01 - Creating and joining GeoDataFrames.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will:\n",
    "* deal with the *GeoJSON* Files to create polygonal plots\n",
    "* learn about projections and coordinate reference\n",
    "* get practice spatially joining data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GeoJSON is another type of files that we can use to store geospatial data and create geospatial visualizations. Unlike shapefiles, GeoJSON is a single file making it easier to work with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first lines (of many) of *nashville-neighborhoods.geojson* look like this,\n",
    "\n",
    "```\n",
    "\"type\":\"Feature\",\n",
    "        \"properties\":{\n",
    "      \"name\":\"Historic Buena Vista\"\n",
    "      },\"geometry\":{\n",
    "          \"type\":\"MultiPolygon\",\"coordinates\":[[[[-86.79511056795417,36.17575964963348],[-86.79403325521203,36.176723819622765],[-86.79395847673587...\n",
    "```\n",
    "\n",
    "The properties are mapped to our geodataframe when reading using geopandas like this..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Historic Buena Vista</td>\n",
       "      <td>MULTIPOLYGON (((-86.79511 36.17576, -86.79403 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charlotte Park</td>\n",
       "      <td>MULTIPOLYGON (((-86.87460 36.15758, -86.87317 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hillwood</td>\n",
       "      <td>MULTIPOLYGON (((-86.87614 36.13554, -86.87583 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Meade</td>\n",
       "      <td>MULTIPOLYGON (((-86.90384 36.12554, -86.90328 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White Bridge</td>\n",
       "      <td>MULTIPOLYGON (((-86.86321 36.12886, -86.86321 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                   name                                           geometry\n",
       "0  Historic Buena Vista  MULTIPOLYGON (((-86.79511 36.17576, -86.79403 ...\n",
       "1        Charlotte Park  MULTIPOLYGON (((-86.87460 36.15758, -86.87317 ...\n",
       "2              Hillwood  MULTIPOLYGON (((-86.87614 36.13554, -86.87583 ...\n",
       "3            West Meade  MULTIPOLYGON (((-86.90384 36.12554, -86.90328 ...\n",
       "4          White Bridge  MULTIPOLYGON (((-86.86321 36.12886, -86.86321 ..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigborhoods = gpd.read_file(\"assets/nashville-neighborhoods.geojson\")\n",
    "neigborhoods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geopandas is installed with some dependencies that makes it easy to deal with:\n",
    "* the vector data which is the geospatial data made up of points, lines and polygons. This is dealt by *fiona* which is a python API for *OGR* (Open GIS Reference). We will deal with vector graphics through these notebooks.\n",
    "* the raster data can be thought as grid maps. This is dealt by *GDAL*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colormaps and legends for geopandas plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can specify a qualitative (categorial) color map (paletted) using the `cmap` argument in the `plot` method of the `GeoDataFrame`. We can also modify some properties of the legend of the mapped colors using the `legend_kwds` argument.\n",
    "\n",
    "\n",
    "```python\n",
    "neigborhoods.plot(\n",
    "    column = \"name\",\n",
    "    legend = True,\n",
    "    cmap = \"Set3\",\n",
    "    legend_kwds = {\n",
    "        \"title\": \"Neighborhood\",\n",
    "        \"loc\": \"upper left\",\n",
    "        \"bbox_to_anchor\": (1, 1.03),\n",
    "        \"ncol\": 3\n",
    "    }\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>email</th>\n",
       "      <th>res_phone</th>\n",
       "      <th>bus_phone</th>\n",
       "      <th>last_name</th>\n",
       "      <th>position</th>\n",
       "      <th>district</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nick</td>\n",
       "      <td>nick.leonardo@nashville.gov</td>\n",
       "      <td>615-509-6334</td>\n",
       "      <td>615-862-6780</td>\n",
       "      <td>Leonardo</td>\n",
       "      <td>Council Member</td>\n",
       "      <td>1</td>\n",
       "      <td>MULTIPOLYGON (((-86.90738 36.39052, -86.90725 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DeCosta</td>\n",
       "      <td>decosta.hastings@nashville.gov</td>\n",
