data-preparation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "### Data Source 1: Accidents in Berlin\n",
    "---"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a73c6a145e998792"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   OBJECTID  LAND  BEZ       LOR          STRASSE  UJAHR  UMONAT  USTUNDE  \\\n0     49090    11   12  12301203     Wittenau Süd   2019       1       13   \n1     49091    11    3   3040818       Pankow Süd   2019       1        9   \n2     49093    11   12  12103115  Breitkopfbecken   2019       3       21   \n3     49096    11    6   6040703       Nikolassee   2019       1        7   \n4     49097    11    7   7030303     Grazer Platz   2019       2       15   \n\n   UWOCHENTAG  UKATEGORIE  ...  IstPKW  IstFuss  IstKrad  IstGkfz  \\\n0           6           3  ...       1        0        0        0   \n1           5           3  ...       1        0        0        0   \n2           6           3  ...       0        0        0        0   \n3           6           2  ...       1        1        0        0   \n4           3           3  ...       1        0        0        0   \n\n   IstSonstige  USTRZUSTAND      LINREFX      LINREFY XGCSWGS84 YGCSWGS84  \n0            0            1  794062,2837  5835083,823  13,34146  52,58609  \n1            0            0  799130,4007  5832327,415  13,41356  52,55862  \n2            0            0   795437,613  5833549,454  13,36034  52,57159  \n3            0            1  786714,3754  5817042,137  13,21777  52,42825  \n4            0            0  796074,3342  5822724,905  13,36007  52,47421  \n\n[5 rows x 24 columns]",
      "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>OBJECTID</th>\n      <th>LAND</th>\n      <th>BEZ</th>\n      <th>LOR</th>\n      <th>STRASSE</th>\n      <th>UJAHR</th>\n      <th>UMONAT</th>\n      <th>USTUNDE</th>\n      <th>UWOCHENTAG</th>\n      <th>UKATEGORIE</th>\n      <th>...</th>\n      <th>IstPKW</th>\n      <th>IstFuss</th>\n      <th>IstKrad</th>\n      <th>IstGkfz</th>\n      <th>IstSonstige</th>\n      <th>USTRZUSTAND</th>\n      <th>LINREFX</th>\n      <th>LINREFY</th>\n      <th>XGCSWGS84</th>\n      <th>YGCSWGS84</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>49090</td>\n      <td>11</td>\n      <td>12</td>\n      <td>12301203</td>\n      <td>Wittenau Süd</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>13</td>\n      <td>6</td>\n      <td>3</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>794062,2837</td>\n      <td>5835083,823</td>\n      <td>13,34146</td>\n      <td>52,58609</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>49091</td>\n      <td>11</td>\n      <td>3</td>\n      <td>3040818</td>\n      <td>Pankow Süd</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>9</td>\n      <td>5</td>\n      <td>3</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>799130,4007</td>\n      <td>5832327,415</td>\n      <td>13,41356</td>\n      <td>52,55862</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>49093</td>\n      <td>11</td>\n      <td>12</td>\n      <td>12103115</td>\n      <td>Breitkopfbecken</td>\n      <td>2019</td>\n      <td>3</td>\n      <td>21</td>\n      <td>6</td>\n      <td>3</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>795437,613</td>\n      <td>5833549,454</td>\n      <td>13,36034</td>\n      <td>52,57159</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>49096</td>\n      <td>11</td>\n      <td>6</td>\n      <td>6040703</td>\n      <td>Nikolassee</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>7</td>\n      <td>6</td>\n      <td>2</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>786714,3754</td>\n      <td>5817042,137</td>\n      <td>13,21777</td>\n      <td>52,42825</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>49097</td>\n      <td>11</td>\n      <td>7</td>\n      <td>7030303</td>\n      <td>Grazer Platz</td>\n      <td>2019</td>\n      <td>2</td>\n      <td>15</td>\n      <td>3</td>\n      <td>3</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>796074,3342</td>\n      <td>5822724,905</td>\n      <td>13,36007</td>\n      <td>52,47421</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 24 columns</p>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the data\n",
    "accidents_2019 = pd.read_csv('raw-data/accidents_Berlin_2019.csv', encoding =\"cp1252\", sep = \";\")\n",
    "\n",
    "# Display the first few rows of the data\n",
    "accidents_2019.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.610291Z",
     "start_time": "2024-04-19T18:17:11.571622Z"
    }
   },
   "id": "6e25c0bc7c2ef6d7",
   "execution_count": 43
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['OBJECTID', 'LAND', 'BEZ', 'LOR', 'STRASSE', 'UJAHR', 'UMONAT',\n       'USTUNDE', 'UWOCHENTAG', 'UKATEGORIE', 'UART', 'UTYP1', 'ULICHTVERH',\n       'IstRad', 'IstPKW', 'IstFuss', 'IstKrad', 'IstGkfz', 'IstSonstige',\n       'USTRZUSTAND', 'LINREFX', 'LINREFY', 'XGCSWGS84', 'YGCSWGS84'],\n      dtype='object')"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the column names (German)\n",
    "accidents_2019.columns"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.615035Z",
     "start_time": "2024-04-19T18:17:11.611355Z"
    }
   },
   "id": "2fccb698bc21015d",
   "execution_count": 44
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# load dataset of 2021\n",
    "accidents_2021 = pd.read_csv('raw-data/accidents_Berlin_2021.csv', encoding =\"cp1252\", sep = \";\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.640968Z",
     "start_time": "2024-04-19T18:17:11.616427Z"
    }
   },
   "id": "c8999fb1292e3cc7",
   "execution_count": 45
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   OBJECTID  LAND  BEZ  LOR_ab_2021  UJAHR  UMONAT  USTUNDE  UWOCHENTAG  \\\n0    219249    11    3    3701658.0   2021      11       18           2   \n1    219248    11    7    7501134.0   2021      12       19           7   \n2    219247    11    4    4100101.0   2021      12       17           4   \n3    219246    11    4    4501041.0   2021      12       15           7   \n4    219243    11   11   11501339.0   2021      12        9           5   \n\n   UKATEGORIE  UART  ...  IstPKW  IstFuss  IstKrad  IstGkfz  IstSonstige  \\\n0           3     0  ...       1        0        1        0            0   \n1           3     6  ...       1        1        0        0            0   \n2           3     5  ...       1        0        0        0            0   \n3           3     5  ...       1        0        1        0            0   \n4           3     3  ...       1        0        0        0            1   \n\n   USTRZUSTAND      LINREFX      LINREFY    XGCSWGS84    YGCSWGS84  \n0            1  800202,0742  5829640,204  13,42689483  52,53393955  \n1            1  798479,5317  5819049,219  13,39209027  52,43995086  \n2            0  793352,6128  5829680,195  13,32624193  52,53802784  \n3            1  792950,0395  5825362,081  13,31652064  52,49953417  \n4            2    807182,01  5825602,793  13,52575163  52,49386714  \n\n[5 rows x 23 columns]",
