notebooks/Geocoding.ipynb

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   "source": [
    "# Geocoding \n",
    "\n",
    "### Source: [Automating GIS-processes 2021](https://autogis-site.readthedocs.io/en/latest/lessons/L3/overview.html)\n",
    "\n",
    "### Data available [here](https://github.com/dahlalex/website/tree/main/notebooks)\n",
    "\n",
    "### The goal is to geocode some addresses and make spatial join."
   ]
  },
  {
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   "execution_count": 1,
   "id": "7e757a89-23e9-4180-858f-fdcd6faaa3ab",
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   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from geopandas.tools import geocode\n",
    "import folium\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import geojson\n",
    "from pyproj import CRS\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0bea3f-772b-44d8-bc24-f72c4a9f030f",
   "metadata": {},
   "source": [
    "### 1- Geocode the addresses"
   ]
  },
  {
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   "execution_count": 2,
   "id": "8416b7e6-a361-4a4a-a190-e4a68f8569f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data (These addresses are located in the Helsinki Region in Southern Finland.)\n",
    "\n",
    "fb = r\"data/addr_autogis.txt\"\n",
    "data = pd.read_csv(fb, sep=\";\")\n"
   ]
  },
  {
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   "execution_count": 3,
   "id": "06042d17-0d69-41ac-ac9c-62f5f21ba1b7",
   "metadata": {},
   "outputs": [
    {
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       "(34, 2)"
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     "execution_count": 3,
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   "source": [
    "data.shape"
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   "id": "2d141512-2c27-49aa-98fc-ecae3a555c05",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>addr</th>\n",
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       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>Itämerenkatu 14, 00101 Helsinki, Finland</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>Kampinkuja 1, 00100 Helsinki, Finland</td>\n",
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       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Kaivokatu 8, 00101 Helsinki, Finland</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>Hermannin rantatie 1, 00580 Helsinki, Finland</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
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       "     id                                           addr\n",
       "0  1000       Itämerenkatu 14, 00101 Helsinki, Finland\n",
       "1  1001          Kampinkuja 1, 00100 Helsinki, Finland\n",
       "2  1002           Kaivokatu 8, 00101 Helsinki, Finland\n",
       "3  1003  Hermannin rantatie 1, 00580 Helsinki, Finland\n",
       "4  1005     Tyynenmerenkatu 9, 00220 Helsinki, Finland"
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   "source": [
    "# Geocode addresses using Nominatim (Open Street Map), \"custom application name\" in the user_agent parameter, \n",
    "# timeout-parameter which specifies how many seconds we will wait for a response from the service.\n",
    "\n",
    "addr = geocode(data[\"addr\"], provider='nominatim', user_agent= \"autogis_alex\", timeout=4)"
   ]
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       "      <td>POINT (24.91556 60.16320)</td>\n",
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       "      <td>POINT (24.93166 60.16905)</td>\n",
       "      <td>Kamppi, 1, Kampinkuja, Kamppi, Helsinki, Helsi...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (24.94179 60.16989)</td>\n",
       "      <td>Kauppakeskus Citycenter, 8, Kaivokatu, Kluuvi,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (24.97835 60.18976)</td>\n",
       "      <td>Hermannin rantatie, Verkkosaari, Kalasatama, S...</td>\n",
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       "      <td>POINT (24.92160 60.15665)</td>\n",
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       "                    geometry  \\\n",
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       "\n",
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       "1  Kamppi, 1, Kampinkuja, Kamppi, Helsinki, Helsi...  \n",
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       "3  Hermannin rantatie, Verkkosaari, Kalasatama, S...  \n",
       "4  Hesburger, 9, Tyynenmerenkatu, Jätkäsaari, Län...  "
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    "addr.head()"
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   "execution_count": 7,
   "id": "5ea2a9d9-fde1-4b2f-b7d3-e07bdb4203b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "addr = addr.join(data)"
   ]
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  {
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   "execution_count": 8,
   "id": "113d426a-eea6-4c8f-aa2e-610e8f9c9bdb",
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       "      <td>POINT (24.92160 60.15665)</td>\n",
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       "\n",