       "      <td>615-779-1565</td>\n",
       "      <td>615-862-6780</td>\n",
       "      <td>Hastings</td>\n",
       "      <td>Council Member</td>\n",
       "      <td>2</td>\n",
       "      <td>MULTIPOLYGON (((-86.75902 36.23091, -86.75909 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nancy</td>\n",
       "      <td>nancy.vanreece@nashville.gov</td>\n",
       "      <td>615-576-0488</td>\n",
       "      <td>615-862-6780</td>\n",
       "      <td>VanReece</td>\n",
       "      <td>Council Member</td>\n",
       "      <td>8</td>\n",
       "      <td>MULTIPOLYGON (((-86.72850 36.28328, -86.72791 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bill</td>\n",
       "      <td>bill.pridemore@nashville.gov</td>\n",
       "      <td>615-915-1419</td>\n",
       "      <td>615-862-6780</td>\n",
       "      <td>Pridemore</td>\n",
       "      <td>Council Member</td>\n",
       "      <td>9</td>\n",
       "      <td>MULTIPOLYGON (((-86.68681 36.28671, -86.68706 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Robert</td>\n",
       "      <td>robert.swope@nashville.gov</td>\n",
       "      <td>615-308-0577</td>\n",
       "      <td>615-862-6780</td>\n",
       "      <td>Swope</td>\n",
       "      <td>Council Member</td>\n",
       "      <td>4</td>\n",
       "      <td>MULTIPOLYGON (((-86.74489 36.05316, -86.74491 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name                           email     res_phone     bus_phone  \\\n",
       "0       Nick     nick.leonardo@nashville.gov  615-509-6334  615-862-6780   \n",
       "1    DeCosta  decosta.hastings@nashville.gov  615-779-1565  615-862-6780   \n",
       "2      Nancy    nancy.vanreece@nashville.gov  615-576-0488  615-862-6780   \n",
       "3       Bill    bill.pridemore@nashville.gov  615-915-1419  615-862-6780   \n",
       "4     Robert      robert.swope@nashville.gov  615-308-0577  615-862-6780   \n",
       "\n",
       "   last_name        position district  \\\n",
       "0   Leonardo  Council Member        1   \n",
       "1   Hastings  Council Member        2   \n",
       "2   VanReece  Council Member        8   \n",
       "3  Pridemore  Council Member        9   \n",
       "4      Swope  Council Member        4   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((-86.90738 36.39052, -86.90725 ...  \n",
       "1  MULTIPOLYGON (((-86.75902 36.23091, -86.75909 ...  \n",
       "2  MULTIPOLYGON (((-86.72850 36.28328, -86.72791 ...  \n",
       "3  MULTIPOLYGON (((-86.68681 36.28671, -86.68706 ...  \n",
       "4  MULTIPOLYGON (((-86.74489 36.05316, -86.74491 ...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "council_dists = gpd.read_file(\"assets/council_districts.geojson\")\n",
    "council_dists.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "council_dists.plot(\n",
    "    column = \"district\",\n",
    "    legend = True,\n",
    "    cmap = \"Set3\",\n",
    "    legend_kwds = {\n",
    "        \"title\": \"District Number\",\n",
    "        \"loc\": \"upper left\",\n",
    "        \"bbox_to_anchor\": (1, 1.03),\n",
    "        \"ncol\": 3,\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>first_name</th>\n",
       "      <th>city</th>\n",
       "      <th>zip</th>\n",
       "      <th>email</th>\n",
       "      <th>state</th>\n",
       "      <th>last_name</th>\n",
       "      <th>address</th>\n",
       "      <th>position</th>\n",
       "      <th>term_expir</th>\n",
       "      <th>district</th>\n",
       "      <th>phone</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dr. Sharon</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>37218</td>\n",
       "      <td>gentryfordistrict1@comcast.net</td>\n",
       "      <td>TN</td>\n",
       "      <td>Gentry</td>\n",
       "      <td>6108 Beals Lane</td>\n",
       "      <td>Member</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>615-268-5269</td>\n",
       "      <td>MULTIPOLYGON (((-86.77136 36.38357, -86.77134 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_name       city    zip                           email state  \\\n",
       "0  Dr. Sharon  Nashville  37218  gentryfordistrict1@comcast.net    TN   \n",
       "\n",
       "  last_name          address position term_expir district         phone  \\\n",