      "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>OBJECTID</th>\n      <th>LAND</th>\n      <th>BEZ</th>\n      <th>LOR_ab_2021</th>\n      <th>UJAHR</th>\n      <th>UMONAT</th>\n      <th>USTUNDE</th>\n      <th>UWOCHENTAG</th>\n      <th>UKATEGORIE</th>\n      <th>UART</th>\n      <th>...</th>\n      <th>IstPKW</th>\n      <th>IstFuss</th>\n      <th>IstKrad</th>\n      <th>IstGkfz</th>\n      <th>IstSonstige</th>\n      <th>USTRZUSTAND</th>\n      <th>LINREFX</th>\n      <th>LINREFY</th>\n      <th>XGCSWGS84</th>\n      <th>YGCSWGS84</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>219249</td>\n      <td>11</td>\n      <td>3</td>\n      <td>3701658.0</td>\n      <td>2021</td>\n      <td>11</td>\n      <td>18</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>800202,0742</td>\n      <td>5829640,204</td>\n      <td>13,42689483</td>\n      <td>52,53393955</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>219248</td>\n      <td>11</td>\n      <td>7</td>\n      <td>7501134.0</td>\n      <td>2021</td>\n      <td>12</td>\n      <td>19</td>\n      <td>7</td>\n      <td>3</td>\n      <td>6</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>798479,5317</td>\n      <td>5819049,219</td>\n      <td>13,39209027</td>\n      <td>52,43995086</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>219247</td>\n      <td>11</td>\n      <td>4</td>\n      <td>4100101.0</td>\n      <td>2021</td>\n      <td>12</td>\n      <td>17</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>793352,6128</td>\n      <td>5829680,195</td>\n      <td>13,32624193</td>\n      <td>52,53802784</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>219246</td>\n      <td>11</td>\n      <td>4</td>\n      <td>4501041.0</td>\n      <td>2021</td>\n      <td>12</td>\n      <td>15</td>\n      <td>7</td>\n      <td>3</td>\n      <td>5</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>792950,0395</td>\n      <td>5825362,081</td>\n      <td>13,31652064</td>\n      <td>52,49953417</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>219243</td>\n      <td>11</td>\n      <td>11</td>\n      <td>11501339.0</td>\n      <td>2021</td>\n      <td>12</td>\n      <td>9</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>807182,01</td>\n      <td>5825602,793</td>\n      <td>13,52575163</td>\n      <td>52,49386714</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of the data\n",
    "accidents_2021.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.649145Z",
     "start_time": "2024-04-19T18:17:11.641691Z"
    }
   },
   "id": "134438d6dcc196a6",
   "execution_count": 46
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "OBJECTID       0\nLAND           0\nBEZ            0\nLOR_ab_2021    1\nUJAHR          0\nUMONAT         0\nUSTUNDE        0\nUWOCHENTAG     0\nUKATEGORIE     0\nUART           0\nUTYP1          0\nULICHTVERH     0\nIstRad         0\nIstPKW         0\nIstFuss        0\nIstKrad        0\nIstGkfz        0\nIstSonstige    0\nUSTRZUSTAND    0\nLINREFX        0\nLINREFY        0\nXGCSWGS84      0\nYGCSWGS84      0\ndtype: int64"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the number of missing values in each column\n",
    "accidents_2021.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.656931Z",
     "start_time": "2024-04-19T18:17:11.649981Z"
    }
   },
   "id": "e2821b1d30c1295c",
   "execution_count": 47
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ObjectID', 'State', 'District', 'LOR_ab_2021', 'AccidentYear',\n",
      "       'AccidentMonth', 'AccidentHour', 'DayOfWeek', 'AccidentCategory',\n",
      "       'AccidentType', 'AccidentTypeDetail', 'LightingCondition',\n",
      "       'InvolvingBike', 'InvolvingCar', 'InvolvingPedestrian',\n",
      "       'InvolvingMotorcycle', 'InvolvingHGV', 'InvolvingOther',\n",
      "       'RoadCondition', 'GraphicCoord1', 'GraphicCoord2', 'LongitudeWGS84',\n",
      "       'LatitudeWGS84'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Dictionary mapping German column names to English\n",
    "translation_dict = {\n",
    "    'OBJECTID': 'ObjectID',\n",
    "    'LAND': 'State',\n",
    "    'BEZ': 'District',\n",
    "    'LOR': 'LOR',\n",
    "    'STRASSE': 'Street',\n",
    "    'UJAHR': 'AccidentYear',\n",
    "    'UMONAT': 'AccidentMonth',\n",
    "    'USTUNDE': 'AccidentHour',\n",
    "    'UWOCHENTAG': 'DayOfWeek',\n",
    "    'UKATEGORIE': 'AccidentCategory',\n",
    "    'UART': 'AccidentType',\n",
    "    'UTYP1': 'AccidentTypeDetail',\n",
    "    'ULICHTVERH': 'LightingCondition',\n",
    "    'IstRad': 'InvolvingBike',\n",
    "    'IstPKW': 'InvolvingCar',\n",
    "    'IstFuss': 'InvolvingPedestrian',\n",
    "    'IstKrad': 'InvolvingMotorcycle',\n",
    "    'IstGkfz': 'InvolvingHGV',\n",
    "    'IstSonstige': 'InvolvingOther',\n",
    "    'USTRZUSTAND': 'RoadCondition',\n",
    "    'LINREFX': 'GraphicCoord1',\n",
    "    'LINREFY': 'GraphicCoord2',\n",
    "    'XGCSWGS84': 'LongitudeWGS84',\n",
    "    'YGCSWGS84': 'LatitudeWGS84'\n",
    "}\n",
    "\n",
    "# Rename the columns\n",
    "accidents_data = accidents_2021.rename(columns=translation_dict)\n",
    "\n",
    "# Display the new column names\n",
    "print(accidents_data.columns)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.661026Z",
     "start_time": "2024-04-19T18:17:11.657620Z"
    }
   },
   "id": "8172026a526b3416",
   "execution_count": 48
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   ObjectID  State  District  LOR_ab_2021  AccidentYear  AccidentMonth  \\\n0    219249     11         3    3701658.0          2021             11   \n1    219248     11         7    7501134.0          2021             12   \n2    219247     11         4    4100101.0          2021             12   \n3    219246     11         4    4501041.0          2021             12   \n4    219243     11        11   11501339.0          2021             12   \n\n   AccidentHour  DayOfWeek  AccidentCategory  AccidentType  ...  InvolvingCar  \\\n0            18          2                 3             0  ...             1   \n1            19          7                 3             6  ...             1   \n2            17          4                 3             5  ...             1   \n3            15          7                 3             5  ...             1   \n4             9          5                 3             3  ...             1   \n\n   InvolvingPedestrian  InvolvingMotorcycle  InvolvingHGV  InvolvingOther  \\\n0                    0                    1             0               0   \n1                    1                    0             0               0   \n2                    0                    0             0               0   \n3                    0                    1             0               0   \n4                    0                    0             0               1   \n\n   RoadCondition  GraphicCoord1  GraphicCoord2  LongitudeWGS84 LatitudeWGS84  \n0              1    800202,0742    5829640,204     13,42689483   52,53393955  \n1              1    798479,5317    5819049,219     13,39209027   52,43995086  \n2              0    793352,6128    5829680,195     13,32624193   52,53802784  \n3              1    792950,0395    5825362,081     13,31652064   52,49953417  \n4              2      807182,01    5825602,793     13,52575163   52,49386714  \n\n[5 rows x 23 columns]",
      "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>ObjectID</th>\n      <th>State</th>\n      <th>District</th>\n      <th>LOR_ab_2021</th>\n      <th>AccidentYear</th>\n      <th>AccidentMonth</th>\n      <th>AccidentHour</th>\n      <th>DayOfWeek</th>\n      <th>AccidentCategory</th>\n      <th>AccidentType</th>\n      <th>...</th>\n      <th>InvolvingCar</th>\n      <th>InvolvingPedestrian</th>\n      <th>InvolvingMotorcycle</th>\n      <th>InvolvingHGV</th>\n      <th>InvolvingOther</th>\n      <th>RoadCondition</th>\n      <th>GraphicCoord1</th>\n      <th>GraphicCoord2</th>\n      <th>LongitudeWGS84</th>\n      <th>LatitudeWGS84</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>219249</td>\n      <td>11</td>\n      <td>3</td>\n      <td>3701658.0</td>\n      <td>2021</td>\n      <td>11</td>\n      <td>18</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>800202,0742</td>\n      <td>5829640,204</td>\n      <td>13,42689483</td>\n      <td>52,53393955</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>219248</td>\n      <td>11</td>\n      <td>7</td>\n      <td>7501134.0</td>\n      <td>2021</td>\n      <td>12</td>\n      <td>19</td>\n      <td>7</td>\n      <td>3</td>\n      <td>6</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>798479,5317</td>\n      <td>5819049,219</td>\n      <td>13,39209027</td>\n      <td>52,43995086</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>219247</td>\n      <td>11</td>\n      <td>4</td>\n      <td>4100101.0</td>\n      <td>2021</td>\n      <td>12</td>\n      <td>17</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>793352,6128</td>\n      <td>5829680,195</td>\n      <td>13,32624193</td>\n      <td>52,53802784</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>219246</td>\n      <td>11</td>\n      <td>4</td>\n      <td>4501041.0</td>\n      <td>2021</td>\n      <td>12</td>\n      <td>15</td>\n      <td>7</td>\n      <td>3</td>\n      <td>5</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>792950,0395</td>\n      <td>5825362,081</td>\n      <td>13,31652064</td>\n      <td>52,49953417</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>219243</td>\n      <td>11</td>\n      <td>11</td>\n      <td>11501339.0</td>\n      <td>2021</td>\n      <td>12</td>\n      <td>9</td>\n      <td>5</td>\n      <td>3</td>\n      <td>3</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>807182,01</td>\n      <td>5825602,793</td>\n      <td>13,52575163</td>\n      <td>52,49386714</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's check the first few rows of the cleaned data\n",
