + "print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we're ready to combine the datasets in an analysis.\n", + "\n", + "**In this case, we want to get data from the census tract within which each school is located.**\n", + "\n", + "But how can we do that? The two datasets don't share a common column to use for a join." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['STATEFP', 'COUNTYFP', 'TRACTCE', 'AFFGEOID', 'GEOID', 'NAME_x', 'LSAD',\n", + " 'ALAND', 'AWATER', 'geometry', 'NAME_y', 'c_race', 'c_white', 'c_black',\n", + " 'c_asian', 'c_latinx', 'state_fips', 'county_fips', 'tract_fips',\n", + " 'med_rent', 'med_hhinc', 'c_tenants', 'c_owners', 'c_renters',\n", + " 'c_movers', 'c_stay', 'c_movelocal', 'c_movecounty', 'c_movestate',\n", + " 'c_moveabroad', 'c_commute', 'c_car', 'c_carpool', 'c_transit',\n", + " 'c_bike', 'c_walk', 'year', 'FIPS_11_digit', 'p_white', 'p_black',\n", + " 'p_asian', 'p_latinx', 'p_owners', 'p_renters', 'p_stay', 'p_movelocal',\n", + " 'p_movecounty', 'p_movestate', 'p_moveabroad', 'p_car', 'p_carpool',\n", + " 'p_transit', 'p_bike', 'p_walk'],\n", + " dtype='object')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tracts_acs_gdf_ac.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['X', 'Y', 'Site', 'Address', 'City', 'State', 'Type', 'API', 'Org',\n", + " 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_gdf.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, they do have a shared relationship by way of space! \n", + "\n", + "So, we'll use a spatial relationship query to figure out the census tract that\n", + "each school is in, then associate the tract's data with that school (as additional data in the school's row).\n", + "This is a **spatial join**!\n", + "\n", + "---------------------------------\n", + "\n", + "### Census Tract Data Associated with Each School\n", + "\n", + "In this case, let's say we're interested in the relationship between the median household income\n", + "in a census tract (`tracts_acs_gdf_ac['med_hhinc']`) and a school's Academic Performance Index\n", + "(`schools_gdf['API']`).\n", + "\n", + "To start, let's take a look at the distributions of our two variables of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1_46_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf.hist('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh, right! Those pesky schools with no reported APIs (i.e. API == 0)! Let's drop those." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf_api = schools_gdf.loc[schools_gdf['API'] > 0, ]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[]],\n", + " dtype=object)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1_49_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "schools_gdf_api.hist('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much better!\n", + "\n", + "Now, maybe we think there ought to be some correlation between the two variables?\n", + "As a first pass at this possibility, let's overlay the two datasets, coloring each one by\n", + "its variable of interest. This should give us a sense of whether or not similar values co-occur." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1_51_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = tracts_acs_gdf_ac.plot(column='med_hhinc', cmap='cividis', figsize=[18,18],\n", + " legend=True, legend_kwds={'label': \"median household income ($)\",\n", + " 'orientation': \"horizontal\"})\n", + "schools_gdf_api.plot(column='API', cmap='cividis', edgecolor='black', alpha=1, ax=ax,\n", + " legend=True, legend_kwds={'label': \"API\", 'orientation': \"horizontal\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatially Joining our Schools and Census Tracts\n", + "\n", + "Though it's hard to say for sure, it certainly looks possible.\n", + "It would be ideal to scatterplot the variables! But in order to do that, \n", + "we need to know the median household income in each school's tract, which\n", + "means we definitely need our **spatial join**!\n", + "\n", + "We'll first take a look at the documentation for the spatial join function, `gpd.sjoin`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function sjoin in module geopandas.tools.sjoin:\n", + "\n", + "sjoin(left_df, right_df, how='inner', op='intersects', lsuffix='left', rsuffix='right')\n", + " Spatial join of two GeoDataFrames.\n", + " \n", + " Parameters\n", + " ----------\n", + " left_df, right_df : GeoDataFrames\n", + " how : string, default 'inner'\n", + " The type of join:\n", + " \n", + " * 'left': use keys from left_df; retain only left_df geometry column\n", + " * 'right': use keys from right_df; retain only right_df geometry column\n", + " * 'inner': use intersection of keys from both dfs; retain only\n", + " left_df geometry column\n", + " op : string, default 'intersects'\n", + " Binary predicate, one of {'intersects', 'contains', 'within'}.\n", + " See http://shapely.readthedocs.io/en/latest/manual.html#binary-predicates.\n", + " lsuffix : string, default 'left'\n", + " Suffix to apply to overlapping column names (left GeoDataFrame).\n", + " rsuffix : string, default 'right'\n", + " Suffix to apply to overlapping column names (right GeoDataFrame).\n", + "\n" + ] + } + ], + "source": [ + "help(gpd.sjoin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like the key arguments to consider are:\n", + "- the two GeoDataFrames (**`left_df`** and **`right_df`**)\n", + "- the type of join to run (**`how`**), which can take the values `left`, `right`, or `inner`\n", + "- the spatial relationship query to use (**`op`**)\n", + "\n", + "**NOTE**:\n", + "- By default `sjoin` is an inner join. It keeps the data from both geodataframes only where the locations spatially intersect.\n", + "\n", + "- By default `sjoin` maintains the geometry of first geodataframe input to the operation. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Which GeoDataFrame are we joining onto which (i.e. which one is getting the other one's data added to it)?\n", + "1. What happened to 'outer' as a join type?\n", + "1. Thus, in our operation, which GeoDataFrame should be the `left_df`, which should be the `right_df`, and `how` do we want our join to run?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alright! Let's run our join!" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts = gpd.sjoin(schools_gdf_api, tracts_acs_gdf_ac, how='left')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking Our Output\n", + "\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "As always, we want to sanity-check our intermediate result before we rush ahead.\n", + "\n", + "One way to do that is to introspect the structure of the result object a bit.\n", + "\n", + "1. What type of object should that have given us?\n", + "1. What should the dimensions of that object be, and why?\n", + "1. If we wanted a visual check of our results (i.e. a plot or map), what could we do?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(325, 64)\n", + "(550, 10)\n", + "(361, 54)\n" + ] + } + ], + "source": [ + "print(schools_jointracts.shape)\n", + "print(schools_gdf.shape)\n", + "print(tracts_acs_gdf_ac.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYSiteAddressCityStateTypeAPIOrggeometry...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0-122.23876137.744764Amelia Earhart Elementary400 Packet Landing RdAlamedaCAES933PublicPOINT (-122.23876 37.74476)...0.9016940.0531200.0133140.0235340.0083380.6807450.0776500.1072930.0047220.019150
1-122.25185637.738999Bay Farm Elementary200 Aughinbaugh WayAlamedaCAES932PublicPOINT (-122.25186 37.73900)...0.9016940.0531200.0133140.0235340.0083380.6807450.0776500.1072930.0047220.019150
2-122.25891537.762058Donald D. Lum Elementary1801 Sandcreek WayAlamedaCAES853PublicPOINT (-122.25892 37.76206)...0.8451200.0902400.0326400.0320000.0000000.6010570.0429330.2470280.0330250.011889
3-122.23484137.765250Edison Elementary2700 Buena Vista AveAlamedaCAES927PublicPOINT (-122.23484 37.76525)...0.9393130.0324920.0230930.0000000.0051020.5618230.0774930.1726500.0188030.036467
4-122.23807837.753964Frank Otis Elementary3010 Fillmore StAlamedaCAES894PublicPOINT (-122.23808 37.75396)...0.9344160.0311220.0107790.0214060.0022770.6455320.0675320.1503980.0150400.031849
\n", + "

5 rows × 64 columns

\n", + "
" + ], + "text/plain": [ + " X Y Site Address \\\n", + "0 -122.238761 37.744764 Amelia Earhart Elementary 400 Packet Landing Rd \n", + "1 -122.251856 37.738999 Bay Farm Elementary 200 Aughinbaugh Way \n", + "2 -122.258915 37.762058 Donald D. Lum Elementary 1801 Sandcreek Way \n", + "3 -122.234841 37.765250 Edison Elementary 2700 Buena Vista Ave \n", + "4 -122.238078 37.753964 Frank Otis Elementary 3010 Fillmore St \n", + "\n", + " City State Type API Org geometry ... \\\n", + "0 Alameda CA ES 933 Public POINT (-122.23876 37.74476) ... \n", + "1 Alameda CA ES 932 Public POINT (-122.25186 37.73900) ... \n", + "2 Alameda CA ES 853 Public POINT (-122.25892 37.76206) ... \n", + "3 Alameda CA ES 927 Public POINT (-122.23484 37.76525) ... \n", + "4 Alameda CA ES 894 Public POINT (-122.23808 37.75396) ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.901694 0.053120 0.013314 0.023534 0.008338 0.680745 \n", + "1 0.901694 0.053120 0.013314 0.023534 0.008338 0.680745 \n", + "2 0.845120 0.090240 0.032640 0.032000 0.000000 0.601057 \n", + "3 0.939313 0.032492 0.023093 0.000000 0.005102 0.561823 \n", + "4 0.934416 0.031122 0.010779 0.021406 0.002277 0.645532 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.077650 0.107293 0.004722 0.019150 \n", + "1 0.077650 0.107293 0.004722 0.019150 \n", + "2 0.042933 0.247028 0.033025 0.011889 \n", + "3 0.077493 0.172650 0.018803 0.036467 \n", + "4 0.067532 0.150398 0.015040 0.031849 \n", + "\n", + "[5 rows x 64 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schools_jointracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confirmed! The output of the our `sjoin` operation is a GeoDataFrame (`schools_jointracts`) with:\n", + "- a row for each school that is located inside a census tract (all of them are)\n", + "- the **point geometry** of that school\n", + "- all of the attribute data columns (non-geometry columns) from both input GeoDataFrames\n", + "\n", + "----------------------------\n", + "\n", + "Let's also take a look at an overlay map of the schools on the tracts.\n", + "If we color the schools categorically by their tracts IDs, then we should see\n", + "that all schools within a given tract polygon are the same color." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1_64_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = tracts_acs_gdf_ac.plot(color='white', edgecolor='black', figsize=[18,18])\n", + "schools_jointracts.plot(column='GEOID', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assessing the Relationship between Median Household Income and API\n", + "\n", + "Fantastic! That looks right!\n", + "\n", + "Now we can create that scatterplot we were thinking about!" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'API')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1_66_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6,6))\n", + "ax.scatter(schools_jointracts.med_hhinc, schools_jointracts.API)\n", + "ax.set_xlabel('median household income ($)')\n", + "ax.set_ylabel('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wow! Just as we suspected based on our overlay map,\n", + "there's a pretty obvious, strong, and positive correlation\n", + "between median household income in a school's tract\n", + "and the school's API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7.3: Aggregation\n", + "\n", + "We just saw that a spatial join in one way to leverage the spatial relationship\n", + "between two datasets in order to create a new, synthetic dataset.\n", + "\n", + "An **aggregation** is another way we can generate new data from this relationship.\n", + "In this case, for each feature in one dataset we find all the features in another\n", + "dataset that satisfy our chosen spatial relationship query with it (e.g. within, intersects),\n", + "then aggregate them using some summary function (e.g. count, mean).\n", + "\n", + "------------------------------------\n", + "\n", + "### Getting the Aggregated School Counts\n", + "\n", + "Let's take this for a spin with our data. We'll count all the schools within each census tract.\n", + "\n", + "Note that we've already done the first step of spatially joining the data from the aggregating features\n", + "(the tracts) onto the data to be aggregated (our schools).\n", + "\n", + "The next step is to group our GeoDataFrame by census tract, and then summarize our data by group.\n", + "We do this using the DataFrame method `groupy`.\n", + "\n", + "To get the correct count, lets rejoin our schools on our tracts, this time keeping all schools\n", + "(not just those with APIs > 0, as before)." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='left')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now for the `groupy` operation.\n", + "\n", + "**NOTE**: We could really use any column, since we're just taking a count. For now we'll just use the school names ('Site')." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Counts, rows and columns: (263, 2)\n", + "Tracts, rows and columns: (361, 54)\n" + ] + }, + { + "data": { + "text/html": [ + "
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\n", + " \n", + "
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\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Above we selected three columns from the input GeoDataFrame to create a subset as input to the dissolve operation. Why?\n", + "1. Why did we run a new spatial join? What would have happened if we had used the `schools_jointracts` object instead?\n", + "1. What explains the dimensions of the new object (361, 2)?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "You responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mapping our Spatial Join Output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also, because our `sjoin` plus `dissolve` pipeline outputs a GeoDataFrame, we can now easily map the school count by census tract!" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/hikarimurayama/Documents/repos/Geospatial-Fundamentals-in-Python/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1_82_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "\n", + "# Display the output of our spatial join\n", + "tracts_schoolcounts.plot(ax=ax,column='Site', \n", + " scheme=\"user_defined\",\n", + " classification_kwds={'bins':[*range(9)]},\n", + " cmap=\"PuRd_r\",\n", + " edgecolor=\"grey\",\n", + " legend=True, \n", + " legend_kwds={'title':'Number of schools'})\n", + "schools_gdf.plot(ax=ax, color='black', markersize=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------\n", + "\n", + "# Exercise: Aggregation\n", + "\n", + "#### What is the mean API of each census tract?\n", + "\n", + "As we mentioned, the spatial aggregation workflow that we just put together above\n", + "could have been used not to generate a new count variable, but also\n", + "to generate any other new variable the results from calling an aggregation function\n", + "on an attribute column.\n", + "\n", + "In this case, we want to calculate and map the mean API of the schools in each census tract.\n", + "\n", + "Copy and paste code from above where useful, then tweak and/or add to that code such that your new code:\n", + "1. joins the schools onto the tracts (**HINT**: make sure to decide whether or not you want to include schools with API = 0!)\n", + "1. dissolves that joined object by the tract IDs, giving you a new GeoDataFrame with each tract's mean API (**HINT**: because this is now a different calculation, different problems may arise and need handling!)\n", + "1. plots the tracts, colored by API scores (**HINT**: overlay the schools points again, visualizing them in a way that will help you visually check your results!)\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "----------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.4 Recap\n", + "We discussed how we can combine datasets to enhance any geospatial data analyses you could do. Key concepts include:\n", + "- Attribute joins\n", + "\t- `.merge()`\n", + "- Spatial joins (order matters!)\n", + "\t- `gpd.sjoin()`\n", + "- Aggregation\n", + "\t-`.groupby()`\n", + "\t- `.dissolve()` (preserves geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
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 D-Lab @ University of California - Berkeley
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 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1.py b/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1.py new file mode 100644 index 0000000..25ae0eb --- /dev/null +++ b/_build/jupyter_execute/ran/07_Joins_and_Aggregation-Copy1.py @@ -0,0 +1,639 @@ +# Lesson 7. Attribute and Spatial Joins + +Now that we understand the logic of spatial relationship queries, +let's take a look at another fundamental spatial operation that relies on them. + +This operation, called a **spatial join**, is the process by which we can +leverage the spatial relationships between distinct datasets to merge +their information into a new, synthetic dataset. + +This operation can be thought as the spatial equivalent of an +**attribute join**, in which multiple tabular datasets can be merged by +aligning matching values in a common column that they both contain. +Thus, we'll start by developing an understanding of this operation first! + +- 7.0 Data Input and Prep +- 7.1 Attribute Joins +- **Exercise**: Choropleth Map +- 7.2 Spatial Joins +- 7.3 Aggregation +- **Exercise**: Aggregation +- 7.4 Recap + +
+ + Instructor Notes + +- Datasets used + - 'notebook_data/census/ACS5yr/census_variables_CA.csv' + - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip' + - 'notebook_data/alco_schools.csv' + +- Expected time to complete + - Lecture + Questions: 45 minutes + - Exercises: 20 minutes + + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +# 7.0 Data Input and Prep + +Let's read in a table of data from the US Census' 5-year American Community Survey (ACS5). + +# Read in the ACS5 data for CA into a pandas DataFrame. +# Note: We force the FIPS_11_digit to be read in as a string to preserve any leading zeroes. +acs5_df = pd.read_csv("notebook_data/census/ACS5yr/census_variables_CA.csv", dtype={'FIPS_11_digit':str}) +acs5_df.head() + +**Brief summary of the data**: + +Below is a table of the variables in this table. They were combined from +different ACS 5 year tables. + +NOTE: +- variables that start with `c_` are counts +- variables that start with `med_` are medians +- variables that end in `_moe` are margin of error estimates +- variables that start with `_p` are proportions calcuated from the counts divided by the table denominator (the total count for whom that variable was assessed) + + +| Variable | Description | +|-----------------|-------------------------------------------------| +|`c_race` |Total population +|`c_white` |Total white non-Latinx +| `c_black` | Total black and African American non-Latinx +| `c_asian` | Total Asian non-Latinx +| `c_latinx` | Total Latinx +| `state_fips` | State level FIPS code +| `county_fips` | County level FIPS code +| `tract_fips` |Tracts level FIPS code +| `med_rent` |Median rent +| `med_hhinc` |Median household income +| `c_tenants` |Total tenants +| `c_owners` |Total owners +| `c_renters` |Total renters +| `c_movers` |Total number of people who moved +| `c_stay` |Total number of people who stayed +| `c_movelocal` |Number of people who moved locally +| `c_movecounty` |Number of people who moved counties +| `c_movestate` | Number of people who moved states +| `c_moveabroad` |Number of people who moved abroad +| `c_commute` |Total number of commuters +| `c_car` | Number of commuters who use a car +| `c_carpool` | Number of commuters who carpool +| `c_transit` |Number of commuters who use public transit +| `c_bike` |Number of commuters who bike +| `c_walk` |Number of commuters who bike +| `year` | ACS data year +| `FIPS_11_digit` | 11-digit FIPS code + + +We're going to drop all of our `moe` columns by identifying all of those that end with `_moe`. We can do that in two steps, first by using `filter` to identify columns that contain the string `_moe`. + +moe_cols = acs5_df.filter(like='_moe',axis=1).columns +moe_cols + +acs5_df.drop(moe_cols, axis=1, inplace=True) + +And lastly, let's grab only the rows for year 2018 and county FIPS code 1 (i.e. Alameda County) + +acs5_df_ac = acs5_df[(acs5_df['year']==2018) & (acs5_df['county_fips']==1)] + +--------------------------------- +Now let's also read in our census tracts again! + +tracts_gdf = gpd.read_file("zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip") + +tracts_gdf.head() + +tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001'] +tracts_gdf_ac.plot() +plt.show() + +# 7.1 Attribute Joins + +**Attribute Joins between Geodataframes and Dataframes** + +*We just mapped the census tracts. But what makes a map powerful is when you map the data associated with the locations.* + +- `tracts_gdf_ac`: These are polygon data in a GeoDataFrame. However, as we saw in the `head` of that dataset, they no attributes of interest! + +- `acs5_df_ac`: These are 2018 ACS data from a CSV file ('census_variables_CA.csv'), imported and read in as a `pandas` DataFrame. However, they have no geometries! + +In order to map the ACS data we need to associate it with the tracts. Let's do that now, by joining the columns from `acs5_df_ac` to the columns of `tracts_gdf_ac` using a common column as the key for matching rows. This process is called an **attribute join**. + + + + + + +-------------------------- + + + + +
+ +
+
+ +#### Question +
+ +The image above gives us a nice conceptual summary of the types of joins we could run. + +1. In general, why might we choose one type of join over another? +1. In our case, do we want an inner, left, right, or outer (AKA 'full') join? + +(**NOTE**: You can read more about merging in `geopandas` [here](http://geopandas.org/mergingdata.html#attribute-joins).) + +Your responses here: + + + + + + + +Okay, here we go! + +Let's take a look at the common column in both our DataFrames. + + +tracts_gdf_ac['GEOID'].head() + +acs5_df_ac['FIPS_11_digit'].head() + + +Note that they are **not named the same thing**. + + That's okay! We just need to know that they contain the same information. + +Also note that they are **not in the same order**. + + That's not only okay... That's the point! (If they were in the same order already then we could just join them side by side, without having Python find and line up the matching rows from each!) + +------------------------------- + +Let's do a `left` join to keep all of the census tracts in Alameda County and only the ACS data for those tracts. + +**NOTE**: To figure out how to do this we could always take a peek at the documentation by calling +`?tracts_gdf_ac.merge`, or `help(tracts_gdf_ac)`. + +# Left join keeps all tracts and the acs data for those tracts +tracts_acs_gdf_ac = tracts_gdf_ac.merge(acs5_df_ac, left_on='GEOID',right_on="FIPS_11_digit", how='left') +tracts_acs_gdf_ac.head(2) + +Let's check that we have all the variables we have in our dataset now. + +list(tracts_acs_gdf_ac.columns) + +
+ +
+
+ +#### Question +
+ +It's always important to run sanity checks on our results, at each step of the way! + +In this case, how many rows and columns should we have? + + +Your response here: + + + + + + + +print("Rows and columns in the Alameda County Census tract gdf:\n\t", tracts_gdf_ac.shape) +print("Row and columns in the ACS5 2018 data:\n\t", acs5_df_ac.shape) +print("Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\n\t", tracts_acs_gdf_ac.shape) + +Let's save out our merged data so we can use it in the final notebook. + +tracts_acs_gdf_ac.to_file('outdata/tracts_acs_gdf_ac.json', driver='GeoJSON') + +## Exercise: Choropleth Map +We can now make choropleth maps using our attribute joined geodataframe. Go ahead and pick one variable to color the map, then map it. You can go back to lesson 5 if you need a refresher on how to make this! + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE + + + + + + +## Double-click to see solution! + + + +------------------- +# 7.2 Spatial Joins + +Great! We've wrapped our heads around the concept of an attribute join. + +Now let's extend that concept to its spatially explicit equivalent: the **spatial join**! + + +
+ +To start, we'll read in some other data: The Alameda County schools data. + +Then we'll work with that data and our `tracts_acs_gdf_ac` data together. + +schools_df = pd.read_csv('notebook_data/alco_schools.csv') +schools_gdf = gpd.GeoDataFrame(schools_df, + geometry=gpd.points_from_xy(schools_df.X, schools_df.Y)) +schools_gdf.crs = "epsg:4326" + +Let's check if we have to transform the schools to match the`tracts_acs_gdf_ac`'s CRS. + +print('schools_gdf CRS:', schools_gdf.crs) +print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs) + +Yes we do! Let's do that. + +**NOTE**: Explicit syntax aiming at that dataset's CRS leaves less room for human error! + +schools_gdf = schools_gdf.to_crs(tracts_acs_gdf_ac.crs) + +print('schools_gdf CRS:', schools_gdf.crs) +print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs) + +Now we're ready to combine the datasets in an analysis. + +**In this case, we want to get data from the census tract within which each school is located.** + +But how can we do that? The two datasets don't share a common column to use for a join. + +tracts_acs_gdf_ac.columns + +schools_gdf.columns + +However, they do have a shared relationship by way of space! + +So, we'll use a spatial relationship query to figure out the census tract that +each school is in, then associate the tract's data with that school (as additional data in the school's row). +This is a **spatial join**! + +--------------------------------- + +### Census Tract Data Associated with Each School + +In this case, let's say we're interested in the relationship between the median household income +in a census tract (`tracts_acs_gdf_ac['med_hhinc']`) and a school's Academic Performance Index +(`schools_gdf['API']`). + +To start, let's take a look at the distributions of our two variables of interest. + +tracts_acs_gdf_ac.hist('med_hhinc') + +schools_gdf.hist('API') + +Oh, right! Those pesky schools with no reported APIs (i.e. API == 0)! Let's drop those. + +schools_gdf_api = schools_gdf.loc[schools_gdf['API'] > 0, ] + +schools_gdf_api.hist('API') + +Much better! + +Now, maybe we think there ought to be some correlation between the two variables? +As a first pass at this possibility, let's overlay the two datasets, coloring each one by +its variable of interest. This should give us a sense of whether or not similar values co-occur. + +ax = tracts_acs_gdf_ac.plot(column='med_hhinc', cmap='cividis', figsize=[18,18], + legend=True, legend_kwds={'label': "median household income ($)", + 'orientation': "horizontal"}) +schools_gdf_api.plot(column='API', cmap='cividis', edgecolor='black', alpha=1, ax=ax, + legend=True, legend_kwds={'label': "API", 'orientation': "horizontal"}) + +### Spatially Joining our Schools and Census Tracts + +Though it's hard to say for sure, it certainly looks possible. +It would be ideal to scatterplot the variables! But in order to do that, +we need to know the median household income in each school's tract, which +means we definitely need our **spatial join**! + +We'll first take a look at the documentation for the spatial join function, `gpd.sjoin`. + +help(gpd.sjoin) + +Looks like the key arguments to consider are: +- the two GeoDataFrames (**`left_df`** and **`right_df`**) +- the type of join to run (**`how`**), which can take the values `left`, `right`, or `inner` +- the spatial relationship query to use (**`op`**) + +**NOTE**: +- By default `sjoin` is an inner join. It keeps the data from both geodataframes only where the locations spatially intersect. + +- By default `sjoin` maintains the geometry of first geodataframe input to the operation. + + +
+ +
+
+ +#### Questions +
+ +1. Which GeoDataFrame are we joining onto which (i.e. which one is getting the other one's data added to it)? +1. What happened to 'outer' as a join type? +1. Thus, in our operation, which GeoDataFrame should be the `left_df`, which should be the `right_df`, and `how` do we want our join to run? + +Your responses here: + + + + + + + + + +Alright! Let's run our join! + +schools_jointracts = gpd.sjoin(schools_gdf_api, tracts_acs_gdf_ac, how='left') + +### Checking Our Output + +
+ +
+ +
+
+ +#### Questions +
+ +As always, we want to sanity-check our intermediate result before we rush ahead. + +One way to do that is to introspect the structure of the result object a bit. + +1. What type of object should that have given us? +1. What should the dimensions of that object be, and why? +1. If we wanted a visual check of our results (i.e. a plot or map), what could we do? + +Your responses here: + + + + + + + + +print(schools_jointracts.shape) +print(schools_gdf.shape) +print(tracts_acs_gdf_ac.shape) + +schools_jointracts.head() + +Confirmed! The output of the our `sjoin` operation is a GeoDataFrame (`schools_jointracts`) with: +- a row for each school that is located inside a census tract (all of them are) +- the **point geometry** of that school +- all of the attribute data columns (non-geometry columns) from both input GeoDataFrames + +---------------------------- + +Let's also take a look at an overlay map of the schools on the tracts. +If we color the schools categorically by their tracts IDs, then we should see +that all schools within a given tract polygon are the same color. + +ax = tracts_acs_gdf_ac.plot(color='white', edgecolor='black', figsize=[18,18]) +schools_jointracts.plot(column='GEOID', ax=ax) + +### Assessing the Relationship between Median Household Income and API + +Fantastic! That looks right! + +Now we can create that scatterplot we were thinking about! + +fig, ax = plt.subplots(figsize=(6,6)) +ax.scatter(schools_jointracts.med_hhinc, schools_jointracts.API) +ax.set_xlabel('median household income ($)') +ax.set_ylabel('API') + +Wow! Just as we suspected based on our overlay map, +there's a pretty obvious, strong, and positive correlation +between median household income in a school's tract +and the school's API. + +# 7.3: Aggregation + +We just saw that a spatial join in one way to leverage the spatial relationship +between two datasets in order to create a new, synthetic dataset. + +An **aggregation** is another way we can generate new data from this relationship. +In this case, for each feature in one dataset we find all the features in another +dataset that satisfy our chosen spatial relationship query with it (e.g. within, intersects), +then aggregate them using some summary function (e.g. count, mean). + +------------------------------------ + +### Getting the Aggregated School Counts + +Let's take this for a spin with our data. We'll count all the schools within each census tract. + +Note that we've already done the first step of spatially joining the data from the aggregating features +(the tracts) onto the data to be aggregated (our schools). + +The next step is to group our GeoDataFrame by census tract, and then summarize our data by group. +We do this using the DataFrame method `groupy`. + +To get the correct count, lets rejoin our schools on our tracts, this time keeping all schools +(not just those with APIs > 0, as before). + +schools_jointracts = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='left') + +Now for the `groupy` operation. + +**NOTE**: We could really use any column, since we're just taking a count. For now we'll just use the school names ('Site'). + +schools_countsbytract = schools_jointracts[['GEOID','Site']].groupby('GEOID', as_index=False).count() +print("Counts, rows and columns:", schools_countsbytract.shape) +print("Tracts, rows and columns:", tracts_acs_gdf_ac.shape) + +# take a look at the data +schools_countsbytract.head() + +### Getting Tract Polygons with School Counts + +The above `groupby` and `count` operations give us the counts we wanted. +- We have the 263 (of 361) census tracts that have at least one school +- We have the number of schools within each of those tracts + +But the output of `groupby` is a plain DataFrame not a GeoDataFrame. + +If we want a GeoDataFrame then we have two options: +1. We could join the `groupby` output to `tracts_acs_gdf_ac` by the attribute `GEOID` +or +2. We could start over, using the GeoDataFrame `dissolve` method, which we can think of as a spatial `groupby`. + + +--------------------------- + +Since we already know how to do an attribute join, we'll do the `dissolve`! + +First, let's run a new spatial join. + +tracts_joinschools = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='right') + +Now, let's run our dissolve! + +tracts_schoolcounts = tracts_joinschools[['GEOID', 'Site', 'geometry']].dissolve(by='GEOID', aggfunc='count') +print("Counts, rows and columns:", tracts_schoolcounts.shape) + +# take a look +tracts_schoolcounts.head() + +Nice! Let's break that down. + +- The `dissolve` operation requires a geometry column and a grouping column (in our case, 'GEOID'). Any geometries within the **same group** will be dissolved if they have the same geometry or nested geometries. + +- The `aggfunc`, or aggregation function, of the dissolve operation will be applied to all numeric columns in the input geodataframe (unless the function is `count` in which case it will count rows.) + +Check out the Geopandas documentation on [dissolve](https://geopandas.org/aggregation_with_dissolve.html?highlight=dissolve) for more information. + + +
+ +
+
+ +#### Questions +
+ +1. Above we selected three columns from the input GeoDataFrame to create a subset as input to the dissolve operation. Why? +1. Why did we run a new spatial join? What would have happened if we had used the `schools_jointracts` object instead? +1. What explains the dimensions of the new object (361, 2)? + +You responses here: + + + + + + + + +### Mapping our Spatial Join Output + +Also, because our `sjoin` plus `dissolve` pipeline outputs a GeoDataFrame, we can now easily map the school count by census tract! + +fig, ax = plt.subplots(figsize = (14,8)) + +# Display the output of our spatial join +tracts_schoolcounts.plot(ax=ax,column='Site', + scheme="user_defined", + classification_kwds={'bins':[*range(9)]}, + cmap="PuRd_r", + edgecolor="grey", + legend=True, + legend_kwds={'title':'Number of schools'}) +schools_gdf.plot(ax=ax, color='black', markersize=2) + +--------------------- + +# Exercise: Aggregation + +#### What is the mean API of each census tract? + +As we mentioned, the spatial aggregation workflow that we just put together above +could have been used not to generate a new count variable, but also +to generate any other new variable the results from calling an aggregation function +on an attribute column. + +In this case, we want to calculate and map the mean API of the schools in each census tract. + +Copy and paste code from above where useful, then tweak and/or add to that code such that your new code: +1. joins the schools onto the tracts (**HINT**: make sure to decide whether or not you want to include schools with API = 0!) +1. dissolves that joined object by the tract IDs, giving you a new GeoDataFrame with each tract's mean API (**HINT**: because this is now a different calculation, different problems may arise and need handling!) +1. plots the tracts, colored by API scores (**HINT**: overlay the schools points again, visualizing them in a way that will help you visually check your results!) + +To see the solution, double-click the Markdown cell below. + +# YOUR CODE HERE: + + + + + + +## Double-click to see solution! + + + +---------------------------- + +## 7.4 Recap +We discussed how we can combine datasets to enhance any geospatial data analyses you could do. Key concepts include: +- Attribute joins + - `.merge()` +- Spatial joins (order matters!) + - `gpd.sjoin()` +- Aggregation + -`.groupby()` + - `.dissolve()` (preserves geometry) + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
