project/report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Realistic alternatives: Substituting the worst train connections with electrified highways."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "<!-- Introduces the problem and motivates your work --> \n",
    "\n",
    "Travel time is one of the most important factors in the decision for a mode of transportation.\n",
    "This project aims to give insights into the extent of the advantage of travel by car over travel by train. \n",
    "Routes which are significantly faster by car are analysed with respect to their degree of electrification.\n",
    "A suggestion for improvements to the charging infrastructure will be made. \n",
    "Infrastructure projects such as new railways take up years in planning and executing.\n",
    "With the spirit of \"free passage for free citizens\" (\"Freie Fahrt für freie Bürger\"), a reasonable and at the same time \n",
    "environmentally friendly alternative is presented: choosing the fast and flexible travel by car, but opting for EVs instead of fossil fuels.\n",
    "\n",
    "#### Main Question\n",
    "Which routes between German cities are the fastest by car (in comparison to travel by train) and should be electrified \n",
    "for a \"quick win\" (compared to new train tracks) in climate action?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Methods\n",
    "<!-- Describes which data sources you used and why, how you built your data pipeline and which data cleaning / transformation steps you followed as well as any problems encountered during data engineering -->\n",
    "#### 2.1 Data Sources\n",
    "The three data sources allow to firstly analyze the connectivity between cities and secondly, they allow for a judgement about the electrification of routes between cities.\n",
    "\n",
    "<b>Datasource 1: Connectivity between largest German Cities (\"Connectivity\")</b>\n",
    "\n",
    "This RDF-Star knowledge graph shows how well the 100 largest (by population) cities in Germany are connected.\n",
    "It allows to compare travel by train, car and plane with respect to travel time. \n",
    "\n",
    "<b>Datasource 2: List of Electric Vehicle (EV) Charging Points in Germany (\"Chargers\")</b>\n",
    "\n",
    "Registry of all publicly accessible charging points registered at the German authority for infrastructure (Bundesnetzagentur).\n",
    "Contains information on location (address and coordinates) and charging point type/capacity.\n",
    "\n",
    "<b>Datasource 3: EV Charging Point Infrastructure in Germany (\"Development\")</b>\n",
    "\n",
    "This overview on the charging point infrastructure in Germany provides information on the number of charging points per federal state, city and district, as well as the historical evolution of the infrastructure.\n",
    "\n",
    "#### 2.2 Data Pipelines\n",
    "The project utilizes three different data pipelines, one for each data source.\n",
    "\n",
    "<b>Data Pipeline 1: Connectivity</b>\n",
    "\n",
    "The data is available as a knowledge graph. In the pipeline, the archived graph is downloaded, unpacked and serialized into a list of triples. Each triple is processed one at a time, where firstly, all triples which contain a formalization of a route are extracted. Secondly, all attributes describing the routes are extracted and both are matched in one tabular data structure. Thirdly, as some graph edges are not labelled in plain text, the labels of all edges are extracted from the graph and replace the data in the data table.\n",
    "Finally, driving durations are converted to minutes and distances are converted to kilometers.\n",
    "\n",
    "<b>Data Pipeline 2: Chargers</b>\n",
    "\n",
    "The pipeline downloads a registry of all ev charging points in Germany. The data is available as a csv file. Firstly, the first rows, containing only a preamble, are removed. Then the column names of the table are replaced with English labels. Afterwards, geographic coordinated as well as the capacity of the chargers are saved as floating point values. Finally, completely empty rows are removed, and the data is saved.\n",
    "\n",
    "<b>Data Pipeline 3: Development</b>\n",
    "\n",
    "The data is published as an Excel sheet, where only one of several tables is relevant to this project. The relevant table and sheet range are selected and saved to a table. The table contains names of all districts, but in no uniform format. Therefore, all pre- and suffixes of district names are removed, to align the names with potential other datasets. For each district, the dataset contains quarterly counts of chargers, split into three categories of chargers (fast, regular and total). For this project, this is too fine-granular, so the number of total charging points in january of every year is selected from the table, and saved as the final dataset.\n",
    "\n",
    "#### 2.3 Challenges\n",
    "The three datasets came either from government projects or government offices, but still turned out to be challenging to work with. \n",