    "accidents_data.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.668425Z",
     "start_time": "2024-04-19T18:17:11.661768Z"
    }
   },
   "id": "6a0b1f39cfa284c2",
   "execution_count": 49
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0    7016\n1    4251\nName: InvolvingBike, dtype: int64"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out the number accidents where a bike was involved\n",
    "accidents_data['InvolvingBike'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.672582Z",
     "start_time": "2024-04-19T18:17:11.669265Z"
    }
   },
   "id": "48e7e78ec1a25e5",
   "execution_count": 50
  },
  {
   "cell_type": "markdown",
   "source": [
    "Insight: We have 4251 reported bike accidents in Berlin in 2019."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c483c336e2bc7d69"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Convert the coordinate columns to floats, replacing commas with periods\n",
    "# Needed for later when plotting the data\n",
    "accidents_data['LongitudeWGS84'] = accidents_data['LongitudeWGS84'].str.replace(',', '.').astype(float)\n",
    "accidents_data['LatitudeWGS84'] = accidents_data['LatitudeWGS84'].str.replace(',', '.').astype(float)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.682447Z",
     "start_time": "2024-04-19T18:17:11.673412Z"
    }
   },
   "id": "ca02fb410745e2c5",
   "execution_count": 51
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "ObjectID                 int64\nState                    int64\nDistrict                 int64\nLOR_ab_2021            float64\nAccidentYear             int64\nAccidentMonth            int64\nAccidentHour             int64\nDayOfWeek                int64\nAccidentCategory         int64\nAccidentType             int64\nAccidentTypeDetail       int64\nLightingCondition        int64\nInvolvingBike            int64\nInvolvingCar             int64\nInvolvingPedestrian      int64\nInvolvingMotorcycle      int64\nInvolvingHGV             int64\nInvolvingOther           int64\nRoadCondition            int64\nGraphicCoord1           object\nGraphicCoord2           object\nLongitudeWGS84         float64\nLatitudeWGS84          float64\ndtype: object"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data types of the columns\n",
    "accidents_data.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.686302Z",
     "start_time": "2024-04-19T18:17:11.683281Z"
    }
   },
   "id": "43e3b3046ffa56d8",
   "execution_count": 52
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# save the cleaned data to a new csv file\n",
    "# accidents_data.to_csv('data/accidents_Berlin_2021.csv', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.688634Z",
     "start_time": "2024-04-19T18:17:11.687102Z"
    }
   },
   "id": "d0275701ff268d3b",
   "execution_count": 53
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Data Source 2: Bike Lanes data via WFS endpoint\n",
    "---"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f8028afda9e372f6"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'owslib'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[54], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mowslib\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mwfs\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m WebFeatureService\n\u001B[1;32m      3\u001B[0m \u001B[38;5;66;03m# The URL of the WFS server you want to connect to\u001B[39;00m\n\u001B[1;32m      4\u001B[0m wfs_url \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhttps://gdi.berlin.de/services/wfs/radverkehrsanlagen?REQUEST=GetCapabilities&SERVICE=wfs\u001B[39m\u001B[38;5;124m'\u001B[39m\n",
      "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'owslib'"
     ]
    }
   ],
   "source": [
    "from owslib.wfs import WebFeatureService\n",
    "\n",
    "# The URL of the WFS server you want to connect to\n",
    "wfs_url = 'https://gdi.berlin.de/services/wfs/radverkehrsanlagen?REQUEST=GetCapabilities&SERVICE=wfs'\n",
    "\n",
    "# Connect to the WFS service\n",
    "wfs = WebFeatureService(url=wfs_url, version='2.0.0')\n",
    "\n",
    "# Specify the layer name and output format\n",
    "layer_name = 'radverkehrsanlagen:b_radverkehrsanlagen'\n",
    "output_format = 'text/xml; subtype=gml/3.1.1'\n",
    "\n",
    "# Fetch features from the layer\n",
    "response = wfs.getfeature(typename=layer_name, outputFormat=output_format)\n",
    "\n",
    "# Since the previous approach to check the content type isn't suitable here, \n",
    "# we'll directly read the response content\n",
    "response_content = response.read()\n",
    "\n",
    "# Check if the response is not empty by checking the size of the response_content\n",
    "if response_content:\n",
    "    # print(response_content[:1000])  # Print the first 1000 bytes to check\n",
    "    \n",
    "    # Save the fetched data to a file\n",
    "    with open('bike_lanes.xml', 'wb') as file:\n",
    "        file.write(response.read())\n",
    "else:\n",
    "    print(\"The response is empty. Check the layer name, output format, and WFS service.\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.711565Z",
     "start_time": "2024-04-19T18:17:11.689368Z"
    }
   },
   "id": "a709be8fa8572bef",
   "execution_count": 54
  },
  {
   "cell_type": "markdown",
   "source": [
    "Running this command in the terminal will convert the XML file to GeoJSON:\n",
    "```bash\n",
    "ogr2ogr -f GeoJSON bike_lanes.geojson bike_lanes.xml\n",
    "```"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "91d8bae70b2ae8a0"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the converted GeoJSON file\n",
    "gdf = gpd.read_file('raw-data/bike_lanes.geojson')\n",
    "\n",
    "# Plot the data\n",
    "gdf.plot()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:11.713338Z",
     "start_time": "2024-04-19T18:17:11.713245Z"
    }
   },
   "id": "aaf0382f1eaa8e3b",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "gdf.head()"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e12c429d8a30a5ea",
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "So far so good - we have the bike lanes data. Now let's see how we can combine the two datasets."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "61009b23dec84b1d"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# translate the column names to English\n",
    "# print values of the 'rva_typ' column\n",
    "gdf['rva_typ'].value_counts()\n",
    "\n",
    "column_names_de = gdf.columns\n",
    "\n",
    "# Translation dictionary\n",
    "translations_en = {\n",
    "    'gml_id': 'gml_id',\n",
    "    'sobj_kz': 'subject_code',\n",
    "    'segm_segm': 'segment',\n",
    "    'segm_bez': 'segment_district',\n",
    "    'stst_str': 'station_street',\n",
    "    'stor_name': 'storage_name',\n",
    "    'ortstl': 'location',\n",
    "    'rva_typ': 'lane_type',\n",