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\n", + "Here's a set of steps that we will implement in the labeled cells below:\n", + "\n", + " 8.1 Read in and Prep Data\n", + "- read in tracts acs joined data\n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- transform it to match the CRS of `tracts_acs_gdf_ac`\n", + "- take a peek\n", + "\n", + "8.2 Spatial Join and Dissolve\n", + "- join the two datasets in such a way that you can then...\n", + "- group by tract and calculate the total grocery-store sales volume\n", + "- don't forget to check the dimensions, contents, and any other relevant aspects of your results\n", + "\n", + "8.3 Plot and Review\n", + "- plot the tracts, coloring them by total grocery-store sales volume\n", + "- plot the grocery stores on top\n", + "- bonus points for devising a nice visualization scheme that helps you heuristically check your results!\n", + "\n", + "\n", + "\n", + "### INSTRUCTIONS:\n", + "**We've written out some of the code for you, but you'll need to replace the ellipses with the correct\n", + "content.**\n", + "\n", + "*You can check your answers by double-clicking on the Markdown cells where indicated.*\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'outdata/tracts_acs_gdf_ac.json'\n", + " - 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: N/A\n", + " - Exercises: 30 minutes\n", + "\n", + "\n", + "\n", + "\n", + "-----------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "---------------------------------------\n", + "\n", + "\n", + "### Install Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------------------\n", + "\n", + "## 8.1 Read in the Prep Data\n", + "\n", + "We first need to prepare our data by loading both our tracts/acs and grocery data, and conduct our usual steps to make there they have the same CRS.\n", + "\n", + "- read in our tracts acs joined data \n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- transform it to match the CRS of `tracts_acs_gdf_ac`\n", + "- take a peek\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m tracts_acs_gdf_ac = gpd.read_file(..)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "# read in tracts acs data\n", + "\n", + "tracts_acs_gdf_ac = gpd.read_file(..)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read our grocery-data CSV into a Pandas DataFrame\n", + "\n", + "grocery_pts_df = pd.read_csv(...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# coerce it to a GeoDataFrame\n", + "\n", + "grocery_pts_gdf = gpd.GeoDataFrame(grocery_pts_df, \n", + " geometry=gpd.points_from_xy(...,...))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define its CRS (NOTE: Use EPSG:4326)\n", + "\n", + "grocery_pts_gdf.crs = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# transform it to match the CRS of tracts_acs_gdf_ac\n", + "\n", + "grocery_pts_gdf.to_crs(..., inplace=...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# take a peek\n", + "\n", + "print(grocery_pts_gdf.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "-----------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.2 Spatial Join and Dissolve\n", + "\n", + "Now that we have our data and they're in the same projection, we're going to conduct an *attribute join* to bring together the two datasets. From there we'll be able to actually *aggregate* our data to count the total sales volume.\n", + "\n", + "- join the two datasets in such a way that you can then...\n", + "- group by tract and calculate the total grocery-store sales volume\n", + "- don't forget to check the dimensions, contents, and any other relevant aspects of your results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# join the two datasets in such a way that you can then...\n", + "\n", + "tracts_joingrocery = gpd.sjoin(..., ..., how= ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# group by tract and calculate the total grocery-store sales volume\n", + "\n", + "tracts_totsalesvol = tracts_joingrocery[['GEOID','geometry','SALESVOL']].dissolve(by= ...,\n", + " aggfunc=..., as_index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# don't forget to check the dimensions, contents, and any other relevant aspects of your results\n", + "\n", + "# check the dimensions\n", + "print('Dimensions of result:', ...)\n", + "print('Dimesions of census tracts:', ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check the result\n", + "print(tracts_totsalesvol.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "----------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.3 Plot and Review\n", + "\n", + "With any time of geospatial analysis you do, it's always nice to plot and visualize your results to check your work and start to understand the full story of your analysis.\n", + "\n", + "- Plot the tracts, coloring them by total grocery-store sales volume\n", + "- Plot the grocery stores on top\n", + "- Bonus points for devising a nice visualization scheme that helps you heuristically check your results!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create the figure and axes\n", + "\n", + "fig, ax = plt.subplots(figsize = (20,20)) \n", + "\n", + "# plot the tracts, coloring by total SALESVOL\n", + "\n", + "tracts_totsalesvol.plot(ax=ax, column= ..., scheme=\"quantiles\", cmap=\"autumn\", edgecolor=\"grey\",\n", + " legend=True, legend_kwds={'title':...})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subset the stores for only those within our tracts, to keep map within region of interest\n", + "\n", + "grocery_pts_gdf_ac = grocery_pts_gdf.loc[..., ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# add the grocery stores, coloring by SALESVOL, for a visual check\n", + "\n", + "grocery_pts_gdf_ac.plot(ax=ax, column= ... , cmap= ..., linewidth= ..., markersize= ...,\n", + " legend=True, legend_kwds={'label': ... , 'orientation': \"horizontal\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "scrolled": false + }, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "-------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "***\n", + "\n", + "# Congrats!! Thanks for Joining Us for Geospatial Fundamentals!!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/ran/08_Pulling_It_All_Together-Copy1.py b/_build/jupyter_execute/ran/08_Pulling_It_All_Together-Copy1.py new file mode 100644 index 0000000..dc10eff --- /dev/null +++ b/_build/jupyter_execute/ran/08_Pulling_It_All_Together-Copy1.py @@ -0,0 +1,265 @@ +# 08. Pulling it all Together + +For this last lesson, we'll practice going through a full workflow!! We'll answer the question: +## What is the total grocery-store sales volume of each census tract? + + +### WORKFLOW: + +
+Here's a set of steps that we will implement in the labeled cells below: + + 8.1 Read in and Prep Data +- read in tracts acs joined data +- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`) +- coerce it to a GeoDataFrame +- define its CRS (EPSG:4326) +- transform it to match the CRS of `tracts_acs_gdf_ac` +- take a peek + +8.2 Spatial Join and Dissolve +- join the two datasets in such a way that you can then... +- group by tract and calculate the total grocery-store sales volume +- don't forget to check the dimensions, contents, and any other relevant aspects of your results + +8.3 Plot and Review +- plot the tracts, coloring them by total grocery-store sales volume +- plot the grocery stores on top +- bonus points for devising a nice visualization scheme that helps you heuristically check your results! + + + +### INSTRUCTIONS: +**We've written out some of the code for you, but you'll need to replace the ellipses with the correct +content.** + +*You can check your answers by double-clicking on the Markdown cells where indicated.* + + +
+ + Instructor Notes + +- Datasets used + - 'outdata/tracts_acs_gdf_ac.json' + - 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv' + +- Expected time to complete + - Lecture + Questions: N/A + - Exercises: 30 minutes + + + + +----------------- + + +--------------------------------------- + + +### Install Packages + +import pandas as pd +import geopandas as gpd + +import matplotlib # base python plotting library +import matplotlib.pyplot as plt # submodule of matplotlib + +# To display plots, maps, charts etc in the notebook +%matplotlib inline + +------------------ + +## 8.1 Read in the Prep Data + +We first need to prepare our data by loading both our tracts/acs and grocery data, and conduct our usual steps to make there they have the same CRS. + +- read in our tracts acs joined data +- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`) +- coerce it to a GeoDataFrame +- define its CRS (EPSG:4326) +- transform it to match the CRS of `tracts_acs_gdf_ac` +- take a peek + + + +# read in tracts acs data + +tracts_acs_gdf_ac = gpd.read_file(..) + +# read our grocery-data CSV into a Pandas DataFrame + +grocery_pts_df = pd.read_csv(...) + +# coerce it to a GeoDataFrame + +grocery_pts_gdf = gpd.GeoDataFrame(grocery_pts_df, + geometry=gpd.points_from_xy(...,...)) + +# define its CRS (NOTE: Use EPSG:4326) + +grocery_pts_gdf.crs = ... + +# transform it to match the CRS of tracts_acs_gdf_ac + +grocery_pts_gdf.to_crs(..., inplace=...) + +# take a peek + +print(grocery_pts_gdf.head()) + +## Double-click here to see solution! + + + +----------------------- + +## 8.2 Spatial Join and Dissolve + +Now that we have our data and they're in the same projection, we're going to conduct an *attribute join* to bring together the two datasets. From there we'll be able to actually *aggregate* our data to count the total sales volume. + +- join the two datasets in such a way that you can then... +- group by tract and calculate the total grocery-store sales volume +- don't forget to check the dimensions, contents, and any other relevant aspects of your results + +# join the two datasets in such a way that you can then... + +tracts_joingrocery = gpd.sjoin(..., ..., how= ...) + +# group by tract and calculate the total grocery-store sales volume + +tracts_totsalesvol = tracts_joingrocery[['GEOID','geometry','SALESVOL']].dissolve(by= ..., + aggfunc=..., as_index=False) + +# don't forget to check the dimensions, contents, and any other relevant aspects of your results + +# check the dimensions +print('Dimensions of result:', ...) +print('Dimesions of census tracts:', ...) + +# check the result +print(tracts_totsalesvol.head()) + +## Double-click here to see solution! + + + +---------------------- + +## 8.3 Plot and Review + +With any time of geospatial analysis you do, it's always nice to plot and visualize your results to check your work and start to understand the full story of your analysis. + +- Plot the tracts, coloring them by total grocery-store sales volume +- Plot the grocery stores on top +- Bonus points for devising a nice visualization scheme that helps you heuristically check your results! + +# create the figure and axes + +fig, ax = plt.subplots(figsize = (20,20)) + +# plot the tracts, coloring by total SALESVOL + +tracts_totsalesvol.plot(ax=ax, column= ..., scheme="quantiles", cmap="autumn", edgecolor="grey", + legend=True, legend_kwds={'title':...}) + +# subset the stores for only those within our tracts, to keep map within region of interest + +grocery_pts_gdf_ac = grocery_pts_gdf.loc[..., ] + +# add the grocery stores, coloring by SALESVOL, for a visual check + +grocery_pts_gdf_ac.plot(ax=ax, column= ... , cmap= ..., linewidth= ..., markersize= ..., + legend=True, legend_kwds={'label': ... , 'orientation': "horizontal"}) + +## Double-click here to see solution! + + + +------------------- + +
+
+
+
+
+
+ +*** + +# Congrats!! Thanks for Joining Us for Geospatial Fundamentals!! + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + + + diff --git a/_config.yml b/_config.yml new file mode 100644 index 0000000..8675647 --- /dev/null +++ b/_config.yml @@ -0,0 +1,48 @@ +####################################################################################### +# Book settings +title: "Geospatial Fundamentals in Python" +author : Hikari Murayama, Drew Hart, Patty Frontiera # The author of the book +copyright : "2020" # Copyright year to be placed in the footer +logo: dlab_logo.png +# Patterns to skip when building the book. Can be glob-style (e.g. "*skip.ipynb") +exclude_patterns : [_build, Thumbs.db, .DS_Store, "**.ipynb_checkpoints"] +# Auto-exclude files not in the toc +only_build_toc_files : false +####################################################################################### +# Execution settings +execute: + execute_notebooks: "off" +####################################################################################### +# HTML-specific settings +html: + favicon : "" # A path to a favicon image + use_edit_page_button : false # Whether to add an "edit this page" button to pages. If `true`, repository information in repository: must be filled in + use_repository_button : true # Whether to add a link to your repository button + use_issues_button : false # Whether to add an "open an issue" button + extra_navbar : Powered by Jupyter Book # Will be displayed underneath the left navbar. + extra_footer : "" # Will be displayed underneath the footer. + google_analytics_id : "" # A GA id that can be used to track book views. + home_page_in_navbar : true # Whether to include your home page in the left Navigation Bar + baseurl : "" # The base URL where your book will be hosted. Used for creating image previews and social links. e.g.: https://mypage.com/mybook/ + comments: + hypothesis : false + utterances : false +####################################################################################### +# LaTeX-specific settings +latex: + latex_engine : pdflatex # one of 'pdflatex', 'xelatex' (recommended for unicode), 'luatex', 'platex', 'uplatex' + use_jupyterbook_latex : true # use jupyterbook-latex for pdf builds as default + +####################################################################################### +# Launch button settings +# launch_buttons: +# notebook_interface : classic # The interface interactive links will activate ["classic", "jupyterlab"] +# binderhub_url : https://mybinder.org # The URL of the BinderHub (e.g., https://mybinder.org) +# jupyterhub_url : "" # The URL of the JupyterHub (e.g., https://datahub.berkeley.edu) +# thebe : false # Add a thebe button to pages (requires the repository to run on Binder) +# colab_url : "" # The URL of Google Colab (https://colab.research.google.com) + +repository: + url : https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python # The URL to your book's repository + path_to_book : "" # A path to your book's folder, relative to the repository root. + branch : master # Which branch of the repository should be used when creating link diff --git a/_toc.yml b/_toc.yml new file mode 100644 index 0000000..8efc86c --- /dev/null +++ b/_toc.yml @@ -0,0 +1,49 @@ + +- file: lessons/intro + numbered: false + + +- part: Introduction to Geospatial Concepts + chapters: + - file: lessons/01_Overview_Geospatial_Data + title: Overview of Geospatial Data + +- part: Getting started with spatial dataframes + chapters: + - file: lessons/02_Introduction_to_GeoPandas + title: Introduction to GeoPandas + - file: lessons/03_CRS_Map_Projections + title: Coordinate Reference Systems (CRS) & Map Projections + - file: lessons/04_More_Data_More_Maps + title: More Data, More Maps! + +- part: Geoprocessing and analysis + chapters: + - file: lessons/06_Spatial_Queries + title: Spatial Queries + - file: lessons/07_Joins_and_Aggregation + title: Attribute and Spatial Joins + +- part: Exercises + chapters: + - file: lessons/08_Pulling_It_All_Together + title: Pulling it All Together + - file: lessons/09_ON_YOUR_OWN_A_Full_Workflow + title: Another Full Workflow + +- part: Get Fancy + chapters: + - file: lessons/10_OPTIONAL_Fetching_Data + title: Read in Data from Online Sources + - file: lessons/11_OPTIONAL_Basemap_with_Contextily + title: Adding Basemaps with Contextily + - file: lessons/12_OPTIONAL_Interactive_Mapping_with_Folium + title: Interactive Mapping with Folium + - file: lessons/13_OPTIONAL_geocoding + title: Geocoding Addresses + - file: lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair + title: Plotting and Mapping with Altair + - file: lessons/15_OPTIONAL_Voronoi_Tessellation + title: Voronoi Tessellation + - file: lessons/16_OPTIONAL_Introduction_to_Raster_Data + title: Introduction to Raster Data \ No newline at end of file diff --git a/dlab_logo.png b/dlab_logo.png new file mode 100644 index 0000000..99e5340 Binary files /dev/null and b/dlab_logo.png differ diff --git a/01.Overview_Geospatial_Data.pdf b/lessons/01.Overview_Geospatial_Data.pdf similarity index 100% rename from 01.Overview_Geospatial_Data.pdf rename to lessons/01.Overview_Geospatial_Data.pdf diff --git a/lessons/01_Overview_Geospatial_Data.ipynb b/lessons/01_Overview_Geospatial_Data.ipynb new file mode 100644 index 0000000..fe8f8cc --- /dev/null +++ b/lessons/01_Overview_Geospatial_Data.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 1. Overview of Geospatial Data\n", + "\n", + "Before diving into any coding, let's first go over some core concepts.\n", + "\n", + "- 1.1 Geospatial Data\n", + "- 1.2 Coordinate Reference Systems\n", + "- 1.3 Types of Spatial Data\n", + "- 1.4 Other Resources\n", + "\n", + "Note that this Jupyterbook covers *a lot*! There's so much to learn and understand about the world of doing geospatial work. But we want you to keep in mind that this really only the start of your journey. All the authors who contributed to this are still learning too :)\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.1 Geospatial Data\n", + "\n", + "So there are a couple of terms that get confused when we're trying to talk about work in this area:\n", + "- *Geographic Information Systems (GIS)*\n", + "- *Geographic Data*\n", + "- *Geospatial Data*\n", + "We'll walk through each of these term-by-term.\n", + "\n", + "**Geographic Information Systems (GIS)** is probably a term that you've heard of before and it integrates many types of data, which includes spatial location. You can think of it as a framework to analyze spatial and geographic data.\n", + "> **Note**: GIS can also be an acronym for Geographic Information Science, which is the study of the study of geographic systems.\n", + "\n", + "**Geographic data** can answer the questions \"where\" and \"what\". To make this a little bit more concrete, let's use this sign in Anatone, WA, USA as an example.\n", + "\n", + "\n", + "\n", + "
Image Credit: Dsdugan at English Wikipedia
\n", + "\n", + "\n", + "Dsdugan at English Wikipedia\n", + "\n", + "Here, our answer to the question to \"where\" is Anatone, WA. The \"what\" question is answered by all the details on the sign, for example we know that the number of dogs in Anatone is 22. These types of details are also called *attributes*.\n", + "\n", + "Another component of geographic data is *metadata*. This component includes things such as when the data was taken, by whom, how, the quality, as wel as other information about the geographic data itself. \n", + "\n", + "**Geospatial Data** is a location that is given by a set of coordinates. For example, the location for Anatone could be specified with a specific latitude and longitude ($46.135570$, $-117.132659$). \n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 Coordinate Reference Systems\n", + "\n", + "A **Coordinate Reference System** or **CRS** is a system for associating specific numerical coordinates with a position on earth. So depending on the CRS that is used the numbers for the latitude and longitude could differ.\n", + "\n", + "\n", + "\n", + "
Image Credit: Wikimedia Commons
\n", + "\n", + "\n", + "There are many CRSs because our understanding and ability to measure the surface of the earth has evolved over time. We can think of these different reasonings as an orange peel or a lamp.\n", + "\n", + "Think if we take a regular orange as our earth:\n", + "\n", + "\n", + "\n", + "
Image Credit: ESRI project package by j_nelson
\n", + "\n", + "\n", + "And the first assumption we make is that it is spherical: \n", + "\n", + "\n", + "\n", + "
Image Credit: ESRI project package by j_nelson
\n", + "\n", + "\n", + "Assuming that it's spherical will introduce some distortion, as well as how I choose to draw all of my continents on it. Plus when I decide to peel it, depending on how I do that, It'll look like different maps on a flat surface:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Image Credit: ESRI project package by j_nelson
\n", + "\n", + "\n", + "Another way to think about this is by thinking about our planet earth as a lamp in a dark room.\n", + "\n", + "\n", + "\n", + "
Image Credit: Brando
\n", + "\n", + "\n", + "\n", + "Depending on factors such as how we tilt the lamp and if our walls our flat the image that we project onto the wall will be different.\n", + "\n", + "*In short, since our earth isn't flat, our earth is distorted to make it feasible to show it on a flat surface*.\n", + "\n", + "\n", + "There are two types of coordinate reference systems.\n", + "- *Geographic CRS*\n", + "- *Projected CRS*\n", + "\n", + "*Geographic CRS* are great for storing data and has units of degrees and are widely used. WGS84 is the most commonly used CRS and is basd on satellites and used by cellphones and GPS. It has the best overall fir for most places on earth. Another common one is NAD83 which is based on both satellite and survey data. It's a great fit for USA based work and is utilized in a lot of federal data products such as the census data. Both of these CRS have *EPSG codes*, which a 4+ digit number used to reference a CRs. For WGS84 the code is 4326, while for NAD83 its 4269. You'll be using these codes when you're using CRS in Python.\n", + "\n", + "*Projected CRS* are good for mapping and spatial analysis. They transform the geographic coordinates (latitude, longitude) to be 2D (X, Y) with units such as meters. All map projections include some type of distortion, whether that be in area, shape, distance or direction. Depending on the CRS it'll probably be minimizing distortion for one of these characteristics. For example, the Mercator projection places importance on shape and direction, but in turn has distorted area as you move away from the equator.\n", + "\n", + "\n", + "\n", + "
Image Credit: QGIS Documentation
\n", + "\n", + "\n", + "\n", + "Of course some projections are worse than others. This joke projection has somehow made all continents look like South America! This story of distortion tells us that some projections are better than others.\n", + "\n", + "\n", + "\n", + "
Image Credit: xkcd comics
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "> **Note**: Here are some videos related to the concept of CRS. \n", + "> - Drawing projections on fruits: [Link](https://www.youtube.com/watch?v=wkK_HsY7S_4&t=399s)\n", + "> - West Wing discussion on using specific projections: [Link](https://www.youtube.com/watch?v=vVX-PrBRtTY&t=55s)\n", + "> - Vox on why world maps are wrong: [Link](https://www.youtube.com/watch?v=kIID5FDi2JQ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 Types of Spatial Data\n", + "\n", + "As you start to gather geospatial data, you'll encounter two types: **vector** and **raster** data.\n", + "\n", + "**Vector data** can be thought of as that that you can connect the dots with. This type of data includes points, lines, and polygons.\n", + "\n", + "\n", + "\n", + "As an example, we can look at these different types of vector data by looking at different data in San Francisco.\n", + "\n", + "\n", + "\n", + "Each of these geometry types can be used for different types of information. Point geometries are great for showing where crimes have occurred historically. Lines can show us the location and length of the freeways in the city. Polygons could help us show information such as population per square mile in different neighborhoods.\n", + "\n", + "Now let's think about what this vector data could look like when you open it up.\n", + "\n", + "\n", + "\n", + "You might get something like this. Each row represents one geospatial feature. So for our second attribute we have the ID number 2, the plot size 20, vegetation type, and a vegetation class of deciduous. Those additional information like the plot size, are **attributes**. These help describe our features. \n", + "\n", + "Furthermore, each of these features have an associated geometry or geometry collection. So in our first table our geometry is a point,\n", + "\n", + "One last thing about vector data-- each group of features is called a layer. So you could have all three of these data, and each dataset would be its own layer. \n", + "\n", + "\n", + "**Raster data** on the other hand is continous. Each location is represented by a grid cell, which are usually all the same size. There a fixed number of rows and columns, and each cell has a value that represents the attribute of interest. \n", + "\n", + "\n", + "\n", + "
Image Credit: Humboldt GSP
\n", + "\n", + "\n", + "\n", + "Raster data should feel familiar to you since images are basically raster data! \n", + "\n", + "Now that we know we have these two types of datasets, we can talk about when to use each. Vector data are better for when you have discreetly bounded data. This could be for counties, rivers, etc. On the other hand, raster data is better for continuous data (like the image we just looked at), or maybe something like temperature, elevation or rainfall.\n", + "\n", + "Now these two datasets come in different file formats, so you’ll know what it is before you pull it in for whatever GIS software you’re using. Some common ones I use are shapefile and geojsons for vector data, and geotiffs for raster data. \n", + "\n", + "| Vector | Raster |\n", + "| ----------- | ----------- |\n", + "| Shapefile (.shp…) | GeoTIFF |\n", + "| GeoJSON, JSON | netCDF |\n", + "| KML | DEM |\n", + "| GeoPackage | |\n", + "\n", + "Although these two types of data look different, and come in different formats, you can still use a combination of raster and vector data to answers questions that you’re probably aiming to answer through your own work.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.4 Other Resources\n", + "\n", + "This is really only a brief introduction to geospatial concepts! If you want to dive a little deeper, here are a couple of resources you can check out:\n", + "\n", + "- [Kaggle Learn: Geospatial Analysis in Python](https://www.kaggle.com/learn/geospatial-analysis), an online interactive tutorial\n", + "\n", + "- [Campbell & Shin, Geographic Information System Basics, v1.0](https://2012books.lardbucket.org/books/geographic-information-system-basics/index.html)\n", + "\n", + "- [ESRI Introduction to Map Design](https://www.esri.com/industries/k-12/education/~/media/Files/Pdfs/industries/k-12/pdfs/intrcart.pdf)\n", + "\n", + "- [AxisMaps Cartography Guide](https://www.axismaps.com/guide/)\n", + "\n", + "- [Gentle Introduction to GIS (QGIS)](https://docs.qgis.org/3.16/en/docs/gentle_gis_introduction/index.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
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 D-Lab @ University of California - Berkeley
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 Team Geo
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" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/02_Introduction_to_GeoPandas.ipynb b/lessons/02_Introduction_to_GeoPandas.ipynb new file mode 100644 index 0000000..e758c62 --- /dev/null +++ b/lessons/02_Introduction_to_GeoPandas.ipynb @@ -0,0 +1,602 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 2. Introduction to Geopandas\n", + "\n", + "In this lesson we'll learn about a package that is core to using geospatial data in Python. We'll go through the structure of the data (it's not too different from regular DataFrames!), geometries, shapefiles, and how to save your hard work.\n", + "\n", + "- 2.1 What is GeoPandas?\n", + "- 2.2 Read in a shapefile\n", + "- 2.3 Explore the GeoDataFrame\n", + "- 2.4 Plot the GeoDataFrame\n", + "- 2.5 Subset the GeoDataFrame\n", + "- 2.6 Save your data\n", + "- 2.7 Recap\n", + "- **Exercise**: IO, Manipulation, and Mapping\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/california_counties/CaliforniaCounties.shp'\n", + " - 'notebook_data/census/Places/cb_2018_06_place_500k.zip'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 5 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 What is GeoPandas?\n", + "\n", + "### GeoPandas and related Geospatial Packages\n", + "\n", + "[GeoPandas](http://geopandas.org/) is a relatively new package that makes it easier to work with geospatial data in Python. In the last few years it has grown more powerful and stable. This is really great because previously it was quite complex to work with geospatial data in Python. GeoPandas is now the go-to package for working with `vector` geospatial data in Python. \n", + "\n", + "> **Protip**: If you work with `raster` data you will want to checkout the [rasterio](https://rasterio.readthedocs.io/en/latest/) package. We will not cover raster data in this tutorial.\n", + "\n", + "### GeoPandas = pandas + geo\n", + "GeoPandas gives you access to all of the functionality of [pandas](https://pandas.pydata.org/), which is the primary data analysis tool for working with tabular data in Python. GeoPandas extends pandas with attributes and methods for working with geospatial data.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries\n", + "\n", + "Let's start by importing the libraries that we will use." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Read in a shapefile\n", + "\n", + "As we discussed in the initial geospatial overview, a *shapefile* is one type of geospatial data that holds vector data. \n", + "\n", + "> To learn more about ESRI Shapefiles, this is a good place to start: [ESRI Shapefile Wiki Page](https://en.wikipedia.org/wiki/Shapefile) \n", + "\n", + "The tricky thing to remember about shapefiles is that they're actually a collection of 3 to 9+ files together. Here's a list of all the files that can make up a shapefile:\n", + " \n", + ">`shp`: The main file that stores the feature geometry\n", + ">\n", + ">`shx`: The index file that stores the index of the feature geometry \n", + ">\n", + ">`dbf`: The dBASE table that stores the attribute information of features \n", + ">\n", + ">`prj`: The file that stores the coordinate system information. (should be required!)\n", + ">\n", + ">`xml`: Metadata —Stores information about the shapefile.\n", + ">\n", + ">`cpg`: Specifies the code page for identifying the character set to be used.\n", + "\n", + "But it remains the most commonly used file format for vector spatial data, and it's really easy to visualize in one go!\n", + "\n", + "Let's try it out with California counties, and use `geopandas` for the first time. `gpd.read_file` is a flexible function that let's you read in many different types of geospatial data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the counties shapefile\n", + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot out California counties\n", + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bam! Amazing! We're off to a running start." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Explore the GeoDataFrame\n", + "\n", + "Before we get in too deep, let's discuss what a *GeoDataFrame* is and how it's different from `pandas` *DataFrames*.\n", + "\n", + "### The GeoPandas GeoDataFrame\n", + "\n", + "A [GeoPandas GeoDataFrame](https://geopandas.org/data_structures.html#geodataframe), or `gdf` for short, is just like a pandas dataframe (`df`) but with an extra geometry column and methods & attributes that work on that column. I repeat because it's important:\n", + "\n", + "> `A GeoPandas GeoDataFrame is a pandas DataFrame with a geometry column and methods & attributes that work on that column.`\n", + "\n", + "> This means all the methods and attributes of a pandas DataFrame also work on a Geopandas GeoDataFrame!!\n", + "\n", + "With that in mind, let's start exploring out dataframe just like we would do in `pandas`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Find the number of rows and columns in counties\n", + "counties.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at the first couple of rows in our geodataframe\n", + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at all the variables included in our data\n", + "counties.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like we have a good amount of information about the total population for different years and the densities, as well as race, age, and occupancy info." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.4 Plot the GeoDataFrame\n", + "\n", + "We're able to plot our GeoDataFrame because of the extra `geometry` column.\n", + "\n", + "### Geopandas Geometries\n", + "There are three main types of geometries that can be associated with your geodataframe: points, lines and polygons:\n", + "\n", + "\n", + "\n", + "In the geodataframe these geometries are encoded in a format known as [Well-Known Text (WKT)](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry). For example:\n", + "\n", + "> - POINT (30 10)\n", + "> - LINESTRING (30 10, 10 30, 40 40)\n", + "> - POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))\n", + ">\n", + "> *where coordinates are separated by a space and coordinate pairs by a comma*\n", + "\n", + "Your geodataframe may also include the variants **multipoints, multilines, and multipolgyons** if the row-level feature of interest is comprised of multiple parts. For example, a geodataframe of states, where one row represents one state, would have a POLYGON geometry for Utah but MULTIPOLYGON for Hawaii, which includes many islands.\n", + "\n", + "> It's ok to mix and match geometries of the same family, e.g., POLYGON and MULTIPOLYGON, in the same geodatafame.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " **Question** What kind of geometry would a roads geodataframe have? What about one that includes landmarks in the San Francisco Bay Area?\n", + "\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the types of geometries in a geodataframe or a subset of the geodataframe by combining the `type` and `unique` methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's check what geometries we have in our counties geodataframe\n", + "counties['geometry'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's check to make sure that we only have polygons and multipolygons \n", + "counties['geometry'].type.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like with other plots you can make in Python, we can start customizing our map with colors, size, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We can run the following line of code to get more info about the parameters we can specify:\n", + "\n", + "?counties.plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make the figure size bigger\n", + "counties.plot(figsize=(6,9))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot(figsize=(6,9), \n", + " edgecolor='grey', # grey colored border lines\n", + " facecolor='pink' , # fill in our counties as pink\n", + " linewidth=2) # make the linedwith a width of 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.5 Subset the GeoDataframe\n", + "\n", + "Since we'll be focusing on Berkeley later in the workshop, let's subset our GeoDataFrame to just be for Alameda County." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# See all county names included in our dataset\n", + "counties['NAME'].values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like Alameda county is specified as \"Alameda\" in this dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create a new geodataframe called `alameda_county` that is a subset of our counties geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county = counties.loc[counties['NAME'] == 'Alameda'].copy().reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot our newly subsetted geodataframe\n", + "alameda_county.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice! Looks like we have what we were looking for.\n", + "\n", + "*FYI*: You can also make dynamic plots of one or more county without saving to a new gdf." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bay_area_counties = ['Alameda', 'Contra Costa', 'Marin', 'Napa', 'San Francisco', \n", + " 'San Mateo', 'Santa Clara', 'Santa Cruz', 'Solano', 'Sonoma']\n", + "counties.loc[counties['NAME'].isin(bay_area_counties)].plot()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.6 Save your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's not forget to save out our Alameda County geodataframe `alameda_county`. This way we won't need to repeat the processing steps and attribute join we did above.\n", + "\n", + "We can save it as a shapefile." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.shp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the problems of saving to a shapefile is that our column names get truncated to 10 characters (a shapefile limitation.) \n", + "\n", + "Instead of renaming all columns with obscure names that are less than 10 characters, we can save our GeoDataFrame to a spatial data file format that does not have this limation - [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) or [GPKG](https://en.wikipedia.org/wiki/GeoPackage) (geopackage) file.\n", + "- These formats have the added benefit of outputting only one file in contrast tothe multi-file shapefile format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.json\", driver=\"GeoJSON\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county.to_file(\"outdata/alameda_county.gpkg\", driver=\"GPKG\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can read these in, just as you would a shapefile with `gpd.read_file`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county_test = gpd.read_file(\"outdata/alameda_county.gpkg\")\n", + "alameda_county_test.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alameda_county_test2 = gpd.read_file(\"outdata/alameda_county.json\")\n", + "alameda_county_test2.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are also many other formats we could use for data output.\n", + "\n", + "**NOTE**: If you're working with point data (i.e. a single latitude and longitude value per feature),\n", + "then CSV might be a good option!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.7 Recap\n", + "\n", + "In this lesson we learned about...\n", + "- The `geopandas` package \n", + "- Reading in shapefiles \n", + " - `gpd.read_file`\n", + "- GeoDataFrame structures\n", + " - `shape`, `head`, `columns`\n", + "- Plotting GeoDataFrames\n", + " - `plot`\n", + "- Subsetting GeoDatFrames\n", + " - `loc`\n", + "- Saving out GeoDataFrames\n", + " - `to_file`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: IO, Manipulation, and Mapping\n", + "\n", + "Now you'll get a chance to practice the operations we learned above.\n", + "\n", + "In the following cell, compose code to:\n", + "\n", + "1. Read in the California places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`)\n", + "2. Subset the data to Berkeley\n", + "3. Plot, and customize as desired\n", + "4. Save out as a shapefile (`outdata/berkeley_places.shp`)\n", + "\n", + "\n", + "*Note: pulling in a zipped shapefile has the same syntax as just pulling in a shapefile. The only difference is that insead of just putting in the filepath you'll want to write `zip://notebook_data/census/Places/cb_2018_06_place_500k.zip`*\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
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 D-Lab @ University of California - Berkeley
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 Team Geo
\n", + "