    "Firstly the districts (\"Landkreise\") were ambiguously named: some contained a prefix (\"Landkreis\") or suffix (\"-Kreis) and some did not. This made it hard to match the districts with the labels of maps, ultimately leading to the decision of plotting information at the state level, and not at the district level.\n",
    "\n",
    "Also, some metrics were just too expensive to calculate (ex. coverage %)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/lisaschmidt/Documents/GitHub/data-engineering-showcase\n"
     ]
    }
   ],
   "source": [
    "%cd ..\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from project.route_processing import aggregate_travel_modes, calculate_absolute_deviation, calculate_relative_deviation, compute_connectivity_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from project.geographical_visualizations import plot_routes_on_germany_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Train Travel to Travel by Car"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, all routes are loaded from the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>transport_type</th>\n",
       "      <th>route</th>\n",
       "      <th>duration</th>\n",
       "      <th>driving_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bremerhaven</td>\n",
       "      <td>Marl</td>\n",
       "      <td>car</td>\n",
       "      <td>LINESTRING(8.586580000000001 53.55175000000000...</td>\n",
       "      <td>152</td>\n",
       "      <td>288.831379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bremerhaven</td>\n",
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       "      <td>Bremerhaven</td>\n",
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       "      <th>3</th>\n",
       "      <td>Bremerhaven</td>\n",
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       "      <td>None</td>\n",
       "      <td>322</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dortmund</td>\n",
       "      <td>Karlsruhe</td>\n",
       "      <td>car</td>\n",
       "      <td>LINESTRING(7.46417 51.51505, 7.461770000000000...</td>\n",
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       "      <td>355.366236</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40510</th>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>Osnabrück</td>\n",
       "      <td>train</td>\n",
       "      <td>None</td>\n",
       "      <td>320</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40511</th>\n",
       "      <td>Halle (Saale)</td>\n",
       "      <td>Bielefeld</td>\n",
       "      <td>car</td>\n",
       "      <td>LINESTRING(11.970030000000001 51.4824400000000...</td>\n",
       "      <td>180</td>\n",
       "      <td>344.566496</td>\n",
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       "      <th>40512</th>\n",
       "      <td>Halle (Saale)</td>\n",
       "      <td>Bielefeld</td>\n",
       "      <td>train</td>\n",
       "      <td>None</td>\n",
       "      <td>231</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40513</th>\n",
       "      <td>Halle (Saale)</td>\n",
       "      <td>Bielefeld</td>\n",
       "      <td>train</td>\n",
       "      <td>None</td>\n",
       "      <td>192</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40514</th>\n",
       "      <td>Halle (Saale)</td>\n",
       "      <td>Bielefeld</td>\n",
       "      <td>train</td>\n",
       "      <td>None</td>\n",
       "      <td>195</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40515 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               start        end transport_type  \\\n",
       "0        Bremerhaven       Marl            car   \n",
       "1        Bremerhaven       Marl          train   \n",
       "2        Bremerhaven       Marl          train   \n",
       "3        Bremerhaven       Marl          train   \n",
       "4           Dortmund  Karlsruhe            car   \n",
       "...              ...        ...            ...   \n",
       "40510       Chemnitz  Osnabrück          train   \n",
       "40511  Halle (Saale)  Bielefeld            car   \n",
       "40512  Halle (Saale)  Bielefeld          train   \n",
       "40513  Halle (Saale)  Bielefeld          train   \n",
       "40514  Halle (Saale)  Bielefeld          train   \n",
       "\n",
       "                                                   route  duration  \\\n",
       "0      LINESTRING(8.586580000000001 53.55175000000000...       152   \n",
       "1                                                   None       320   \n",
       "2                                                   None       385   \n",
       "3                                                   None       322   \n",
       "4      LINESTRING(7.46417 51.51505, 7.461770000000000...       200   \n",
       "...                                                  ...       ...   \n",
       "40510                                               None       320   \n",
       "40511  LINESTRING(11.970030000000001 51.4824400000000...       180   \n",