    "    'sorvt_typ': 'service_type',\n",
    "    'laenge': 'length_in_m',\n",
    "    'b_pflicht': 'use_mandatory',\n",
    "    'geometry': 'geometry'\n",
    "}\n",
    "\n",
    "# Translate the columns in the GeoDataFrame\n",
    "gdf_renamed = gdf.rename(columns=translations_en)\n",
    "\n",
    "# Display the first few rows of the translated GeoDataFrame\n",
    "gdf_renamed.head()"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "fb30e3e075a6f2ad",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# drop column 'service_type'\n",
    "gdf_renamed = gdf_renamed.drop(columns='service_type')\n",
    "\n",
    "# translate values of the 'rva_type' column\n",
    "gdf_renamed['lane_type'].value_counts()\n",
    "\n",
    "# Translation dictionary for 'lane_type'\n",
    "translations_lane_type = {\n",
    "    'Radwege': 'bike path',\n",
    "    'Schutzstreifen': 'protected lane',\n",
    "    'Radfahrstreifen': 'bicycle lane',\n",
    "    'Bussonderfahrstreifen': 'bus priority lane'\n",
    "}\n",
    "\n",
    "# Translate the values in the 'lane_type' column\n",
    "gdf_renamed['lane_type'] = gdf_renamed['lane_type'].replace(translations_lane_type)\n",
    "\n",
    "# translate ja, nein in 'use_mandatory' column\n",
    "gdf_renamed['use_mandatory'].value_counts()\n",
    "\n",
    "# Translation dictionary for 'use_mandatory'\n",
    "translations_use_mandatory = {\n",
    "    'ja': 'yes',\n",
    "    'nein': 'no'\n",
    "}\n",
    "\n",
    "# Translate the values in the 'use_mandatory' column\n",
    "gdf_renamed['use_mandatory'] = gdf_renamed['use_mandatory'].map(translations_use_mandatory)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ba2c9c92524b9a48",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Display the first few rows of the translated GeoDataFrame\n",
    "print(gdf_renamed.head())"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c1a3005afe93fe6d",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Check the CRS of the GeoDataFrame\n",
    "if gdf_renamed.crs != 'epsg:4326':\n",
    "    gdf_renamed = gdf_renamed.to_crs('epsg:4326')"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d5918efbb5aa8b91",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Save the translated GeoDataFrame to a new GeoJSON file\n",
    "gdf_renamed.to_file('data/bike_lanes_Berlin.geojson', driver='GeoJSON')"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a239d6564f602930",
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Data Source 3: Air Quality Data retrieved from API Luftmessnetz Berlin\n",
    "---\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "747de3b3186d9dfb"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "# Fetch the list of active stations from the API\n",
    "stations = requests.get('https://luftdaten.berlin.de/api/stations?active=true').json()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:34.062125Z",
     "start_time": "2024-04-19T18:17:33.966581Z"
    }
   },
   "id": "35eeac273f584248",
   "execution_count": 55
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "              name   code   codeEu  \\\n0      010 Wedding  mc010  DEBE010   \n1   018 Schöneberg  mc018  DEBE018   \n2  027 Marienfelde  mc027  DEBE027   \n3    032 Grunewald  mc032  DEBE032   \n4     042 Neukölln  mc042  DEBE034   \n\n                                      address          lat          lng  \\\n0  13353 Wedding, Amrumer Str./Limburger Str.  52.54291000  13.34926000   \n1              10823 Berlin, Belziger Str. 52  52.48579000  13.34885000   \n2                12307 Berlin, Schichauweg 60  52.39840000  13.36807500   \n3                      14193 Berlin, Jagen 91  52.47319000  13.22514000   \n4                 12047 Berlin, Nansenstr. 10  52.48945100  13.43084400   \n\n   active stationgroups             measuringStart measuringEnd  \\\n0    True  [background]  1984-11-01T00:00:00+01:00         None   \n1    True  [background]  1986-11-01T00:00:00+01:00         None   \n2    True      [suburb]  1989-04-01T00:00:00+02:00         None   \n3    True      [suburb]  1986-10-01T00:00:00+01:00         None   \n4    True  [background]  1986-03-01T00:00:00+01:00         None   \n\n   measuringHeight                                                url  \\\n0              NaN  http://www.stadtentwicklung.berlin.de/umwelt/u...   \n1              NaN  http://www.stadtentwicklung.berlin.de/umwelt/u...   \n2              NaN  http://www.stadtentwicklung.berlin.de/umwelt/u...   \n3              3.0  http://www.stadtentwicklung.berlin.de/umwelt/u...   \n4              NaN  http://www.stadtentwicklung.berlin.de/umwelt/u...   \n\n                                         information  \\\n0  Die Station befindet sich an der Beuth Hochsch...   \n1  Tempelhof-Schöneberg, auf dem Gelände des Sena...   \n2  Tempelhof-Schöneberg, auf dem Versuchsfeld Mar...   \n3  Charlottenburg-Wilmersdorf, im Forst Grunewald...   \n4  Neukölln, auf dem Gelände einer Kindertagesstätte   \n\n                                          components  \\\n0  [pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...   \n1  [no_1h, no_24h, no_1m, no_1y, no2_1h, no2_24h,...   \n2  [pm10_24h, no_1h, no_24h, no_1m, no_1y, no2_1h...   \n3  [pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...   \n4  [pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...   \n\n                                    activeComponents  \\\n0  [pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...   \n1  [no_1h, no_24h, no_1m, no_1y, no2_1h, no2_24h,...   \n2  [no_1h, no_24h, no_1m, no_1y, no2_1h, no2_24h,...   \n3  [pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...   \n4  [pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...   \n\n                                            partials                  lqis  \\\n0        [pm2_gravi_24h, pm2_gravi_1m, pm2_gravi_1y]  [lqi, o3, pm10, no2]   \n1                                                 []            [lqi, no2]   \n2                                                 []        [lqi, o3, no2]   \n3                                                 []  [lqi, o3, pm10, no2]   \n4  [pm2_gravi_24h, pm2_gravi_1m, pm2_gravi_1y, pm...  [lqi, o3, pm10, no2]   \n\n                                             exceeds  \n0  [no2_1h, o3_1h, o3_8hg, pm10_24h, no2_1y, pm10...  \n1                                   [no2_1h, no2_1y]  \n2                    [no2_1h, o3_1h, o3_8hg, no2_1y]  \n3  [no2_1h, o3_1h, o3_8hg, pm10_24h, no2_1y, pm10...  \n4  [no2_1h, o3_1h, o3_8hg, pm10_24h, no2_1y, pm10...  ",
      "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>code</th>\n      <th>codeEu</th>\n      <th>address</th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>active</th>\n      <th>stationgroups</th>\n      <th>measuringStart</th>\n      <th>measuringEnd</th>\n      <th>measuringHeight</th>\n      <th>url</th>\n      <th>information</th>\n      <th>components</th>\n      <th>activeComponents</th>\n      <th>partials</th>\n      <th>lqis</th>\n      <th>exceeds</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>010 Wedding</td>\n      <td>mc010</td>\n      <td>DEBE010</td>\n      <td>13353 Wedding, Amrumer Str./Limburger Str.</td>\n      <td>52.54291000</td>\n      <td>13.34926000</td>\n      <td>True</td>\n      <td>[background]</td>\n      <td>1984-11-01T00:00:00+01:00</td>\n      <td>None</td>\n      <td>NaN</td>\n      <td>http://www.stadtentwicklung.berlin.de/umwelt/u...</td>\n      <td>Die Station befindet sich an der Beuth Hochsch...</td>\n      <td>[pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...</td>\n      <td>[pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...</td>\n      <td>[pm2_gravi_24h, pm2_gravi_1m, pm2_gravi_1y]</td>\n      <td>[lqi, o3, pm10, no2]</td>\n      <td>[no2_1h, o3_1h, o3_8hg, pm10_24h, no2_1y, pm10...