\n", + " \n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/03_CRS_Map_Projections.ipynb b/lessons/03_CRS_Map_Projections.ipynb new file mode 100644 index 0000000..32c99ab --- /dev/null +++ b/lessons/03_CRS_Map_Projections.ipynb @@ -0,0 +1,853 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 3. Coordinate Reference Systems (CRS) & Map Projections\n", + "\n", + "Building off of what we learned in the previous notebook, we'll get to understand an integral aspect of geospatial data: Coordinate Reference Systems.\n", + "\n", + "- 3.1 California County Shapefile\n", + "- 3.2 USA State Shapefile\n", + "- 3.3 Plot the Two Together\n", + "- 3.4 Coordinate Reference System (CRS)\n", + "- 3.5 Getting the CRS\n", + "- 3.6 Setting the CRS\n", + "- 3.7 Transforming or Reprojecting the CRS\n", + "- 3.8 Plotting States and Counties Togther\n", + "- 3.9 Recap\n", + "- **Exercise**: CRS Management\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - ‘notebook_data/california_counties/CaliforniaCounties.shp’\n", + " - ‘notebook_data/us_states/us_states.shp’\n", + " - ‘notebook_data/census/Places/cb_2018_06_place_500k.zip’\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 10 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 California County shapefile\n", + "Let's go ahead and bring back in our California County shapefile. As before, we can read the file in using `gpd.read_file` and plot it straight away." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')\n", + "counties.plot(color='darkgreen')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even if we have an awesome map like this, sometimes we want to have more geographical context, or we just want additional information. We're going to try **overlaying** our counties GeoDataFrame on our USA states shapefile." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 USA State shapefile\n", + "\n", + "We're going to bring in our states geodataframe, and let's do the usual operations to start exploring our data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in states shapefile\n", + "states = gpd.read_file('notebook_data/us_states/us_states.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at the first few rows\n", + "states.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Count how many rows and columns we have\n", + "states.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot our states data\n", + "states.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might have noticed that our plot extends beyond the 50 states (which we also saw when we executed the `shape` method). Let's double check what states we have included in our data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states['STATE'].values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beyond the 50 states we seem to have American Samoa, Puerto Rico, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands included in this geodataframe. To make our map cleaner, let's limit the states to the contiguous states (so we'll also exclude Alaska and Hawaii)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define list of non-contiguous states\n", + "non_contiguous_us = [ 'American Samoa','Puerto Rico','Guam',\n", + " 'Commonwealth of the Northern Mariana Islands',\n", + " 'United States Virgin Islands', 'Alaska','Hawaii']\n", + "# Limit data according to above list\n", + "states_limited = states.loc[~states['STATE'].isin(non_contiguous_us)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot it\n", + "states_limited.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To prepare for our mapping overlay, let's make our states a nice, light grey color." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited.plot(color='lightgrey', figsize=(10,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Plot the two together\n", + "\n", + "Now that we have both geodataframes in our environment, we can plot both in the same figure.\n", + "\n", + "**NOTE**: To do this, note that we're getting a Matplotlib Axes object (`ax`), then explicitly adding each our layers to it\n", + "by providing the `ax=ax` argument to the `plot` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh no, what happened here?\n", + "\n", + " **Question** Without looking ahead, what do you think happened?\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "If you look at the numbers we have on the x and y axes in our two plots, you'll see that the county data has much larger numbers than our states data. It's represented in some different type of unit other than decimal degrees! \n", + "\n", + "In fact, that means if we zoom in really close into our plot we'll probably see the states data plotted. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "counties.plot(color='darkgreen',ax=ax)\n", + "states_limited.plot(color='lightgrey', ax=ax)\n", + "ax.set_xlim(-140,-50)\n", + "ax.set_ylim(20,50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a key issue that you'll have to resolve time and time again when working with geospatial data!\n", + "\n", + "It all revolves around **coordinate reference systems** and **projections**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----------------------------\n", + "\n", + "## 3.4 Coordinate Reference Systems (CRS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **Question** Do you have experience with Coordinate Reference Systems?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

As a refresher, a CRS describes how the coordinates in a geospatial dataset relate to locations on the surface of the earth. \n", + "\n", + "A `geographic CRS` consists of: \n", + "- a 3D model of the shape of the earth (a **datum**), approximated as a sphere or spheroid (aka ellipsoid)\n", + "- the **units** of the coordinate system (e.g, decimal degrees, meters, feet) and \n", + "- the **origin** (i.e. the 0,0 location), specified as the meeting of the **equator** and the **prime meridian**( \n", + "\n", + "A `projected CRS` consists of\n", + "- a geographic CRS\n", + "- a **map projection** and related parameters used to transform the geographic coordinates to `2D` space.\n", + " - a map projection is a mathematical model used to transform coordinate data\n", + "\n", + "### A Geographic vs Projected CRS\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### There are many, many CRSs\n", + "\n", + "Theoretically the number of CRSs is unlimited!\n", + "\n", + "Why? Primariy, because there are many different definitions of the shape of the earth, multiplied by many different ways to cast its surface into 2 dimensions. Our understanding of the earth's shape and our ability to measure it has changed greatly over time.\n", + "\n", + "#### Why are CRSs Important?\n", + "\n", + "- You need to know the data about your data (or `metadata`) to use it appropriately.\n", + "\n", + "\n", + "- All projected CRSs introduce distortion in shape, area, and/or distance. So understanding what CRS best maintains the characteristics you need for your area of interest and your analysis is important.\n", + "\n", + "\n", + "- Some analysis methods expect geospatial data to be in a projected CRS\n", + " - For example, `geopandas` expects a geodataframe to be in a projected CRS for area or distance based analyses.\n", + "\n", + "\n", + "- Some Python libraries, but not all, implement dynamic reprojection from the input CRS to the required CRS and assume a specific CRS (WGS84) when a CRS is not explicitly defined.\n", + "\n", + "\n", + "- Most Python spatial libraries, including Geopandas, require geospatial data to be in the same CRS if they are being analysed together.\n", + "\n", + "#### What you need to know when working with CRSs\n", + "\n", + "- What CRSs used in your study area and their main characteristics\n", + "- How to identify, or `get`, the CRS of a geodataframe\n", + "- How to `set` the CRS of geodataframe (i.e. define the projection)\n", + "- Hot to `transform` the CRS of a geodataframe (i.e. reproject the data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Codes for CRSs commonly used with CA data\n", + "\n", + "CRSs are typically referenced by an [EPSG code](http://wiki.gis.com/wiki/index.php/European_Petroleum_Survey_Group). \n", + "\n", + "It's important to know the commonly used CRSs and their EPSG codes for your geographic area of interest. \n", + "\n", + "For example, below is a list of commonly used CRSs for California geospatial data along with their EPSG codes.\n", + "\n", + "##### Geographic CRSs\n", + "-`4326: WGS84` (units decimal degrees) - the most commonly used geographic CRS\n", + "\n", + "-`4269: NAD83` (units decimal degrees) - the geographic CRS customized to best fit the USA. This is used by all Census geographic data.\n", + "\n", + "> `NAD83 (epsg:4269)` are approximately the same as `WGS84(epsg:4326)` although locations can differ by up to 1 meter in the continental USA and elsewhere up to 3m. That is not a big issue with census tract data as these data are only accurate within +/-7meters.\n", + "##### Projected CRSs\n", + "\n", + "-`5070: CONUS NAD83` (units meters) projected CRS for mapping the entire contiguous USA (CONUS)\n", + "\n", + "-`3857: Web Mercator` (units meters) conformal (shape preserving) CRS used as the default in web mapping\n", + "\n", + "-`3310: CA Albers Equal Area, NAD83` (units meters) projected CRS for CA statewide mapping and spatial analysis\n", + "\n", + "-`26910: UTM Zone 10N, NAD83` (units meters) projected CRS for northern CA mapping & analysis\n", + "\n", + "-`26911: UTM Zone 11N, NAD83` (units meters) projected CRS for Southern CA mapping & analysis\n", + "\n", + "-`102641 to 102646: CA State Plane zones 1-6, NAD83` (units feet) projected CRS used for local analysis.\n", + "\n", + "You can find the full CRS details on the website https://www.spatialreference.org" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.5 Getting the CRS\n", + "\n", + "### Getting the CRS of a gdf\n", + "\n", + "GeoPandas GeoDataFrames have a `crs` attribute that returns the CRS of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.crs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can clearly see from those two printouts (even if we don't understand all the content!),\n", + "the CRSs of our two datasets are different! **This explains why we couldn't overlay them correctly!**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------------------------\n", + "The above CRS definition specifies \n", + "- the name of the CRS (`WGS84`), \n", + "- the axis units (`degree`)\n", + "- the shape (`datum`),\n", + "- and the origin (`Prime Meridian`, and the equator)\n", + "- and the area for which it is best suited (`World`)\n", + "\n", + "> Notes:\n", + "> - `geocentric` latitude and longitude assume a spherical (round) model of the shape of the earth\n", + "> - `geodetic` latitude and longitude assume a spheriodal (ellipsoidal) model, which is closer to the true shape.\n", + "> - `geodesy` is the study of the shape of the earth." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE**: If you print a `crs` call, Python will just display the EPSG code used to initiate the CRS object. Depending on your versions of Geopandas and its dependencies, this may or may not look different from what we just saw above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(states_limited.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.6 Setting the CRS\n", + "\n", + "You can also set the CRS of a gdf using the `crs` attribute. You would set the CRS if is not defined or if you think it is incorrectly defined.\n", + "\n", + "> In desktop GIS terminology setting the CRS is called **defining the CRS**\n", + "\n", + "As an example, let's set the CRS of our data to `None`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# first set the CRS to None\n", + "states_limited.crs = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check it again\n", + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "...hummm...\n", + "\n", + "If a variable has a null value (None) then displaying it without printing it won't display anything!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check it again\n", + "print(states_limited.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll set it back to its correct CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set it to 4326\n", + "states_limited.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show it\n", + "states_limited.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE**: You can set the CRS to anything you like, but **that doesn't make it correct**! This is because setting the CRS does not change the coordinate data; it just tells the software how to interpret it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.7 Transforming or Reprojecting the CRS\n", + "You can transform the CRS of a geodataframe with the `to_crs` method.\n", + "\n", + "\n", + "> In desktop GIS terminology transforming the CRS is called **projecting the data** (or **reprojecting the data**)\n", + "\n", + "When you do this you want to save the output to a new GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited_utm10 = states_limited.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now take a look at the CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "states_limited_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see the result immediately by plotting the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# plot geographic gdf\n", + "states_limited.plot();\n", + "plt.axis('square');\n", + "\n", + "# plot utm gdf\n", + "states_limited_utm10.plot();\n", + "plt.axis('square')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your thoughts here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What two key differences do you see between the two plots above?\n", + "1. Do either of these plotted USA maps look good?\n", + "1. Try looking at the common CRS EPSG codes above and see if any of them look better for the whole country than what we have now. Then try transforming the states data to the CRS that you think would be best and plotting it. (Use the code cell two cells below.)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Double-click to see solution!**\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.8 Plotting states and counties together\n", + "\n", + "Now that we know what a CRS is and how we can set them, let's convert our counties GeoDataFrame to match up with out states' crs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert counties data to NAD83 \n", + "counties_utm10 = counties.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties_utm10.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot it together!\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "states_limited_utm10.plot(color='lightgrey', ax=ax)\n", + "counties_utm10.plot(color='darkgreen',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we know that the best CRS to plot the contiguous US from the above question is 5070, let's also transform and plot everything in that CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties_conus = counties.to_crs(\"epsg:5070\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "states_limited_conus.plot(color='lightgrey', ax=ax)\n", + "counties_conus.plot(color='darkgreen',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.9 Recap\n", + "\n", + "In this lesson we learned about...\n", + "- Coordinate Reference Systems \n", + "- Getting the CRS of a geodataframe\n", + " - `crs`\n", + "- Transforming/repojecting CRS\n", + " - `to_crs`\n", + "- Overlaying maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: CRS Management\n", + "\n", + "Now it's time to take a crack and managing the CRS of a new dataset. In the code cell below, write code to:\n", + "\n", + "1. Bring in the CA places data (`notebook_data/census/Places/cb_2018_06_place_500k.zip`)\n", + "2. Check if the CRS is EPSG code 26910. If not, transform the CRS\n", + "3. Plot the California counties and places together.\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/04_More_Data_More_Maps.ipynb b/lessons/04_More_Data_More_Maps.ipynb new file mode 100644 index 0000000..95c48aa --- /dev/null +++ b/lessons/04_More_Data_More_Maps.ipynb @@ -0,0 +1,650 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 4. More Data, More Maps!\n", + "\n", + "Now that we know how to pull in data, check and transform Coordinate Reference Systems (CRS), and plot GeoDataFrames together - let's practice doing the same thing with other geometry types. In this notebook we'll be bringing in bike boulevards and schools, which will get us primed to think about spatial relationship questions.\n", + "\n", + "- 4.1 Berkeley Bike Boulevards\n", + "- 4.2 Alameda County Schools\n", + "- **Exercise**: Even More Data!\n", + "- 4.3 Map Overlays with Matplotlib\n", + "- 4.4 Recap\n", + "- **Exercise**: Overlay Mapping\n", + "- 4.5 Teaser for Day 2\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/parcels/parcel_pts_rand30pct.geojson'\n", + " - ‘notebook_data/berkeley/BerkeleyCityLimits.shp’\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.1 Berkeley Bike Boulevards\n", + "\n", + "We're going to bring in data bike boulevards in Berkeley. Note two things that are different from our previous data:\n", + "- We're bringing in a [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) this time and not a shapefile\n", + "- We have a **line** geometry GeoDataFrame (our county and states data had **polygon** geometries)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual, we'll want to do our usual data exploration..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our bike boulevard data includes the following information:\n", + " - `BB_STRNAM` - bike boulevard Streetname\n", + " - `BB_STRID` - bike boulevard Street ID\n", + " - `BB_FRO` - bike boulevard origin street\n", + " - `BB_TO` - bike boulevard end street\n", + " - `BB_SECID`- bike boulevard section id\n", + " - `DIR_` - cardinal directions the bike boulevard runs\n", + " - `Status` - status on whether the bike boulevard exists\n", + " - `ALT_bikeCA` - ? \n", + " - `Shape_len` - length of the boulevard in meters \n", + " - `len_km` - length of the boulevard in kilometers\n", + " - `geometry`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "Why are there 211 features when we only have 8 bike boulevards?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your reponse here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig,ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds.plot(ax=ax)\n", + "bike_blvds.head(1).plot(color='orange',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now take a look at our CRS..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's tranform our CRS to UTM Zone 10N, NAD83 that we used in the last lesson." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10 = bike_blvds.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2 Alameda County Schools\n", + "\n", + "Alright! Now that we have our bike boulevard data squared away, we're going to bring in our Alameda County school data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **Questions** \n", + "\n", + "Without looking ahead:\n", + "\n", + "1. Is this a geodataframe? \n", + "2. How do you know?\n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your reponse here:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "This is not a GeoDataFrame! A couple of clues to figure that out are..\n", + "\n", + "1. We're pulling in a Comma Separated Value (CSV) file, which is not a geospatial data format\n", + "2. There is no geometry column (although we do have latitude and longitude values)\n", + "\n", + "\n", + "-------------------------------\n", + "\n", + "Although our school data is not starting off as a GeoDataFrame, we actually have the tools and information to make it one. Using the `gpd.GeoDataFrame` constructor, we can transform our plain DataFrame into a GeoDataFrame (specifying the geometry information and then the CRS)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(schools_gdf.crs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.crs = \"epsg:4326\"\n", + "schools_gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll notice that the shape is the same from what we had as a dataframe, just with the added `geometry` column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "schools_gdf.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And with it being a GeoDataFrame, we can plot it as we did for our other data sets.\n", + "Notice that we have our first **point** geometry GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But of course we'll want to transform the CRS, so that we can later plot it with our bike boulevard data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf_utm10 = schools_gdf.to_crs( \"epsg:26910\")\n", + "schools_gdf_utm10.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*In Lesson 2 we discussed that you can save out GeoDataFrames in multiple file formats. You could opt for a GeoJSON, a shapefile, etc... for point data sets it is also an option to save it out as a CSV since the geometry isn't complicated*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Even More Data!\n", + "Let's play around with another point GeoDataFrame.\n", + "\n", + "In the code cell provided below, compose code to:\n", + "\n", + "1. Read in the parcel points data (`notebook_data/parcels/parcel_pts_rand30pct.geojson`)\n", + "2. Transform the CRS to 26910\n", + "3. Plot and customize as desired!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.3 Map Overlays with Matplotlib\n", + "\n", + "No matter the geometry type we have for our GeoDataFrame, we can create overlay plots.\n", + "\n", + "Since we've already done the legwork of transforming our CRS, we can go ahead and plot them together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds_utm10.plot(ax=ax, color='red')\n", + "schools_gdf_utm10.plot(ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to answer questions like *\"What schools are close to bike boulevards in Berkeley?\"*, the above plot isn't super helpful, since the extent covers all of Alameda county.\n", + "\n", + "Luckily, GeoDataFrames have an easy method to extract the minimium and maximum values for both x and y, so we can use that information to set the bounds for our plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "minx, miny, maxx, maxy = bike_blvds.total_bounds\n", + "print(minx, miny, maxx, maxy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `xlim` and `ylim` we can zoom in to see if there are schools proximal to the bike boulevards." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "bike_blvds_utm10.plot(ax=ax, color='red')\n", + "schools_gdf_utm10 .plot(ax=ax)\n", + "plt.xlim(minx, maxx)\n", + "plt.ylim(miny, maxy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.4 Recap\n", + "\n", + "In this lesson we learned a several new skills:\n", + "- Transformed an a-spatial dataframe into a geospatial one\n", + " - `gpd.GeoDataFrame`\n", + "- Worked with point and line GeoDataFrames\n", + "- Overlayed point and line GeoDataFrames\n", + "- Limited the extent of a map\n", + " - `total_bounds`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Overlay Mapping\n", + "\n", + "Let's take some time to practice reading in and reconciling new datasets, then mapping them together.\n", + "\n", + "In the code cell provided below, write code to:\n", + "\n", + "1. Bring in your Berkeley places shapefile (and don't forget to check/transform the crs!) (`notebook_data/berkeley/BerkeleyCityLimits.shp`)\n", + "1. Overlay the parcel points on top of the bike boulevards\n", + "1. Create the same plot but limit it to the extent of Berkeley city limits\n", + "\n", + "***BONUS***: *Add the Berkeley outline to your last plot!*\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click the see the solution!\n", + "\n", + "\n", + "\n", + "-----------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.5 Teaser for Day 2...\n", + "\n", + "You may be wondering if and how we could make our maps more interesting and informative than this.\n", + "\n", + "To give you a tantalizing taste of Day 2, the answer is: Yes, we can! And here's how!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = schools_gdf_utm10.plot(column='Org', cmap='winter', \n", + " markersize=35, edgecolor='black',\n", + " linewidth=0.5, alpha=1, figsize=[9, 9],\n", + " legend=True)\n", + "ax.set_title('Public and Private Schools, Alameda County')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/05_Data-Driven_Mapping.ipynb b/lessons/05_Data-Driven_Mapping.ipynb new file mode 100644 index 0000000..ec25627 --- /dev/null +++ b/lessons/05_Data-Driven_Mapping.ipynb @@ -0,0 +1,889 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 5. Data-driven Mapping\n", + "\n", + "*Data-driven mapping* refers to the process of using data values to determine the symbology of mapped features. Color, shape, and size are the three most common symbology types used in data-driven mapping.\n", + "Data-driven maps are often refered to as thematic maps.\n", + "\n", + "\n", + "- 5.1 Choropleth Maps\n", + "- 5.2 Issues with Visualization\n", + "- 5.3 Classification Schemes\n", + "- 5.4 Point Maps\n", + "- 5.5 Mapping Categorical Data\n", + "- 5.6 Recap\n", + "- **Exercise**: Data-Driven Mapping\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/california_counties/CaliforniaCounties.shp'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + "- Expected time to complete\n", + " - Lecture + Questions: 30 minutes\n", + " - Exercises: 15 minutes\n", + "\n", + "\n", + "\n", + "### Types of Thematic Maps\n", + "\n", + "There are two primary types of maps used to convey data values:\n", + "\n", + "- `Choropleth maps`: set the color of areas (polygons) by data value\n", + "- `Point symbol maps`: set the color or size of points by data value\n", + "\n", + "We will discuss both of these types of maps in more detail in this lesson. But let's take a quick look at choropleth maps. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.1 Choropleth Maps\n", + "Choropleth maps are the most common type of thematic map.\n", + "\n", + "Let's take a look at how we can use a geodataframe to make a choropleth map.\n", + "\n", + "We'll start by reloading our counties dataset from Day 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties = gpd.read_file('notebook_data/california_counties/CaliforniaCounties.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a plain map of our polygons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for comparison, let's create a choropleth map by setting the color of the county based on the values in the population per square mile (`POP12_SQMI`) column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot(column='POP12_SQMI', figsize=(10,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's really the heart of it. To set the color of the features based on the values in a column, set the `column` argument to the column name in the gdf.\n", + "> **Protip:** \n", + "- You can quickly right-click on the plot and save to a file or open in a new browser window." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default map colors are linearly scaled to data values. This is called a `proportional color map`.\n", + "\n", + "- The great thing about `proportional color maps` is that you can visualize the full range of data values.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also add a legend, and even tweak its display." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.plot(column='POP12_SQMI', figsize=(10,10), legend=True,\n", + " legend_kwds={'label': \"Population Density per mile$^2$\",\n", + " 'orientation': \"horizontal\"},)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "Why are we plotting `POP12_SQMI` instead of `POP2012`?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Note: Types of Color Maps\n", + "\n", + "There are a few different types of color maps (or color palettes), each of which has a different purpose:\n", + "- *diverging* - a \"diverging\" set of colors are used so emphasize mid-range values as well as extremes.\n", + "- *sequential* - usually with a single color hue to emphasize changes in magnitude, where darker colors typically mean higher values\n", + "- *qualitative* - a diverse set of colors to identify categories and avoid implying quantitative significance.\n", + "\n", + "\n", + "\n", + "
Image Credit: Dsdugan at English Wikipedia
\n", + "\n", + "> **Pro-tip**: You can actually see all your color map options if you misspell what you put in `cmap` and try to run-in. Try it out!\n", + "\n", + "> **Pro-tip**: Sites like [ColorBrewer](https://colorbrewer2.org/#type=sequential&scheme=Blues&n=3) let's you play around with different types of color maps. If you want to create your own, [The Python Graph Gallery](https://python-graph-gallery.com/python-colors/) is a way to see what your Python color options are.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.2 Issues with Visualization\n", + "\n", + "### Types of choropleth data\n", + "\n", + "There are several types of quantitative data variables that can be used to create a choropleth map. Let's consider these in terms of our ACS data.\n", + "\n", + "- **Count**\n", + " - counts, aggregated by feature\n", + " - *e.g. population within a census tract*\n", + "\n", + "- **Density**\n", + " - count, aggregated by feature, normalized by feature area\n", + " - *e.g. population per square mile within a census tract*\n", + "\n", + "- **Proportions / Percentages**\n", + " - value in a specific category divided by total value across in all categories\n", + " - *e.g. proportion of the tract population that is white compared to the total tract population*\n", + "\n", + "- **Rates / Ratios**\n", + " - value in one category divided by value in another category\n", + " - *e.g. homeowner-to-renter ratio would be calculated as the number of homeowners (c_owners/ c_renters)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpretability of plotted data\n", + "The goal of a choropleth map is to use color to visualize the spatial distribution of a quantitative variable.\n", + "\n", + "Brighter or richer colors are typically used to signify higher values.\n", + "\n", + "A big problem with choropleth maps is that our eyes are drawn to the color of larger areas, even if the values being mapped in one or more smaller areas are more important.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see just this sort of problem in our population-density map. \n", + "\n", + "***Why does our map not look that interesting?*** Take a look at the histogram below, then consider the following question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.hist(counties['POP12_SQMI'],bins=40)\n", + "plt.title('Population Density per mile$^2$')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What county does that outlier represent? What problem does that pose?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.3 Classification schemes\n", + "\n", + "Let's try to make our map more interpretable!\n", + "\n", + "The common alternative to a proportionial color map is to use a **classification scheme** to create a **graduated color map**. This is the standard way to create a **choropleth map**.\n", + "\n", + "A **classification scheme** is a method for binning continuous data values into 4-7 classes (the default is 5) and map those classes to a color palette. \n", + "\n", + "### The commonly used classifications schemes:\n", + "\n", + "- **Equal intervals**\n", + " - equal-size data ranges (e.g., values within 0-10, 10-20, 20-30, etc.)\n", + " - pros:\n", + " - best for data spread across entire range of values\n", + " - easily understood by map readers\n", + " - cons:\n", + " - but avoid if you have highly skewed data or a few big outliers\n", + " \n", + " \n", + "- **Quantiles**\n", + " - equal number of observations in each bin\n", + " - pros:\n", + " - looks nice, becuase it best spreads colors across full set of data values\n", + " - thus, it's often the default scheme for mapping software\n", + " - cons:\n", + " - bin ranges based on the number of observations, not on the data values\n", + " - thus, different classes can have very similar or very different values.\n", + " \n", + " \n", + "- **Natural breaks**\n", + " - minimize within-class variance and maximize between-class differences\n", + " - e.g. 'fisher-jenks'\n", + " - pros:\n", + " - great for exploratory data analysis, because it can identify natural groupings\n", + " - cons:\n", + " - class breaks are best fit to one dataset, so the same bins can't always be used for multiple years\n", + " \n", + " \n", + "- **Manual** \n", + " - classifications are user-defined\n", + " - pros: \n", + " - especially useful if you want to slightly change the breaks produced by another scheme\n", + " - can be used as a fixed set of breaks to compare data over time\n", + " - cons:\n", + " - more work involved" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classification schemes and GeoDataFrames\n", + "\n", + "Classification schemes can be implemented using the geodataframe `plot` method by setting a value for the **scheme** argument. This requires the [pysal](https://pysal.org/) and [mapclassify](https://pysal.org/mapclassify) libraries to be installed in your Python environment. \n", + "\n", + "Here is a list of the `classification schemes` names that we will use:\n", + "- `equalinterval`, `quantiles`,`fisherjenks`,`naturalbreaks`, and `userdefined`.\n", + "\n", + "For more information about these classification schemes see the [pysal mapclassifiers web page](https://pysal.org/mapclassify/api.html) or check out the help docs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------\n", + "\n", + "### Classification schemes in action\n", + "\n", + "Let's redo the last map using the `quantile` classification scheme.\n", + "\n", + "- What is different about the code? About the output map?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Plot population density - mile^2\n", + "fig, ax = plt.subplots(figsize = (10,5)) \n", + "counties.plot(column='POP12_SQMI', \n", + " scheme=\"quantiles\",\n", + " legend=True,\n", + " ax=ax\n", + " )\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: For interval notation\n", + "- A square bracket is *inclusive*\n", + "- A parentheses is *exclusive*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### User Defined Classification Schemes\n", + "\n", + "You may get pretty close to your final map without being completely satisfied. In this case you can manually define a classification scheme.\n", + "\n", + "Let's customize our map with a `user-defined` classification scheme where we manually set the breaks for the bins using the `classification_kwds` argument." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "counties.plot(column='POP12_SQMI',\n", + " legend=True, \n", + " cmap=\"RdYlGn\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[50,100,200,300,400]},\n", + " ax=ax)\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we are customizing our plot, we can also edit our legend to specify and format the text so that it's easier to read.\n", + "\n", + "- We'll use `legend_labels_list` to customize the labels for group in the legend." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "counties.plot(column='POP12_SQMI',\n", + " legend=True, \n", + " cmap=\"RdYlGn\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[50,100,200,300,400]},\n", + " ax=ax)\n", + "\n", + "# Create the labels for the legend\n", + "legend_labels_list = ['<50','50 to 100','100 to 200','200 to 300','300 to 400','>400']\n", + "\n", + "# Apply the labels to the plot\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "ax.set_title(\"Population Density per Sq Mile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's plot a ratio\n", + "\n", + "If we look at the columns in our dataset, we see we have a number of variables\n", + "from which we can calculate proportions, rates, and the like.\n", + "\n", + "Let's try that out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counties.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (15,6)) \n", + "\n", + "# Plot percent hispanic as choropleth\n", + "counties.plot(column=(counties['HISPANIC']/counties['POP2012'] * 100), \n", + " legend=True, \n", + " cmap=\"Blues\", \n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[20,40,60,80]},\n", + " edgecolor=\"grey\",\n", + " linewidth=0.5,\n", + " ax=ax)\n", + "\n", + "legend_labels_list = ['<20%','20% - 40%','40% - 60%','60% - 80%','80% - 100%']\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "ax.set_title(\"Percent Hispanic Population\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What new options and operations have we added to our code?\n", + "1. Based on our code, what title would you give this plot to describe what it displays?\n", + "1. How many bins do we specify in the `legend_labels_list` object, and how many bins are in the map legend? Why?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.4 Point maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choropleth maps are great, but mapping using point symbols enables us to visualize our spatial data in another way. \n", + "\n", + "If you know both mapping methods you can expand how much information you can show in one map. \n", + "\n", + "For example, point maps are a great way to map `counts` because the varying sizes of areas are deemphasized.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----------------------\n", + "Let's read in some point data on Alameda County schools." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We got it from a plain CSV file, let's coerce it to a GeoDataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can map it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.plot()\n", + "plt.title('Alameda County Schools')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Proportional Color Maps\n", + "**Proportional color maps** linearly scale the `color` of a point symbol by the data values.\n", + "\n", + "Let's try this by creating a map of `API`. API stands for *Academic Performance Index*, which is a measurement system that looks at the performance of an individual school." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.plot(column=\"API\", cmap=\"gist_heat\", \n", + " edgecolor=\"grey\", figsize=(10,8), legend=True)\n", + "plt.title(\"Alameda County, School API scores\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you see that continuous color bar in the legend you know that the mapping of data values to colors is not classified.\n", + "\n", + "\n", + "### Graduated Color Maps\n", + "\n", + "We can also create **graduated color maps** by binning data values before associating them with colors. These are just like choropleth maps, except that the term \"choropleth\" is only used with polygon data. \n", + "\n", + "Graduated color maps use the same syntax as the choropleth maps above - you create them by setting a value for `scheme`. \n", + "\n", + "Below, we copy the code we used above to create a choropleth, but we change the name of the geodataframe to use the point gdf. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (15,6)) \n", + "\n", + "# Plot percent non-white with graduated colors\n", + "schools_gdf.plot(column='API', \n", + " legend=True, \n", + " cmap=\"Blues\",\n", + " scheme='user_defined', \n", + " classification_kwds={'bins':[0,200,400,600,800]},\n", + " edgecolor=\"grey\",\n", + " linewidth=0.5,\n", + " #markersize=60,\n", + " ax=ax)\n", + "\n", + "# Create a custom legend\n", + "legend_labels_list = ['0','< 200','< 400','< 600','< 800','>= 800']\n", + "\n", + "# Apply the legend to the map\n", + "for j in range(0,len(ax.get_legend().get_texts())):\n", + " ax.get_legend().get_texts()[j].set_text(legend_labels_list[j])\n", + "\n", + "# Create the plot\n", + "plt.tight_layout()\n", + "plt.title(\"Alameda County, School API scores\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf['API'].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the syntax for a choropleth and graduated color map is the same,\n", + "although some options only apply to one or the other.\n", + "\n", + "For example, uncomment the `markersize` parameter above to see how you can further customize a graduated color map." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graduated symbol maps\n", + "\n", + "`Graduated symbol maps` are also a great method for mapping points. These are just like graduated color maps but instead of associating symbol color with data values they associate point size. Similarly,graduated symbol maps use `classification schemes` to set the size of point symbols. \n", + "\n", + "> We demonstrate how to make graduated symbol maps along with some other mapping techniques in the `Optional Mapping notebook` which we encourage you to explore on your own. (***Coming Soon***)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.5 Mapping Categorical Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mapping categorical data, also called qualitative data, is a bit more straightforward. There is no need to scale or classify data values. The goal of the color map is to provide a contrasting set of colors so as to clearly delineate different categories. Here's a point-based example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.plot(column='Org', categorical=True, legend=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.6 Recap\n", + "We learned about important data driven mapping strategies and mapping concepts and can leverage what many of us know about `matplotlib`\n", + "- Choropleth Maps\n", + "- Point maps\n", + "- Color schemes \n", + "- Classifications" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise: Data-Driven Mapping\n", + "\n", + "Point and polygons are not the only geometry-types that we can use in data-driven mapping!\n", + "\n", + "Run the next cell to load a dataset containing Berkeley's bicycle boulevards (which we'll be using more in the following notebook).\n", + "\n", + "Then in the following cell, write your own code to:\n", + "1. plot the bike boulevards;\n", + "2. color them by status (find the correct column in the head of the dataframe, displayed below);\n", + "3. color them using a fitting, good-looking qualitative colormap that you choose from [The Matplotlib Colormap Reference](https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html);\n", + "4. set the line width to 5 (check the plot method's documentation to find the right argument for this!);\n", + "4. add the argument `figsize=[20,20]`, to make your map nice and big and visible!\n", + " \n", + "Then answer the questions posed in the last cell.\n", + "\n", + "
