Spatial Join.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46bc9a6f",
   "metadata": {},
   "source": [
    "# Spatial joins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdfecc91",
   "metadata": {},
   "source": [
    "Goals of this notebook:\n",
    "\n",
    "- Based on the `countries` and `cities` dataframes, determine for each city the country in which it is located.\n",
    "- To solve this problem, we will use the the concept of a 'spatial join' operation: combining information of geospatial datasets based on their spatial relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee1cd75",
   "metadata": {},
   "source": [
    "## import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f485cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import geopandas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d983be2f",
   "metadata": {},
   "source": [
    "## import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3103a76",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d3a949e",
   "metadata": {},
   "source": [
    "## Joining dataframes\n",
    "\n",
    "Pandas provides functionality to join or merge dataframes in different ways, see https://chrisalbon.com/python/data_wrangling/pandas_join_merge_dataframe/ for an overview and https://pandas.pydata.org/pandas-docs/stable/merging.html for the full documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c46ecc",
   "metadata": {},
   "source": [
    "To illustrate the concept of joining the information of two dataframes with pandas, let's take a small subset of our `cities` and `countries` datasets: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d12a1e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "cities2 = cities[cities['name'].isin(['Kampala', 'Nairobi', 'Dodoma', 'Kigali'])].copy()\n",
    "cities2['iso_a3'] = ['RWA', 'TZ', 'UG', 'KE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1bfad7e3",
   "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>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>iso_a3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kigali</td>\n",
       "      <td>POINT (30.05859 -1.95164)</td>\n",
       "      <td>RWA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Dodoma</td>\n",
       "      <td>POINT (35.75000 -6.18331)</td>\n",
       "      <td>TZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Kampala</td>\n",
       "      <td>POINT (32.58138 0.31860)</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>Nairobi</td>\n",
       "      <td>POINT (36.81471 -1.28140)</td>\n",
       "      <td>KE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name                   geometry iso_a3\n",
       "15    Kigali  POINT (30.05859 -1.95164)    RWA\n",
       "25    Dodoma  POINT (35.75000 -6.18331)     TZ\n",
       "58   Kampala   POINT (32.58138 0.31860)     UG\n",
       "228  Nairobi  POINT (36.81471 -1.28140)     KE"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "93abc91a",
   "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>iso_a3</th>\n",
       "      <th>name</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_a3                  name      continent\n",
       "0    AFG           Afghanistan           Asia\n",
       "1    AGO                Angola         Africa\n",
       "2    ALB               Albania         Europe\n",
       "3    ARE  United Arab Emirates           Asia\n",
       "4    ARG             Argentina  South America"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries2 = countries[['iso_a3', 'name', 'continent']]\n",
    "countries2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a455a7c9",
   "metadata": {},
   "source": [
    "We added a 'iso_a3' column to the `cities` dataset, indicating a code of the country of the city. This country code is also present in the `countries` dataset, which allows us to merge those two dataframes based on the common column.\n",
    "\n",
    "Joining the `cities` dataframe with `countries` will transfer extra information about the countries (the full name, the continent) to the `cities` dataframe, based on a common key:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0290b79",
   "metadata": {},
   "source": [
    "## spatial relationships between objects\n",
    "\n",
    "In the previous notebook , we have seen the notion of spatial relationships between geometry objects: within, contains, intersects, ...\n",
    "\n",
    "In this case, we know that each of the cities is located *within* one of the countries, or the other way around that each country can *contain* multiple cities.\n",
    "\n",
    "We can test such relationships using the methods we have seen in the previous notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b5e7e3e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kenya = countries.loc[countries['name'] == 'Kenya', 'geometry'].squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5702cce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "238    False\n",
       "239    False\n",
       "240    False\n",
       "241    False\n",
       "242    False\n",
       "Length: 243, dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.within(Kenya)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eca3bcac",
   "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>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>Nairobi</td>\n",
       "      <td>POINT (36.81471 -1.28140)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name                   geometry\n",
       "228  Nairobi  POINT (36.81471 -1.28140)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities[cities.within(Kenya)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0a6ef3",
   "metadata": {},
   "source": [
    "## Spatial join operation\n",
    "\n",
    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
    "<b>SPATIAL JOIN</b> = *transferring attributes from one layer to another based on their spatial relationship* <br><br>\n",
    "\n",
    "\n",
    "Different parts of this operations:\n",
    "\n",
    "<ul>\n",
    "  <li>The GeoDataFrame to which we want add information</li>\n",
    "  <li>The GeoDataFrame that contains the information we want to add </li>\n",
    "  <li>The spatial relationship we want to use to match both datasets ('intersects', 'contains', 'within')</li>\n",
    "  <li>The type of join: left or inner join</li>\n",
    "</ul>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9cafde2",
   "metadata": {},
   "source": [
    "In this case, we want to join the `cities` dataframe with the information of the `countries` dataframe, based on the spatial relationship between both datasets.\n",
    "\n",
    "We use the [`geopandas.sjoin`](http://geopandas.readthedocs.io/en/latest/reference/geopandas.sjoin.html) function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9bf1b236",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined = geopandas.sjoin(cities, countries, op='within', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "738513b9",
   "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>name_left</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>name_right</th>\n",
