09_ON_YOUR_OWN_A_Full_Workflow.ipynb

{
 "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",
    "<!--\n",
    "#SOLUTION:\n",
    "# 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()\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": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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",
    "<!--\n",
    "#SOLUTION:\n",
    "\n",
    "# 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()\n",
    "\n",
    "# Check CRS\n",
    "print('polling_ac_gdf:', polling_ac_gdf.crs)\n",
    "print('tracts_gdf_ac CRS:', tracts_gdf_ac.crs)\n",
    "\n",
    "# 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\")\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",
    "<!--\n",
    "#SOLUTION:\n",
    "\n",
    "# Spatial join tracts/acs with the polling data (keep the tracts geometry!)\n",
    "polls_jointracts = gpd.sjoin(tracts_gdf_ac_utm10, polling_ac_gdf_utm10, how='left')\n",
    "\n",
    "# Plot it to make sure you have the right geometry\n",
    "polls_jointracts.plot()\n",
    "\n",
    "# Check out your data and its dimensions\n",
    "print(polls_jointracts.shape)\n",
    "polls_jointracts.head()\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": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRACTCE</th>\n",
       "      <th>geometry</th>\n",
       "      <th>NAME_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400100</td>\n",
       "      <td>POLYGON ((566221.610 4193371.510, 566659.969 4...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400200</td>\n",
       "      <td>POLYGON ((565335.837 4188666.188, 565441.159 4...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>400300</td>\n",
       "      <td>POLYGON ((564744.993 4188317.651, 564946.532 4...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400400</td>\n",
       "      <td>POLYGON ((564950.988 4188518.225, 564992.933 4...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>400500</td>\n",
       "      <td>POLYGON ((564276.448 4189213.844, 564317.359 4...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "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",
    "<!--\n",
    "#SOLUTION:\n",
    "\n",
    "# Use `dissolve` to count the number of polls we have\n",
    "polls_countsbytract = polls_jointracts[['TRACTCE', 'NAME_right', 'geometry']].dissolve(by='TRACTCE', aggfunc='count').reset_index()\n",
    "polls_countsbytract.head()\n",
    "\n",
    "# rename the column to be for the number of polling stations\n",
    "polls_countsbytract.rename(columns={'NAME_right': 'Num_Polling'}, inplace=True)\n",
    "\n",
    "# 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,column='Num_Polling', \n",
    "                         cmap=\"YlGn\",\n",
    "                         edgecolor=\"grey\",\n",
    "                         legend=True)\n",
    "polling_ac_gdf_utm10.plot(ax=ax, color='orange', edgecolor='black', markersize=3)\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",
    "<!--\n",
    "#SOLUTION:\n",
    "\n",
    "# merge onto census tract data\n",
    "tracts_gdf_ac =  tracts_gdf_ac.merge(polls_countsbytract[['TRACTCE', 'Num_Polling']], left_on='TRACTCE',right_on=\"TRACTCE\", how='inner') \n",
    "tracts_gdf_ac.head()\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",
    "#SOLUTION:\n",
    "\n",
    "berkeley_places = gpd.read_file(\"outdata/berkeley_places.shp\")\n",
    "berkeley_places.crs\n",
    "berkeley_places_utm10 = berkeley_places.to_crs(\"epsg:26910\")\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",
    "<!--\n",
    "#SOLUTION:\n",
    "\n",
    "# Bring in bus routes, transform CRS to 26910\n",
    "bus_routes = gpd.read_file('zip://notebook_data/transportation/Fall20Routeshape.zip')\n",
    "bus_routes_utm10 = bus_routes.to_crs(\"epsg:26910\")\n",
    "bus_routes_utm10.head()\n",
    "\n",
    "# Look at intersection between bus routes and Berkeley\n",
    "bus_routes_berkeley = bus_routes_utm10.intersects(berkeley_places_utm10.geometry.squeeze())\n",
    "\n",
    "# Plot results of intersection\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "berkeley_places_utm10.plot(ax=ax)\n",
    "bus_routes_utm10.loc[bus_routes_berkeley].plot(ax=ax, column ='PUB_RTE')\n",
    "\n",
    "bus_berk = bus_routes_utm10.loc[bus_routes_berkeley].reset_index(drop=True)\n",
    "\n",
    "bus_berk.length\n",
    "\n",
    "# Clip bus routes to everything that is inside the berkley outline\n",
    "bus_berk_clip = gpd.clip(bus_berk,berkeley_places_utm10)\n",
    "\n",
    "# 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')\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",
    "<!--\n",
    "#SOLUTION:\n",
    "\n",
    "bus_berk_buf =bus_berk_clip.buffer(distance=100)\n",
    "\n",
    "polling_berk = polling_ac_gdf_utm10.intersects(berkeley_places_utm10.geometry.squeeze())\n",
    "\n",
    "polling_berk_gdf = polling_ac_gdf_utm10[polling_berk].reset_index(drop=True)\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')\n",
    "\n",
    "\n",
    "polling_berk_gdf.distance(bus_berk_clip.unary_union)\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# You're done!!!! \n",
    "<img src=\"https://pbs.twimg.com/media/EIEHbKvXUAATXhi?format=jpg&name=large\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<div style=\"display:inline-block;vertical-align:middle;\">\n",
    "<a href=\"https://dlab.berkeley.edu/\" target=\"_blank\"><img src =\"assets/images/dlab_logo.png\" width=\"75\" align=\"left\">\n",
    "</a>\n",
    "</div>\n",
    "\n",
    "<div style=\"display:inline-block;vertical-align:middle;\">\n",
    "    <div style=\"font-size:larger\">&nbsp;D-Lab @ University of California - Berkeley</div>\n",
    "    <div>&nbsp;Team Geo<div>\n",
    "</div>\n",
    "        \n",
    "\n",
    "\n"
   ]
  }
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