\n", + "\n", + "\n", + "To see the solution, double-click the Markdown cell below.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What does that map indicate about the status of the Berkeley bike boulevards?\n", + "1. What does that map indicate about the status of your Berkeley bike-boulevard *dataset*?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/06_Spatial_Queries.ipynb b/lessons/06_Spatial_Queries.ipynb new file mode 100644 index 0000000..18cf6f3 --- /dev/null +++ b/lessons/06_Spatial_Queries.ipynb @@ -0,0 +1,1058 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 6. Spatial Queries\n", + "\n", + "In spatial analysis, our goal is not just to make nice maps,\n", + "but to actually run analyses that leverage the explicitly spatial\n", + "nature of our data. The process of doing this is known as \n", + "**spatial analysis**.\n", + "\n", + "To construct spatial analyses, we string together series of spatial\n", + "operations in such a way that the end result answers our question of interest.\n", + "There are many such spatial operations. These are known as **spatial queries**.\n", + "\n", + "\n", + "- 6.0 Load and prep some data\n", + "- 6.1 Measurement Queries\n", + "- 6.2 Relationship Queries\n", + "- **Exercise**: Spatial Relationship Query\n", + "- 6.3 Proximity Analysis\n", + "- **Exercise**: Proximity Analysis\n", + "- 6.4 Recap\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip'\n", + " - 'notebook_data/protected_areas/CPAD_2020a_Units.shp'\n", + " - 'notebook_data/berkeley/BerkeleyCityLimits.shp'\n", + " - 'notebook_data/alco_schools.csv'\n", + " - 'notebook_data/transportation/BerkeleyBikeBlvds.geojson'\n", + " - 'notebook_data/transportation/bart.csv'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------\n", + "\n", + "We will start by reviewing the most\n", + "fundamental set, which we'll refer to as **spatial queries**.\n", + "These can be divided into:\n", + "\n", + "- Measurement queries\n", + " - What is feature A's **length**?\n", + " - What is feature A's **area**?\n", + " - What is feature A's **perimeter**?\n", + " - What is feature A's **distance** from feature B?\n", + " - etc.\n", + "- Relationship queries\n", + " - Is feature A **within** feature B?\n", + " - Does feature A **intersect** with feature B?\n", + " - Does feature A **cross** feature B?\n", + " - etc.\n", + " \n", + "We'll work through examples of each of those types of queries.\n", + "\n", + "Then we'll see an example of a very common spatial analysis that \n", + "is a conceptual amalgam of those two types: **proximity analysis**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.0 Load and prep some data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read in our census tracts data again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\")\n", + "census_tracts.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "census_tracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we'll grab just the Alameda Country tracts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac = census_tracts.loc[census_tracts['COUNTYFP']=='001'].reset_index(drop=True)\n", + "census_tracts_ac.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.1 Measurement Queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll start off with some simple measurement queries.\n", + "\n", + "For example, here's how we can get the areas of each of our census tracts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay! \n", + "\n", + "We got... \n", + "\n", + "numbers!\n", + "\n", + "...?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. What do those numbers mean?\n", + "1. What are our units?\n", + "1. And if we're not sure, how might be find out?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at our CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ah-hah! We're working in an unprojected CRS, with units of decimal degrees.\n", + "\n", + "**When doing spatial analysis, we will almost always want to work in a projected CRS\n", + "that has natural distance units, such as meters!**\n", + "\n", + "Time to project!\n", + "\n", + "(As previously, we'll use UTM Zone 10N with a NAD83 data.\n", + "This is a good choice for our region of interest.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10 = census_tracts_ac.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's try our area calculation again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks much more reasonable!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What are our units now?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may have noticed that our census tracts already have an area column in them.\n", + "\n", + "Let's do a sanity check on our results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# calculate the area for the 0th feature\n", + "census_tracts_ac_utm10.area[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get the area for the 0th feature according to its 'ALAND' attribute\n", + "census_tracts['ALAND'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check equivalence of the calculated areas and the 'ALAND' column\n", + "census_tracts_ac_utm10['ALAND'].values == census_tracts_ac_utm10.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What explains this disagreement? Are the calculated areas incorrect?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also sum the area for Alameda county by adding `.sum()` to the end of our area calculation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.area.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can actually look up how large Alameda County is to check our work.The county is 739 miles2, which is around 1,914,001,213 meters2. I'd say we're pretty close!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As it turns out, we can similarly use another attribute\n", + "to get the features' lengths.\n", + "\n", + "**NOTE**: In this case, given we're\n", + "dealing with polygons, this is equivalent to getting the features' perimeters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.length" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.2 Relationship Queries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GBP2Co-TutCH" + }, + "source": [ + "\n", + "[Spatial relationship queries](https://en.wikipedia.org/wiki/Spatial_relation) consider how two geometries or sets of geometries relate to one another in space. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jgUkeehpCqnS" + }, + "source": [ + "Here is a list of the most commonly used GeoPandas methods to test spatial relationships.\n", + "\n", + "- [within](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.within)\n", + "- [contains](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.contains) (the inverse of `within`)\n", + "- [intersects](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.intersects)\n", + "\n", + "
\n", + "There several other GeoPandas spatial relationship predicates but they are more complex to properly employ. For example the following two operations only work with geometries that are completely aligned.\n", + "\n", + "- [touches](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.touches)\n", + "- [equals](http://geopandas.org/reference.html?highlight=distance#geopandas.GeoSeries.equals)\n", + "\n", + "\n", + "All of these methods takes the form:\n", + "\n", + " Geoseries.(geometry)\n", + " \n", + "For example:\n", + "\n", + " Geoseries.contains(geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------------\n", + "\n", + "Let's load a new dataset to demonstrate these queries.\n", + "\n", + "This is a dataset containing all the protected areas (parks and the like) in California." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas = gpd.read_file('./notebook_data/protected_areas/CPAD_2020a_Units.shp')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Does this need to be reprojected too?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes it does!\n", + "\n", + "Let's reproject it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas_utm10 = pas.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One common use for spatial queries is for spatial subsetting of data.\n", + "\n", + "In our case, lets use **intersects** to\n", + "find all of the parks that have land in Alameda County.\n", + "\n", + "But before we do that, let's take another look at our geometries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.geometry.type.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because we nave census tracts, each of these rows is either a Polygon or a MultiPolygon. For our relationship query we can actually simplify our geometry to be one polygon by using `unary_union`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "census_tracts_ac_utm10.geometry.unary_union" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(census_tracts_ac_utm10.geometry.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can go ahead and conduct our operation `intersects`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas_in_ac = pas_utm10.intersects(census_tracts_ac_utm10.geometry.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we scroll the resulting GeoDataFrame to the right we'll see that \n", + "the `COUNTY` column of our resulting subset gives us a good sanity check on our results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas_in_ac" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pas_utm10[pas_in_ac].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So does this overlay plot!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = census_tracts_ac_utm10.plot(color='gray', figsize=[12,16])\n", + "pas_utm10[pas_in_ac].plot(ax=ax, column='ACRES', cmap='summer', legend=True,\n", + " edgecolor='black', linewidth=0.4, alpha=0.8,\n", + " legend_kwds={'label': \"acres\",\n", + " 'orientation': \"horizontal\"})\n", + "ax.set_title('Protected areas in Alameda County, colored by area', size=18);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# color by county?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Spatial Relationship Query\n", + "\n", + "Let's use a spatial relationship query to create a new dataset containing Berkeley schools!\n", + "\n", + "Run the next two cells to load datasets containing Berkeley's city boundary and Alameda County's\n", + "schools and to reproject them to EPSG: 26910.\n", + "\n", + "Then in the following cell, write your own code to:\n", + "1. subset the schools for only those `within` Berkeley\n", + "2. plot the Berkeley boundary and then the schools as an overlay map\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the Berkeley boundary\n", + "berkeley = gpd.read_file(\"notebook_data/berkeley/BerkeleyCityLimits.shp\")\n", + "\n", + "# transform to EPSG:26910\n", + "berkeley_utm10 = berkeley.to_crs(\"epsg:26910\")\n", + "\n", + "# display\n", + "berkeley_utm10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the Alameda County schools CSV\n", + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "\n", + "# coerce it to a GeoDataFrame\n", + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "# define its unprojected (EPSG:4326) CRS\n", + "schools_gdf.crs = \"epsg:4326\"\n", + "\n", + "# transform to EPSG:26910\n", + "schools_gdf_utm10 = schools_gdf.to_crs( \"epsg:26910\")\n", + "\n", + "# display\n", + "schools_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Double-click to see solution!\n", + "\n", + "\n", + "\n", + "-------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.3 Proximity Analysis\n", + "\n", + "Now that we've seen the basic idea of spatial measurement and relationship queries,\n", + "let's take a look at a common analysis that combines those concepts: **promximity analysis**.\n", + "\n", + "Proximity analysis seeks to identify all features in a focal feature set\n", + "that are within some maximum distance of features in a reference feature set.\n", + "\n", + "A common workflow for this analysis is:\n", + "\n", + "1. Buffer (i.e. add a margin around) the reference dataset, out to the maximum distance.\n", + "1. Run a spatial relationship query to find all focal features that intersect (or are within) the buffer.\n", + "\n", + "---------------------------------\n", + "\n", + "Let's read in our bike boulevard data again.\n", + "\n", + "Then we'll find out which of our Berkeley schools are within a block's distance (200 m) of the boulevards." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds = gpd.read_file('notebook_data/transportation/BerkeleyBikeBlvds.geojson')\n", + "bike_blvds.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, we need to reproject the boulevards to our projected CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10 = bike_blvds.to_crs( \"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create our 200 meter bike boulevard buffers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10.crs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_buf = bike_blvds_utm10.buffer(distance=200)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_utm10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bike_blvds_buf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's overlay everything." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "berkeley_utm10.plot(color='lightgrey', ax=ax)\n", + "bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "bike_blvds_utm10.plot(ax=ax)\n", + "berkeley_schools.plot(color='purple',ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Looks like we're all ready to run our intersection to complete the proximity analysis.\n", + "\n", + "\n", + "**NOTE**: In order to subset with our buffers we need to call the `unary_union` attribute of the buffer object.\n", + "This gives us a single unified polygon, rather than a series of multipolygons representing buffers around each of the points in our multilines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_near_blvds = berkeley_schools.within(bike_blvds_buf.unary_union)\n", + "blvd_schools = berkeley_schools[schools_near_blvds]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's overlay again, to see if the schools we subsetted make sense." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "berkeley_utm10.plot(color='lightgrey', ax=ax)\n", + "bike_blvds_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "bike_blvds_utm10.plot(ax=ax)\n", + "berkeley_schools.plot(color='purple',ax=ax)\n", + "blvd_schools.plot(color='yellow', markersize=50, ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to find the shortest distance from one school to the bike boulevards, we can use the `distance` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley_schools.distance(bike_blvds_utm10.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Proximity Analysis\n", + "\n", + "Now it's your turn to try out a proximity analysis!\n", + "\n", + "Run the next cell to load our BART-system data, reproject it to EPSG: 26910, and subset it to Berkeley.\n", + "\n", + "Then in the following cell, write your own code to find all schools within walking distance (1 km) of a BART station.\n", + "\n", + "As a reminder, let's break this into steps:\n", + "1. buffer your Berkeley BART stations to 1 km (**HINT**: remember your units!)\n", + "2. use the schools' `within` attribute to check whether or not they're within the buffers (**HINT**: don't forget the `unary_union`!)\n", + "3. subset the Berkeley schools using the object returned by your spatial relationship query\n", + "\n", + "4. as always, plot your results for a good visual check!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the BART stations from CSV\n", + "bart_stations = pd.read_csv('notebook_data/transportation/bart.csv')\n", + "# coerce to a GeoDataFrame\n", + "bart_stations_gdf = gpd.GeoDataFrame(bart_stations, \n", + " geometry=gpd.points_from_xy(bart_stations.lon, bart_stations.lat))\n", + "# define its unprojected (EPSG:4326) CRS\n", + "bart_stations_gdf.crs = \"epsg:4326\"\n", + "# transform to UTM Zone 10 N (EPSG:26910)\n", + "bart_stations_gdf_utm10 = bart_stations_gdf.to_crs( \"epsg:26910\")\n", + "# subset to Berkeley\n", + "berkeley_bart = bart_stations_gdf_utm10[bart_stations_gdf_utm10.within(berkeley_utm10.unary_union)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Double-click to see solution!\n", + "\n", + "\n", + "\n", + "----------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.4 Recap\n", + "Leveraging what we've learned in our earlier lessons, we got to work with map overlays and start answering questions related to proximity. Key concepts include:\n", + "- Measuring area and length\n", + "\t- `.area`, \n", + "\t- `.length`\n", + "- Relationship Queries\n", + "\t- `.intersects()`\n", + "\t- `.within()`\n", + "- Buffer analysis\n", + "\t- `.buffer()`\n", + "\t- `.distance()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/07_Joins_and_Aggregation.ipynb b/lessons/07_Joins_and_Aggregation.ipynb new file mode 100644 index 0000000..4f662b3 --- /dev/null +++ b/lessons/07_Joins_and_Aggregation.ipynb @@ -0,0 +1,1180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 7. Attribute and Spatial Joins\n", + "\n", + "Now that we understand the logic of spatial relationship queries,\n", + "let's take a look at another fundamental spatial operation that relies on them.\n", + "\n", + "This operation, called a **spatial join**, is the process by which we can\n", + "leverage the spatial relationships between distinct datasets to merge\n", + "their information into a new, synthetic dataset.\n", + "\n", + "This operation can be thought as the spatial equivalent of an\n", + "**attribute join**, in which multiple tabular datasets can be merged by\n", + "aligning matching values in a common column that they both contain.\n", + "Thus, we'll start by developing an understanding of this operation first!\n", + "\n", + "- 7.0 Data Input and Prep\n", + "- 7.1 Attribute Joins\n", + "- **Exercise**: Choropleth Map\n", + "- 7.2 Spatial Joins\n", + "- 7.3 Aggregation\n", + "- **Exercise**: Aggregation\n", + "- 7.4 Recap\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'notebook_data/census/ACS5yr/census_variables_CA.csv'\n", + " - 'notebook_data/census/Tracts/cb_2013_06_tract_500k.zip'\n", + " - 'notebook_data/alco_schools.csv'\n", + " \n", + "- Expected time to complete\n", + " - Lecture + Questions: 45 minutes\n", + " - Exercises: 20 minutes\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.0 Data Input and Prep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's read in a table of data from the US Census' 5-year American Community Survey (ACS5)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the ACS5 data for CA into a pandas DataFrame.\n", + "# Note: We force the FIPS_11_digit to be read in as a string to preserve any leading zeroes.\n", + "acs5_df = pd.read_csv(\"notebook_data/census/ACS5yr/census_variables_CA.csv\", dtype={'FIPS_11_digit':str})\n", + "acs5_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Brief summary of the data**:\n", + "\n", + "Below is a table of the variables in this table. They were combined from \n", + "different ACS 5 year tables.\n", + "\n", + "NOTE:\n", + "- variables that start with `c_` are counts\n", + "- variables that start with `med_` are medians\n", + "- variables that end in `_moe` are margin of error estimates\n", + "- variables that start with `_p` are proportions calcuated from the counts divided by the table denominator (the total count for whom that variable was assessed)\n", + "\n", + "\n", + "| Variable | Description |\n", + "|-----------------|-------------------------------------------------|\n", + "|`c_race` |Total population \n", + "|`c_white` |Total white non-Latinx\n", + "| `c_black` | Total black and African American non-Latinx\n", + "| `c_asian` | Total Asian non-Latinx\n", + "| `c_latinx` | Total Latinx\n", + "| `state_fips` | State level FIPS code\n", + "| `county_fips` | County level FIPS code\n", + "| `tract_fips` |Tracts level FIPS code\n", + "| `med_rent` |Median rent\n", + "| `med_hhinc` |Median household income\n", + "| `c_tenants` |Total tenants\n", + "| `c_owners` |Total owners\n", + "| `c_renters` |Total renters\n", + "| `c_movers` |Total number of people who moved\n", + "| `c_stay` |Total number of people who stayed\n", + "| `c_movelocal` |Number of people who moved locally\n", + "| `c_movecounty` |Number of people who moved counties\n", + "| `c_movestate` | Number of people who moved states\n", + "| `c_moveabroad` |Number of people who moved abroad\n", + "| `c_commute` |Total number of commuters\n", + "| `c_car` | Number of commuters who use a car\n", + "| `c_carpool` | Number of commuters who carpool\n", + "| `c_transit` |Number of commuters who use public transit\n", + "| `c_bike` |Number of commuters who bike\n", + "| `c_walk` |Number of commuters who bike\n", + "| `year` | ACS data year\n", + "| `FIPS_11_digit` | 11-digit FIPS code\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're going to drop all of our `moe` columns by identifying all of those that end with `_moe`. We can do that in two steps, first by using `filter` to identify columns that contain the string `_moe`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "moe_cols = acs5_df.filter(like='_moe',axis=1).columns\n", + "moe_cols" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df.drop(moe_cols, axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And lastly, let's grab only the rows for year 2018 and county FIPS code 1 (i.e. Alameda County)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df_ac = acs5_df[(acs5_df['year']==2018) & (acs5_df['county_fips']==1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------------------\n", + "Now let's also read in our census tracts again!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001']\n", + "tracts_gdf_ac.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.1 Attribute Joins\n", + "\n", + "**Attribute Joins between Geodataframes and Dataframes**\n", + "\n", + "*We just mapped the census tracts. But what makes a map powerful is when you map the data associated with the locations.*\n", + "\n", + "- `tracts_gdf_ac`: These are polygon data in a GeoDataFrame. However, as we saw in the `head` of that dataset, they no attributes of interest!\n", + "\n", + "- `acs5_df_ac`: These are 2018 ACS data from a CSV file ('census_variables_CA.csv'), imported and read in as a `pandas` DataFrame. However, they have no geometries!\n", + "\n", + "In order to map the ACS data we need to associate it with the tracts. Let's do that now, by joining the columns from `acs5_df_ac` to the columns of `tracts_gdf_ac` using a common column as the key for matching rows. This process is called an **attribute join**.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "The image above gives us a nice conceptual summary of the types of joins we could run.\n", + "\n", + "1. In general, why might we choose one type of join over another?\n", + "1. In our case, do we want an inner, left, right, or outer (AKA 'full') join? \n", + "\n", + "(**NOTE**: You can read more about merging in `geopandas` [here](http://geopandas.org/mergingdata.html#attribute-joins).)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, here we go!\n", + "\n", + "Let's take a look at the common column in both our DataFrames.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_ac['GEOID'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "acs5_df_ac['FIPS_11_digit'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Note that they are **not named the same thing**. \n", + " \n", + " That's okay! We just need to know that they contain the same information.\n", + "\n", + "Also note that they are **not in the same order**. \n", + " \n", + " That's not only okay... That's the point! (If they were in the same order already then we could just join them side by side, without having Python find and line up the matching rows from each!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------------------\n", + "\n", + "Let's do a `left` join to keep all of the census tracts in Alameda County and only the ACS data for those tracts.\n", + "\n", + "**NOTE**: To figure out how to do this we could always take a peek at the documentation by calling\n", + "`?tracts_gdf_ac.merge`, or `help(tracts_gdf_ac)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Left join keeps all tracts and the acs data for those tracts\n", + "tracts_acs_gdf_ac = tracts_gdf_ac.merge(acs5_df_ac, left_on='GEOID',\n", + " right_on=\"FIPS_11_digit\", how='left')\n", + "tracts_acs_gdf_ac.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check that we have all the variables we have in our dataset now." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "list(tracts_acs_gdf_ac.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "It's always important to run sanity checks on our results, at each step of the way!\n", + "\n", + "In this case, how many rows and columns should we have?\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your response here:\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Rows and columns in the Alameda County Census tract gdf:\\n\\t\", tracts_gdf_ac.shape)\n", + "print(\"Row and columns in the ACS5 2018 data:\\n\\t\", acs5_df_ac.shape)\n", + "print(\"Rows and columns in the Alameda County Census tract gdf joined to the ACS data:\\n\\t\", tracts_acs_gdf_ac.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's save out our merged data so we can use it in the final notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_acs_gdf_ac.to_file('outdata/tracts_acs_gdf_ac.json', driver='GeoJSON')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: Choropleth Map\n", + "We can now make choropleth maps using our attribute joined geodataframe. Go ahead and pick one variable to color the map, then map it. You can go back to lesson 5 if you need a refresher on how to make this!\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Double-click to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------------------\n", + "## 7.2 Spatial Joins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! We've wrapped our heads around the concept of an attribute join.\n", + "\n", + "Now let's extend that concept to its spatially explicit equivalent: the **spatial join**!\n", + "\n", + "\n", + "
\n", + "\n", + "To start, we'll read in some other data: The Alameda County schools data.\n", + "\n", + "Then we'll work with that data and our `tracts_acs_gdf_ac` data together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_df = pd.read_csv('notebook_data/alco_schools.csv')\n", + "schools_gdf = gpd.GeoDataFrame(schools_df, \n", + " geometry=gpd.points_from_xy(schools_df.X, schools_df.Y))\n", + "schools_gdf.crs = \"epsg:4326\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check if we have to transform the schools to match the`tracts_acs_gdf_ac`'s CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('schools_gdf CRS:', schools_gdf.crs)\n", + "print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes we do! Let's do that.\n", + "\n", + "**NOTE**: Explicit syntax aiming at that dataset's CRS leaves less room for human error!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf = schools_gdf.to_crs(tracts_acs_gdf_ac.crs)\n", + "\n", + "print('schools_gdf CRS:', schools_gdf.crs)\n", + "print('tracts_acs_gdf_ac CRS:', tracts_acs_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we're ready to combine the datasets in an analysis.\n", + "\n", + "**In this case, we want to get data from the census tract within which each school is located.**\n", + "\n", + "But how can we do that? The two datasets don't share a common column to use for a join." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_acs_gdf_ac.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, they do have a shared relationship by way of space! \n", + "\n", + "So, we'll use a spatial relationship query to figure out the census tract that\n", + "each school is in, then associate the tract's data with that school (as additional data in the school's row).\n", + "This is a **spatial join**!\n", + "\n", + "---------------------------------\n", + "\n", + "### Census Tract Data Associated with Each School\n", + "\n", + "In this case, let's say we're interested in the relationship between the median household income\n", + "in a census tract (`tracts_acs_gdf_ac['med_hhinc']`) and a school's Academic Performance Index\n", + "(`schools_gdf['API']`).\n", + "\n", + "To start, let's take a look at the distributions of our two variables of interest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_acs_gdf_ac.hist('med_hhinc')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf.hist('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oh, right! Those pesky schools with no reported APIs (i.e. API == 0)! Let's drop those." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf_api = schools_gdf.loc[schools_gdf['API'] > 0, ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_gdf_api.hist('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much better!\n", + "\n", + "Now, maybe we think there ought to be some correlation between the two variables?\n", + "As a first pass at this possibility, let's overlay the two datasets, coloring each one by\n", + "its variable of interest. This should give us a sense of whether or not similar values co-occur." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = tracts_acs_gdf_ac.plot(column='med_hhinc', cmap='cividis', figsize=[18,18],\n", + " legend=True, legend_kwds={'label': \"median household income ($)\",\n", + " 'orientation': \"horizontal\"})\n", + "schools_gdf_api.plot(column='API', cmap='cividis', edgecolor='black', alpha=1, ax=ax,\n", + " legend=True, legend_kwds={'label': \"API\", 'orientation': \"horizontal\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatially Joining our Schools and Census Tracts\n", + "\n", + "Though it's hard to say for sure, it certainly looks possible.\n", + "It would be ideal to scatterplot the variables! But in order to do that, \n", + "we need to know the median household income in each school's tract, which\n", + "means we definitely need our **spatial join**!\n", + "\n", + "We'll first take a look at the documentation for the spatial join function, `gpd.sjoin`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(gpd.sjoin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like the key arguments to consider are:\n", + "- the two GeoDataFrames (**`left_df`** and **`right_df`**)\n", + "- the type of join to run (**`how`**), which can take the values `left`, `right`, or `inner`\n", + "- the spatial relationship query to use (**`op`**)\n", + "\n", + "**NOTE**:\n", + "- By default `sjoin` is an inner join. It keeps the data from both geodataframes only where the locations spatially intersect.\n", + "\n", + "- By default `sjoin` maintains the geometry of first geodataframe input to the operation. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Which GeoDataFrame are we joining onto which (i.e. which one is getting the other one's data added to it)?\n", + "1. What happened to 'outer' as a join type?\n", + "1. Thus, in our operation, which GeoDataFrame should be the `left_df`, which should be the `right_df`, and `how` do we want our join to run?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alright! Let's run our join!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts = gpd.sjoin(schools_gdf_api, tracts_acs_gdf_ac, how='left')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking Our Output\n", + "\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "As always, we want to sanity-check our intermediate result before we rush ahead.\n", + "\n", + "One way to do that is to introspect the structure of the result object a bit.\n", + "\n", + "1. What type of object should that have given us?\n", + "1. What should the dimensions of that object be, and why?\n", + "1. If we wanted a visual check of our results (i.e. a plot or map), what could we do?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Your responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(schools_jointracts.shape)\n", + "print(schools_gdf.shape)\n", + "print(tracts_acs_gdf_ac.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confirmed! The output of the our `sjoin` operation is a GeoDataFrame (`schools_jointracts`) with:\n", + "- a row for each school that is located inside a census tract (all of them are)\n", + "- the **point geometry** of that school\n", + "- all of the attribute data columns (non-geometry columns) from both input GeoDataFrames\n", + "\n", + "----------------------------\n", + "\n", + "Let's also take a look at an overlay map of the schools on the tracts.\n", + "If we color the schools categorically by their tracts IDs, then we should see\n", + "that all schools within a given tract polygon are the same color." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = tracts_acs_gdf_ac.plot(color='white', edgecolor='black', figsize=[18,18])\n", + "schools_jointracts.plot(column='GEOID', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assessing the Relationship between Median Household Income and API\n", + "\n", + "Fantastic! That looks right!\n", + "\n", + "Now we can create that scatterplot we were thinking about!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(6,6))\n", + "ax.scatter(schools_jointracts.med_hhinc, schools_jointracts.API)\n", + "ax.set_xlabel('median household income ($)')\n", + "ax.set_ylabel('API')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wow! Just as we suspected based on our overlay map,\n", + "there's a pretty obvious, strong, and positive correlation\n", + "between median household income in a school's tract\n", + "and the school's API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.3. Aggregation\n", + "\n", + "We just saw that a spatial join in one way to leverage the spatial relationship\n", + "between two datasets in order to create a new, synthetic dataset.\n", + "\n", + "An **aggregation** is another way we can generate new data from this relationship.\n", + "In this case, for each feature in one dataset we find all the features in another\n", + "dataset that satisfy our chosen spatial relationship query with it (e.g. within, intersects),\n", + "then aggregate them using some summary function (e.g. count, mean).\n", + "\n", + "------------------------------------\n", + "\n", + "### Getting the Aggregated School Counts\n", + "\n", + "Let's take this for a spin with our data. We'll count all the schools within each census tract.\n", + "\n", + "Note that we've already done the first step of spatially joining the data from the aggregating features\n", + "(the tracts) onto the data to be aggregated (our schools).\n", + "\n", + "The next step is to group our GeoDataFrame by census tract, and then summarize our data by group.\n", + "We do this using the DataFrame method `groupy`.\n", + "\n", + "To get the correct count, lets rejoin our schools on our tracts, this time keeping all schools\n", + "(not just those with APIs > 0, as before)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_jointracts = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='left')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now for the `groupy` operation.\n", + "\n", + "**NOTE**: We could really use any column, since we're just taking a count. For now we'll just use the school names ('Site')." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schools_countsbytract = schools_jointracts[['GEOID','Site']].groupby('GEOID', as_index=False).count()\n", + "print(\"Counts, rows and columns:\", schools_countsbytract.shape)\n", + "print(\"Tracts, rows and columns:\", tracts_acs_gdf_ac.shape)\n", + "\n", + "# take a look at the data\n", + "schools_countsbytract.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting Tract Polygons with School Counts\n", + "\n", + "The above `groupby` and `count` operations give us the counts we wanted.\n", + "- We have the 263 (of 361) census tracts that have at least one school\n", + "- We have the number of schools within each of those tracts\n", + "\n", + "But the output of `groupby` is a plain DataFrame not a GeoDataFrame.\n", + "\n", + "If we want a GeoDataFrame then we have two options:\n", + "1. We could join the `groupby` output to `tracts_acs_gdf_ac` by the attribute `GEOID`\n", + "or\n", + "2. We could start over, using the GeoDataFrame `dissolve` method, which we can think of as a spatial `groupby`. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------------\n", + "\n", + "Since we already know how to do an attribute join, we'll do the `dissolve`!\n", + "\n", + "First, let's run a new spatial join." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_joinschools = gpd.sjoin(schools_gdf, tracts_acs_gdf_ac, how='right')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_joinschools.geometry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's run our dissolve!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_schoolcounts = tracts_joinschools[['GEOID', 'Site', 'geometry']].dissolve(by='GEOID', aggfunc='count')\n", + "print(\"Counts, rows and columns:\", tracts_schoolcounts.shape)\n", + "\n", + "# take a look\n", + "tracts_schoolcounts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice! Let's break that down.\n", + "\n", + "- The `dissolve` operation requires a geometry column and a grouping column (in our case, 'GEOID'). Any geometries within the **same group** will be dissolved if they have the same geometry or nested geometries. \n", + " \n", + "- The `aggfunc`, or aggregation function, of the dissolve operation will be applied to all numeric columns in the input geodataframe (unless the function is `count` in which case it will count rows.) \n", + "\n", + "Check out the Geopandas documentation on [dissolve](https://geopandas.org/aggregation_with_dissolve.html?highlight=dissolve) for more information.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Above we selected three columns from the input GeoDataFrame to create a subset as input to the dissolve operation. Why?\n", + "1. Why did we run a new spatial join? What would have happened if we had used the `schools_jointracts` object instead?\n", + "1. What explains the dimensions of the new object (361, 2)?" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "You responses here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mapping our Spatial Join Output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also, because our `sjoin` plus `dissolve` pipeline outputs a GeoDataFrame, we can now easily map the school count by census tract!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "\n", + "# Display the output of our spatial join\n", + "tracts_schoolcounts.plot(ax=ax,column='Site', \n", + " scheme=\"user_defined\",\n", + " classification_kwds={'bins':[*range(9)]},\n", + " cmap=\"PuRd_r\",\n", + " edgecolor=\"grey\",\n", + " legend=True, \n", + " legend_kwds={'title':'Number of schools'})\n", + "schools_gdf.plot(ax=ax, color='black', markersize=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------\n", + "\n", + "## Exercise: Aggregation\n", + "\n", + "#### What is the mean API of each census tract?\n", + "\n", + "As we mentioned, the spatial aggregation workflow that we just put together above\n", + "could have been used not to generate a new count variable, but also\n", + "to generate any other new variable the results from calling an aggregation function\n", + "on an attribute column.\n", + "\n", + "In this case, we want to calculate and map the mean API of the schools in each census tract.\n", + "\n", + "Copy and paste code from above where useful, then tweak and/or add to that code such that your new code:\n", + "1. joins the schools onto the tracts (**HINT**: make sure to decide whether or not you want to include schools with API = 0!)\n", + "1. dissolves that joined object by the tract IDs, giving you a new GeoDataFrame with each tract's mean API (**HINT**: because this is now a different calculation, different problems may arise and need handling!)\n", + "1. plots the tracts, colored by API scores (**HINT**: overlay the schools points again, visualizing them in a way that will help you visually check your results!)\n", + "\n", + "To see the solution, double-click the Markdown cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR CODE HERE:\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Double-click to see solution!\n", + "\n", + "\n", + "\n", + "----------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.4 Recap\n", + "We discussed how we can combine datasets to enhance any geospatial data analyses you could do. Key concepts include:\n", + "- Attribute joins\n", + "\t- `.merge()`\n", + "- Spatial joins (order matters!)\n", + "\t- `gpd.sjoin()`\n", + "- Aggregation\n", + "\t-`.groupby()`\n", + "\t- `.dissolve()` (preserves geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/08_Pulling_It_All_Together.ipynb b/lessons/08_Pulling_It_All_Together.ipynb new file mode 100644 index 0000000..61edb37 --- /dev/null +++ b/lessons/08_Pulling_It_All_Together.ipynb @@ -0,0 +1,449 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 08. Pulling it all Together\n", + "\n", + "For this last lesson, we'll practice going through a full workflow!! We'll answer the question:\n", + "## What is the total grocery-store sales volume of each census tract?\n", + "\n", + "\n", + "### WORKFLOW:\n", + "\n", + "