       "      <th>continent</th>\n",
       "      <th>pop_est</th>\n",
       "      <th>gdp_md_est</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vatican City</td>\n",
       "      <td>POINT (12.45339 41.90328)</td>\n",
       "      <td>79.0</td>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>6.213780e+07</td>\n",
       "      <td>2221000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>POINT (12.44177 43.93610)</td>\n",
       "      <td>79.0</td>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>6.213780e+07</td>\n",
       "      <td>2221000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vaduz</td>\n",
       "      <td>POINT (9.51667 47.13372)</td>\n",
       "      <td>9.0</td>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Europe</td>\n",
       "      <td>8.754413e+06</td>\n",
       "      <td>416600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lobamba</td>\n",
       "      <td>POINT (31.20000 -26.46667)</td>\n",
       "      <td>152.0</td>\n",
       "      <td>SWZ</td>\n",
       "      <td>Swaziland</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1.467152e+06</td>\n",
       "      <td>11060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>POINT (6.13000 49.61166)</td>\n",
       "      <td>97.0</td>\n",
       "      <td>LUX</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>Europe</td>\n",
       "      <td>5.941300e+05</td>\n",
       "      <td>58740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>POINT (-43.22697 -22.92308)</td>\n",
       "      <td>22.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>South America</td>\n",
       "      <td>2.073534e+08</td>\n",
       "      <td>3081000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>POINT (-46.62697 -23.55673)</td>\n",
       "      <td>22.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>South America</td>\n",
       "      <td>2.073534e+08</td>\n",
       "      <td>3081000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>POINT (151.18323 -33.91807)</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2.323241e+07</td>\n",
       "      <td>1189000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>POINT (103.85387 1.29498)</td>\n",
       "      <td>111.0</td>\n",
       "      <td>MYS</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3.138199e+07</td>\n",
       "      <td>863000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>POINT (114.18306 22.30693)</td>\n",
       "      <td>30.0</td>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1.379303e+09</td>\n",
       "      <td>21140000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name_left                     geometry  index_right iso_a3  \\\n",
       "0      Vatican City    POINT (12.45339 41.90328)         79.0    ITA   \n",
       "1        San Marino    POINT (12.44177 43.93610)         79.0    ITA   \n",
       "2             Vaduz     POINT (9.51667 47.13372)          9.0    AUT   \n",
       "3           Lobamba   POINT (31.20000 -26.46667)        152.0    SWZ   \n",
       "4        Luxembourg     POINT (6.13000 49.61166)         97.0    LUX   \n",
       "..              ...                          ...          ...    ...   \n",
       "238  Rio de Janeiro  POINT (-43.22697 -22.92308)         22.0    BRA   \n",
       "239       São Paulo  POINT (-46.62697 -23.55673)         22.0    BRA   \n",
       "240          Sydney  POINT (151.18323 -33.91807)          8.0    AUS   \n",
       "241       Singapore    POINT (103.85387 1.29498)        111.0    MYS   \n",
       "242       Hong Kong   POINT (114.18306 22.30693)         30.0    CHN   \n",
       "\n",
       "     name_right      continent       pop_est  gdp_md_est  \n",
       "0         Italy         Europe  6.213780e+07   2221000.0  \n",
       "1         Italy         Europe  6.213780e+07   2221000.0  \n",
       "2       Austria         Europe  8.754413e+06    416600.0  \n",
       "3     Swaziland         Africa  1.467152e+06     11060.0  \n",
       "4    Luxembourg         Europe  5.941300e+05     58740.0  \n",
       "..          ...            ...           ...         ...  \n",
       "238      Brazil  South America  2.073534e+08   3081000.0  \n",
       "239      Brazil  South America  2.073534e+08   3081000.0  \n",
       "240   Australia        Oceania  2.323241e+07   1189000.0  \n",
       "241    Malaysia           Asia  3.138199e+07    863000.0  \n",
       "242       China           Asia  1.379303e+09  21140000.0  \n",
       "\n",
       "[243 rows x 8 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "703f7a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Asia             59\n",
       "Africa           57\n",
       "Europe           46\n",
       "North America    26\n",
       "South America    14\n",
       "Oceania           8\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined['continent'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "890dcdec",
   "metadata": {},
   "source": [
    "## The overlay operation\n",
    "\n",
    "In the spatial join operation above, we are not changing the geometries itself. We are not joining geometries, but joining attributes based on a spatial relationship between the geometries. This also means that the geometries need to at least overlap partially.\n",
    "\n",
    "If you want to create new geometries based on joining (combining) geometries of different dataframes into one new dataframe (eg by taking the intersection of the geometries), you want an **overlay** operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e15bc715",
   "metadata": {},
   "outputs": [],
   "source": [
    "africa = countries[countries['continent'] == 'Africa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9d65d75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "africa.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "04a21299",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp/ipykernel_5152/1163649495.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'buffer' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  cities['geometry'] = cities.buffer(2)\n"
     ]
    }
   ],
   "source": [
    "cities['geometry'] = cities.buffer(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "689187b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "geopandas.overlay(africa, cities, how='difference').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c9b1d5f",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
    "<b>REMEMBER</b> <br>\n",
    "\n",
    "<ul>\n",
    "  <li>**Coordinate Reference**: For you to overlay data they must be in same coordinate reference system</li>\n",
    "  <li>**Spatial join**: transfer attributes from one dataframe to another based on the spatial relationship</li>\n",
    "  <li>**Spatial overlay**: construct new geometries based on spatial operation between both dataframes (and combining attributes of both dataframes)</li>\n",
    "</ul>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae49d30f",
   "metadata": {},
   "source": [
    "Thats it for the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28a1bd7f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}