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>018 Schöneberg</td>\n      <td>mc018</td>\n      <td>DEBE018</td>\n      <td>10823 Berlin, Belziger Str. 52</td>\n      <td>52.48579000</td>\n      <td>13.34885000</td>\n      <td>True</td>\n      <td>[background]</td>\n      <td>1986-11-01T00:00:00+01:00</td>\n      <td>None</td>\n      <td>NaN</td>\n      <td>http://www.stadtentwicklung.berlin.de/umwelt/u...</td>\n      <td>Tempelhof-Schöneberg, auf dem Gelände des Sena...</td>\n      <td>[no_1h, no_24h, no_1m, no_1y, no2_1h, no2_24h,...</td>\n      <td>[no_1h, no_24h, no_1m, no_1y, no2_1h, no2_24h,...</td>\n      <td>[]</td>\n      <td>[lqi, no2]</td>\n      <td>[no2_1h, no2_1y]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>027 Marienfelde</td>\n      <td>mc027</td>\n      <td>DEBE027</td>\n      <td>12307 Berlin, Schichauweg 60</td>\n      <td>52.39840000</td>\n      <td>13.36807500</td>\n      <td>True</td>\n      <td>[suburb]</td>\n      <td>1989-04-01T00:00:00+02:00</td>\n      <td>None</td>\n      <td>NaN</td>\n      <td>http://www.stadtentwicklung.berlin.de/umwelt/u...</td>\n      <td>Tempelhof-Schöneberg, auf dem Versuchsfeld Mar...</td>\n      <td>[pm10_24h, no_1h, no_24h, no_1m, no_1y, no2_1h...</td>\n      <td>[no_1h, no_24h, no_1m, no_1y, no2_1h, no2_24h,...</td>\n      <td>[]</td>\n      <td>[lqi, o3, no2]</td>\n      <td>[no2_1h, o3_1h, o3_8hg, no2_1y]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>032 Grunewald</td>\n      <td>mc032</td>\n      <td>DEBE032</td>\n      <td>14193 Berlin, Jagen 91</td>\n      <td>52.47319000</td>\n      <td>13.22514000</td>\n      <td>True</td>\n      <td>[suburb]</td>\n      <td>1986-10-01T00:00:00+01:00</td>\n      <td>None</td>\n      <td>3.0</td>\n      <td>http://www.stadtentwicklung.berlin.de/umwelt/u...</td>\n      <td>Charlottenburg-Wilmersdorf, im Forst Grunewald...</td>\n      <td>[pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...</td>\n      <td>[pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...</td>\n      <td>[]</td>\n      <td>[lqi, o3, pm10, no2]</td>\n      <td>[no2_1h, o3_1h, o3_8hg, pm10_24h, no2_1y, pm10...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>042 Neukölln</td>\n      <td>mc042</td>\n      <td>DEBE034</td>\n      <td>12047 Berlin, Nansenstr. 10</td>\n      <td>52.48945100</td>\n      <td>13.43084400</td>\n      <td>True</td>\n      <td>[background]</td>\n      <td>1986-03-01T00:00:00+01:00</td>\n      <td>None</td>\n      <td>NaN</td>\n      <td>http://www.stadtentwicklung.berlin.de/umwelt/u...</td>\n      <td>Neukölln, auf dem Gelände einer Kindertagesstätte</td>\n      <td>[pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...</td>\n      <td>[pm10_1h, pm10_24h, pm10_24hg, pm10_1m, pm10_1...</td>\n      <td>[pm2_gravi_24h, pm2_gravi_1m, pm2_gravi_1y, pm...</td>\n      <td>[lqi, o3, pm10, no2]</td>\n      <td>[no2_1h, o3_1h, o3_8hg, pm10_24h, no2_1y, pm10...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert json to pandas dataframe\n",
    "stations_df = pd.DataFrame(stations)\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "stations_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:34.739821Z",
     "start_time": "2024-04-19T18:17:34.710459Z"
    }
   },
   "id": "e5fc3121ae58a5",
   "execution_count": 56
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "              name   code   codeEu  \\\n0      010 Wedding  mc010  DEBE010   \n1   018 Schöneberg  mc018  DEBE018   \n2  027 Marienfelde  mc027  DEBE027   \n3    032 Grunewald  mc032  DEBE032   \n4     042 Neukölln  mc042  DEBE034   \n\n                                      address          lat          lng  \n0  13353 Wedding, Amrumer Str./Limburger Str.  52.54291000  13.34926000  \n1              10823 Berlin, Belziger Str. 52  52.48579000  13.34885000  \n2                12307 Berlin, Schichauweg 60  52.39840000  13.36807500  \n3                      14193 Berlin, Jagen 91  52.47319000  13.22514000  \n4                 12047 Berlin, Nansenstr. 10  52.48945100  13.43084400  ",
      "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>code</th>\n      <th>codeEu</th>\n      <th>address</th>\n      <th>lat</th>\n      <th>lng</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>010 Wedding</td>\n      <td>mc010</td>\n      <td>DEBE010</td>\n      <td>13353 Wedding, Amrumer Str./Limburger Str.</td>\n      <td>52.54291000</td>\n      <td>13.34926000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>018 Schöneberg</td>\n      <td>mc018</td>\n      <td>DEBE018</td>\n      <td>10823 Berlin, Belziger Str. 52</td>\n      <td>52.48579000</td>\n      <td>13.34885000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>027 Marienfelde</td>\n      <td>mc027</td>\n      <td>DEBE027</td>\n      <td>12307 Berlin, Schichauweg 60</td>\n      <td>52.39840000</td>\n      <td>13.36807500</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>032 Grunewald</td>\n      <td>mc032</td>\n      <td>DEBE032</td>\n      <td>14193 Berlin, Jagen 91</td>\n      <td>52.47319000</td>\n      <td>13.22514000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>042 Neukölln</td>\n      <td>mc042</td>\n      <td>DEBE034</td>\n      <td>12047 Berlin, Nansenstr. 10</td>\n      <td>52.48945100</td>\n      <td>13.43084400</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop unnecessary columns\n",
    "stations_df = stations_df.iloc[:, 0:6]\n",
    "stations_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:35.281096Z",
     "start_time": "2024-04-19T18:17:35.275708Z"
    }
   },
   "id": "201616bba5625053",
   "execution_count": 57
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Extract the station names and coordinates\n",
    "station_codes = [station['code'] for station in stations]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:36.471963Z",
     "start_time": "2024-04-19T18:17:36.469706Z"
    }
   },
   "id": "eca6e81866d8b6e5",
   "execution_count": 58
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for station mc010 fetched successfully.\n",
      "No data found for station mc018.\n",
      "No data found for station mc027.\n",
      "Data for station mc032 fetched successfully.\n",
      "Data for station mc042 fetched successfully.\n",
      "Data for station mc077 fetched successfully.\n",
      "Data for station mc085 fetched successfully.\n",
      "No data found for station mc115.\n",
      "Data for station mc117 fetched successfully.\n",
      "Data for station mc124 fetched successfully.\n",
      "No data found for station mc145.\n",
      "Data for station mc171 fetched successfully.\n",
      "Data for station mc174 fetched successfully.\n",
      "No data found for station mc282.\n",
      "Data for station mc190 fetched successfully.\n",
      "Data for station MC221 fetched successfully.\n",
      "No data found for station mc144.\n"
     ]
    }
   ],
   "source": [
    "# trying to access the data for each station and check if successful\n",
    "responses = []\n",
    "\n",
    "# loop through the station codes and get the data for each station, check if response is empty\n",
    "for code in station_codes:\n",
    "    url = f\"https://luftdaten.berlin.de/api/stations/{code}/data?core=pm10&period=24h&timespan=custom&start=01.01.2021&end=31.12.2021\"\n",
    "    response = requests.get(url)\n",
    "    if response.json():\n",
    "        print(f\"Data for station {code} fetched successfully.\")\n",
    "        # add the response to the list of responses\n",
    "        responses.append(response)\n",
    "    else:\n",
    "        print(f\"No data found for station {code}.\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:47.755270Z",
     "start_time": "2024-04-19T18:17:46.222400Z"
    }
   },
   "id": "1d75effc03b861aa",
   "execution_count": 60
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "         date  value pollutant   code\n0  2021-12-31    8.0      pm10  mc010\n1  2021-12-30   13.0      pm10  mc010\n2  2021-12-29   17.0      pm10  mc010\n3  2021-12-28   39.0      pm10  mc010\n4  2021-12-27   39.0      pm10  mc010",