\n", + "Here's a set of steps that we will implement in the labeled cells below:\n", + "\n", + " 8.1 Read in and Prep Data\n", + "- read in tracts acs joined data\n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- transform it to match the CRS of `tracts_acs_gdf_ac`\n", + "- take a peek\n", + "\n", + "8.2 Spatial Join and Dissolve\n", + "- join the two datasets in such a way that you can then...\n", + "- group by tract and calculate the total grocery-store sales volume\n", + "- don't forget to check the dimensions, contents, and any other relevant aspects of your results\n", + "\n", + "8.3 Plot and Review\n", + "- plot the tracts, coloring them by total grocery-store sales volume\n", + "- plot the grocery stores on top\n", + "- bonus points for devising a nice visualization scheme that helps you heuristically check your results!\n", + "\n", + "\n", + "\n", + "### INSTRUCTIONS:\n", + "**We've written out some of the code for you, but you'll need to replace the ellipses with the correct\n", + "content.**\n", + "\n", + "*You can check your answers by double-clicking on the Markdown cells where indicated.*\n", + "\n", + "\n", + "
\n", + "\n", + " Instructor Notes\n", + "\n", + "- Datasets used\n", + " - 'outdata/tracts_acs_gdf_ac.json'\n", + " - 'notebook_data/other/ca_grocery_stores_2019_wgs84.csv'\n", + "\n", + "- Expected time to complete\n", + " - Lecture + Questions: N/A\n", + " - Exercises: 30 minutes\n", + "\n", + "\n", + "\n", + "\n", + "-----------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "---------------------------------------\n", + "\n", + "\n", + "### Install Packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------------------\n", + "\n", + "## 8.1 Read in the Prep Data\n", + "\n", + "We first need to prepare our data by loading both our tracts/acs and grocery data, and conduct our usual steps to make there they have the same CRS.\n", + "\n", + "- read in our tracts acs joined data \n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/other/ca_grocery_stores_2019_wgs84.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- transform it to match the CRS of `tracts_acs_gdf_ac`\n", + "- take a peek\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in tracts acs data\n", + "\n", + "tracts_acs_gdf_ac = gpd.read_file(..)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read our grocery-data CSV into a Pandas DataFrame\n", + "\n", + "grocery_pts_df = pd.read_csv(...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# coerce it to a GeoDataFrame\n", + "\n", + "grocery_pts_gdf = gpd.GeoDataFrame(grocery_pts_df, \n", + " geometry=gpd.points_from_xy(...,...))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define its CRS (NOTE: Use EPSG:4326)\n", + "\n", + "grocery_pts_gdf.crs = ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# transform it to match the CRS of tracts_acs_gdf_ac\n", + "\n", + "grocery_pts_gdf.to_crs(..., inplace=...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grocery_pts_gdf.crs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# take a peek\n", + "\n", + "print(grocery_pts_gdf.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "-----------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.2 Spatial Join and Dissolve\n", + "\n", + "Now that we have our data and they're in the same projection, we're going to conduct an *attribute join* to bring together the two datasets. From there we'll be able to actually *aggregate* our data to count the total sales volume.\n", + "\n", + "- join the two datasets in such a way that you can then...\n", + "- group by tract and calculate the total grocery-store sales volume\n", + "- don't forget to check the dimensions, contents, and any other relevant aspects of your results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# join the two datasets in such a way that you can then...\n", + "\n", + "tracts_joingrocery = gpd.sjoin(..., ..., how= ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# group by tract and calculate the total grocery-store sales volume\n", + "\n", + "tracts_totsalesvol = tracts_joingrocery[['GEOID','geometry','SALESVOL']].dissolve(by= ...,\n", + " aggfunc=..., as_index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# don't forget to check the dimensions, contents, and any other relevant aspects of your results\n", + "\n", + "# check the dimensions\n", + "print('Dimensions of result:', ...)\n", + "print('Dimesions of census tracts:', ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check the result\n", + "print(tracts_totsalesvol.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "----------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.3 Plot and Review\n", + "\n", + "With any time of geospatial analysis you do, it's always nice to plot and visualize your results to check your work and start to understand the full story of your analysis.\n", + "\n", + "- Plot the tracts, coloring them by total grocery-store sales volume\n", + "- Plot the grocery stores on top\n", + "- Bonus points for devising a nice visualization scheme that helps you heuristically check your results!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create the figure and axes\n", + "\n", + "fig, ax = plt.subplots(figsize = (20,20)) \n", + "\n", + "# plot the tracts, coloring by total SALESVOL\n", + "\n", + "tracts_totsalesvol.plot(ax=ax, column= ..., scheme=\"quantiles\", cmap=\"autumn\", edgecolor=\"grey\",\n", + " legend=True, legend_kwds={'title':...})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subset the stores for only those within our tracts, to keep map within region of interest\n", + "\n", + "grocery_pts_gdf_ac = grocery_pts_gdf.loc[..., ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# add the grocery stores, coloring by SALESVOL, for a visual check\n", + "\n", + "grocery_pts_gdf_ac.plot(ax=ax, column= ... , cmap= ..., linewidth= ..., markersize= ...,\n", + " legend=True, legend_kwds={'label': ... , 'orientation': \"horizontal\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "scrolled": false + }, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n", + "\n", + "-------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "\n", + "***\n", + "\n", + "## Congrats!! Thanks for Joining Us for Geospatial Fundamentals!!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/09_ON_YOUR_OWN_A_Full_Workflow.ipynb b/lessons/09_ON_YOUR_OWN_A_Full_Workflow.ipynb new file mode 100644 index 0000000..8361545 --- /dev/null +++ b/lessons/09_ON_YOUR_OWN_A_Full_Workflow.ipynb @@ -0,0 +1,680 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson 9. On Your Own: A Full Workflow\n", + "Now is your chance to pull everything we've learned together and answer the questions: \n", + "- How many polling stations are in each census tract in Alameda County?\n", + "- Which polling stations are within walking distance (100m) from a bus route in Berkeley?\n", + "- How far are these polling stations from the bus routes in Berkeley?\n", + "\n", + "**All on your own!!**\n", + "\n", + "- 9.1 Polling Station Locations\n", + "- 9.2 Tracts data \n", + "- 9.3 Spatial Join \n", + "- 9.4 Aggregate number of stations by census tracts\n", + "- 9.5 Attribute join back to tracts data\n", + "- 9.6 Berkeley outline\n", + "- 9.7 Bus routes\n", + "- 9.8 Polling station distance from bus routes\n", + "\n", + "*We've written out some of the code for you, and you can check your answers by clicking on the toggle solution button*\n", + " \n", + "### Install Packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.1 Polling Station Locations\n", + "\n", + "We'll be using the 2020 General Election voting locations for Alameda County for this analysis. Since the data is *aspatial* we'll need to coerce it to be a geodataframe and define a CRS.\n", + "\n", + "- read our grocery-data CSV into a Pandas DataFrame (it lives at `'notebook_data/ac_voting_locations.csv`)\n", + "- coerce it to a GeoDataFrame\n", + "- define its CRS (EPSG:4326)\n", + "- plot it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pull in polling location\n", + "\n", + "# polling_ac_df = pd.read_csv(...)\n", + "# polling_ac_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make into geo data frame\n", + "\n", + "# polling_ac_gdf = gpd.GeoDataFrame(..., \n", + "# geometry=gpd.points_from_xy(...,...))\n", + "# polling_ac_gdf.crs = ...\n", + "\n", + "# plot it \n", + "\n", + "# polling_ac_gdf.plot(...)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.2 Tracts data\n", + "\n", + "Since we want to answer the question **How many polling stations are in each census tract?**, we'll pull in our tracts data.\n", + "\n", + "- Bring in the census tracts data which lives at `notebook_data/census/Tracts/cb_2013_06_tract_500k.zip`\n", + "- Narrow it down to Alameda County\n", + "- Check CRS\n", + "- Transform CRS to 26910 if needed\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bring in census tracts\n", + "# tracts_gdf = gpd.read_file(...)\n", + "\n", + "# Narrow it down to Alameda County\n", + "# tracts_gdf_ac = tracts_gdf[...]\n", + "# tracts_gdf_ac.plot()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check CRS\n", + "print('polling_ac_gdf:', ...)\n", + "print('tracts_gdf_ac CRS:', ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform CRS\n", + "polling_ac_gdf_utm10 = ...\n", + "tracts_gdf_ac_utm10 = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.3 Spatial Join\n", + "\n", + "Alright, now our data is all ready to go! We're going to do a *spatial join* to answer our question about polling stations in each tract.\n", + "\n", + "- Spatial join tracts/acs with the polling data (keep the tracts geometry!)\n", + "- Plot it to make sure you have the right geometry\n", + "- Check out your data and its dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Spatial join tracts/acs with the polling data (keep the tracts geometry!)\n", + "\n", + "# polls_jointracts = gpd.sjoin(..., ... , how=...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot it to make sure you have the right geometry\n", + "\n", + "# polls_jointracts.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check out your data and its dimensions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.4 Aggregate number of stations by census tracts\n", + "\n", + "Now that we have a GeoDataFrame with all our polling and tract data, we'll need to *aggregate* to actually count the number of stations we have\n", + "\n", + "- Use `dissolve` to count the number of polls we have\n", + "- Create a choropleth map base don the number of stations there are" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use `dissolve` to count the number of polls we have\n", + "\n", + "# polls_countsbytract = polls_jointracts[['TRACTCE', 'NAME_right', \n", + "# 'geometry']].dissolve(by=..., \n", + "# aggfunc=...).reset_index()\n", + "# polls_countsbytract.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# rename the column to be for the number of polling stations (you dont have to change anything here)\n", + "\n", + "# polls_countsbytract.rename(columns={'NAME_right': 'Num_Polling'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a choropleth map base don the number of stations there are\n", + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "\n", + "# polls_countsbytract.plot(ax=ax,\n", + "# column=..., \n", + "# cmap=...,\n", + "# edgecolor=\"grey\",\n", + "# legend=True)\n", + "\n", + "# polling_ac_gdf_utm10.plot(ax=ax, color=..., edgecolor=..., markersize= ...)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.5 Attribute join back to tracts data\n", + "\n", + "Amazing! Now that we have this information let's do an *attribute join* to add this data into our tracts data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# merge onto census tract data\n", + "\n", + "# tracts_gdf_ac = tracts_gdf_ac.merge(polls_countsbytract[['TRACTCE', 'Num_Polling']], left_on= ...,right_on= ... , how= ... ) \n", + "# tracts_gdf_ac.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.6 Berkeley outline\n", + "\n", + "To answer our question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* we'll need to know where Berkeley is! This is the perfect time to bring our Berkeley places data in.\n", + "\n", + "- Read in `outdata/berkeley_places.shp`\n", + "- Check the CRS\n", + "- Transform CRS if necessary to EPSG:26910" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in outdata/berkeley_places.shp\n", + "# berkeley_places = gpd.read_file(...)\n", + "\n", + "# Check the CRS\n", + "\n", + "\n", + "# Transform CRS if necessary to EPSG:26910\n", + "berkeley_places_utm10 = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.7 Bus routes\n", + "\n", + "- Bring in bus routes ('notebook_data/transportation/Fall20Routeshape.zip'), transform CRS to 26910\n", + "- Intersect bus routes with Berkeley\n", + "- Plot results of intersection\n", + "- Clip bus routes to everything that is inside the berkley outline\n", + "- Plot bus routes on top of Berkeley outline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Bring in bus routes, transform CRS to 26910\n", + "bus_routes = ...\n", + "# bus_routes_utm10 = bus_routes.to_crs(...)\n", + "# bus_routes_utm10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at intersection between bus routes and Berkeley\n", + "# bus_routes_berkeley = .intersects(... .geometry.squeeze())\n", + "\n", + "# Create new geodataframe from these results\n", + "# bus_berk = bus_routes_utm10.loc[bus_routes_berkeley].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot results of intersection\n", + "\n", + "# fig, ax = plt.subplots(figsize=(10,10))\n", + "# berkeley_places_utm10.plot(ax=ax)\n", + "# bus_berk.plot(ax=ax, column ='PUB_RTE')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# BONUS: Look at route length\n", + "# bus_berk.length" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Clip bus routes to everything that is inside the berkley outline\n", + "# bus_berk_clip = gpd.clip(...,...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Plot bus routes on top of Berkeley outline\n", + "# fig, ax = plt.subplots(figsize=(10,10))\n", + "# berkeley_places_utm10.plot(ax=ax)\n", + "# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.6 Polling stations within walking distance of bus routes\n", + "\n", + "Now we can really answer the question *Which polling stations are within walking distance (100m) from a bus route in Berkeley?* \n", + "\n", + "- Create buffer around bus route for 100m\n", + "- Intersect polling locations in Alameda County with Berkeley outline \n", + "- Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations\n", + "- Calculate the distance from polling stations to the closest bus route" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create buffer around bus route for 100m\n", + "# bus_berk_buf =bus_berk_clip.buffer(distance= ...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Intersect polling locations in Alameda County with Berkeley outline\n", + "# polling_berk = ... .intersects(berkeley_places_utm10.geometry.squeeze())\n", + "\n", + "# polling_berk_gdf = polling_ac_gdf_utm10[polling_berk].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot Berkeley outline, bus routes, the bus routes buffer, and polling locations\n", + "\n", + "# fig, ax = plt.subplots(figsize=(10,10))\n", + "# berkeley_places_utm10.plot(ax=ax)\n", + "# bus_berk_buf.plot(color='pink', ax=ax, alpha=0.5)\n", + "# bus_berk_clip.plot(ax=ax, column ='PUB_RTE')\n", + "# polling_berk_gdf.plot(ax=ax, color= 'yellow')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the distance from polling stations to the closest bus route\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## You're done!!!! \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/10_OPTIONAL_Fetching_Data.ipynb b/lessons/10_OPTIONAL_Fetching_Data.ipynb new file mode 100644 index 0000000..6046084 --- /dev/null +++ b/lessons/10_OPTIONAL_Fetching_Data.ipynb @@ -0,0 +1,745 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 10. Read in Data from Online Sources + CSV to Geodataframe\n", + "\n", + "In this optional notebook we'll be going over how to read data into a notebook from online sources.\n", + "\n", + "- [10.1 Introduction ](#section1)\n", + "- [10.2 Read File from a url](#section2)\n", + "- [10.3 Read File from an API](#section3)\n", + "- [10.4 Read in Data from a Pyhton Library](#section4)\n", + "- [10.5 Exercise](#section5)\n", + "- [10.6 Read in Data from a CSV and convert to geodataframe](#section6)\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + " \n", + "**DEVELOPER NOTES**:\n", + "- Datasets used:\n", + " - Census Data: https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_tract_500k.zip\n", + " - SF Bikeway Data: https://data.sfgov.org/api/geospatial/ygmz-vaxd?method=export&format=GeoJSON\n", + " - Berkeley Bikeway Data: https://data.cityofberkeley.info/api/geospatial/fgw9-98ic?method=export&format=GeoJSON\n", + " - OSMNX Library SF and Berkeley Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.1 Introduction\n", + "\n", + "In the past examples, the data we have imported into our notebooks has come either from previously downloaded and saved files or from the census API. The goal of this notebook is to present other ways of accessing data, either from **urls**, other **APIs** or from predetermined **Python libraries**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set-Up\n", + "Let's import the packages we need before we get started." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import collections\n", + "import requests \n", + "from urllib.request import urlopen, Request\n", + "\n", + "import json # for working with JSON data\n", + "import geojson # ditto for GeoJSON data - an extension of JSON with support for geographic data\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "%matplotlib inline \n", + "import matplotlib.pyplot as plt # more plotting stuff" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.2 Read File from a url\n", + "\n", + "The following link shows the different shapefile data available through the Census Bureau [website](https://www2.census.gov/geo/tiger/GENZ2018/shp/). Clicking on any of the files will dowload the .zip file unto your computer.\n", + "\n", + "This notebook will show a workaround to access the file directly from the notebook, without having to go through the process of previously downloading the shapefile.\n", + "\n", + "For this example, we will download the cities for the state of California ([cb_2018_06_tract_500k.zip](https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_tract_500k.zip)). Remember that California's State FIPS code is 06, which is how we recognize that this dataset is associated with the State of California." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the data from the url, read it using geopandas and create a subset of only Berkeley places" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll save the data from the url into a variable called *ca_places*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ca_places = \"https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_place_500k.zip\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll use geopandas to read the file and then we'll visualize it to make sure we read it properly" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "places = gpd.read_file(ca_places)\n", + "places.plot(); ### This takes a little bit, because the file is fairly large" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### CONFIRM THAT THIS IS TRUE\n", + "Notice that there are some spaces inside the boundaries of the state of California that are empty. These are unincorporated areas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, say we are only interested in the City of Berkeley. Let's examine the file to see how we could select the polygon fob Berkeley. We'll take a look at which columns are included in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "places.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try filtering by Name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "berkeley = places[places['NAME']=='Berkeley']\n", + "berkeley.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Awesome! This worked! Now we have a polygon with the boundaries of the City of Berkeley." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.3 Read in file from a API\n", + "\n", + "In this section, we will be reading a file using an API, or Application Programming Interface. APIs are very useful, because they allow two different portals to talk to each other. For more information on APIs, take a look [here](https://en.wikipedia.org/wiki/Application_programming_interface).\n", + "\n", + "In this case, we will be using the City of Berkeley Open Data Portal's API to read in information on the bike network to out notebook.\n", + "\n", + "Below you can find more information both on the City of Berkeley's Open Data portal and on the bike network data.\n", + "\n", + "### Berkeley Open Data portal\n", + "https://data.cityofberkeley.info/\n", + "\n", + "### Berkeley Bike Network data\n", + "https://data.cityofberkeley.info/Transportation/Bicycle-Boulevards/fgw9-98ic\n", + "\n", + "\n", + "We will be reading the geospatial data for the bike network of the City of Berkeley." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, first we'll save the data from the url into a variable called *berkeley_bike_ways* and then we'll read it using geopandas." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley_bike_ways = \"https://data.cityofberkeley.info/api/geospatial/fgw9-98ic?method=export&format=GeoJSON\"\n", + "bikes = gpd.read_file(berkeley_bike_ways)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll plot the bikeways on top of the City of Berkeley polygon that we imported from the Census Bureau url" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (10,8)) \n", + "berkeley.plot(ax=ax)\n", + "bikes.plot(ax=ax)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oops! Where did the bike lanes go? Well, python uses a default color for all plots, so the bike paths were plotted on top of the polygon in the exact same color. Let's try to plot the bike lanes yellow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (10,8)) \n", + "berkeley.plot(ax=ax)\n", + "bikes.plot(ax=ax, color=\"yellow\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have a map that shows where the bike network of the City of Berkeley is located." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.4 Read in data via a Python library (OSMnx)\n", + "\n", + "OSMnx is a Python library that lets you access Open Street Map's street networks through an API.\n", + "\n", + "You can explore more of Open Street Maps [here](https://www.openstreetmap.org/)\n", + "\n", + "You can access the full documentation of OSMnx [here](https://osmnx.readthedocs.io/en/stable/index.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to install library\n", + "# !pip install osmnx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the below cell does not run, you need to install the library first, by uncommmenting and running the cell above\n", + "\n", + "> **Note**\n", + ">\n", + "> If you get a `numpy` associated error you may need to uninstall and reinstall `numpy` as well as set up tools. Run the following lines of code in your terminal:\n", + ">\n", + " pip uninstall -y numpy\n", + " pip uninstall -y setuptools\n", + " pip install setuptools\n", + " pip install numpy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import osmnx as ox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the osmnx library to access data from Open Street Maps. Let's try to load the Berkeley street map. \n", + "We are using the graph_from_place function. To see the full documentation for the function, go to this link: https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.graph.graph_from_place.\n", + "\n", + "\n", + "We need to define two arguments for the function: the **query** and the **network type**\n", + "\n", + "- **Query**: For cities in the US, the query should follow the following format: \"City Name, State Abbreviation, USA\"\n", + " \n", + " \n", + "- **Network Type**: This is where we define which network we are interested in. Some of the available options are:\n", + " - all\n", + " - drive\n", + " - walk\n", + " - bike\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try to read the data for the vehicular network for Berkeley" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "place = \"Berkeley, CA, USA\"\n", + "graph = ox.graph_from_place(place, network_type='drive')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This took a while to read. Let's take a look at how many elements were loaded from OSM for Berkeley" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "len(graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the data type" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "type(graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a new format. To get this into something that is familiar to us, we are going to extract the nodes and links by using the *graph_to_gdfs* function, which converts our data from a graph to two geodataframes. Because a street network is made up from nodes and links, and our geodatraframes can only have one geography type, the *graph_to_gdfs* returns 2 geodataframes: a node (point) and a street (line) geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "nodes, streets = ox.graph_to_gdfs(graph)\n", + "streets.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's try to put everything together in the same map (the limits of the city, the bike lanes and the streets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (10,8)) \n", + "berkeley.plot(ax=ax)\n", + "streets.plot(ax=ax, color=\"grey\")\n", + "bikes.plot(ax=ax, color=\"yellow\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another feature that we can extract form OSMnx is the bus stops. To do this, we use the pois_from_place function (see full documentation [here](https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.pois.pois_from_place))\n", + "\n", + "This function requires two arguments: the **query** (same as above) and the **tag**:\n", + "\n", + "- **Query**: For cities in the US, the query should follow the following format: \"City Name, State Abbreviation, USA\"\n", + " \n", + " \n", + "- **Tag**: This is where we define which tags we are interested in. There are many options available. You can find a list of tag features [here](https://wiki.openstreetmap.org/wiki/Map_Features#Highway). These tags are coded as dictionaries. Bus stops are a value defined under the key highway, therefore, the format to call for bus stops looks like this: {'highway':'bus_stop'}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's access the bus stops using the same query defined for Berkeley\n", + "\n", + "> **Note**\n", + ">\n", + ">If you are using an older version of `osmnx` you would be able to use the function `pois_from_place`. This and other functions such as `footprints_from_place` are deprecated as of July 2020. `geometries_from_place` is meant to replace these functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### fetch and map POIs from osmnx\n", + "busstops = ox.geometries_from_place(place, tags = {'highway':'bus_stop'})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's check the data type busstops was read as" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "type(busstops)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, busstops is already a geodataframe. Therefore, we can plot it as it is unto out map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (10,8)) \n", + "berkeley.plot(ax=ax)\n", + "streets.plot(ax=ax, color=\"grey\")\n", + "bikes.plot(ax=ax, color=\"yellow\")\n", + "busstops.plot(ax=ax, color=\"white\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.5 Exercise\n", + "\n", + "Repeat above for SF. The link for accessing the bikeways for SF is already given to you below.\n", + "\n", + "### SF Open Data portal\n", + "\n", + "https://datasf.org/opendata/\n", + "\n", + "#### SF Bike Network data\n", + "https://data.sfgov.org/Transportation/SFMTA-Bikeway-Network/ygmz-vaxd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sf_bike_ways = \"https://data.sfgov.org/api/geospatial/ygmz-vaxd?method=export&format=GeoJSON\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10.6 Read in Data from a CSV and convert to geodataframe\n", + "\n", + "In this example, we'll learn how to read a csv file with latitude and longitude coordinates and convert it to a geodataframe for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Read in CSV file\n", + "stations = pd.read_csv(\"notebook_data/transportation/bart.csv\")\n", + "stations.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now want to convert the csv file into a Point geodataframe, so we can produce maps and access the geospatial analysis tools.\n", + "\n", + "We do this below with the geopandas `GeoDataFrame` function which takes as input\n", + "\n", + "1. a pandas dataframe here `stations`, and\n", + "2. `geometry` for each row in the dataframe.\n", + "\n", + "We create the geometry using the geopandas `points_from_xy` function, using the data in the `lon` and `lat` columns of the pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#Convert the DataFrame to a GeoDataFrame. \n", + "bart_gdf = gpd.GeoDataFrame(stations, geometry=gpd.points_from_xy(stations.lon, stations.lat)) \n", + "\n", + "# and take a look\n", + "bart_gdf.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have a map of BART stations! You can use this approach with any CSV file that has columns of x,y coordinates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10.7 Exercises\n", + "\n", + "\n", + "\n", + "Set the CRS for `bart_gdf` to WGS84" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is the url for the 2018 census county geographic boundary file.\n", + "\n", + "* Read in the county file\n", + "* Subset on Marin County\n", + "* Plot Marin County with the Bart stations you transformed\n", + "* Question: what should do if the county name is not unique?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Census Counties file for the USA\n", + "county_file = \"https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_500k.zip\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Double-click here to see solution!\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/11_OPTIONAL_Basemap_with_Contextily.ipynb b/lessons/11_OPTIONAL_Basemap_with_Contextily.ipynb new file mode 100644 index 0000000..48d7b31 --- /dev/null +++ b/lessons/11_OPTIONAL_Basemap_with_Contextily.ipynb @@ -0,0 +1,839 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 11. Adding Basemaps with Contextily\n", + "\n", + "If you work with geospatial data in Python, you most likely are familiar with the fantastic [GeoPandas](https://geopandas.org/) library. GeoPandas leverages the power of [Matplotlib](https://matplotlib.org/) to enable users to make maps of their data. However, until recently, it has not been easy to add basemaps to these maps. Basemaps are the contextual map data, like Google Maps, on top of which geospatial data are often displayed.\n", + "\n", + "\n", + "The new Python library [contextily](https://github.com/geopandas/contextily), which stands for *context map tiles*, now makes it possible and relatively straight forward to add basemaps to Geopandas maps. Below we walk through a few common workflows for doing this.\n", + "\n", + "First, let's load are libraries. This assumes you have the following Python libraries installed in your environment:\n", + "\n", + "- pandas\n", + "- matplotlib\n", + "- geopandas (and all dependancies)\n", + "- contextily\n", + "- descartes" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import contextily as cx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read data into a Geopandas GeoDataFrame\n", + "\n", + "Fetch the census places data to map. Census places includes cities and other populated places. Here we fetch the 2019 cartographic boundary (`cb_`) file of California (`06`) places." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ca_places = \"https://www2.census.gov/geo/tiger/GENZ2019/shp/cb_2019_06_place_500k.zip\"\n", + "places = gpd.read_file(ca_places)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the geodatarame `plot` method to make a quick map." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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STATEFPPLACEFPPLACENSAFFGEOIDGEOIDNAMELSADALANDAWATERgeometry
00636490024101021600000US06364900636490Industry2530529397723181POLYGON ((-118.05750 34.01640, -118.05603 34.0...
10640130024116201600000US06401300640130Lancaster25244187339681671POLYGON ((-118.32517 34.75176, -118.32073 34.7...
20675000024119871600000US06750000675000Stockton251610256317985703POLYGON ((-121.41881 38.04418, -121.41801 38.0...
30643000024108661600000US06430000643000Long Beach2513130222275937543MULTIPOLYGON (((-118.12890 33.75801, -118.1273...
40678106024120421600000US06781060678106Tehama2520572100POLYGON ((-122.13364 40.02417, -122.13295 40.0...