       "0    Gentry  6108 Beals Lane   Member       2016        1  615-268-5269   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((-86.77136 36.38357, -86.77134 ...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_dists = gpd.read_file(\"assets/school-district.geojson\")\n",
    "school_dists.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "school_dists.plot(\n",
    "    column = \"district\",\n",
    "    legend = True,\n",
    "    cmap = \"Set3\",\n",
    "    legend_kwds = {\n",
    "        \"title\": \"District Number\",\n",
    "        \"loc\": \"upper left\",\n",
    "        \"bbox_to_anchor\": (1, 1.03)\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neigborhoods.plot(\n",
    "    column = \"name\",\n",
    "    cmap = \"summer\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Projections and Coordinate Reference Systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A map projection is the way we visualize the 3d world into a 2d plane. The most common projection is the [Mercator projection](https://en.wikipedia.org/wiki/Mercator_projection#:~:text=The%20Mercator%20projection%20(%2Fm%C9%99r,cartographer%20Gerardus%20Mercator%20in%201569.&text=The%20map%20is%20thereby%20conformal,objects%20away%20from%20the%20equator.)). Setting the coordinate reference system (CRS) to the `GeoDataFrame` tells `geopandas` how to interpret the longitude and latitude coordinates. The common CRSs are:\n",
    "* EPSG:4326 : this is used by Google Earth and the units here are decimal degrees.\n",
    "* EPSG:3857 : this is used by Google Maps, Bing Maps, Open Street Maps and the units here are meters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geometry Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create the geometry columns from a dataframe. We can use the `gemoetry` module in the `shapely` library to import a `Point` class that will construct our representation of the geometry. `shapely` is a module that allows us to create and work with points, lines, and ploygons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\U\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:118: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n",
      "  arr = construct_1d_object_array_from_listlike(values)\n"
     ]
    }
   ],
   "source": [
    "from shapely.geometry import Point\n",
    "\n",
    "schools = pd.read_csv(\"assets/schools.csv\")\n",
    "\n",
    "schools[\"geometry\"] = schools.apply(lambda row: Point(row.Longitude, row.Latitude), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To construct a `GeoDataFrame` from the `pd.DataFrame`, we should call the `GeoDataFrame` constructor and pass the `pd.DataFrame` object with specifying the `crs` and `geometry` to the CRS we are going to use, usually `epsg:4326` and the geometry column which is `schools.geometry` here.\n",
    "\n",
    "```python\n",
    "schools_geo_df = gpd.DataFrame(\n",
    "    schools,\n",
    "    crs = \"epsg:4326\",\n",
    "    geometry = schools.geometry\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools_geo_df = gpd.GeoDataFrame(\n",
    "    schools,\n",
    "    crs = \"epsg:4326\",\n",
    "    geometry = schools.geometry\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>School ID</th>\n",
       "      <th>School Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Phone Number</th>\n",
       "      <th>School State ID</th>\n",
       "      <th>School Level</th>\n",
       "      <th>Lowest Grade</th>\n",
       "      <th>Highest Grade</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>School Website</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Mapped Location</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>496</td>\n",
       "      <td>A. Z. Kelley Elementary</td>\n",
       "      <td>5834 Pettus Road</td>\n",
       "      <td>Antioch</td>\n",
       "      <td>TN</td>\n",
       "      <td>37013</td>\n",
       "      <td>(615)941-7535</td>\n",
       "      <td>1</td>\n",
       "      <td>Elementary School</td>\n",
       "      <td>Grade P3</td>\n",
       "      <td>Grade 4</td>\n",
       "      <td>36.021817</td>\n",
       "      <td>-86.658848</td>\n",
       "      <td>http://schools.mnps.org/az-kelley-elementary-s...</td>\n",
       "      <td>Cane Ridge</td>\n",
       "      <td>(36.02181712, -86.65884778)</td>\n",
       "      <td>POINT (-86.65885 36.02182)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>375</td>\n",
       "      <td>Alex Green Elementary</td>\n",