      "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>date</th>\n      <th>value</th>\n      <th>pollutant</th>\n      <th>code</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2021-12-31</td>\n      <td>8.0</td>\n      <td>pm10</td>\n      <td>mc010</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2021-12-30</td>\n      <td>13.0</td>\n      <td>pm10</td>\n      <td>mc010</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2021-12-29</td>\n      <td>17.0</td>\n      <td>pm10</td>\n      <td>mc010</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2021-12-28</td>\n      <td>39.0</td>\n      <td>pm10</td>\n      <td>mc010</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2021-12-27</td>\n      <td>39.0</td>\n      <td>pm10</td>\n      <td>mc010</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the date range and pollutants of interest\n",
    "date_start = \"01.01.2021\"\n",
    "date_end = \"31.12.2021\"\n",
    "pollutants = [\"pm10\", \"pm25\"]\n",
    "\n",
    "# Function to fetch air quality data for a given station and pollutant\n",
    "def fetch_pollutant_data(code, pollutant):\n",
    "    url = f\"https://luftdaten.berlin.de/api/stations/{code}/data?core={pollutant}&period=24h&timespan=custom&start={date_start}&end={date_end}\"\n",
    "    response = requests.get(url)\n",
    "    if response.status_code == 200:\n",
    "        data = response.json()\n",
    "        return [{\"date\": item['datetime'].split('T')[0], \"value\": item['value'], \"pollutant\": pollutant} for item in data]\n",
    "    else:\n",
    "        return []\n",
    "\n",
    "# Initialize an empty list to hold all pollutant data\n",
    "all_pollutant_data = []\n",
    "\n",
    "# Loop through each station and each pollutant, fetching and appending the data\n",
    "for station in stations_df.to_dict('records'):\n",
    "    for pollutant in pollutants:\n",
    "        pollutant_data = fetch_pollutant_data(station['code'], pollutant)\n",
    "        # Add station code to each data point for merging later\n",
    "        for data_point in pollutant_data:\n",
    "            data_point['code'] = station['code']\n",
    "        all_pollutant_data.extend(pollutant_data)\n",
    "\n",
    "# Convert the collected pollutant data into a DataFrame\n",
    "pollutant_df = pd.DataFrame(all_pollutant_data)\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "pollutant_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:17:56.607233Z",
     "start_time": "2024-04-19T18:17:52.217019Z"
    }
   },
   "id": "5b98150c4f1f8c57",
   "execution_count": 61
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          name   code   codeEu                                     address  \\\n",
      "0  010 Wedding  mc010  DEBE010  13353 Wedding, Amrumer Str./Limburger Str.   \n",
      "1  010 Wedding  mc010  DEBE010  13353 Wedding, Amrumer Str./Limburger Str.   \n",
      "2  010 Wedding  mc010  DEBE010  13353 Wedding, Amrumer Str./Limburger Str.   \n",
      "3  010 Wedding  mc010  DEBE010  13353 Wedding, Amrumer Str./Limburger Str.   \n",
      "4  010 Wedding  mc010  DEBE010  13353 Wedding, Amrumer Str./Limburger Str.   \n",
      "\n",
      "           lat          lng       date  value pollutant  \n",
      "0  52.54291000  13.34926000 2021-12-31    8.0      pm10  \n",
      "1  52.54291000  13.34926000 2021-12-30   13.0      pm10  \n",
      "2  52.54291000  13.34926000 2021-12-29   17.0      pm10  \n",
      "3  52.54291000  13.34926000 2021-12-28   39.0      pm10  \n",
      "4  52.54291000  13.34926000 2021-12-27   39.0      pm10  \n"
     ]
    }
   ],
   "source": [
    "# Convert date strings to datetime objects for both DataFrames\n",
    "pollutant_df['date'] = pd.to_datetime(pollutant_df['date'])\n",
    "\n",
    "# Merge the pollutant data with the stations DataFrame based on the station code\n",
    "# and optionally the date if your stations_df includes a 'date' column\n",
    "merged_df = pd.merge(stations_df, pollutant_df, on='code', how='left')\n",
    "\n",
    "# You now have a merged DataFrame `merged_df` with stations and their corresponding air quality data\n",
    "print(merged_df.head())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:18:03.812423Z",
     "start_time": "2024-04-19T18:18:03.785194Z"
    }
   },
   "id": "2dfc0c13dc86fa1a",
   "execution_count": 62
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Save the merged DataFrame to a new CSV file\n",
    "merged_df.to_csv('data/air_quality_Berlin_2021_all.csv', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:18:33.412630Z",
     "start_time": "2024-04-19T18:18:33.255018Z"
    }
   },
   "id": "9e48b9c5ab05d422",
   "execution_count": 64
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot pollutant values as line plot \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Filter the DataFrame for a specific pollutant\n",
    "pm10_data = merged_df[merged_df['pollutant'] == 'pm10']\n",
    "\n",
    "# Group the data by date and calculate the mean value for each day\n",
    "daily_pm10 = pm10_data.groupby('date')['value'].mean()\n",
    "\n",
    "# Plot the daily PM10 values\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(daily_pm10.index, daily_pm10.values, marker='o', color='b', label='PM10')\n",
    "plt.title('Daily PM10 Values in Berlin (2021)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('PM10 Value')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:18:36.884494Z",
     "start_time": "2024-04-19T18:18:36.734825Z"
    }
   },
   "id": "723f3c10f02ad7",
   "execution_count": 65
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<folium.folium.Map at 0x147d6f110>",
      "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n&lt;html&gt;\n&lt;head&gt;\n    \n    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n    \n        &lt;script&gt;\n            L_NO_TOUCH = false;\n            L_DISABLE_3D = false;\n        &lt;/script&gt;\n    \n    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://code.jquery.com/jquery-3.7.1.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n    \n            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n            &lt;style&gt;\n                #map_22577fe541991d31b20f04e042b14eb0 {\n                    position: relative;\n                    width: 100.0%;\n                    height: 100.0%;\n                    left: 0.0%;\n                    top: 0.0%;\n                }\n                .leaflet-container { font-size: 1rem; }\n            &lt;/style&gt;\n        \n&lt;/head&gt;\n&lt;body&gt;\n    \n    \n            &lt;div class=&quot;folium-map&quot; id=&quot;map_22577fe541991d31b20f04e042b14eb0&quot; &gt;&lt;/div&gt;\n        \n&lt;/body&gt;\n&lt;script&gt;\n    \n    \n            var map_22577fe541991d31b20f04e042b14eb0 = L.map(\n                &quot;map_22577fe541991d31b20f04e042b14eb0&quot;,\n                {\n                    center: [52.52, 13.405],\n                    crs: L.CRS.EPSG3857,\n                    zoom: 11,\n                    zoomControl: true,\n                    preferCanvas: false,\n                }\n            );\n\n            \n\n        \n    \n            var tile_layer_4a3084132edc5b9c6b4d9d659f9f3e4d = L.tileLayer(\n                &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n                {&quot;attribution&quot;: &quot;\\u0026copy; \\u003ca href=\\&quot;https://www.openstreetmap.org/copyright\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e contributors&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 19, &quot;maxZoom&quot;: 19, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n            );\n        \n    \n            tile_layer_4a3084132edc5b9c6b4d9d659f9f3e4d.addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var marker_1e80f095b840156a92018bd298bd93ba = L.marker(\n                [52.54291, 13.34926],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_6607732c68e61039549bac096ee437a4 