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" + ], + "text/plain": [ + " STATEFP PLACEFP PLACENS AFFGEOID GEOID NAME LSAD \\\n", + "0 06 36490 02410102 1600000US0636490 0636490 Industry 25 \n", + "1 06 40130 02411620 1600000US0640130 0640130 Lancaster 25 \n", + "2 06 75000 02411987 1600000US0675000 0675000 Stockton 25 \n", + "3 06 43000 02410866 1600000US0643000 0643000 Long Beach 25 \n", + "4 06 78106 02412042 1600000US0678106 0678106 Tehama 25 \n", + "\n", + " ALAND AWATER geometry \n", + "0 30529397 723181 POLYGON ((-118.05750 34.01640, -118.05603 34.0... \n", + "1 244187339 681671 POLYGON ((-118.32517 34.75176, -118.32073 34.7... \n", + "2 161025631 7985703 POLYGON ((-121.41881 38.04418, -121.41801 38.0... \n", + "3 131302222 75937543 MULTIPOLYGON (((-118.12890 33.75801, -118.1273... \n", + "4 2057210 0 POLYGON ((-122.13364 40.02417, -122.13295 40.0... " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "places.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can subset the data by selecting a row or rows by place name. Let's select the city of Berkeley, CA." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley = places[places['NAME']=='Berkeley']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "berkeley.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Contextily to add a basemap\n", + "\n", + "Above we can see the map of the boundary of the city of Berkeley, CA. The axis labels display the longitude and latitude coordinates for the bounding extent of the city.\n", + "\n", + "Let's use `contextily` in it's most simple form to add a basemap to provide the geographic context for Berkeley. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = berkeley.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a few important things to note about the above code.\n", + "\n", + "- We use `matplotlib` to define the plot canvas as `ax`.\n", + "- We then add the contextily basemap to the map with the code `cx.add_basemap(ax)`\n", + "\n", + "Additionally, we **dynamically transform the coordinate reference system**, or CRS, of the Berkeley geodataframe from geographic lat/lon coordinates to `web mercator` using the method **to_crs('EPSG:3857')**. [Web mercator](https://en.wikipedia.org/wiki/Web_Mercator_projection) is the default CRS used by all web map tilesets. It is referenced by a the code `EPSG:3857` where [EPSG](https://en.wikipedia.org/wiki/EPSG_Geodetic_Parameter_Dataset) stands for the the initials of the organization that created these codes (the European Petroleum Survey Group).\n", + "\n", + "Let's clean up the map by adding some code to change the symbology of the Berkeley city boundary. This will highlight the value of adding a basemap.\n", + "\n", + "First, let's map the boundary with out a fill color." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "berkeley.plot(edgecolor=\"red\", facecolor=\"none\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's build on those symbology options and add the contextily basemap." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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4-122.26712037.828705MACARTHURBARTALA
\n", + "
" + ], + "text/plain": [ + " lon lat STATION OPERATOR COUNTY\n", + "0 -122.283348 37.874061 NORTH BERKELEY BART ALA\n", + "1 -122.268249 37.869689 DOWNTOWN BERKELEY BART ALA\n", + "2 -122.270119 37.853207 ASHBY BART ALA\n", + "3 -122.251777 37.844510 ROCKRIDGE BART ALA\n", + "4 -122.267120 37.828705 MACARTHUR BART ALA" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bart.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting Point Data in a dataframe to Geospatial Data in a geodataframe\n", + "\n", + "Because these data are in a CSV file we read them into a Pandas DataFrame.\n", + "\n", + "In order to map these data we need to convert these data to a GeoPandas GeoDataFame. To do this, we need to specify:\n", + "\n", + "- the data, here the geodataframe `bart`\n", + "- the coordinate data, here `bart['X']` and `bart['Y']`\n", + "- the CRS of the bart coordinate data, here `EPSG:4326`\n", + "\n", + "The CRS code 'EPSG:4326' stands for the World Geodectic System of 1984, or WGS84. This is the most commonly used CRS for geographic (lat/lon) coordinate data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DhNdrlIdPBJ0pZRfD6zXKoUi/mdEvpQwQXq9RDj4RdK6UAcLrNcrBJ4LOlTJAeDVgOfhE0LlSBgivBiwHnwg6V8pRDPB6jTLwwr3OlTZAWDn4RNAZB4gmPIZuZecAkcKTqcxKmqRsh8fQzRwgUnkM3cwBIpXH0M0cIFJ5DN3MScpUHkM3ayNASFoBfAM4PTn+toj4uKTdQPV0OgxMR8SGBuWHgRuB9UAAvx8R386i8t1WHySqCUoHCSuLdloQJ4AtETEjaRC4X9JdEXFJ9QBJNwDPpZT/NPC1iPgNSacBKzuudY94qNPKrmUOIipmkruDyS2q+yUJuBi4pb6spFcAbwNuSh7rxYiY7rzaveGhTiu7tpKUkgYkTQDHgLsj4oGa3ecBz0bEEw2Kvg6YAj4vab+kGyWdkfIcV0galzQ+NTW1sFfRJR7qtLJrK0BExFySX1gNbJK0vmb3ZTRoPSSWA78A/ENEbASeB65OeY5dETEWEWMjIyPt1r+rPNRZTnv2T7J55z2svfpONu+8p9RXoFrQMGfSPbgP2A4gaTlwEbA7pcgR4EhNi+M2KgEjFzzUWT6+TN18LQOEpJFkJAJJQ8BW4FCyeytwKCKONCobEf8LPCOp+o16B/BYp5XuFV83onycd5qvnVGMs4CbJQ1QCSi3RsQdyb5LqeteSBoFboyI85NNHwL+ORnBeAr4vUxq3iNeLlwuzjvN1zJARMTDwMaUfZc32HYUOL/m/gQwtugamvXQ6PAQkw2CQVnzTp5qbVbDeaf5PNXarIan2M/nAGFWx3mnU9zFMLNUDhBmlsoBwsxSOUCYWSoHCDNL5QBhZqk8zGmWkSL+0JIDhFkGinr1MXcxzDJQ1FWgDhBmGSjqKlAHCLMMFPXqYw4QZhko6ipQJynNMlDUVaAOEGYZKeIqUHcxzCyVA4SZpXKAMLNUDhBmlsoBwsxSKSJaH9VjkqaAH/S5GmcCP3IdANejXt7q8dqIWNTvWS7JALEUSBqPiL7+nsdSqIPrUe56uIthZqkcIMwslQNEul39rgBLow7getQrTT2cgzCzVG5BmFkqBwgzS1WqACHpNyUdlPRTSWM1298paZ+kR5J/tyTbV0q6U9KhpNzOFo//Gkkzkj7Sj3pI2iRpIrkdkPTePtWjYfk+1ONVku5N3pPPNqtDN+uRHLtD0pOSDkvalmU9kn1/JekZSTNNHvc0SZ9Pyh+Q9Kut/iZERGluwBuAdcB9wFjN9o3AaPL/9cBk8v+VwNuT/58GfBN4d5PH/xLwReAj/ahHctzy5P9nAceq93tcj4bl+1CPM4BfBt4PfLZfnw/gHOAAcDqwFvgeMJBVPZL7b0ne85kmj/sB4PPJ/18N7AOWNfublOp6EBHxOICk+u37a+4eBFZIOj0iXgDuTY55UdJDwOpGjy3pQuAp4Pl+1SM5rmoF0DQD3cV6pJU/0eN6PA/cL+nnGj1vr+oBXAB8IXn935f0JLAJ+HZG9TgREd9pVKbOOcB/JI91TNI0MAY8mFagVF2MNv06sL/+wyxpGPg1kj9w3b4zgD8Dru1nPZL9vyjpIPAI8P6IONmPerQq34d6ZGUx9VgFPFNz/0iyLfN6tHAAuEDScklrgXOBs5sVKFwLQtLXgZ9tsOujEfGvLcq+EbgOeFfd9uXALcBnIuKpBkWvBf42ImaqEbxP9SAiHgDeKOkNwM2SPkSlOdnTetSX79ffo8Fj9aMejU7rH5N0TZb1aMPnqHRfxqmsdfoW0PwE0qpfVsQbdX27ZNtq4L+BzQ2O/xyVNz/t8b4JPJ3cpoEfAx/sdT0aHH9v/eP3qh7Nyvf67wFcThs5iC5+PnYAO2ru7wXemnU9kv2pOYgGx34LOKfZMYVrQSxG0jy8k8qb+F91+/4SeCXwvrTyEXFezfHXUHmTWmbNs65H0mx8JiJOSnotlUTX032oR2r5XtYjKxnU4yvAv0j6G2AUeD1N+v2LqUeb5VdSmRz5vKR3Aicj4rGmhdqNNkW4Ae+l0v87ATwL7E22/zmV5OJEze3VVKJ1AI/XbH9fUuY9wF80eI5raD2K0ZV6AL9DJXk1ATwEXNinejQs34/3hUqA/DEwkzxH6hmzy/X4KJXRi8M0GQlbTD2SfZ9Myvw0+feaBu/LmuT5Hwe+TmUZeNPvjKdam1kqj2KYWSoHCDNL5QBhZqkcIMwslQOEmaVygDCzVA4QZpbq/wEzynQvnomLFQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Convert the DataFrame to a GeoDataFrame. \n", + "bart_gdf = gpd.GeoDataFrame(bart, geometry=gpd.points_from_xy(bart['lon'], \n", + " bart['lat']), \n", + " crs='EPSG:4326') \n", + "\n", + "# and take a look\n", + "bart_gdf.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have the BART data in a geodataframe we can use the same commands as we did above to map it with a contextily basemap." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have the full range of `matplotlib` style options to enhance the map, a few of which are shown in the example below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = bart_gdf.to_crs('EPSG:3857').plot(\n", + " color=\"red\",\n", + " edgecolor=\"black\",\n", + " markersize=50, \n", + " figsize=(9, 9))\n", + "\n", + "ax.set_title('Bay Area Bart Stations')\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Changing the Basemap\n", + "\n", + "By default `contextiley` returns maptiles from the OpenStreetmap Mapnik basemap. However, ther are other available tilesets from different providers. These tilesets are stored in the contextily `cx.providers` dictionary.\n", + "\n", + "That's a large dictionary and you can view it. Alternatively, and more simply, you can access the list of the providers in this dictionary using the command `cs.providers.keys`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['OpenStreetMap', 'OpenSeaMap', 'OpenPtMap', 'OpenTopoMap', 'OpenRailwayMap', 'OpenFireMap', 'SafeCast', 'Thunderforest', 'OpenMapSurfer', 'Hydda', 'MapBox', 'Stamen', 'Esri', 'OpenWeatherMap', 'HERE', 'FreeMapSK', 'MtbMap', 'CartoDB', 'HikeBike', 'BasemapAT', 'nlmaps', 'NASAGIBS', 'NLS', 'JusticeMap', 'Wikimedia', 'GeoportailFrance', 'OneMapSG'])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# change basemap - can be one of these\n", + "# first see available provider names\n", + "cx.providers.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Once you have the list of providers, you can find the names of their specific tilesets. \n", + "\n", + "Below, we retrieve the list of the tilesets available from the provider `CartoDB`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['Positron', 'PositronNoLabels', 'PositronOnlyLabels', 'DarkMatter', 'DarkMatterNoLabels', 'DarkMatterOnlyLabels', 'Voyager', 'VoyagerNoLabels', 'VoyagerOnlyLabels', 'VoyagerLabelsUnder'])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Then find the names of the tile sets for a specific provider\n", + "cx.providers.CartoDB.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can specify a different tileset using the **source** argument to the `add_basemap` method." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['WorldStreetMap', 'DeLorme', 'WorldTopoMap', 'WorldImagery', 'WorldTerrain', 'WorldShadedRelief', 'WorldPhysical', 'OceanBasemap', 'NatGeoWorldMap', 'WorldGrayCanvas'])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cx.providers.Esri.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/contextily/tile.py:632: UserWarning: The inferred zoom level of 11 is not valid for the current tile provider (valid zooms: 1 - 9).\n", + " warnings.warn(msg)\n" + ] + }, + { + "ename": "ConnectionError", + "evalue": "('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mConnectionResetError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m 700\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 446\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 441\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1346\u001b[0m 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Connection reset by peer", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mProtocolError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m resp = conn.urlopen(\n\u001b[0m\u001b[1;32m 440\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 754\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 755\u001b[0;31m retries = retries.increment(\n\u001b[0m\u001b[1;32m 756\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_method_retryable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 532\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m 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"\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m 700\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;31m# Otherwise it looks like a bug in the 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url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 540\u001b[0m }\n\u001b[1;32m 541\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 654\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 657\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mProtocolError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 500\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mMaxRetryError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mConnectionError\u001b[0m: ('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Change the basemap provider and tileset\n", + "ax = bart_gdf.to_crs('EPSG:3857').plot(figsize=(9, 9))\n", + "cx.add_basemap(ax, source=cx.providers.NASAGIBS.ModisTerraTrueColorCR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning More\n", + "\n", + "Above, we prove a very short introduction to the excellent `contextily` library. You can find more detailed information on the `contextily` homepage, available at: [https://github.com/geopandas/contextily](https://github.com/geopandas/contextily). We especially encourage you to check out the notebook examples provided in that github repo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb b/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb new file mode 100644 index 0000000..f3cf55d --- /dev/null +++ b/lessons/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb @@ -0,0 +1,2042 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 12. Interactive Mapping with Folium\n", + "\n", + "In previous lessons we used `Geopandas` and `matplotlib` to create choropleth and point maps of our data. In this notebook we will take it to the next level by creating `interactive maps` with the **folium** library. \n", + "\n", + "\n", + "\n", + ">### References\n", + ">\n", + ">This notebook provides an introduction to `folium`. To see what else you can do, check out the references listed below.\n", + ">\n", + "> - [Folium web site](https://github.com/python-visualization/folium)\n", + ">\n", + "> - [Folium notebook examples](https://nbviewer.jupyter.org/github/python-visualization/folium/tree/master/examples/)\n", + "\n", + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline \n", + "\n", + "import folium # popular python web mapping tool for creating Leaflet maps\n", + "import folium.plugins\n", + "\n", + "# Supress minor warnings about the syntax of CRS definitions, \n", + "# ie \"init=epsg:4269\" vs \"epsg:4269\"\n", + "import warnings\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Check your version of `folium` and `geopandas`.\n", + "\n", + "Folium is a new and evolving Python library so make sure you have version 0.10.1 or later installed." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unknown\n" + ] + } + ], + "source": [ + "print(folium.__version__) # Make sure you have version 0.10.1 or later of folium!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9.0\n" + ] + } + ], + "source": [ + "print(gpd.__version__) # Make sure you have version 0.7.0 or later of GeoPandas!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 12.1 Introduction\n", + "\n", + "Interactive maps serve two very important purposes in geospatial analysis. First, they provde new tools for exploratory data analysis. With an interactive map you can:\n", + "- `pan` over the mapped data, \n", + "- `zoom` into a smaller arear that is not easily visible when the full extent of the map is displayed, and \n", + "- `click` on or `hover` over a feature to see more information about it.\n", + "\n", + "Second, when saved and shared, interactive maps provide a new tool for communicating the results of your analysis and for inviting your online audience to actively explore your work.\n", + "\n", + "For those of you who work with tools like ArcGIS or QGIS, interactive maps also make working in the jupyter notebook environment a bit more like working in a desktop GIS.\n", + "\n", + "The goal of this notebook is to show you how to create an interactive map with your geospatial data so that you can better analyze your data and save your output to share with others. \n", + "\n", + "After completing this lesson you will be able to create an interactive map like the one shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%html\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.2 Interactive Mapping with Folium\n", + "\n", + "Under the hood, `folium` is a Python package for creating interactive maps with [Leaflet](https://leafletjs.com), a popular javascript web mapping library. \n", + "\n", + "Let's start by creating a interactive map with the `folium.Map` function and display it in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a new folium map and save it to the variable name map1\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " width=\"100%\", # the width & height of the output map\n", + " height=500, # in pixels (int) or in percent of available space (str)\n", + " zoom_start=13) # the zoom level for the data to be displayed (3-20)\n", + "\n", + "map1 # display the map in the notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's discuss the map above and the code we used to generate it.\n", + "\n", + "At any time you can enter the following command to get help with `folium.Map`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment to see help docs\n", + "?folium.Map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make another folium map using the code below:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new folium map and save it to the variable name map1\n", + "#\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " #width=800, # the width & height of the output map\n", + " #height=600, # in pixels or in percent of available space\n", + " zoom_start=13) # the zoom level for the data to be displayed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "- What's new in the code?\n", + "\n", + "- How do you think that will change the map?\n", + "\n", + "Let's display the map and see what changes..." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "map1 # display map in notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the map changes when you change the underlying **tileset** from the default, which is `OpenStreetMap`, to `CartoDB Positron`. \n", + "> [OpenStreetMap](https://www.openstreetmap.org/#map=5/38.007/-95.844) is the largest free and open source dataset of geographic information about the world. So it is the default basemap for a lot of mapping tools and libraries.\n", + "\n", + "- You can find a list of the available tilesets you can use in the help documentation (`folium.Map?`), a snippet of which is shown below:\n", + "\n", + "
\n",
+    "Generate a base map of given width and height with either default\n",
+    "tilesets or a custom tileset URL. The following tilesets are built-in\n",
+    "to Folium. Pass any of the following to the \"tiles\" keyword:\n",
+    "\n",
+    "    - \"OpenStreetMap\"\n",
+    "    - \"Mapbox Bright\" (Limited levels of zoom for free tiles)\n",
+    "    - \"Mapbox Control Room\" (Limited levels of zoom for free tiles)\n",
+    "    - \"Stamen\" (Terrain, Toner, and Watercolor)\n",
+    "    - \"Cloudmade\" (Must pass API key)\n",
+    "    - \"Mapbox\" (Must pass API key)\n",
+    "    - \"CartoDB\" (positron and dark_matter)\n",
+    "
\n", + "\n", + "\n", + "#### Exercise\n", + "\n", + "Take a few minutes to try some of the different tilesets in the code below and see how they change the output map. *Avoid the ones that don't require an API key*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make changes to the code below to change the folium Map\n", + "## Try changing the values for the zoom_start and tiles parameters.\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='Stamen Watercolor', # basemap aka baselay or tile set\n", + " width=800, # the width & height of the output map\n", + " height=500, # in pixels or percent of available space\n", + " zoom_start=13) # the zoom level for the data to be displayed\n", + "\n", + "#display the map\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.3 Adding a Map Layer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have created a folium map, let's add our California County data to the map. \n", + "\n", + "First, let's read that data into a Geopandas geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Alameda county census tract data with the associated ACS 5yr variables.\n", + "ca_counties_gdf = gpd.read_file(\"notebook_data/california_counties/CaliforniaCounties.shp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Take another brief look at the geodataframe to recall the contents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# take a look at first two rows\n", + "ca_counties_gdf.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# take a look at all column names\n", + "ca_counties_gdf.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding a layer with folium.GeoJson\n", + "\n", + "Folium provides a number of ways to add vector data - points, lines, and polygons - to a map. \n", + "\n", + "The data we are working with are in Geopandas geodataframes. The main folium function for adding these to the map is `folium.GeoJson`.\n", + "\n", + "Let's build on our last map and add the census tracts as a `folium.GeoJson` layer. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB positron', # basemap aka baselay or tile set\n", + " width=800, # the width & height of the output map\n", + " height=600, # in pixels or in percent of available space\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts to the map\n", + "folium.GeoJson(ca_counties_gdf).add_to(map1)\n", + "\n", + "#display the map\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That was pretty straight-forward, but `folium.GeoJSON` provides a lot of arguments for customizing the display of the data in the map. We will review some of these soon. However, at any time you can get more information about `folium.GeoJSON` by taking a look at the function documentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view documentation\n", + "# folium.GeoJson?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking and Transforming the CRS\n", + "\n", + "It's always a good idea to check the **CRS** of your geodata before doing anything with that data. This is true when we use `folium` to make an interactive map. \n", + "\n", + "Here is how folium deals with the CRS of a geodataframe before mapping it:\n", + "- Folium checks to see if the gdf has a defined CRS\n", + " - If the CRS is not defined, it assumes the data to be in the WGS84 CRS (epsg=4326).\n", + " - If the CRS is defined, it will be transformed dynamically to WGS84 before mapping.\n", + "\n", + "\n", + "So, if your map data doesn't show up where at all or where you think it should, check the CRS of your data!\n", + "- If it is not defined, define it.\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "- What is the CRS of the tract data?\n", + "- How is folium dealing with the CRS of this gdf?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check the CRS of the data \n", + "print(...)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for answers*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Styling features with `folium.GeoJson`\n", + "\n", + "Let's dive deeper into the `folium.GeoJson` function. Below is an excerpt from the help documentation for the function that shows all the available function arguments that we can set.\n", + "\n", + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "What argument do we use to style the color for our polygons?\n", + "\n", + "
\n",
+    "folium.GeoJson(\n",
+    "    data,\n",
+    "    style_function=None,\n",
+    "    highlight_function=None,\n",
+    "    name=None,\n",
+    "    overlay=True,\n",
+    "    control=True,\n",
+    "    show=True,\n",
+    "    smooth_factor=None,\n",
+    "    tooltip=None,\n",
+    "    embed=True,\n",
+    ")\n",
+    "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's examine the options for the `style_function` in more detail since we will use these to change the style of our mapped data.\n", + "\n", + "\n", + "`style_function = lambda x: {` apply to all features being mapped (ie, all rows in the geodataframe) \n", + "`'weight': line_weight,` set the thickness of a line or polyline where <1 is thin, >1 thick, 1 = default \n", + "`'opacity': line_opacity,` set opacity where 1 is solid, 0.5 is semi-opaque and 0 is transparent \n", + "`'color': line_color` set the color of the line, eg \"red\" or some hexidecimal color value\n", + "`'fillOpacity': opacity,` set opacity of the fill of a polygon \n", + "`'fillColor': color` set color of the fill of a polygon \n", + "`'dashArray': '5, 5'` set line pattern to a dash of 5 pixels on, off \n", + "`}`\n", + "\n", + "\n", + "\n", + "Ok! Let's try setting the style of our census tract by defining a style function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts gdf layer\n", + "# setting the style of the data\n", + "folium.GeoJson(ca_counties_gdf,\n", + " style_function = lambda x: {\n", + " 'weight':2,\n", + " 'color':\"white\",\n", + " 'opacity':1,\n", + " 'fillColor':\"red\",\n", + " 'fillOpacity':0.6\n", + " }\n", + " ).add_to(map1)\n", + "\n", + "\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "Copy the code from our last map and paste it below. Take a few minutes edit the code to change the style of the census tract polygons.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='Stamen Watercolor',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts gdf layer\n", + "# setting the style of the data\n", + "folium.GeoJson(ca_counties_gdf,\n", + " style_function = lambda x: {\n", + " 'weight':3,\n", + " 'color':\"black\",\n", + " 'opacity':1,\n", + " 'fillColor':\"none\",\n", + " 'fillOpacity':0.6\n", + " }\n", + " ).add_to(map1)\n", + "\n", + "\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding a Tooltip\n", + "\n", + "A `tooltip` can be added to a folium.GeoJson map layer to display data values when the mouse hovers over a feature.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Double check what columns we have\n", + "ca_counties_gdf.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?folium.GeoJsonTooltip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts gdf layer\n", + "folium.GeoJson(ca_counties_gdf,\n", + " style_function = lambda x: {\n", + " 'weight':2,\n", + " 'color':\"white\",\n", + " 'opacity':1,\n", + " 'fillColor':\"red\",\n", + " 'fillOpacity':0.6\n", + " },\n", + " \n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['NAME','POP2012','POP12_SQMI' ], \n", + " aliases=['County', 'Population', 'Population Density (mi2)'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map1)\n", + "\n", + "\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As always, you can get more help by reading the documentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view help\n", + "#folium.GeoJsonTooltip?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "\n", + "Edit the code in the cell below to `add` the median age(`MED_AGE`) to the tooltip." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map1 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "# Add the census tracts gdf layer\n", + "folium.GeoJson(ca_counties_gdf,\n", + " style_function = lambda x: {\n", + " 'weight':2,\n", + " 'color':\"white\",\n", + " 'opacity':1,\n", + " 'fillColor':\"red\",\n", + " 'fillOpacity':0.6\n", + " },\n", + " \n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['NAME','POP2012','POP12_SQMI','MED_AGE' ], \n", + " aliases=['County', 'Population', 'Population Density (mi2)', 'Median Age'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map1)\n", + "\n", + "\n", + "map1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for answers*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.4 Data Mapping\n", + "\n", + "Above, we set the style for all of the census tracts to the same fill and outline colors and opacity values. \n", + "\n", + "Let's take a look at how we would use the `data values` to set the color values for the polygons. This is called a `choropleth` map or, more generally, a `thematic map`.\n", + "\n", + "The `folium.Choropleth` function can be used for this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view help docs\n", + "## folium.Choropleth?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `folium.Choropleth`, we will use some of the same style parameters that we used with `folium.GeoJson`.\n", + "\n", + "We will also use some new parameters, as shown below.\n", + "\n", + "First, let's take a look at the data we will map to refresh our knowledge." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(ca_counties_gdf.columns)\n", + "ca_counties_gdf.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create a choropleth map of total population, which is in the `c_race` column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ca_counties_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map2 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "\n", + "# Add the Choropleth layer\n", + "folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), # The object with the geospatial data\n", + " data=ca_counties_gdf, # The object with the attribute data (can be same)\n", + " columns=['NAME','POP2012'], # the ID and data columns in the data objects\n", + " key_on=\"feature.id\", # the ID in the geo_data object (don't change)\n", + " fill_color=\"Reds\", # The color palette (or color map) - see help\n", + " fill_opacity=0.65,\n", + " line_color=\"grey\",\n", + " legend=True,\n", + " legend_name=\"Population\",\n", + " ).add_to(map2)\n", + "\n", + "# Display the map\n", + "map2 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choropleth Mapping with Folium - discussion\n", + "\n", + "Let's discuss the following lines from the code above in more detail.\n", + "\n", + "
\n",
+    "# Add the Choropleth layer\n",
+    "folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),\n",
+    "           data=ca_counties_gdf, \n",
+    "           columns=['NAME','POP2012'],\n",
+    "           key_on=\"feature.id\",\n",
+    "           fill_color=\"Reds\",                               \n",
+    "           ...)\n",
+    "\n",
+    "\n",
+    "
\n", + "\n", + "`geo_data` and the `data`: we need to identify the objects that contains both because they could be different objects. In our example they are in the same object.\n", + "\n", + "`ca_counties_gdf.set_index('NAME')`: We need to **set_index('NAME')** in order to identify the column in `geo_data` that will be used to `join` the geometries in the `geo_data` to the data values in `data`.\n", + "\n", + "`columns=['NAME','POP2012']`: we identify in `data` (1) the column that will join these `data` to `geo_data` and (2) the second column is the column with the values that will determine the color.\n", + "\n", + "`fill_color=\"Reds\":` Here we identify the name of the color palette that we will use to style the polygons. These will be the same as the `matplotlib` colormaps.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question\n", + "Recall our discussion about best practices for choropleth maps. Is population count an appropriate variable to plot as a choropleth? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your thoughts here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "\n", + "Copy and paste the code from above into the cell below to create a choropleth map of population density (`POP12_SQMI`).\n", + "\n", + "Feel free to experiment with any of the `folium.Choropleth` style parameters, especially the `fill_color` which needs to be one of the `color brewer palettes` listed below:\n", + "\n", + "
\n",
+    "fill_color: string, default 'blue'\n",
+    "    Area fill color. Can pass a hex code, color name, or if you are\n",
+    "    binding data, one of the following color brewer palettes:\n",
+    "    'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',\n",
+    "    'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.\n",
+    "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here\n", + "# Define the basemap\n", + "map2 = folium.Map(location=[37.7749, -122.4194], # lat, lon around which to center the map\n", + " tiles='Stamen Toner',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "\n", + "# Add the Choropleth layer \n", + "folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), # The object with the geospatial data\n", + " data=ca_counties_gdf, # The object with the attribute data (can be same)\n", + " columns=['NAME','POP12_SQMI'], # the ID and data columns in the data objects\n", + " key_on=\"feature.id\", # the ID in the geo_data object (don't change)\n", + " fill_color=\"RdPu\", # The color palette (or color map) - see help\n", + " fill_opacity=0.8).add_to(map2)\n", + "\n", + "map2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for answers*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choropleth Maps with Tooltips\n", + "\n", + "You can add a `tooltip` to a folium.Choropleth map but the process is not straigthforward. The `folium.Choropleth` function does not have a tooltip argument the way `folium.GeoJson` does.\n", + "\n", + "The workaround is to add the layer as both a `folium.Choropleth` layer and as a `folium.GeoJson` layer and bind the tooltip to the GeoJson layer.\n", + "\n", + "Let's check it out below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map3 = folium.Map(location=[37.8721, -122.2578], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " width=1000, # the width & height of the output map\n", + " height=600, # in pixels\n", + " zoom_start=6) # the zoom level for the data to be displayed\n", + "\n", + "\n", + "# Add the Choropleth layer\n", + "folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'), # The object with the geospatial data\n", + " data=ca_counties_gdf, # The object with the attribute data (can be same)\n", + " columns=['NAME','POP2012'], # the ID and data columns in the data objects\n", + " key_on=\"feature.id\", # the ID in the geo_data object (don't change)\n", + " fill_color=\"Reds\", # The color palette (or color map) - see help\n", + " fill_opacity=0.65,\n", + " line_color=\"grey\",\n", + " legend=True,\n", + " legend_name=\"Population\",\n", + " ).add_to(map3)\n", + "\n", + "# ADD the same geodataframe to the map to display a tooltip\n", + "layer2 = folium.GeoJson(ca_counties_gdf,\n", + " style_function=lambda x: {'color':'transparent','fillColor':'transparent'},\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['NAME','POP2012'], \n", + " aliases=['County', 'Population'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " highlight_function=lambda x: {'weight':3,'color':'white'}\n", + ").add_to(map3)\n", + "\n", + "\n", + "\n", + "map3 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Question \n", + "Do you notice anything different about the `style_function` for layer2 above?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "Redo the above choropleth map code to map population density. Add both population and population density to the tooltip. Don't forget to update the legend name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.5 Overlays\n", + "\n", + "We can overlay other geospatial data on our folium maps.\n", + "\n", + "Let's say we want to focus the previous choropleth map with tooltips (`map3`) on the City of Berkeley. We can fetch the border of the city from our census Places dataset. These data can be downloaded from the Census website. We use the cartographic boundary files not the TIGER line files as these look better on a map (clipped to shoreline). \n", + "\n", + "Specifically, we will fetch the city boundaries from the following census cartographic boundary file:\n", + "\n", + "- https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_06_place_500k.zip\n", + "\n", + "Then we can overlay the border of the city on the map and set the initial zoom to the center of the Berkeley boundary.\n", + "\n", + "Let's try that.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to read in the census places data and create a subset geodataframe for our city of interest, here Berkeley." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "places = gpd.read_file(\"zip://notebook_data/census/Places/cb_2018_06_place_500k.zip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "places.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "berkeley = places[places.NAME=='Berkeley'].copy()\n", + "berkeley.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the Berkeley geodataframe to make sure it looks ok." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "berkeley.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Create a new map centered on Berkeley\n", + "berkeley_map = folium.Map(location=[berkeley.centroid.y.mean(), \n", + " berkeley.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " width=800,height=600,\n", + " zoom_start=13)\n", + "\n", + "\n", + "# Add the census tract polygons as a choropleth map\n", + "layer1=folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),\n", + " data=ca_counties_gdf,\n", + " columns=['NAME','POP2012'],\n", + " fill_color=\"Reds\",\n", + " fill_opacity=0.65,\n", + " line_color=\"grey\", #\"white\",\n", + " line_weight=1,\n", + " line_opacity=1,\n", + " key_on=\"feature.id\",\n", + " legend=True,\n", + " legend_name=\"Population\",\n", + " highlight=True\n", + " ).add_to(berkeley_map)\n", + "\n", + "# Add the berkeley boundary - note the fill color\n", + "layer2 = folium.GeoJson(data=berkeley,\n", + " name='Berkeley',smooth_factor=2,\n", + " style_function=lambda x: {'color':'black',\n", + " 'opacity':1,\n", + " 'fillColor':\n", + " 'transparent',\n", + " 'weight':3},\n", + " ).add_to(berkeley_map)\n", + "\n", + "# Add the tooltip for the census tracts as its own layer\n", + "layer3 = folium.GeoJson(ca_counties_gdf,\n", + " style_function=lambda x: {'color':'transparent','fillColor':'transparent'},\n", + " tooltip=folium.features.GeoJsonTooltip(\n", + " fields=['NAME','POP2012'], \n", + " aliases=['County', 'Population'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " highlight_function=lambda x: {'weight':3,'color':'white'}\n", + ").add_to(berkeley_map)\n", + "\n", + "berkeley_map # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "Any questions about the above map?\n", + "\n", + "Does the code for the Berkeley map above differ from our previous choropleth map code?\n", + "\n", + "Does the order of layer2 & layer3 matter (can they be switched?)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "\n", + "Redo the above map with population density. Create and display the Oakland city boundary on the map instead of Berkeley and center the map on Oakland." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for solution*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.6 Mapping Points and Lines\n", + "\n", + "We can also add points and lines to a folium map.\n", + "\n", + "Let's overlay BART stations as points and BART lines as lines to the interactive map. For the Bay Area these are data are available from the [Metropoliton Transportation Commission (MTC) Open Data portal](http://opendata.mtc.ca.gov/datasets).