       "      <td>3921 Lloyd Road</td>\n",
       "      <td>Whites Creek</td>\n",
       "      <td>TN</td>\n",
       "      <td>37189</td>\n",
       "      <td>(615)876-5105</td>\n",
       "      <td>5</td>\n",
       "      <td>Elementary School</td>\n",
       "      <td>Grade P4</td>\n",
       "      <td>Grade 4</td>\n",
       "      <td>36.252961</td>\n",
       "      <td>-86.832229</td>\n",
       "      <td>http://schools.mnps.org/alex-green-elementary-...</td>\n",
       "      <td>Whites Creek</td>\n",
       "      <td>(36.2529607, -86.8322292)</td>\n",
       "      <td>POINT (-86.83223 36.25296)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   School ID              School Name    Street Address          City State  \\\n",
       "0        496  A. Z. Kelley Elementary  5834 Pettus Road       Antioch    TN   \n",
       "1        375    Alex Green Elementary   3921 Lloyd Road  Whites Creek    TN   \n",
       "\n",
       "   ZIP Code   Phone Number  School State ID       School Level Lowest Grade  \\\n",
       "0     37013  (615)941-7535                1  Elementary School     Grade P3   \n",
       "1     37189  (615)876-5105                5  Elementary School     Grade P4   \n",
       "\n",
       "  Highest Grade   Latitude  Longitude  \\\n",
       "0       Grade 4  36.021817 -86.658848   \n",
       "1       Grade 4  36.252961 -86.832229   \n",
       "\n",
       "                                      School Website       Cluster  \\\n",
       "0  http://schools.mnps.org/az-kelley-elementary-s...    Cane Ridge   \n",
       "1  http://schools.mnps.org/alex-green-elementary-...  Whites Creek   \n",
       "\n",
       "               Mapped Location                    geometry  \n",
       "0  (36.02181712, -86.65884778)  POINT (-86.65885 36.02182)  \n",
       "1    (36.2529607, -86.8322292)  POINT (-86.83223 36.25296)  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools_geo_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the CRS of the geometry column of `GeoDataFrame` using the `to_crs` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "schools_geo_df.geometry = schools_geo_df.geometry.to_crs(epsg = 3857)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>School ID</th>\n",
       "      <th>School Name</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Phone Number</th>\n",
       "      <th>School State ID</th>\n",
       "      <th>School Level</th>\n",
       "      <th>Lowest Grade</th>\n",
       "      <th>Highest Grade</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>School Website</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Mapped Location</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>496</td>\n",
       "      <td>A. Z. Kelley Elementary</td>\n",
       "      <td>5834 Pettus Road</td>\n",
       "      <td>Antioch</td>\n",
       "      <td>TN</td>\n",
       "      <td>37013</td>\n",
       "      <td>(615)941-7535</td>\n",
       "      <td>1</td>\n",
       "      <td>Elementary School</td>\n",
       "      <td>Grade P3</td>\n",
       "      <td>Grade 4</td>\n",
       "      <td>36.021817</td>\n",
       "      <td>-86.658848</td>\n",
       "      <td>http://schools.mnps.org/az-kelley-elementary-s...</td>\n",
       "      <td>Cane Ridge</td>\n",
       "      <td>(36.02181712, -86.65884778)</td>\n",
       "      <td>POINT (-9646818.808 4303623.790)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>375</td>\n",
       "      <td>Alex Green Elementary</td>\n",
       "      <td>3921 Lloyd Road</td>\n",
       "      <td>Whites Creek</td>\n",
       "      <td>TN</td>\n",
       "      <td>37189</td>\n",
       "      <td>(615)876-5105</td>\n",
       "      <td>5</td>\n",
       "      <td>Elementary School</td>\n",
       "      <td>Grade P4</td>\n",
       "      <td>Grade 4</td>\n",
       "      <td>36.252961</td>\n",
       "      <td>-86.832229</td>\n",
       "      <td>http://schools.mnps.org/alex-green-elementary-...</td>\n",
       "      <td>Whites Creek</td>\n",
       "      <td>(36.2529607, -86.8322292)</td>\n",
       "      <td>POINT (-9666119.539 4335484.432)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   School ID              School Name    Street Address          City State  \\\n",
       "0        496  A. Z. Kelley Elementary  5834 Pettus Road       Antioch    TN   \n",