       "                                             address    id  \\\n",
       "0  Ruoholahti, 14, Itämerenkatu, Ruoholahti, Läns...  1000   \n",
       "1  Kamppi, 1, Kampinkuja, Kamppi, Helsinki, Helsi...  1001   \n",
       "2  Kauppakeskus Citycenter, 8, Kaivokatu, Kluuvi,...  1002   \n",
       "3  Hermannin rantatie, Verkkosaari, Kalasatama, S...  1003   \n",
       "4  Hesburger, 9, Tyynenmerenkatu, Jätkäsaari, Län...  1005   \n",
       "\n",
       "                                            addr  \n",
       "0       Itämerenkatu 14, 00101 Helsinki, Finland  \n",
       "1          Kampinkuja 1, 00100 Helsinki, Finland  \n",
       "2           Kaivokatu 8, 00101 Helsinki, Finland  \n",
       "3  Hermannin rantatie 1, 00580 Helsinki, Finland  \n",
       "4     Tyynenmerenkatu 9, 00220 Helsinki, Finland  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8d701468-ee5d-4381-9106-5724f045162b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dahla\\miniconda3\\envs\\autogis2021\\lib\\site-packages\\geopandas\\io\\file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    }
   ],
   "source": [
    "out_fp= r\"data/addr_helsinki.shp\"\n",
    "addr.to_file(out_fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e884bf-38b5-4751-834d-e838ef2b13ce",
   "metadata": {},
   "source": [
    "### 2- Get data (Population grid 250m x 250m) that contains population information from the Helsinki Region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "942c505c-be82-4c5f-bc4f-3a0dd6fbd516",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the url for web feature service\n",
    "url = 'https://kartta.hsy.fi/geoserver/wfs'\n",
    "\n",
    "# Specify parameters (read data in json format). \n",
    "# Available feature types in this particular data source: http://geo.stat.fi/geoserver/vaestoruutu/wfs?service=wfs&version=2.0.0&request=describeFeatureType\n",
    "params = dict(service='WFS', \n",
    "              version='2.0.0', \n",
    "              request='GetFeature', \n",
    "              typeName='asuminen_ja_maankaytto:Vaestotietoruudukko_2018', \n",
    "              outputFormat='json')\n",
    "\n",
    "# Fetch data from WFS using requests\n",
    "r = requests.get(url, params=params)\n",
    "\n",
    "# Create GeoDataFrame from geojson\n",
    "pop = gpd.GeoDataFrame.from_features(geojson.loads(r.content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac69d487-c3be-426b-a058-ef500bd843f8",
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       "                                            geometry  index  asukkaita  \\\n",
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       "0       28.0      99        99        99        99        99        99   \n",
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       "\n",
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       "0        99        99         99  \n",
       "1        99        99         99  \n",
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "pop.head()"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96cf2dd8-3617-4844-8daa-13a12fdeca13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the name of a column\n",
    "pop = pop.rename(columns={\"asukkaita\":\"pop18\"})\n",
    "\n",
    "# get rid of unnecessary columns\n",
    "pop = pop[[\"pop18\", \"geometry\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "58734b09-7355-4acd-aa97-74140848dfac",
   "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>pop18</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON ((25472499.995 6689749.005, 25472499.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((25472499.995 6685998.998, 25472499.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>POLYGON ((25472499.995 6684249.004, 25472499.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((25472499.995 6683999.005, 25472499.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>POLYGON ((25472499.995 6682998.998, 25472499.9...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop18                                           geometry\n",
       "0      9  POLYGON ((25472499.995 6689749.005, 25472499.9...\n",
       "1      5  POLYGON ((25472499.995 6685998.998, 25472499.9...\n",
       "2      8  POLYGON ((25472499.995 6684249.004, 25472499.9...\n",
       "3      5  POLYGON ((25472499.995 6683999.005, 25472499.9...\n",
       "4     11  POLYGON ((25472499.995 6682998.998, 25472499.9..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06820635-0d27-4873-96d1-47e1353539a9",
   "metadata": {},
   "source": [
    "### 3- join the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f695b9fc-8a36-4bd7-892a-393b35759a9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:4326\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# check the coordinate system\n",
    "\n",
    "print(addr.crs)\n",
    "print(pop.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "89f9ee1c-6068-451a-9856-795a705448c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the coordinate reference system as ETRS GK-25 (EPSG:3879) because we know what it is based on the population grid metadata.\n",