\n", + "\n", + "We're going to try pulling in BART station data that we downloaded from the website and subsetted from the passenger-rail-stations. You can learn more about the dataset through here: http://opendata.mtc.ca.gov/datasets/passenger-rail-stations-2019\n", + "\n", + "As usual, let's try pulling in the data and inspect the first couple of rows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load light rail stop data\n", + "railstops = gpd.read_file(\"zip://notebook_data/transportation/Passenger_Rail_Stations_2019.zip\") \n", + "railstops.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Subset to keep just bart stations\n", + "bart_stations = railstops[railstops['agencyname']=='BART'].sort_values(by=\"station_na\")\n", + "bart_stations.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Repeat for the rail lines\n", + "rail_lines = gpd.read_file(\"zip://notebook_data/transportation/Passenger_Railways_2019.zip\") \n", + "rail_lines.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rail_lines.operator.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subset by operator to get the bart lines\n", + "bart_lines = rail_lines[rail_lines['operator']=='BART']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Check the CRS of the geodataframes\n", + "print(bart_stations.crs)\n", + "print(bart_lines.crs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Quick plot\n", + "bart_stations.plot()\n", + "bart_lines.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have fetched and checked the Bart data, let's do a quick folium map with it.\n", + "\n", + "We will use `folium.GeoJson` to add these data to the map, just as we used it previously for the census tract polygons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bart Map\n", + "map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " width=800,height=600,\n", + " zoom_start=10)\n", + "\n", + "\n", + "folium.GeoJson(bart_lines).add_to(map4)\n", + "\n", + "folium.GeoJson(bart_stations).add_to(map4)\n", + "\n", + "\n", + "map4 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also add tooltips, just as we did previously." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bart Map\n", + "map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " #width=800,height=600,\n", + " zoom_start=10)\n", + "\n", + "# Add Bart lines\n", + "folium.GeoJson(bart_lines,\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['operator' ],\n", + " aliases=['Line operator'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map4)\n", + "\n", + "# Add Bart stations\n", + "folium.GeoJson(bart_stations,\n", + " tooltip=folium.GeoJsonTooltip(fields=['ts_locatio'], \n", + " aliases=['Stop Name'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map4)\n", + "\n", + "\n", + "map4 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's pretty cool, but don't you just want to click on those marker points to get a `popup` rather than hovering over for a `tooltip`?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mapping Points\n", + "\n", + "So far we have used `folium.GeoJson` to map our BART points. By default this uses the push-pin marker symbology made popular by Google Maps. \n", + "\n", + "Under the hood, folium.GeoJson uses the default object type `folium.Marker` when the input data are points.\n", + "\n", + "This is helpful to know because `folium.Marker` has a few options that allow further customization of our points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view help docs\n", + "folium.Marker?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's explicitly add the Bart Stations as points so we can change the `tooltips` to `popups`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Bart Map\n", + "map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " #width=800,height=800,\n", + " zoom_start=10)\n", + "\n", + "# Add Bart lines\n", + "folium.GeoJson(bart_lines,\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['operator' ],\n", + " aliases=['Line operator'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map4)\n", + "\n", + "# Add Bart stations\n", + "bart_stations.apply(lambda row:\n", + " folium.Marker(\n", + " location=[row['geometry'].y, row['geometry'].x],\n", + " popup=row['ts_locatio'],\n", + " ).add_to(map4), axis=1)\n", + "\n", + "map4 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That `folium.Marker` code is a bit more complex than `folium.GeoJson` and may not be worth it unless you really want that popup behavior.\n", + "\n", + "But let's see what else we can do with a `folium.Marker` by viewing the next map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Bart Map\n", + "map4 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " #width=800,height=600,\n", + " zoom_start=10)\n", + "\n", + "# Add BART lines\n", + "folium.GeoJson(bart_lines,\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['operator' ],\n", + " aliases=['Line operator'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map4)\n", + "\n", + "# Add BART Stations\n", + "icon_url = \"https://gomentumstation.net/wp-content/uploads/2018/08/Bay-area-rapid-transit-1000.png\"\n", + "bart_stations.apply(lambda row:\n", + " folium.Marker(\n", + " location=[row['geometry'].y,row['geometry'].x],\n", + " popup=row['ts_locatio'],\n", + " icon=folium.features.CustomIcon(icon_url,icon_size=(20, 20)),\n", + " ).add_to(map4), axis=1)\n", + "\n", + "map4 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exercise\n", + "\n", + "Copy and paste the code for the previous cell into the next cell and \n", + "1. change the bart icon to \"https://ya-webdesign.com/transparent450_/train-emoji-png-14.png\"\n", + "2. change the popup back to a tooltip." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Click here for solution*\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### folium.CircleMarkers\n", + "\n", + "You may prefer to customize points as `CircleMarkers` instead of the icon or pushpin Marker style. This allows you to set size and color of a marker, either manually or as a function of a data variable.\n", + "\n", + "Let's look at some code for doing this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " #width=1000, # the width & height of the output map\n", + " #height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "# Add BART Lines\n", + "folium.GeoJson(bart_lines).add_to(map5)\n", + "\n", + "\n", + "# Add BART Stations\n", + "bart_stations.apply(lambda row:\n", + " folium.CircleMarker(\n", + " location=[row['geometry'].y, row['geometry'].x],\n", + " radius=10,\n", + " color='purple',\n", + " fill=True,\n", + " fill_color='purple',\n", + " popup=row['ts_locatio'],\n", + " ).add_to(map5), \n", + " axis=1)\n", + "\n", + "\n", + "map5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### folium.Circle \n", + "\n", + "You can also set the size of your circles to a fixed radius, in meters, using `folium.Circle`. This is great for exploratory data analysis. For example, you can see what the census tract values are within 500 meters of a BART station." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view\n", + "#?folium.Circle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], # lat, lon around which to center the map\n", + " tiles='CartoDB Positron',\n", + " #width=1000, # the width & height of the output map\n", + " #height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "# Add BART Lines\n", + "folium.GeoJson(bart_lines).add_to(map5)\n", + "\n", + "\n", + "# Add BART Stations\n", + "bart_stations.apply(lambda row:\n", + " folium.Circle(\n", + " location=[row['geometry'].y, row['geometry'].x],\n", + " radius=500,\n", + " color='purple',\n", + " fill=True,\n", + " fill_color='purple',\n", + " popup=row['ts_locatio'],\n", + " ).add_to(map5), \n", + " axis=1)\n", + "\n", + "\n", + "map5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Question\n", + "
\n", + "\n", + "What do you notice about the size of the circles as you zoom in/out when you compare folium.Circles and folium.CircleMarkers?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Proportional Symbol Maps\n", + "\n", + "One of the advantages of the `folium.CircleMarker` is that we can set the size of the map to vary based on a data value.\n", + "\n", + "To give this a try, let's add a fake column to the `bart_stations` gdf called millions_served and set it to a value between 1 and 10." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# add a column to the bart stations gdf\n", + "bart_stations['millions_served'] = np.random.randint(1,10, size=len(bart_stations))\n", + "bart_stations.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the basemap\n", + "map5 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()],\n", + " tiles='CartoDB Positron',\n", + " #width=1000, # the width & height of the output map\n", + " #height=600, # in pixels\n", + " zoom_start=10) # the zoom level for the data to be displayed\n", + "\n", + "folium.GeoJson(bart_lines).add_to(map5)\n", + "\n", + "# Add BART Stations as CircleMarkers\n", + "# Here, some knowlege of Python string formatting is useful\n", + "bart_stations.apply(lambda row:\n", + " folium.CircleMarker(\n", + " location=[row['geometry'].y, row['geometry'].x],\n", + " radius=row['millions_served'],\n", + " color='purple',\n", + " fill=True,\n", + " fill_color='purple',\n", + " tooltip = \"Bart Station: %s
Millions served: %s\" % (row['ts_locatio'], row['millions_served'])\n", + " \n", + " ).add_to(map5), axis=1)\n", + "map5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So if you hover over our BART stations, you see that we've formatted it nicely! Using some HTML and Python string formatting we can make our `tooltip` easier to read. \n", + "\n", + "If you want to learn more about customizing these, you can [go check this out to learn HTML basics](https://www.w3schools.com/html/html_basic.asp). You can then [go here to learn about Python string formatting](https://python-reference.readthedocs.io/en/latest/docs/str/formatting.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.7 Creating and Saving a folium Interactive Map\n", + "\n", + "Now that you have seen most of the ways you can add a geodataframe to a folium map, let's create one big map that includes several of our geodataframes.\n", + "\n", + "To control the display of the data layers, we will add a `folium.LayerControl`\n", + "\n", + "- A `folium.LayerControl` will allow you to toggle on/off a map's visible layers. \n", + "\n", + "- In order to add a layer to the LayerControl, the layer must have value set for its `name`.\n", + "\n", + "Let's take a look. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new map centered on the census tract data\n", + "map6 = folium.Map(location=[bart_stations.centroid.y.mean(), bart_stations.centroid.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " #width=800,height=600,\n", + " zoom_start=10)\n", + "\n", + "# Add the counties polygons as a choropleth map\n", + "layer1=folium.Choropleth(geo_data=ca_counties_gdf.set_index('NAME'),\n", + " data=ca_counties_gdf,\n", + " columns=['NAME','POP2012'],\n", + " fill_color=\"Reds\",\n", + " fill_opacity=0.65,\n", + " line_color=\"grey\", #\"white\",\n", + " line_weight=1,\n", + " line_opacity=1,\n", + " key_on=\"feature.id\",\n", + " legend=True,\n", + " legend_name=\"Population\",\n", + " highlight=True,\n", + " name=\"Counties\"\n", + " ).add_to(map6)\n", + "\n", + "# Add the tooltip for the counties as its own layer\n", + "# Don't display in the Layer control!\n", + "layer2 = folium.GeoJson(ca_counties_gdf,\n", + " style_function=lambda x: {'color':'transparent','fillColor':'transparent'},\n", + " tooltip=folium.features.GeoJsonTooltip(\n", + " fields=['NAME','POP2012'], \n", + " aliases=['Name', 'Population'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " highlight_function=lambda x: {'weight':3,'color':'white'}\n", + ").add_to(layer1.geojson)\n", + "\n", + "# Add Bart lines\n", + "folium.GeoJson(bart_lines,\n", + " name=\"Bart Lines\",\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['operator' ],\n", + " aliases=['Line operator'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map6)\n", + "\n", + "\n", + "# Add Bart stations\n", + "folium.GeoJson(bart_stations,\n", + " name=\"Bart stations\",\n", + " tooltip=folium.GeoJsonTooltip(fields=['ts_locatio' ], \n", + " aliases=['Stop Name'],\n", + " labels=True,\n", + " localize=True\n", + " ),\n", + " ).add_to(map6)\n", + "\n", + "# ADD LAYER CONTROL\n", + "folium.LayerControl(collapsed=False).add_to(map6)\n", + "\n", + "map6 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "
\n", + "\n", + "#### Questions\n", + "
\n", + "\n", + "1. Take a look at the help docs `folium.LayerControl?`. What parameter would move the location of the LayerControl? What parameter would allow it to be closed by default?\n", + "\n", + "2. Take a look at the way we added `layer2` above (this has the census tract tooltips). How has the code we use to add the layer to the map changed? Why do you think we made this change?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to view\n", + "#folium.LayerControl?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving to an html file\n", + "\n", + "By saving our map to a html we can use it later as something to add to a website or email to a colleague.\n", + "\n", + "You can save any of the maps you have in the notebook using this syntax:\n", + "\n", + "> map_name.save(\"file_name.html\")\n", + "\n", + "Let's try that." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "map6.save('outdata/bartmap.html')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find your html file on your computer and double-click on it to open it in a browser." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extra Challenge\n", + "\n", + "Check out the notebook examples and find one to try with the data we have used in this notebook. I recommend the following.\n", + "\n", + "- [Mini-maps](https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/MiniMap.ipynb)\n", + "- [Dual-map](https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/plugin-DualMap.ipynb) (choropleth maps two census tract vars)\n", + "- [Search](https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/plugin-Search.ipynb) (e.g., for a Bart Station by name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 12.8 Recap\n", + "Here we learned about the wonderful world of `Folium`! We created interactive maps-- whether it be choropleth, points, lines, symbols... we mapped it all. \n", + "\n", + "Below you'll find a list of key functionalities we learned:\n", + "- Interactive mapping\n", + "\t- `folium.Map()`\n", + "- Adding a map layer\n", + "\t- `.add_to()`\n", + "\t- `folium.Choropleth()`\n", + "\t\t- `geo_data`\n", + "\t\t- `columns`\n", + "\t\t- `fill_color`\n", + "\t- `folium.GeoJson()`\n", + "\t\t- `style_function`\n", + "\t- `folium.Marker()`\n", + "\t\t- `icon`\n", + "\t- `folium.CircleMarker()`\n", + "\t\t- `radius`\n", + "- Adding a Tooltip\n", + "\t- `folium.GeoJsonTooltip`\n", + "\t- `folium.features.GeoJsonTooltip`\n", + "- Adding layer control\n", + "\t- `folium.LayerControl()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Important note\n", + "\n", + "The folium library changes often so I recommend you update your package frequently. This will give you increased functionality and may make future code easier to write. However, it might cause your existing code to break.\n", + "\n", + "### References\n", + "\n", + "This notebook provides an introduction to `folium`. To see what else you can do, check out the references listed below.\n", + "\n", + "- [Folium web site](https://github.com/python-visualization/folium)\n", + "\n", + "- [Folium notebook examples](https://nbviewer.jupyter.org/github/python-visualization/folium/tree/master/examples/)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/13_OPTIONAL_geocoding.ipynb b/lessons/13_OPTIONAL_geocoding.ipynb new file mode 100755 index 0000000..b3b67ff --- /dev/null +++ b/lessons/13_OPTIONAL_geocoding.ipynb @@ -0,0 +1,399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geocoding Addresses in Python\n", + "\n", + "This notebook demonstrates how to geocode a dataframe of addresses" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import our packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import contextily as cx\n", + "import matplotlib.pyplot as plt\n", + "import folium\n", + "\n", + "# FOR geocoding\n", + "import geopy\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sample Data\n", + "Let's use as our sample data a CSV file of Alameda County Schools." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df0 = pd.read_csv(\"./notebook_data/alco_schools.csv\")\n", + "df0.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that this datafile already has coordinates, but we will ignore those columns and subset it to Berkeley schools only for our geocoding example. We will also only keep public schools to limit the number of addresses to be geocoded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = df0[(df0['City']=='Berkeley' )& (df0['Org']== 'Public')][['Site','Address','City','State']].reset_index(drop=True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.shape # SEE HOW MANY SCHOOLS WILL BE GEOCODED" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we create a column that has all address components as this format is favored by many geocoders." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['full_address'] = df['Address'] +' '+ df['City']+ ' '+ df['State']\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a GeoDataFrame\n", + "We will create a Geopandas Geodataframe that has no geometry so that we can use GeoPandas functionality for geocoding." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = gpd.GeoDataFrame(data=df, \n", + " geometry=None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Geocoders and associated parameters\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##################################################################\n", + "## Geocoder to use \n", + "## see https://geopy.readthedocs.io/en/latest/\n", + "## and https://geopandas.org/geocoding.html\n", + "##################################################################\n", + "\n", + "# By default, the geocode function uses the GeoCode.Farm geocoding API with a rate limitation applied. \n", + "# But a different geocoding service can be specified (we really like the google geocoder!)\n", + "# Set your Google geocoding API Key if you want to geocode using that API\n", + "geocoder_name = 'Nominatim' # or \"GoogleV3\" or None to skip geocoding step\n", + "geocoder_apikey = None # None if not required or google api key, or other api key\n", + "geopy.geocoders.options.default_user_agent = 'D-Lab GeoFUN Workshop at UC Berkeley'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test the geocoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# test the geocoder\n", + "if geocoder_name is not None: \n", + " print(\"Geocoding is enabled with this geocoder:\", geocoder_name)\n", + " \n", + " if geocoder_apikey is None: \n", + " x= gpd.tools.geocode('1600 pennsylvania ave. washington, dc', provider=geocoder_name)['geometry'].squeeze()\n", + " \n", + " else:\n", + " x=gpd.tools.geocode('1600 pennsylvania ave. washington, dc', provider=geocoder_name, api_key=geocoder_apikey)['geometry'].squeeze()\n", + "else:\n", + " print(\"Geocoding is NOT enabled.\")\n", + " \n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make a Geocoding Function\n", + "\n", + "We can apply a geocoding function to a pandas dataframe to geocode all rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def geocode_one_address(addr, geocoder_name=geocoder_name, geocoder_apikey=geocoder_apikey):\n", + " '''\n", + " Function to geocode an input address IFF geom is None\n", + " Use geopy with google geocoder to geocode addresses.\n", + " Requires the api_key value to be set prior to running this function\n", + " \n", + " Parameters:\n", + " addr (str): address to geocode, eg \"1 Main St, Oakland, CA\"\n", + " geocoder_name (str): name of geocoder (\"nominatim\" or \"GoogleV3\")\n", + " geocoder_apikey (str): api_key if needed by geocoder\n", + " Returns: \n", + " geom (POINT): a point geometry or None if unsuccessful\n", + " \n", + " ''' \n", + " \n", + " if addr != None:\n", + " tempaddr = addr\n", + " \n", + " print(\"...geocoding this address: [%s]\" % tempaddr)\n", + " \n", + " try:\n", + " if geocoder_apikey == None:\n", + " return gpd.tools.geocode(tempaddr, provider=geocoder_name)['geometry'].squeeze()\n", + " else:\n", + " return gpd.tools.geocode(tempaddr, provider=geocoder_name, api_key=geocoder_apikey)['geometry'].squeeze()\n", + " except:\n", + " print(\"...Problem with address: \", tempaddr)\n", + " return None\n", + "\n", + " else: \n", + " print(\"No address to geocode\")\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# test geocoding function on one address\n", + "x = geocode_one_address('1600 pennsylvania ave. washington, dc')\n", + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#batch geocode addresses in a data frame\n", + "if geocoder_name is None:\n", + " print(\"Geocoding is NOT enabled.\")\n", + " print(\"Will NOT geocode addresses\")\n", + "else:\n", + " print(\"Geocoding is enabled with this geocoder:\", geocoder_name)\n", + " print(\"Ready to Geocode addresses\")\n", + " \n", + " if geocoder_apikey is None: \n", + " gdf['geometry'] = gdf.apply(lambda x: geocode_one_address(x['full_address']), axis=1)\n", + " else:\n", + " gdf['geometry'] = gdf.apply(lambda x: geocode_one_address(x['full_address']), axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set the CRS\n", + "Since we now have geographic coordinates we need to set the Coordinate Reference System of the data (WGS84)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = gdf.set_crs(epsg=4326)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Map the geocoded Addresses" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add basemap with Contextily\n", + "We can map the schools that were successfully geocoded, i.e. where the geometry is not equal to None." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = gdf[gdf.geometry!=None].to_crs('EPSG:3857').plot(figsize=(9, 9), color=\"red\")\n", + "cx.add_basemap(ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interactive Map with Folium\n", + "\n", + "We can create an interactive map of the schools that were successfully geocoded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "map1 = folium.Map(location=[gdf.geometry.y.mean(), gdf.geometry.x.mean()], \n", + " tiles='CartoDB Positron',\n", + " zoom_start=12)\n", + "\n", + "folium.GeoJson(gdf[gdf.geometry!=None],\n", + " tooltip=folium.GeoJsonTooltip(\n", + " fields=['Site'], \n", + " aliases=[\"\"],\n", + " #labels=True,\n", + " localize=True)\n", + " ).add_to(map1)\n", + "\n", + "map1 # show map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save output to GeoJson File" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save Geodataframe to file\n", + "#gdf.to_file(\"my_geocoded_schools.geojson\", driver='GeoJSON')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb b/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb new file mode 100644 index 0000000..40005f3 --- /dev/null +++ b/lessons/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb @@ -0,0 +1,1294 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 14. Making Plots and Maps with Altair\n", + "\n", + "The Python Altair library is great because it works with both pandas dataframes and geopandas geodataframes. It allows you to create all kinds of plots and also to make makes. Moreover the plots can be linked to the maps (but not vice versa) so that selecting data on the plot in turn highlights the geographies for related areas. We demonstrate this below with census data.\n", + "\n", + "This is powerful because you can do all this with just one Python library - instead of learning one for plotting and one for mapping. You can do this with matplotlib as well but the Altair syntax is a bit less complex.\n", + "\n", + "\n", + "For more information see the Altair website: https://altair-viz.github.io/" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#Import libraries including altair\n", + "import numpy as np\n", + "import pandas as pd\n", + "import altair as alt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment & Install or Upgrade geopandas if necessary\n", + "#!pip install GeoPandas==0.8.2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/geo_env2/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import geopandas as gpd" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "census_income_CA_2018.csv census_variables_CA_2013.zip\r\n", + "census_mhhinc_CA_county_2018.csv census_variables_CA_2018.csv\r\n", + "census_tracts_CA_2018.zip census_variables_CA_2018.zip\r\n", + "census_variables_CA.csv s4_cenvars_CA.csv\r\n", + "census_variables_CA_2013.csv s4_cenvars_CA_2018.csv\r\n" + ] + } + ], + "source": [ + "!ls notebook_data/census/ACS5yr/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load ACS 5 year (2014 - 2018) data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"notebook_data/census/ACS5yr/census_variables_CA_2018.csv\", dtype={'FIPS_11_digit':str})" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NAMEc_racec_whitec_blackc_asianc_latinxc_race_moec_white_moec_black_moec_asian_moe...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0Census Tract 8.02, Merced County, California3996160950231208232324936103...0.8498610.1465890.0035510.0000000.0000000.8243080.1221420.0126350.0000000.000000
1Census Tract 9.01, Merced County, California38361402973422204951864625...0.8284430.1490880.0195610.0015860.0013220.7879250.0671700.0000000.0000000.096604
2Census Tract 15.02, Merced County, California24931581812421542271052257...0.8537870.1049010.0182260.0097210.0133660.6448150.0941600.0083430.0119190.057211
3Census Tract 9.02, Merced County, California98113752871358417279686383621...0.8912110.0956770.0043020.0000000.0088100.9085480.0439620.0000000.0000000.007598
4Census Tract 12, Merced County, California543121871373582388450266104140...0.9201410.0588240.0053980.0107970.0048400.8387240.0642450.0004430.0000000.012406
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" + ], + "text/plain": [ + " NAME c_race c_white c_black \\\n", + "0 Census Tract 8.02, Merced County, California 3996 1609 50 \n", + "1 Census Tract 9.01, Merced County, California 3836 1402 97 \n", + "2 Census Tract 15.02, Merced County, California 2493 158 18 \n", + "3 Census Tract 9.02, Merced County, California 9811 3752 87 \n", + "4 Census Tract 12, Merced County, California 5431 2187 137 \n", + "\n", + " c_asian c_latinx c_race_moe c_white_moe c_black_moe c_asian_moe ... \\\n", + "0 231 2082 323 249 36 103 ... \n", + "1 34 2220 495 186 46 25 ... \n", + "2 124 2154 227 105 22 57 ... \n", + "3 1358 4172 796 863 83 621 ... \n", + "4 358 2388 450 266 104 140 ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.849861 0.146589 0.003551 0.000000 0.000000 0.824308 \n", + "1 0.828443 0.149088 0.019561 0.001586 0.001322 0.787925 \n", + "2 0.853787 0.104901 0.018226 0.009721 0.013366 0.644815 \n", + "3 0.891211 0.095677 0.004302 0.000000 0.008810 0.908548 \n", + "4 0.920141 0.058824 0.005398 0.010797 0.004840 0.838724 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.122142 0.012635 0.000000 0.000000 \n", + "1 0.067170 0.000000 0.000000 0.096604 \n", + "2 0.094160 0.008343 0.011919 0.057211 \n", + "3 0.043962 0.000000 0.000000 0.007598 \n", + "4 0.064245 0.000443 0.000000 0.012406 \n", + "\n", + "[5 rows x 66 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Take a look at the data\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 8057 entries, 0 to 8056\n", + "Data columns (total 66 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 NAME 8057 non-null object \n", + " 1 c_race 8057 non-null int64 \n", + " 2 c_white 8057 non-null int64 \n", + " 3 c_black 8057 non-null int64 \n", + " 4 c_asian 8057 non-null int64 \n", + " 5 c_latinx 8057 non-null int64 \n", + " 6 c_race_moe 8057 non-null int64 \n", + " 7 c_white_moe 8057 non-null int64 \n", + " 8 c_black_moe 8057 non-null int64 \n", + " 9 c_asian_moe 8057 non-null int64 \n", + " 10 c_latinx_moe 8057 non-null int64 \n", + " 11 state_fips 8057 non-null int64 \n", + " 12 county_fips 8057 non-null int64 \n", + " 13 tract_fips 8057 non-null int64 \n", + " 14 med_rent 7906 non-null float64\n", + " 15 med_hhinc 7965 non-null float64\n", + " 16 c_tenants 8057 non-null int64 \n", + " 17 c_owners 8057 non-null int64 \n", + " 18 c_renters 8057 non-null int64 \n", + " 19 med_rent_moe 7846 non-null float64\n", + " 20 med_hhinc_moe 7945 non-null float64\n", + " 21 c_tenants_moe 8057 non-null int64 \n", + " 22 c_owners_moe 8057 non-null int64 \n", + " 23 c_renters_moe 8057 non-null int64 \n", + " 24 c_movers 8057 non-null int64 \n", + " 25 c_stay 8057 non-null int64 \n", + " 26 c_movelocal 8057 non-null int64 \n", + " 27 c_movecounty 8057 non-null int64 \n", + " 28 c_movestate 8057 non-null int64 \n", + " 29 c_moveabroad 8057 non-null int64 \n", + " 30 c_movers_moe 8057 non-null int64 \n", + " 31 c_stay_moe 8057 non-null int64 \n", + " 32 c_movelocal_moe 8057 non-null int64 \n", + " 33 c_movecounty_moe 8057 non-null int64 \n", + " 34 c_movestate_moe 8057 non-null int64 \n", + " 35 c_moveabroad_moe 8057 non-null int64 \n", + " 36 c_commute 8057 non-null int64 \n", + " 37 c_car 8057 non-null int64 \n", + " 38 c_carpool 8057 non-null int64 \n", + " 39 c_transit 8057 non-null int64 \n", + " 40 c_bike 8057 non-null int64 \n", + " 41 c_walk 8057 non-null int64 \n", + " 42 c_commute_moe 8057 non-null int64 \n", + " 43 c_car_moe 8057 non-null int64 \n", + " 44 c_carpool_moe 8057 non-null int64 \n", + " 45 c_transit_moe 8057 non-null int64 \n", + " 46 c_bike_moe 8057 non-null int64 \n", + " 47 c_walk_moe 8057 non-null int64 \n", + " 48 year 8057 non-null int64 \n", + " 49 FIPS_11_digit 8057 non-null object \n", + " 50 p_white 8012 non-null float64\n", + " 51 p_black 8012 non-null float64\n", + " 52 p_asian 8012 non-null float64\n", + " 53 p_latinx 8012 non-null float64\n", + " 54 p_owners 7981 non-null float64\n", + " 55 p_renters 7981 non-null float64\n", + " 56 p_stay 8012 non-null float64\n", + " 57 p_movelocal 8012 non-null float64\n", + " 58 p_movecounty 8012 non-null float64\n", + " 59 p_movestate 8012 non-null float64\n", + " 60 p_moveabroad 8012 non-null float64\n", + " 61 p_car 7992 non-null float64\n", + " 62 p_carpool 7992 non-null float64\n", + " 63 p_transit 7992 non-null float64\n", + " 64 p_bike 7992 non-null float64\n", + " 65 p_walk 7992 non-null float64\n", + "dtypes: float64(20), int64(44), object(2)\n", + "memory usage: 4.1+ MB\n" + ] + } + ], + "source": [ + "# See what columns we have complete data for (no nulls) and what the datatypes are\n", + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Subset the data so we are only looking at Alameda County (fips code == 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df2 = df[df.county_fips==1]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NAMEc_racec_whitec_blackc_asianc_latinxc_race_moec_white_moec_black_moec_asian_moe...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
266Census Tract 4415.01, Alameda County, California6570677111474057036314883389...0.9258970.0395930.0104760.0198740.0041600.7617610.1139400.0548120.0120850.003453
267Census Tract 4047, Alameda County, California207915151341991751331376289...0.8918260.0283900.0376900.0318160.0102790.5320930.1776740.1581400.0065120.005581
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" + ], + "text/plain": [ + " NAME c_race c_white \\\n", + "266 Census Tract 4415.01, Alameda County, California 6570 677 \n", + "267 Census Tract 4047, Alameda County, California 2079 1515 \n", + "\n", + " c_black c_asian c_latinx c_race_moe c_white_moe c_black_moe \\\n", + "266 111 4740 570 363 148 83 \n", + "267 134 199 175 133 137 62 \n", + "\n", + " c_asian_moe ... p_stay p_movelocal p_movecounty p_movestate \\\n", + "266 389 ... 0.925897 0.039593 0.010476 0.019874 \n", + "267 89 ... 0.891826 0.028390 0.037690 0.031816 \n", + "\n", + " p_moveabroad p_car p_carpool p_transit p_bike p_walk \n", + "266 0.004160 0.761761 0.113940 0.054812 0.012085 0.003453 \n", + "267 0.010279 0.532093 0.177674 0.158140 0.006512 0.005581 \n", + "\n", + "[2 rows x 66 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make an Altair scatter plot \n", + "\n", + "that visualizes the relationship between median household income and the percent of households that are owner-occupied.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(df2).mark_circle(size=50).encode(\n", + " x='med_hhinc',\n", + " y='p_owners'\n", + ").properties(\n", + " height=350, width=500\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(361, 66)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cb_2013_06_tract_500k.zip \u001b[31mcb_2018_06_tract_500k.shp.ea.iso.xml\u001b[m\u001b[m\r\n", + "cb_2017_06_tract_500k.zip \u001b[31mcb_2018_06_tract_500k.shp.iso.xml\u001b[m\u001b[m\r\n", + "cb_2018_06_tract_500k.cpg cb_2018_06_tract_500k.shx\r\n", + "cb_2018_06_tract_500k.dbf cb_2018_06_tract_500k.zip\r\n", + "cb_2018_06_tract_500k.prj oakland_tracts_2018.zip\r\n", + "cb_2018_06_tract_500k.shp\r\n" + ] + } + ], + "source": [ + "!ls notebook_data/census/Tracts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in the Census Tract geographic data\n", + "\n", + "into a GeoPandas GeoDataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "tracts = gpd.read_file('zip://./notebook_data/census/Tracts/cb_2018_06_tract_500k.zip')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " STATEFP COUNTYFP TRACTCE AFFGEOID GEOID NAME LSAD \\\n", + "0 06 009 000300 1400000US06009000300 06009000300 3 CT \n", + "1 06 011 000300 1400000US06011000300 06011000300 3 CT \n", + "\n", + " ALAND AWATER geometry \n", + "0 457009794 394122 POLYGON ((-120.76399 38.21389, -120.76197 38.2... \n", + "1 952744514 195376 POLYGON ((-122.50006 39.12232, -122.50022 39.1... " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tracts.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tracts.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Subset to keep only the tracts for Alameda County" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "tracts=tracts[tracts.COUNTYFP=='001']" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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STATEFPCOUNTYFPTRACTCEAFFGEOIDGEOIDNAME_xLSADALANDAWATERgeometry...p_stayp_movelocalp_movecountyp_movestatep_moveabroadp_carp_carpoolp_transitp_bikep_walk
0060014251011400000US06001425101060014251014251.01CT5908702045459POLYGON ((-122.31419 37.84231, -122.29923 37.8......0.8652390.0365240.0358940.0371540.0251890.5509980.1075390.1696230.0155210.062084