       "1        375    Alex Green Elementary   3921 Lloyd Road  Whites Creek    TN   \n",
       "\n",
       "   ZIP Code   Phone Number  School State ID       School Level Lowest Grade  \\\n",
       "0     37013  (615)941-7535                1  Elementary School     Grade P3   \n",
       "1     37189  (615)876-5105                5  Elementary School     Grade P4   \n",
       "\n",
       "  Highest Grade   Latitude  Longitude  \\\n",
       "0       Grade 4  36.021817 -86.658848   \n",
       "1       Grade 4  36.252961 -86.832229   \n",
       "\n",
       "                                      School Website       Cluster  \\\n",
       "0  http://schools.mnps.org/az-kelley-elementary-s...    Cane Ridge   \n",
       "1  http://schools.mnps.org/alex-green-elementary-...  Whites Creek   \n",
       "\n",
       "               Mapped Location                          geometry  \n",
       "0  (36.02181712, -86.65884778)  POINT (-9646818.808 4303623.790)  \n",
       "1    (36.2529607, -86.8322292)  POINT (-9666119.539 4335484.432)  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schools_geo_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatially joining two GeoDataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we have the 2 following geodataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'School Districts')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize = (20, 6))\n",
    "\n",
    "council_dists.plot(\n",
    "    column = \"district\",\n",
    "    legend = True,\n",
    "    cmap = \"Set3\",\n",
    "    legend_kwds = {\n",
    "        \"title\": \"District Number\",\n",
    "        \"loc\": \"upper left\",\n",
    "        \"bbox_to_anchor\": (1, 1.03),\n",
    "        \"ncol\": 3,\n",
    "    },\n",
    "    ax = ax[0]\n",
    ")\n",
    "\n",
    "ax[0].set_title(\"Council Districts\")\n",
    "\n",
    "school_dists.plot(\n",
    "    column = \"district\",\n",
    "    legend = True,\n",
    "    cmap = \"Set3\",\n",
    "    legend_kwds = {\n",
    "        \"title\": \"District Number\",\n",
    "        \"loc\": \"upper left\",\n",
    "        \"bbox_to_anchor\": (1, 1.03)\n",
    "    },\n",
    "    ax = ax[1]\n",
    ")\n",
    "\n",
    "ax[1].set_title(\"School Districts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to know if there are council districts are contained completely within a school district ? Or if there are any council district intersects with any school district ? Here, spatial joining the 2 geodataframes can be very helpful.\n",
    "\n",
    "We can spatially join the 2 geodataframes using the `sjoin` function.\n",
    "\n",
    "\n",
    "```python\n",
    "gpd.sjoin(\n",
    "    first_geo_df,\n",
    "    second_goe_df,\n",
    "    predicate = join operation\n",
    ")\n",
    "```\n",
    "\n",
    "The `predicate` argument here is the type of operation we want to join the two geodataframes based on. It can be:\n",
    "\n",
    "* `\"intersects\"` : return all observations where the 2 geodataframes *intersect* each other.\n",
    "* `\"contains\"` : return all observations where the first dataframe *compeletely contains* the second one.\n",
    "* `\"within\"` : return all observations where the first dataframe *are completely within* the second one.\n",
    "\n",
    "And we can use of the shape of the resulting geodataframe to know how many observations results from the joining operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of council districts within school districts is 11\n"
     ]
    }
   ],
   "source": [
    "within_gdf = gpd.sjoin(council_dists, school_dists, predicate = \"within\")\n",
    "\n",
    "print(\"Number of council districts within school districts is {}\".format(within_gdf.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of school districts contain school districts is 11\n"
     ]
    }
   ],
   "source": [
    "contains_gdf = gpd.sjoin(school_dists, council_dists, predicate = \"contains\")\n",
    "\n",
    "print(\"Number of school districts contain school districts is {}\".format(contains_gdf.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of school districts contain school districts is 100\n"
     ]
    }
   ],
   "source": [
    "intersects_gdf = gpd.sjoin(school_dists, council_dists, predicate = \"intersects\")\n",
    "\n",
    "print(\"Number of school districts contain school districts is {}\".format(intersects_gdf.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>first_name_left</th>\n",