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_1e80f095b840156a92018bd298bd93ba.setIcon(icon_6607732c68e61039549bac096ee437a4);\n        \n    \n        var popup_4621daa33180262fc6119d431cb017f2 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_99aea26902c68ef34bfdc3ca5d5a1bcb = $(`&lt;div id=&quot;html_99aea26902c68ef34bfdc3ca5d5a1bcb&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;010 Wedding&lt;/div&gt;`)[0];\n                popup_4621daa33180262fc6119d431cb017f2.setContent(html_99aea26902c68ef34bfdc3ca5d5a1bcb);\n            \n        \n\n        marker_1e80f095b840156a92018bd298bd93ba.bindPopup(popup_4621daa33180262fc6119d431cb017f2)\n        ;\n\n        \n    \n    \n            var marker_0534dd3d6f7de97d18e63b34f860852e = L.marker(\n                [52.48579, 13.34885],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_176003c53076d8e19e3053a33ab3c385 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_0534dd3d6f7de97d18e63b34f860852e.setIcon(icon_176003c53076d8e19e3053a33ab3c385);\n        \n    \n        var popup_2606c9d3d3541410bd83db01f4a7f6e9 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_97943fb930eb55a4f0ce2249290b0c8a = $(`&lt;div id=&quot;html_97943fb930eb55a4f0ce2249290b0c8a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;018 Schöneberg&lt;/div&gt;`)[0];\n                popup_2606c9d3d3541410bd83db01f4a7f6e9.setContent(html_97943fb930eb55a4f0ce2249290b0c8a);\n            \n        \n\n        marker_0534dd3d6f7de97d18e63b34f860852e.bindPopup(popup_2606c9d3d3541410bd83db01f4a7f6e9)\n        ;\n\n        \n    \n    \n            var marker_f179166159b13b068d4a2418083d5a52 = L.marker(\n                [52.3984, 13.368075],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_aa8fc9a41d7615949684300b44bd89da = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_f179166159b13b068d4a2418083d5a52.setIcon(icon_aa8fc9a41d7615949684300b44bd89da);\n        \n    \n        var popup_11daf65a9225d65bea0ea207cb4f478c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_9507bcc9b4d5ab0aa554f205bb42d0f7 = $(`&lt;div id=&quot;html_9507bcc9b4d5ab0aa554f205bb42d0f7&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;027 Marienfelde&lt;/div&gt;`)[0];\n                popup_11daf65a9225d65bea0ea207cb4f478c.setContent(html_9507bcc9b4d5ab0aa554f205bb42d0f7);\n            \n        \n\n        marker_f179166159b13b068d4a2418083d5a52.bindPopup(popup_11daf65a9225d65bea0ea207cb4f478c)\n        ;\n\n        \n    \n    \n            var marker_31bf1cd980bd78984f8fc307a8b09162 = L.marker(\n                [52.47319, 13.22514],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_d773a72999e38f69d8c2cba76115ec27 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_31bf1cd980bd78984f8fc307a8b09162.setIcon(icon_d773a72999e38f69d8c2cba76115ec27);\n        \n    \n        var popup_11a162816ff1673d2ad11c8c104ccc68 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_0f078b7d2e38482a0a7ec59bf31b87c9 = $(`&lt;div id=&quot;html_0f078b7d2e38482a0a7ec59bf31b87c9&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;032 Grunewald&lt;/div&gt;`)[0];\n                popup_11a162816ff1673d2ad11c8c104ccc68.setContent(html_0f078b7d2e38482a0a7ec59bf31b87c9);\n            \n        \n\n        marker_31bf1cd980bd78984f8fc307a8b09162.bindPopup(popup_11a162816ff1673d2ad11c8c104ccc68)\n        ;\n\n        \n    \n    \n            var marker_5ff1c822967bfe1a10892fc0428baf19 = L.marker(\n                [52.489451, 13.430844],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_5e71fa8b7349eb4761d84223d2a6b8a1 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_5ff1c822967bfe1a10892fc0428baf19.setIcon(icon_5e71fa8b7349eb4761d84223d2a6b8a1);\n        \n    \n        var popup_399d5268bbe9048fb648809d4097a150 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_44076d2f7bb48fe1b3f569b835e8df28 = $(`&lt;div id=&quot;html_44076d2f7bb48fe1b3f569b835e8df28&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;042 Neukölln&lt;/div&gt;`)[0];\n                popup_399d5268bbe9048fb648809d4097a150.setContent(html_44076d2f7bb48fe1b3f569b835e8df28);\n            \n        \n\n        marker_5ff1c822967bfe1a10892fc0428baf19.bindPopup(popup_399d5268bbe9048fb648809d4097a150)\n        ;\n\n        \n    \n    \n            var marker_db0f4acf83003359edde711eab45e7f0 = L.marker(\n                [52.6441, 13.4833],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_dc78e381e4e35d11605ee3415e76ffae = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_db0f4acf83003359edde711eab45e7f0.setIcon(icon_dc78e381e4e35d11605ee3415e76ffae);\n        \n    \n        var popup_356c4035cd28fc1e6affdf790a1ad01e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_f1be97668fcf1bc9078fec3df3478a48 = $(`&lt;div id=&quot;html_f1be97668fcf1bc9078fec3df3478a48&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;077 Buch&lt;/div&gt;`)[0];\n                popup_356c4035cd28fc1e6affdf790a1ad01e.setContent(html_f1be97668fcf1bc9078fec3df3478a48);\n            \n        \n\n        marker_db0f4acf83003359edde711eab45e7f0.bindPopup(popup_356c4035cd28fc1e6affdf790a1ad01e)\n        ;\n\n        \n    \n    \n            var marker_88c7e77eea0ec441ba8f86f0e67dca23 = L.marker(\n                [52.447697, 13.64705],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_90afec5194e256a094490b48f942f4f9 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_88c7e77eea0ec441ba8f86f0e67dca23.setIcon(icon_90afec5194e256a094490b48f942f4f9);\n        \n    \n        var popup_79be0ebb2bd26f301eeaf51f71b7747b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_7776363d4830b934f1ca04a226b74670 = $(`&lt;div id=&quot;html_7776363d4830b934f1ca04a226b74670&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;085 Friedrichshagen&lt;/div&gt;`)[0];\n                popup_79be0ebb2bd26f301eeaf51f71b7747b.setContent(html_7776363d4830b934f1ca04a226b74670);\n            \n        \n\n        marker_88c7e77eea0ec441ba8f86f0e67dca23.bindPopup(popup_79be0ebb2bd26f301eeaf51f71b7747b)\n        ;\n\n        \n    \n    \n            var marker_2fe9bff4357ecf071aa9b4e4fdc9201d = L.marker(\n                [52.50663, 13.332976],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_7aed5a0e02290532d36daefdfee3d705 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_2fe9bff4357ecf071aa9b4e4fdc9201d.setIcon(icon_7aed5a0e02290532d36daefdfee3d705);\n        \n    \n        var popup_b28f1a1d9ac7a70ce23366c3990535d6 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_c3997170805a0ca537c2e58b784b4a27 = $(`&lt;div id=&quot;html_c3997170805a0ca537c2e58b784b4a27&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;115 Hardenbergplatz&lt;/div&gt;`)[0];\n                popup_b28f1a1d9ac7a70ce23366c3990535d6.setContent(html_c3997170805a0ca537c2e58b784b4a27);\n            \n        \n\n        marker_2fe9bff4357ecf071aa9b4e4fdc9201d.bindPopup(popup_b28f1a1d9ac7a70ce23366c3990535d6)\n        ;\n\n        \n    \n    \n            var marker_a6543e4995cdad68e97b5f71f4ad45cc = L.marker(\n                [52.46362, 13.31832],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_9059298e7eb87ee46bf3bfc9e4448325 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_a6543e4995cdad68e97b5f71f4ad45cc.setIcon(icon_9059298e7eb87ee46bf3bfc9e4448325);\n        \n    \n        var popup_0965c9cb15845e60397f687da3fda66f = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_57d19ae5dd7dd33544699d54d5d50634 = $(`&lt;div id=&quot;html_57d19ae5dd7dd33544699d54d5d50634&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;117 Schildhornstraße&lt;/div&gt;`)[0];\n                popup_0965c9cb15845e60397f687da3fda66f.setContent(html_57d19ae5dd7dd33544699d54d5d50634);\n            \n        \n\n        marker_a6543e4995cdad68e97b5f71f4ad45cc.bindPopup(popup_0965c9cb15845e60397f687da3fda66f)\n        ;\n\n        \n    \n    \n            var marker_2ac553fb86137267181a4aef721a73f0 = L.marker(\n                [52.438115, 