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" + ], + "text/plain": [ + " STATEFP COUNTYFP TRACTCE AFFGEOID GEOID NAME_x LSAD \\\n", + "0 06 001 425101 1400000US06001425101 06001425101 4251.01 CT \n", + "1 06 001 428600 1400000US06001428600 06001428600 4286 CT \n", + "\n", + " ALAND AWATER geometry ... \\\n", + "0 590870 2045459 POLYGON ((-122.31419 37.84231, -122.29923 37.8... ... \n", + "1 898967 1080420 POLYGON ((-122.27993 37.76818, -122.27849 37.7... ... \n", + "\n", + " p_stay p_movelocal p_movecounty p_movestate p_moveabroad p_car \\\n", + "0 0.865239 0.036524 0.035894 0.037154 0.025189 0.550998 \n", + "1 0.767469 0.067846 0.110467 0.036532 0.017686 0.550140 \n", + "\n", + " p_carpool p_transit p_bike p_walk \n", + "0 0.107539 0.169623 0.015521 0.062084 \n", + "1 0.019048 0.270588 0.034734 0.035294 \n", + "\n", + "[2 rows x 76 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tracts2.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Thematic Map\n", + "\n", + "Use the Geopandas Plot method to create a map of tracts colored by median household income values." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tracts2.plot(column='med_hhinc', legend=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make the same map with Altair" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alt.Chart(tracts2).mark_geoshape().encode(\n", + " color='med_hhinc'\n", + ").properties(\n", + " width=500,\n", + " height=300\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Link Atair Scatterplot and Map" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First create a selection object\n", + "my_selection = alt.selection_interval()\n", + "\n", + "# Create a background map\n", + "background_map = alt.Chart(tracts2).mark_geoshape(\n", + " fill= 'lightgray',\n", + " stroke = 'white'\n", + ").properties(\n", + " width=400,\n", + " height=300\n", + ")\n", + "\n", + "# Create the interactive scatterplot\n", + "# by addng the selection object\n", + "the_scatterplot = alt.Chart(tracts2).mark_circle(size=50).encode(\n", + " x='med_hhinc',\n", + " y='p_owners'\n", + ").properties(\n", + " width=375,\n", + " height=300\n", + ").add_selection(\n", + " my_selection\n", + ")\n", + "\n", + "# Create the interactive map\n", + "# by adding the selection object\n", + "income_map = alt.Chart(tracts2).mark_geoshape().encode(\n", + " color='med_hhinc'\n", + ").properties(\n", + " width=400,\n", + " height=350\n", + ").transform_filter(\n", + " my_selection\n", + ")\n", + "\n", + "# Link the maps (background_map and income_map)\n", + "# to the scatterplot (the_scatterplot)\n", + "the_scatterplot | (background_map + income_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try dragging a box around a subset of the points on the scatterplot and see what happens to the map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/15_OPTIONAL_Voronoi_Tessellation.ipynb b/lessons/15_OPTIONAL_Voronoi_Tessellation.ipynb new file mode 100644 index 0000000..b9ba3d0 --- /dev/null +++ b/lessons/15_OPTIONAL_Voronoi_Tessellation.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 15. Voronoi Tessellation\n", + "\n", + "In some of the earlier lessons we dicussed how to conduct *proximity analyses* using buffer polygons. We looked at how accessible schools were via bike paths in Berkeley. Instead of using a buffers drawn at differnt radii around our locations or objects of interest, we could also use something called a **Voronoi diagram**.\n", + "\n", + "\n", + "\n", + "As seen above, we have a bunch of **Voronoi cells** that are delineated by encompassing all locations that are closest to our point of interest than any other points. \n", + "\n", + "In this notebook, we'll experiment with making these type of diagrams in Python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "import random\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll be using a Python package called `geovoronoi`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from geovoronoi.plotting import subplot_for_map, plot_voronoi_polys_with_points_in_area\n", + "from geovoronoi import voronoi_regions_from_coords, points_to_coords" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 15.1 Polling locations\n", + "\n", + "We'll be using the 2020 General Election voting locations for Alameda County for this analysis. Since the data is aspatial we'll need to coerce it to be a geodataframe and define a CRS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pull in polling location\n", + "polling_ac_df = pd.read_csv('notebook_data/ac_voting_locations.csv')\n", + "polling_ac_df.head()\n", + "\n", + "# Make into geo data frame\n", + "polling_ac_gdf = gpd.GeoDataFrame(polling_ac_df, \n", + " geometry=gpd.points_from_xy(polling_ac_df.X, polling_ac_df.Y))\n", + "polling_ac_gdf.crs = \"epsg:4326\"\n", + "\n", + "polling_ac_gdf.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 15.2 Census tracts\n", + "We'll also bring in our census tracts data for Alameda county." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bring in census tracts\n", + "tracts_gdf = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\")\n", + "\n", + "# Narrow it down to Alameda County\n", + "tracts_gdf_ac = tracts_gdf[tracts_gdf['COUNTYFP']=='001']\n", + "tracts_gdf_ac.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make sure we can use it with our polling locations data, we'll check the Coordinate Reference System (CRS)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check CRS\n", + "print('polling_ac_gdf:', polling_ac_gdf.crs)\n", + "print('tracts_gdf_ac CRS:', tracts_gdf_ac.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Uh oh! It looks like they have different CRS. We'll transform them both\n", + "> Note: If you need a refresher on CRS check out Lesson 3, Coordinate Reference Systems (CRS) & Map Projections" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform CRS\n", + "polling_ac_gdf_utm10 = polling_ac_gdf.to_crs(\"epsg:26910\")\n", + "tracts_gdf_ac_utm10 = tracts_gdf_ac.to_crs(\"epsg:26910\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now let's plot them together to see how the polling locations are spread across the county." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize = (14,8)) \n", + "\n", + "tracts_gdf_ac_utm10.plot(ax=ax,color='lightgrey',\n", + " legend=True)\n", + "polling_ac_gdf_utm10.plot(ax=ax, color='seagreen', markersize=9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 15.3 Voronoi Tessellation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make our Voronoi geometries, we'll be using the `voronoi_regions_from_coords` from the `geovoronai` package. Let's check the helper function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?voronoi_regions_from_coords" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll see that the helper function says *enerate Voronoi regions from NumPy array of 2D coordinates or list of Shapely Point objects in `coord`*. That means instead of GeoDataframe as an input, we'll need to first convert all our geometries to numpy arrays. \n", + "\n", + "We can easily do this by using `points_to_coords`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "polling_array = points_to_coords(polling_ac_gdf_utm10.geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now we're ready to run our voronoi region creation! We put in two inputs: our polling locations as a numpy array and our tracts boundary (which we created using `unary_union`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "region_polys, region_pts = voronoi_regions_from_coords(polling_array, tracts_gdf_ac_utm10.unary_union)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll also notice we get two outputs from our line of code. The first object, in our case `region_polys` gives us the shape of the Voronoi geometry, while `region_pts` gives us the list of points.\n", + "\n", + "To easily plot our points, we can use the `plot_voronoi_polys_with_points_in_area` which takes the following arguments:\n", + "- `ax`: Matplotlib axes object on which you want to plot\n", + "- `area_shape`: the boundary shape that encompasses our Voronoi regions. In our case this is the shape of Alameda County.\n", + "- `region_polys`: The dictionary that we got from above that gives the IDs and the polygons of our Voronoi geoemtries.\n", + "- `points`: The numpy array of our shapely point objects, which we got above as `region_pts`\n", + "\n", + "There are more arguments than this that you can use to customize your plot. Uncomment the code below to see the helper file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ?plot_voronoi_polys_with_points_in_area" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = subplot_for_map(figsize=(10,10))\n", + "plot_voronoi_polys_with_points_in_area(ax, tracts_gdf_ac_utm10.unary_union, \n", + " region_polys, \n", + " polling_array, \n", + " region_pts,\n", + " points_markersize=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ta-da!!!! \n", + "\n", + "## 15.4 Voronoi colored by an attribute\n", + "\n", + "Now we can go a step beyond this by changing the colors of each of our Voronoi regions based on a certain attribute.\n", + "\n", + "To do that, let's first get all of our region geometries as a list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "list_polys = list(region_polys.values())\n", + "list_polys[0:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we'll replace our point geometries in our original polling locations geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "polling_v = gpd.GeoDataFrame(polling_ac_gdf_utm10.drop('geometry',axis=1),\n", + " geometry=list_polys)\n", + "polling_v.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Say we had a number of votes cast count for every polling location. We'll randomly generate it here..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "polling_v['votes_cast'] = random.sample(range(10000,50000), polling_v.shape[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can now color our polygons based on the number of votes cast there." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax= plt.subplots(figsize=(10,6))\n", + "polling_v.plot(column='votes_cast', cmap='Purples', legend=True, ax=ax)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/16_OPTIONAL_Introduction_to_Raster_Data.ipynb b/lessons/16_OPTIONAL_Introduction_to_Raster_Data.ipynb new file mode 100644 index 0000000..5eebb3b --- /dev/null +++ b/lessons/16_OPTIONAL_Introduction_to_Raster_Data.ipynb @@ -0,0 +1,877 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 16. Introduction to Raster Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a very brief introduction to reading raster data and basic manipulations in Python. We'll walk through one of the most commonly used raster python packages, `rasterio`. We'll be using the [National Land Cover Database (NLCD)](https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend) from 2011 that was downloaded from [here](https://viewer.nationalmap.gov/basic).\n", + "\n", + "\n", + "\n", + "> Note: They also have a [cool online viewer](https://www.mrlc.gov/viewer/) that is free and open access." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "import matplotlib # base python plotting library\n", + "import matplotlib.pyplot as plt # submodule of matplotlib\n", + "from matplotlib.patches import Patch\n", + "\n", + "import json\n", + "import numpy as np\n", + "\n", + "# To display plots, maps, charts etc in the notebook\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use raster data we'll be using the `rasterio` package, which is a popular package that helps you read, write, and manipulate raster data. We'll also be using `rasterstats`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import rasterio\n", + "from rasterio.plot import show, plotting_extent\n", + "from rasterio.mask import mask\n", + "\n", + "from rasterstats import zonal_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.1 Import data and plot\n", + "\n", + "To open our NLCD subset data, we'll use the `rasterio.open` function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011 = rasterio.open('notebook_data/raster/nlcd2011_sf.tif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check out what we get." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's dissect this output here. We can look at the helper documentation for clues." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?rasterio.open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which reads that the function returns a ``DatasetReader`` or ``DatasetWriter`` object. Unlike in `GeoPandas` which we've been utilizing a lot of, we don't have a directly editable object here. However, `rasterio` does have functions in place where we can still use this returned object directly.\n", + "\n", + "For example, we can easily plot our NLCD data using `rasterio.plot.show`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rasterio.plot.show(nlcd_2011)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And just like how we formatted our `matplotlib` plots when we were using GeoDataFrames, we can still do that with this raster plotting function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?rasterio.plot.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,8))\n", + "plt_nlcd = rasterio.plot.show(nlcd_2011, cmap='Pastel2', ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Take note of what you think could be improved here... we'll come back to this)\n", + "\n", + "We can also plot a histogram of our data in a very similar way." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rasterio.plot.show_hist(nlcd_2011, bins=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that we have more values on the lower end than on the higher end. To really understand the values that we see here let's [take a look at the legend](https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend).\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.2 Raster data structure\n", + "\n", + "> *Note:* If you need a refresher on what raster data is and relevant terminology. Check out the first lesson that covers geospatial topics\n", + "\n", + "Now that we have a basic grasp on how to pull in and plot raster data, we can dig a little deeper to see what information we have.\n", + "\n", + "First let's check the number of bands there are in our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011.count" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case we only have 1 band. If you're pulling in aerial image, you might have 3 bands (red, green, blue). In the case you're bringing in remote sensing data like Landsat or MODIS you might have more!\n", + "\n", + "Not let's check out what meta data we have." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "nlcd_2011.meta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we have a lot of good information here. Let's unpack it:\n", + "\n", + "- `driver`: the file type (simialr to what we see in `open` and Geopandas `open`)\n", + "- `dtype`: the data type of each of your pixels\n", + "- `nodata`: the value that is set for no data pixels\n", + "- `width`: the number of pixels wide your dataset is\n", + "- `height`: the number of pixels high your dataset is\n", + "- `count`: the number of bands in your dataset\n", + "- `crs`: the coordiante reference system (CRS) of your data\n", + "- `transform`: the affine transform matrix that tell us which pixel locations in each row and column align with spatial locations (longitude, latitude)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also get similar information by calling `profile`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011.profile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, but now we want to actually access our data. We can read in our data as a Numpy ndarray." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011_array = nlcd_2011.read()\n", + "nlcd_2011_array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can call shape and see we have a 3D array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011_array.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much like other Numpy arrays, we can look at the min, mean, and max of our data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Minimum: \", np.nanmin(nlcd_2011_array))\n", + "print(\"Max: \", np.nanmean(nlcd_2011_array))\n", + "print(\"Mean: \", np.nanmax(nlcd_2011_array))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And since we have our data in an array form now, we can plot it using not a `rasterio` function, but simply `plt.imshow`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(nlcd_2011_array[0,:,:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that we specified this plotting by making our array 2D. This gives us more flexibility about how we want to create our plots. You can do something like this:\n", + "\n", + "> This definitely looks more scary than it actually is. Essentially we are:\n", + "> 1. constructing a full color spectrum with all the colors we want\n", + "> 2. If values are outside of this range, we set the color tot white\n", + "> 3. we set the boudnaries for each of these colors so we know which color to assign to what value\n", + "> 4. we create legend labels for our legend\n", + ">\n", + "> This process is only really needed if we want to have a color map for specific values outside of a specific named `matplotlib` named color map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the colors you want\n", + "cmap = matplotlib.colors.ListedColormap(['royalblue', #11\n", + " 'white', #12\n", + " 'beige', #21\n", + " 'salmon', #22\n", + " 'red', #23\n", + " 'darkred', #24\n", + " 'grey', #31\n", + " 'yellowgreen', #41\n", + " 'darkgreen', #42\n", + " 'lightgreen', # 43\n", + " 'darkgoldenrod', #51\n", + " 'tan', # 52\n", + " 'wheat', # 71\n", + " 'darkkhaki', #72\n", + " 'darkseagreen', #73\n", + " 'mediumseagreen', #74\n", + " 'gold', #81\n", + " 'chocolate', #82\n", + " 'lightsteelblue', #90\n", + " 'steelblue', #95\n", + " ])\n", + "cmap.set_under('#FFFFFF')\n", + "cmap.set_over('#FFFFFF')\n", + "# Define a normalization from values -> colors\n", + "norm = matplotlib.colors.BoundaryNorm([10.5,\n", + " 11.5,\n", + " 12.5,\n", + " 21.5,\n", + " 22.5,\n", + " 23.5,\n", + " 24.5,\n", + " 31.5,\n", + " 41.5, \n", + " 42.5,\n", + " 43.5,\n", + " 51.5,\n", + " 52.5,\n", + " 71.5,\n", + " 72.5,\n", + " 73.5,\n", + " 74.5,\n", + " 81.5,\n", + " 82.5,\n", + " 90.5,\n", + " 95.5,\n", + " ],20)\n", + "\n", + "\n", + "legend_labels = { 'royalblue':'Open Water', \n", + " 'white':'Perennial Ice/Snow',\n", + " 'beige':'Developed, Open Space',\n", + " 'salmon':'Developed, Low Intensity',\n", + " 'red':'Developed, Medium Intensity',\n", + " 'darkred':'Developed High Intensity',\n", + " 'grey':'Barren Land (Rock/Sand/Clay)',\n", + " 'yellowgreen':'Deciduous Forest',\n", + " 'darkgreen':'Evergreen Forest',\n", + " 'lightgreen':'Mixed Forest',\n", + " 'darkgoldenrod':'Dwarf Scrub',\n", + " 'tan':'Shrub/Scrub',\n", + " 'wheat':'Grassland/Herbaceous',\n", + " 'darkkhaki':'Sedge/Herbaceous',\n", + " 'darkseagreen':'Lichens',\n", + " 'mediumseagreen':'Moss',\n", + " 'gold':'Pasture/Hay',\n", + " 'chocolate':'Cultivated Crops',\n", + " 'lightsteelblue':'Woody Wetlands',\n", + " 'steelblue':'Emergent Herbaceous Wetlands'}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 8))\n", + "plt_nlcd = ax.imshow(nlcd_2011_array[0,:,:], cmap=cmap, norm=norm)\n", + "ax.set_title('NLCD 2011', fontsize=30)\n", + "\n", + "# Remove axes\n", + "ax.set_frame_on(False)\n", + "plt.setp(ax.get_xticklabels(), visible=False)\n", + "plt.setp(ax.get_yticklabels(), visible=False)\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "\n", + "# Add color bar\n", + "patches = [Patch(color=color, label=label)\n", + " for color, label in legend_labels.items()]\n", + "\n", + "fig.legend(handles=patches, facecolor=\"white\",bbox_to_anchor=(1.1, 1.05))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.2 Mask raster data\n", + "\n", + "*Masking* is a common action that is done with raster data where you \"mask\" everything outside of a certain geometry.\n", + "\n", + "To do this let's first bring in the san francisco county data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Bring in census tracts\n", + "tracts_gdf = gpd.read_file(\"zip://notebook_data/census/Tracts/cb_2013_06_tract_500k.zip\").to_crs('epsg:4326')\n", + "\n", + "# Narrow it down to San Francisco County\n", + "tracts_gdf_sf = tracts_gdf[tracts_gdf['COUNTYFP']=='075']\n", + "\n", + "tracts_gdf_sf.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We forgot about the Farollon islands! Let's crop those out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Crop out Farallon\n", + "tracts_gdf_sf = tracts_gdf_sf.cx[-122.8:-122.35, 37.65:37.85].copy().reset_index(drop=True)\n", + "\n", + "tracts_gdf_sf.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll want to check the crs of our GeoDataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_sf.crs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will call the `mask` function from `rasterio`. Let's look at the documentation first." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We actually recommend using the `rioxarray` method instesd. So we'll import a new package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import rioxarray as rxr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Open our same NLCD data..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nlcd_2011 = rxr.open_rasterio('notebook_data/raster/nlcd2011_sf.tif',\n", + " masked=True).squeeze()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reproject our NLCD to be in the same coordinate reference system as the san francisco data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from rasterio.crs import CRS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!rio --version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Currently doesn't work\n", + "# Issue: https://github.com/mapbox/rasterio/issues/2103\n", + "test = nlcd_2011.rio.reproject(tracts_gdf_sf.crs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And clip our data to the san francisco geometry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clipped = test.rio.clip(tracts_gdf_sf.geometry, tracts_gdf_sf.crs, drop=False, invert=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can easily plot this using `.plot()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clipped.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can also make a pretty map like we did before." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8, 8))\n", + "clipped.plot(cmap=cmap, norm=norm, ax=ax, add_colorbar=False)\n", + "ax.set_title('NLCD 2011 (Cropped)', fontsize=30)\n", + "\n", + "# Add color bar\n", + "patches = [Patch(color=color, label=label)\n", + " for color, label in legend_labels.items()]\n", + "\n", + "fig.legend(handles=patches, facecolor=\"white\",bbox_to_anchor=(1.1, 1.05))\n", + "\n", + "# Remove axes\n", + "ax.set_frame_on(False)\n", + "plt.setp(ax.get_xticklabels(), visible=False)\n", + "plt.setp(ax.get_yticklabels(), visible=False)\n", + "ax.set_xticks([])\n", + "ax.set_yticks([])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and you can save your work out to a new file!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clipped.rio.to_raster(\"outdata/nlcd2011_sf_cropped.tif\", tiled=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.3 Aggregate raster to vector\n", + "\n", + "Another common step we see in a lot of raster work flows is questions that go along the lines of \"How do I find the average of my raster within my vector data shapes\"?\n", + "\n", + "We can do this by *aggregating* to our vector data. For this example we'll ask the question, \"What is the majority class I have in each of the census tracts in San Francisco?\"\n", + "\n", + "For this we'll turn to the `rasterstas` pacakge which has a handy function called `zonal_stats`. By default, the function will give us the minimum, maximum, mean, and count. But there also a lot more statistics that the function can return beyond this:\n", + "- sum\n", + "- std\n", + "- median\n", + "- majority\n", + "- minority\n", + "- unique\n", + "- range\n", + "- nodata\n", + "- percentile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we'll first bring back our clipped census tracts shapefile we have for san francisco." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_sf.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we'll check out the `zonal_stats` documentation to get a better sense of how we can customize the arguments to better fit our needs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?zonal_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which doesn't tell us a ton. Since we don't have `gen_zonal_stas` loaded, we can go look at the documentation online: https://pythonhosted.org/rasterstats/rasterstats.html\n", + "\n", + "After we check that out, let's get on rolling and actually get our zonal stats by census tract." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with rasterio.open('notebook_data/raster/nlcd2011_sf.tif') as src:\n", + " affine = src.transform\n", + " array = src.read(1)\n", + " df_zonal_stats = pd.DataFrame(zonal_stats(tracts_gdf_sf, array, affine=affine, stats=['majority', 'unique']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's a lot going on in the cell above, let's break it down:\n", + "- `affine` object grabbed the transform of our raster data\n", + "- `array` object read the first band we have in our raster dataset\n", + "- `df_zonal_stats` has the results of our `zonal_stats` and then coerced it to be a dataframe.\n", + "\n", + "So from that caell, we get `df_zonal_stats` which looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_zonal_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So now, we can merge this back onto our geodataframe so we can add the majority classes and unique number of classes as attributes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracts_gdf_sf_zs = pd.concat([tracts_gdf_sf, df_zonal_stats[['majority','unique']]], axis=1) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can make a map that shows, for example, the majority class we have in each census tract." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(8,8))\n", + "tracts_gdf_sf_zs.plot(column='majority', cmap=cmap, norm=norm, ax=ax)\n", + "\n", + "# Add color bar\n", + "patches = [Patch(color=color, label=label)\n", + " for color, label in legend_labels.items()]\n", + "\n", + "fig.legend(handles=patches, facecolor=\"white\",bbox_to_anchor=(1.1, 1.05))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 16.4 Other resources\n", + "We really only grazed the surface here. We've linked a couple of resources that dive into raster data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [EarthLab](https://www.earthdatascience.org)\n", + "- [Software Carpentry](https://carpentries-incubator.github.io/geospatial-python/aio/index.html)\n", + "- [Intro to Python GIS](https://automating-gis-processes.github.io/CSC/index.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "
 D-Lab @ University of California - Berkeley
\n", + "
 Team Geo
\n", + "
\n", + " \n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "geo_env2", + "language": "python", + "name": "geo_env2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/lessons/99_Questions_Answers.md b/lessons/99_Questions_Answers.md new file mode 100644 index 0000000..2b5bbb2 --- /dev/null +++ b/lessons/99_Questions_Answers.md @@ -0,0 +1,127 @@ +# Common questions and answers + +This document lists comment questions and their respective answers pointing to specific parts of the workshop files.  + +I’m having trouble installing `GeoPandas` on a Windows computer. +- I’m having trouble installing `GeoPandas` on a Mac and I usually use pip install. +- When using pip to install GeoPandas, you need to make sure that all dependencies are installed correctly. Fiona provides binary wheels with the dependencies included for Mac and Linux. The easiest way to attempt to fix this, first order, is to uninstall geopandas and it’s dependencies and reinstall. + +I’m having trouble with packages versions not working with each other. +- You can try creating a virtual environment, see the bottom of the [README](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/README.md) + +What’s the difference between `GeoPandas` and `Pandas`? +- [Lesson 2.1](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/02_Introduction_to_GeoPandas.ipynb) + +How do I read in geospatial data vector file formats? +- `gpd.read_file` is a great function that reads in multiple vector data file formats. +- [Lesson 2.2 and 2.6](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/02_Introduction_to_GeoPandas.ipynb) + +How do I save geospatial data file formats? +- [Lesson 2.6](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/02_Introduction_to_GeoPandas.ipynb) + +What are Coordinate Reference Systems +- [Lesson 1](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/01.Overview_Geospatial_Data.pdf) +- [Lesson 3.4](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/03_CRS_Map_Projections.ipynb) + +I’m trying to plot two shapefile together but they’re not showing up +- This is the #1 folks run into! It’s most likely that the CRS for your two datasets are different. +- [Lesson 3.1-3.3](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/03_CRS_Map_Projections.ipynb) + +How do I get the CRS of my data and transform it? +- [Lesson 3.5, 3.7](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/03_CRS_Map_Projections.ipynb) + +How do I set the CRS of my data if it’s missing? +- [Lesson 3.6](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/03_CRS_Map_Projections.ipynb) + +I have a CSV that has latitude and longitude values, how do I coerce it to be a GeoDataFrame? +- [Lesson 4.2](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/04_More_Data_More_Maps.ipynb) + +How do I find the geospatial extent of my data? +- Use `total_bounds` +- [Lesson 4.3](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/04_More_Data_More_Maps.ipynb) + +How do I create a choropleth map? +- [Lesson 5.1](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/05_Data-Driven_Mapping.ipynb) + +What kinds of color maps are there? +- [Lesson 5.1](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/05_Data-Driven_Mapping.ipynb) + +What types of data is best for choropleth mapping? +- [Lesson 5.2](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/05_Data-Driven_Mapping.ipynb) + +What is a classification scheme and how do I use different ones in Python? +- [Lesson 5.3](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/05_Data-Driven_Mapping.ipynb) + +Can I define my own classification scheme? +- Yes! +- [Lesson 5.3](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/05_Data-Driven_Mapping.ipynb) + +How do I create a point map? +- [Lesson 5.4](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/05_Data-Driven_Mapping.ipynb) + +How does mapping categorical data different from mapping quantitative data? +- It’s basically the same except you’ll have to specify that it’s categorical. +- [Lesson 5.5](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/05_Data-Driven_Mapping.ipynb) + +How do I calculate the area or length of my GeoDataFrame? +- [Lesson 6.1](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/06_Spatial_Queries.ipynb) + +What is a relationship query? +- [Lesson 6.2](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/06_Spatial_Queries.ipynb) + +How do I do a proximity analysis? +- [Lesson 6.3](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/06_Spatial_Queries.ipynb) + +How do I know what units my buffer size is in? +- The units are what your CRS says they are. +- [Lesson 6.3](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/06_Spatial_Queries.ipynb) + +Can I do a merge like I do in Pandas for GeoDataFrames? +- Yes! +- [Lesson 7.1](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/07_Joins_and_Aggregation.ipynb) + +What is a spatial join and how do I do it? +- [Lesson 7.2](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/07_Joins_and_Aggregation.ipynb) + +What’s the best way to aggregate my geospatial data (for example, after doing a join)? +- Using `.dissolve` is better than a `groupby` since it’ll preserve your geometries. +- [Lesson 7.3](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/07_Joins_and_Aggregation.ipynb) + +Do you have any full workflows we can work through and ask questions about? +- Yes, we have two! +- [Lesson 8](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/08_Pulling_It_All_Together.ipynb) +- [Lesson 9](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/09_ON_YOUR_OWN_A_Full_Workflow.ipynb) + +How do I fetch and use geospatial data without downloading it as a file? +- [Lesson 10](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/10_OPTIONAL_Fetching_Data.ipynb) + +How do I create maps with basemaps? +- [Lesson 11](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/11_OPTIONAL_Basemap_with_Contextily.ipynb) + +How do I create interactive maps? +- [Lesson 12](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/12_OPTIONAL_Interactive_Mapping_with_Folium.ipynb) + +How do I geocode address in Python? +- [Lesson 13](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/13_OPTIONAL_geocoding.ipynb) + +Is there a package to do both panda and geopandas plots with some interactive functionality? +- Try `Altair`! +- [Lesson 14](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb) + +How do I do a Voronoi Tessellation? +- [Lesson 15](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/14_OPTIONAL_Plotting_and_Mapping_with_Altair.ipynb) + +I want to start using raster data. Where’s a good place ot start? +- [Lesson 16](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python/blob/master/16_OPTIONAL_Introduction_to_Raster_Data.ipynb) + +--- +
+ + +
+ +
+    
 D-Lab @ University of California - Berkeley
+    
 Team Geo
+
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The Python programming language is a great platform for exploring these data and integrating them into your research. This JupyterBook explores everything from *A to Z* to get started to work with Geospatial data in Python. We then take you all the way to *fancy* to work with online data sources, basemaps, interactive maps, geocoding, tessellation, and raster data. + +### 1. Getting Started with Spatial Dataframes + +Part one will introduce basic methods for working with geospatial data in Python using the [GeoPandas library](https://geopandas.org). You will learn how to import and export spatial data and store them as GeoPandas GeoDataFrames (or spatial dataframes). We will explore and compare several methods for mapping the data including the GeoPandas plot function and the matplotlib library. We will review coordinate reference systems and methods for reading, defining and transforming these. + + +### 2. Geoprocessing and Analysis + +Part two dives deeper into data driven mapping in Python, using color palettes and data classification to communicate information with maps. We will also introduce basic methods for processing spatial data, which are the building blocks of common spatial analysis workflows. + + +### 3. Exercises + +Part 3 provides two full workflows for you to try to work through on your own. These exercises uses techniques and concepts from both the first and second parts. + +### 4. Get Fancy + +Part 4 dives builds off of the foundational work from the earlier sections. The topics included involve: +- Reading in online sources data +- Adding basemaps +- Creating interactive maps +- Geocoding addresses +- Using Altair for plotting +- Creating voronoi tessellations +- Starting out with raster data + + +### Pre-requisites + +#### Knowledge Requirements +You'll probably get the most out of this workshop if you have a basic foundation in Python and Pandas, similar to what you would have from taking the D-Lab Python Fundamentals workshop series. Here are a couple of suggestions for materials to check-out prior to the workshop. + +`D-Lab Workshops`: + - [Python Fundamentals](https://github.com/dlab-berkeley/python-fundamentals) + - [Pandas](https://github.com/dlab-berkeley/introduction-to-pandas) + +`Other`: + - [Learn Python on Kaggle](https://www.kaggle.com/learn/python) + - [Programming in Python - Software Carpentry](http://swcarpentry.github.io/python-novice-inflammation/) + - [Learn Pandas on Kaggle](https://www.kaggle.com/learn/pandas) + - [Plotting in Python - Software Carpentry](http://swcarpentry.github.io/python-novice-gapminder/) +: Basic knowledge of geospatial data is expected. R experience equivalent to the D-Lab R Fundamentals workshop series is required to follow along with the tutorial. Knowledge of ggplot helpful. + +#### Technology Requirements: + +Bring a laptop with Python and the following packages installed: pandas, geopandas, matplotlib, descartes and dependencies. More details are provided on the workshop github page https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-Python). + + +## 1.0 Python and Jupyter Notebook installation + +There are many ways to install python and python libraries, distributed as packages, on your computer. Here is one way that we recommend. + + +* Anaconda installs IDEs and several important packages like NumPy, Pandas, and so on, and this is a really convenient package which can be downloaded and installed. + +Anaconda is a free and open-source distribution of Python. Anaconda installs IDEs (integrated development environments, aka where you can write and run code) and several important packages like NumPy and Pandas, making it a really convenient package to use. + +### 1.1 Download Anaconda: + +Follow this link to download Anaconda: https://www.anaconda.com/distribution. The same link can be used for Mac, Windows, and Linux. + + +We recommend downloading the latest version, which will be Python 3. +![downloadinstruc](assets/images/anaconda_download_instructions.png) + +Open the .exe file that was downloaded and follow the instructions in the installation wizard prompt. + +### 1.2 Launch Anaconda and open a Jupyter Notebook + +Once installation is complete open Anaconda Navigator and launch Jupyter Notebook. +![launchnav](assets/images/anaconda_navigator_launch.png) + +Jupyter Notebook will open in your web browser (it does not require internet to work). In Jupyter, navigate to the folder where you saved the code file you plan to use and open the .ipynb file (the extension for Jupyter Notebook files written in Python) to view it in the Notebook. + +## 2.0 Installing Geopandas + +- From within Anaconda Navigator click on the `Environments` selection in the left sidebar menu +> ![anacondanav](assets/images/anaconda1_navigator_home.png) + +- Click on the arrow to the right of your `base (root)` environment and select **Open Terminal** + +> ![anacondanav](assets/images/anaconda2_base_open_teriminal.png) + +- This will give you access to the command line interface (CLI) on your computer in a window that looks like this: + +> ![openterminal](assets/images/anaconda2_base_open_teriminal.png) + +- Install some needed software by entering the following commands, one at a time: + +``` +conda install python=3 geopandas +conda install juypter +conda install matplotlib +conda install descartes +conda install mapclassify +conda install contextily +``` +Once you have those libraries all installed you will be able to go to Anaconda Navigator, launch a `Jupyter Notebook`, navigate to the workshop files and run all of the notebooks. + + +*Optionally you can create a virtual environment In the terminal window, type the **conda** commands shown on the [GeoPandas website](https://geopandas.org/install.html#creating-a-new-environment) for installing Geopandas in a virtual environment. These are:* + +```` +conda create -n geo_env +conda activate geo_env +conda config --env --add channels conda-forge +conda config --env --set channel_priority strict +conda install python=3 geopandas +```` + +*After creating your virtual environment, you can process and install the rest of your packages listed above. You will be able to select your `geo_env` in Anaconda Navigator.* + + + +--- +
+ + +
+ +
+
 D-Lab @ University of California - Berkeley
+
 Team Geo
+
+ + diff --git a/lessons/notebook_data/README.md b/lessons/notebook_data/README.md new file mode 100644 index 0000000..4ec8001 --- /dev/null +++ b/lessons/notebook_data/README.md @@ -0,0 +1,3 @@ +# Data Folder + +This is a holding place for the notebook data during development. diff --git a/notebook_data/ac_voting_locations.csv b/lessons/notebook_data/ac_voting_locations.csv similarity index 100% rename from notebook_data/ac_voting_locations.csv rename to lessons/notebook_data/ac_voting_locations.csv diff --git a/notebook_data/alco_schools.csv b/lessons/notebook_data/alco_schools.csv similarity index 100% rename from notebook_data/alco_schools.csv rename to lessons/notebook_data/alco_schools.csv diff --git a/notebook_data/bartmap_example.html b/lessons/notebook_data/bartmap_example.html similarity index 100% rename from notebook_data/bartmap_example.html rename to lessons/notebook_data/bartmap_example.html diff --git a/notebook_data/berkeley/BerkeleyCityLimits.cpg 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