       "      <th>email_left</th>\n",
       "      <th>res_phone</th>\n",
       "      <th>bus_phone</th>\n",
       "      <th>last_name_left</th>\n",
       "      <th>position_left</th>\n",
       "      <th>district_left</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>first_name_right</th>\n",
       "      <th>city</th>\n",
       "      <th>zip</th>\n",
       "      <th>email_right</th>\n",
       "      <th>state</th>\n",
       "      <th>last_name_right</th>\n",
       "      <th>address</th>\n",
       "      <th>position_right</th>\n",
       "      <th>term_expir</th>\n",
       "      <th>district_right</th>\n",
       "      <th>phone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nick</td>\n",
       "      <td>nick.leonardo@nashville.gov</td>\n",
       "      <td>615-509-6334</td>\n",
       "      <td>615-862-6780</td>\n",
       "      <td>Leonardo</td>\n",
       "      <td>Council Member</td>\n",
       "      <td>1</td>\n",
       "      <td>MULTIPOLYGON (((-86.90738 36.39052, -86.90725 ...</td>\n",
       "      <td>0</td>\n",
       "      <td>Dr. Sharon</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>37218</td>\n",
       "      <td>gentryfordistrict1@comcast.net</td>\n",
       "      <td>TN</td>\n",
       "      <td>Gentry</td>\n",
       "      <td>6108 Beals Lane</td>\n",
       "      <td>Member</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>615-268-5269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DeCosta</td>\n",
       "      <td>decosta.hastings@nashville.gov</td>\n",
       "      <td>615-779-1565</td>\n",
       "      <td>615-862-6780</td>\n",
       "      <td>Hastings</td>\n",
       "      <td>Council Member</td>\n",
       "      <td>2</td>\n",
       "      <td>MULTIPOLYGON (((-86.75902 36.23091, -86.75909 ...</td>\n",
       "      <td>0</td>\n",
       "      <td>Dr. Sharon</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>37218</td>\n",
       "      <td>gentryfordistrict1@comcast.net</td>\n",
       "      <td>TN</td>\n",
       "      <td>Gentry</td>\n",
       "      <td>6108 Beals Lane</td>\n",
       "      <td>Member</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>615-268-5269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nancy</td>\n",
       "      <td>nancy.vanreece@nashville.gov</td>\n",
       "      <td>615-576-0488</td>\n",
       "      <td>615-862-6780</td>\n",
       "      <td>VanReece</td>\n",
       "      <td>Council Member</td>\n",
       "      <td>8</td>\n",
       "      <td>MULTIPOLYGON (((-86.72850 36.28328, -86.72791 ...</td>\n",
       "      <td>1</td>\n",
       "      <td>Jill</td>\n",
       "      <td>Madison</td>\n",
       "      <td>37115</td>\n",
       "      <td>jill.speering@mnps.org</td>\n",
       "      <td>TN</td>\n",
       "      <td>Speering</td>\n",
       "      <td>1033 Falls Avenue</td>\n",
       "      <td>Vice-Chair</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>615-562-5234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bill</td>\n",
       "      <td>bill.pridemore@nashville.gov</td>\n",
       "      <td>615-915-1419</td>\n",
       "      <td>615-862-6780</td>\n",
       "      <td>Pridemore</td>\n",
       "      <td>Council Member</td>\n",
       "      <td>9</td>\n",
       "      <td>MULTIPOLYGON (((-86.68681 36.28671, -86.68706 ...</td>\n",
       "      <td>1</td>\n",
       "      <td>Jill</td>\n",
       "      <td>Madison</td>\n",
       "      <td>37115</td>\n",
       "      <td>jill.speering@mnps.org</td>\n",
       "      <td>TN</td>\n",
       "      <td>Speering</td>\n",
       "      <td>1033 Falls Avenue</td>\n",
       "      <td>Vice-Chair</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>615-562-5234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name_left                      email_left     res_phone     bus_phone  \\\n",
       "0            Nick     nick.leonardo@nashville.gov  615-509-6334  615-862-6780   \n",
       "1         DeCosta  decosta.hastings@nashville.gov  615-779-1565  615-862-6780   \n",
       "2           Nancy    nancy.vanreece@nashville.gov  615-576-0488  615-862-6780   \n",
       "3            Bill    bill.pridemore@nashville.gov  615-915-1419  615-862-6780   \n",
       "\n",
       "  last_name_left   position_left district_left  \\\n",