    "pop = pop.set_crs(\"EPSG:3879\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04715c60-8634-4df0-899a-588e017cb5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let’s re-project addresses to the projection of the population layer\n",
    "addr.to_crs(pop.crs, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "37d0a8d8-6073-47b0-bdf7-80c5b80bf9b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:3879\n",
      "EPSG:3879\n"
     ]
    }
   ],
   "source": [
    "print(addr.crs)\n",
    "print(pop.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "71456480-7b0f-4567-b1e7-8ee4e69a3ee7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 4)\n",
      "(5832, 2)\n"
     ]
    }
   ],
   "source": [
    "print(addr.shape)\n",
    "print(pop.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b6d14e5-d74f-48df-b5a6-682f406e36d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a spatial join\n",
    "join = gpd.sjoin(addr, pop, how= \"inner\", predicate=\"within\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "525651c5-f0bb-4da2-99b1-cbf0c0134337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 6)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7c4ec256-128a-4fb0-b61d-07d18cc1457d",
   "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>geometry</th>\n",
       "      <th>address</th>\n",
       "      <th>id</th>\n",
       "      <th>addr</th>\n",
       "      <th>index_right</th>\n",
       "      <th>pop18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (25495311.608 6672258.695)</td>\n",
       "      <td>Ruoholahti, 14, Itämerenkatu, Ruoholahti, Läns...</td>\n",
       "      <td>1000</td>\n",
       "      <td>Itämerenkatu 14, 00101 Helsinki, Finland</td>\n",
       "      <td>3252</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (25496206.216 6672909.016)</td>\n",
       "      <td>Kamppi, 1, Kampinkuja, Kamppi, Helsinki, Helsi...</td>\n",
       "      <td>1001</td>\n",
       "      <td>Kampinkuja 1, 00100 Helsinki, Finland</td>\n",
       "      <td>3364</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>POINT (25496230.571 6672892.612)</td>\n",
       "      <td>Alepa, 1, Urho Kekkosen katu, Kamppi, Helsinki...</td>\n",
       "      <td>1031</td>\n",
       "      <td>Urho Kekkosen katu 1, 00100 Helsinki, Finland</td>\n",
       "      <td>3364</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (25496768.622 6673002.004)</td>\n",
       "      <td>Kauppakeskus Citycenter, 8, Kaivokatu, Kluuvi,...</td>\n",
       "      <td>1002</td>\n",
       "      <td>Kaivokatu 8, 00101 Helsinki, Finland</td>\n",
       "      <td>3488</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (25498798.583 6675215.263)</td>\n",
       "      <td>Hermannin rantatie, Verkkosaari, Kalasatama, S...</td>\n",
       "      <td>1003</td>\n",
       "      <td>Hermannin rantatie 1, 00580 Helsinki, Finland</td>\n",
       "      <td>3822</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            geometry  \\\n",
       "0   POINT (25495311.608 6672258.695)   \n",
       "1   POINT (25496206.216 6672909.016)   \n",
       "30  POINT (25496230.571 6672892.612)   \n",
       "2   POINT (25496768.622 6673002.004)   \n",
       "3   POINT (25498798.583 6675215.263)   \n",
       "\n",
       "                                              address    id  \\\n",
       "0   Ruoholahti, 14, Itämerenkatu, Ruoholahti, Läns...  1000   \n",
       "1   Kamppi, 1, Kampinkuja, Kamppi, Helsinki, Helsi...  1001   \n",
       "30  Alepa, 1, Urho Kekkosen katu, Kamppi, Helsinki...  1031   \n",
       "2   Kauppakeskus Citycenter, 8, Kaivokatu, Kluuvi,...  1002   \n",
       "3   Hermannin rantatie, Verkkosaari, Kalasatama, S...  1003   \n",
       "\n",
       "                                             addr  index_right  pop18  \n",
       "0        Itämerenkatu 14, 00101 Helsinki, Finland         3252    515  \n",
       "1           Kampinkuja 1, 00100 Helsinki, Finland         3364    182  \n",
       "30  Urho Kekkosen katu 1, 00100 Helsinki, Finland         3364    182  \n",
       "2            Kaivokatu 8, 00101 Helsinki, Finland         3488     38  \n",
       "3   Hermannin rantatie 1, 00580 Helsinki, Finland         3822     61  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf98b884-642e-4beb-b077-87c72457d67a",
   "metadata": {},
   "source": [
    "### 4- plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ef533d2-8567-40f6-9fe2-71533f724655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "\n",
    "pop.plot(ax=ax, color=\"gray\")\n",
    "      \n",
    "  \n",
    "join.plot(ax= ax, column= \"pop18\", scheme= \"quantiles\", cmap=\"YlOrRd\", markersize=40,\n",
    "         legend= True, legend_kwds={'loc': 'lower right'})\n",
    "\n",
    "\n",
    "plt.title(\"Amount of inhabitants living close the the Addresses\")\n",
    "\n",
    "ax.axis('off')\n",
    "\n",
    "outfp = r\"static_maps/addr_pop2018.png\"\n",
    "plt.savefig(outfp, facecolor=\"white\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea8589db-39f3-4b56-96a5-b472ce11da27",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}