13.38772],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_578ef67818781c189c00c7574808d0cd = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_2ac553fb86137267181a4aef721a73f0.setIcon(icon_578ef67818781c189c00c7574808d0cd);\n        \n    \n        var popup_76131f4631e885bf3b21b55da46870d3 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_d2a881320bec6607e4a46825e6d85ff8 = $(`&lt;div id=&quot;html_d2a881320bec6607e4a46825e6d85ff8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;124 Mariendorfer Damm&lt;/div&gt;`)[0];\n                popup_76131f4631e885bf3b21b55da46870d3.setContent(html_d2a881320bec6607e4a46825e6d85ff8);\n            \n        \n\n        marker_2ac553fb86137267181a4aef721a73f0.bindPopup(popup_76131f4631e885bf3b21b55da46870d3)\n        ;\n\n        \n    \n    \n            var marker_ae67e5748b0c8577cd18fe49be60d914 = L.marker(\n                [52.65327, 13.29608],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_f65ea708745c02e86e7347ac7e2e0084 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_ae67e5748b0c8577cd18fe49be60d914.setIcon(icon_f65ea708745c02e86e7347ac7e2e0084);\n        \n    \n        var popup_701ce3303f737c12fad56fbb5182afba = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_8cf793b3163c4b8acfc857c644d7d3f3 = $(`&lt;div id=&quot;html_8cf793b3163c4b8acfc857c644d7d3f3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;145 Frohnau&lt;/div&gt;`)[0];\n                popup_701ce3303f737c12fad56fbb5182afba.setContent(html_8cf793b3163c4b8acfc857c644d7d3f3);\n            \n        \n\n        marker_ae67e5748b0c8577cd18fe49be60d914.bindPopup(popup_701ce3303f737c12fad56fbb5182afba)\n        ;\n\n        \n    \n    \n            var marker_2b24e44902b3629b31a3d5ead74816f0 = L.marker(\n                [52.51361, 13.41883],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_09f54a779f2e2725dcf235986070354f = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_2b24e44902b3629b31a3d5ead74816f0.setIcon(icon_09f54a779f2e2725dcf235986070354f);\n        \n    \n        var popup_c60d5747d47331aa57565f931f03196f = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_35b35ac875551e10128d006c32c21357 = $(`&lt;div id=&quot;html_35b35ac875551e10128d006c32c21357&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;171 Mitte&lt;/div&gt;`)[0];\n                popup_c60d5747d47331aa57565f931f03196f.setContent(html_35b35ac875551e10128d006c32c21357);\n            \n        \n\n        marker_2b24e44902b3629b31a3d5ead74816f0.bindPopup(popup_c60d5747d47331aa57565f931f03196f)\n        ;\n\n        \n    \n    \n            var marker_fad1ddfb9c7bf0a7800b7151c62e0cf3 = L.marker(\n                [52.51407, 13.46993],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_17456c273d908f988c3aee63e1ef8a45 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_fad1ddfb9c7bf0a7800b7151c62e0cf3.setIcon(icon_17456c273d908f988c3aee63e1ef8a45);\n        \n    \n        var popup_7a1e519d66e16e20b223237f257a79a7 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_1edf89af3b387f2d9f173d3d76875c74 = $(`&lt;div id=&quot;html_1edf89af3b387f2d9f173d3d76875c74&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;174 Frankfurter Allee&lt;/div&gt;`)[0];\n                popup_7a1e519d66e16e20b223237f257a79a7.setContent(html_1edf89af3b387f2d9f173d3d76875c74);\n            \n        \n\n        marker_fad1ddfb9c7bf0a7800b7151c62e0cf3.bindPopup(popup_7a1e519d66e16e20b223237f257a79a7)\n        ;\n\n        \n    \n    \n            var marker_c7984f349fcc5123031c159c51ff3516 = L.marker(\n                [52.4853, 13.5295],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_37bb1357611b5f1e7565e5bfaa5e8cdd = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_c7984f349fcc5123031c159c51ff3516.setIcon(icon_37bb1357611b5f1e7565e5bfaa5e8cdd);\n        \n    \n        var popup_80a2b3d5e6b59c695d105508510e36f1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_17d3a4240dca160067788b9244ecadb7 = $(`&lt;div id=&quot;html_17d3a4240dca160067788b9244ecadb7&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;282 Karlshorst&lt;/div&gt;`)[0];\n                popup_80a2b3d5e6b59c695d105508510e36f1.setContent(html_17d3a4240dca160067788b9244ecadb7);\n            \n        \n\n        marker_c7984f349fcc5123031c159c51ff3516.bindPopup(popup_80a2b3d5e6b59c695d105508510e36f1)\n        ;\n\n        \n    \n    \n            var marker_1ffbdac6daa2aed3f076c3c4651f5a52 = L.marker(\n                [52.509813, 13.382377],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_515518dfac6fb91fcc18e18a6360f0df = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_1ffbdac6daa2aed3f076c3c4651f5a52.setIcon(icon_515518dfac6fb91fcc18e18a6360f0df);\n        \n    \n        var popup_c112dbb80c7be97111d62ffb35f13175 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_9eea66561b9eabb240d25b2d6f61e14b = $(`&lt;div id=&quot;html_9eea66561b9eabb240d25b2d6f61e14b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;190 Leipziger Straße&lt;/div&gt;`)[0];\n                popup_c112dbb80c7be97111d62ffb35f13175.setContent(html_9eea66561b9eabb240d25b2d6f61e14b);\n            \n        \n\n        marker_1ffbdac6daa2aed3f076c3c4651f5a52.bindPopup(popup_c112dbb80c7be97111d62ffb35f13175)\n        ;\n\n        \n    \n    \n            var marker_9f9951942db86be2986f7ca2e2cc435a = L.marker(\n                [52.483812, 13.429672],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_b30bae80603dc238ac4537ef7ca2c8b5 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_9f9951942db86be2986f7ca2e2cc435a.setIcon(icon_b30bae80603dc238ac4537ef7ca2c8b5);\n        \n    \n        var popup_35d7077c2e1e83d5d76e7c678f85eb53 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_ef92805a4b523acc2340909916ee209d = $(`&lt;div id=&quot;html_ef92805a4b523acc2340909916ee209d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;221 Karl-Marx-Straße&lt;/div&gt;`)[0];\n                popup_35d7077c2e1e83d5d76e7c678f85eb53.setContent(html_ef92805a4b523acc2340909916ee209d);\n            \n        \n\n        marker_9f9951942db86be2986f7ca2e2cc435a.bindPopup(popup_35d7077c2e1e83d5d76e7c678f85eb53)\n        ;\n\n        \n    \n    \n            var marker_61134886f4d3f0b319454c4712658743 = L.marker(\n                [52.46747, 13.44121],\n                {}\n            ).addTo(map_22577fe541991d31b20f04e042b14eb0);\n        \n    \n            var icon_57cf464aab8701e403d82b679fe734eb = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_61134886f4d3f0b319454c4712658743.setIcon(icon_57cf464aab8701e403d82b679fe734eb);\n        \n    \n        var popup_55fad6835ac9dcc8e75f47c2be8f2d39 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_fd3151ac078fc9f6671775ba71258a7b = $(`&lt;div id=&quot;html_fd3151ac078fc9f6671775ba71258a7b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;144 Silbersteinstraße&lt;/div&gt;`)[0];\n                popup_55fad6835ac9dcc8e75f47c2be8f2d39.setContent(html_fd3151ac078fc9f6671775ba71258a7b);\n            \n        \n\n        marker_61134886f4d3f0b319454c4712658743.bindPopup(popup_55fad6835ac9dcc8e75f47c2be8f2d39)\n        ;\n\n        \n    \n&lt;/script&gt;\n&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot stations on map\n",
    "import folium\n",
    "\n",
    "# Create a map centered around Berlin\n",
    "m = folium.Map(location=[52.52, 13.4050], zoom_start=11)\n",
    "\n",
    "# Add markers for each station\n",
    "for _, row in stations_df.iterrows():\n",
    "    folium.Marker(\n",
    "        location=[row['lat'], row['lng']],\n",
    "        popup=row['name'],\n",
    "        icon=folium.Icon(color='blue', icon='info-sign')\n",
    "    ).add_to(m)\n",
    "    \n",
    "m"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-19T18:18:38.545935Z",
     "start_time": "2024-04-19T18:18:38.524038Z"
    }
   },
   "id": "1413f302c2c29fe4",
   "execution_count": 66
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}