       "0       Leonardo  Council Member             1   \n",
       "1       Hastings  Council Member             2   \n",
       "2       VanReece  Council Member             8   \n",
       "3      Pridemore  Council Member             9   \n",
       "\n",
       "                                            geometry  index_right  \\\n",
       "0  MULTIPOLYGON (((-86.90738 36.39052, -86.90725 ...            0   \n",
       "1  MULTIPOLYGON (((-86.75902 36.23091, -86.75909 ...            0   \n",
       "2  MULTIPOLYGON (((-86.72850 36.28328, -86.72791 ...            1   \n",
       "3  MULTIPOLYGON (((-86.68681 36.28671, -86.68706 ...            1   \n",
       "\n",
       "  first_name_right       city    zip                     email_right state  \\\n",
       "0       Dr. Sharon  Nashville  37218  gentryfordistrict1@comcast.net    TN   \n",
       "1       Dr. Sharon  Nashville  37218  gentryfordistrict1@comcast.net    TN   \n",
       "2             Jill    Madison  37115          jill.speering@mnps.org    TN   \n",
       "3             Jill    Madison  37115          jill.speering@mnps.org    TN   \n",
       "\n",
       "  last_name_right            address position_right term_expir district_right  \\\n",
       "0          Gentry    6108 Beals Lane         Member       2016              1   \n",
       "1          Gentry    6108 Beals Lane         Member       2016              1   \n",
       "2        Speering  1033 Falls Avenue     Vice-Chair       2016              3   \n",
       "3        Speering  1033 Falls Avenue     Vice-Chair       2016              3   \n",
       "\n",
       "          phone  \n",
       "0  615-268-5269  \n",
       "1  615-268-5269  \n",
       "2  615-562-5234  \n",
       "3  615-562-5234  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "within_gdf.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note:*** if there are columns with the same name in the 2 geodataframes, the geodataframe resulted from the join operation will append `\"_left\"` and `\"_right\"` to know which column are from which dataframe. We can change the name of these columns explictly using `rename` method of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "within_gdf.rename(\n",
    "    columns = {\n",
    "        \"district_left\": \"council_district\",\n",
    "        \"district_right\": \"school_district\"\n",
    "    },\n",
    "    inplace = True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe which school districts contain the most council districts using a simple groupping and counting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>council_district</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_district</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 council_district\n",
       "school_district                  \n",
       "3                               3\n",
       "1                               2\n",
       "9                               2\n",
       "2                               1\n",
       "5                               1\n",
       "6                               1\n",
       "8                               1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "within_gdf[[\"council_district\", \"school_district\"]].groupby(\"school_district\").agg(\"count\").sort_values(\"council_district\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "contains_gdf.rename(\n",
    "    columns = {\n",
    "        \"district_right\": \"council_district\",\n",
    "        \"district_left\": \"school_district\"\n",
    "    },\n",
    "    inplace = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>council_district</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_district</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 council_district\n",
       "school_district                  \n",
       "3                               3\n",
       "1                               2\n",
       "9                               2\n",
       "2                               1\n",
       "5                               1\n",
       "6                               1\n",
       "8                               1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contains_gdf[[\"council_district\", \"school_district\"]].groupby(\"school_district\").agg(\"count\").sort_values(\"council_district\", ascending = False)"
   ]
  }
 ],
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