       "40512                                               None       231   \n",
       "40513                                               None       192   \n",
       "40514                                               None       195   \n",
       "\n",
       "       driving_distance  \n",
       "0            288.831379  \n",
       "1                   NaN  \n",
       "2                   NaN  \n",
       "3                   NaN  \n",
       "4            355.366236  \n",
       "...                 ...  \n",
       "40510               NaN  \n",
       "40511        344.566496  \n",
       "40512               NaN  \n",
       "40513               NaN  \n",
       "40514               NaN  \n",
       "\n",
       "[40515 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnx = sqlite3.connect('data/connections.sqlite')\n",
    "df_connections = pd.read_sql_query(\"SELECT * FROM connections\", cnx)\n",
    "df_connections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, all routes taken by car and by train are made comparable by aggregating the routes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>start</th>\n",
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       "      <th>absolute_deviation</th>\n",
       "      <th>relative_deviation</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>Erfurt</td>\n",
       "      <td>Marl</td>\n",
       "      <td>-196</td>\n",
       "      <td>-93.779904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5801</th>\n",
       "      <td>Marl</td>\n",
       "      <td>Erfurt</td>\n",
       "      <td>-194</td>\n",
       "      <td>-93.269231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4958</th>\n",
       "      <td>Leipzig</td>\n",
       "      <td>Marl</td>\n",
       "      <td>-181</td>\n",
       "      <td>-74.485597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3096</th>\n",
       "      <td>Halle (Saale)</td>\n",
       "      <td>Marl</td>\n",
       "      <td>-163</td>\n",
       "      <td>-73.755656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5813</th>\n",
       "      <td>Marl</td>\n",
       "      <td>Halle (Saale)</td>\n",
       "      <td>-160</td>\n",
       "      <td>-73.732719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>Bottrop</td>\n",
       "      <td>Marl</td>\n",
       "      <td>298</td>\n",
       "      <td>1655.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3782</th>\n",
       "      <td>Herne</td>\n",
       "      <td>Marl</td>\n",
       "      <td>271</td>\n",
       "      <td>1806.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5820</th>\n",
       "      <td>Marl</td>\n",
       "      <td>Herne</td>\n",
       "      <td>305</td>\n",
       "      <td>1906.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7507</th>\n",
       "      <td>Recklinghausen</td>\n",
       "      <td>Marl</td>\n",
       "      <td>320</td>\n",
       "      <td>3200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5857</th>\n",
       "      <td>Marl</td>\n",
       "      <td>Recklinghausen</td>\n",
       "      <td>349</td>\n",
       "      <td>3490.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9702 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               start             end  absolute_deviation  relative_deviation\n",
       "1920          Erfurt            Marl                -196          -93.779904\n",
       "5801            Marl          Erfurt                -194          -93.269231\n",
       "4958         Leipzig            Marl                -181          -74.485597\n",
       "3096   Halle (Saale)            Marl                -163          -73.755656\n",
       "5813            Marl   Halle (Saale)                -160          -73.732719\n",
       "...              ...             ...                 ...                 ...\n",
       "646          Bottrop            Marl                 298         1655.555556\n",
       "3782           Herne            Marl                 271         1806.666667\n",
       "5820            Marl           Herne                 305         1906.250000\n",
       "7507  Recklinghausen            Marl                 320         3200.000000\n",
       "5857            Marl  Recklinghausen                 349         3490.000000\n",
       "\n",
       "[9702 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_connectivity = aggregate_travel_modes(df_connections)\n",
    "df_connectivity['absolute_deviation'] = df_connectivity.apply(calculate_absolute_deviation, axis=1)\n",
    "df_connectivity['relative_deviation'] = df_connectivity.apply(calculate_relative_deviation, axis=1)\n",
    "df_connectivity[[\"start\", \"end\", \"absolute_deviation\", \"relative_deviation\"]].sort_values(by=\"relative_deviation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The city Marl is involved in all outliers, interestingly, as lower and upper outlier, suggesting that the measurements were faulty, or influenced by a incident on the routes. For the following analysis, the routes involving Marl are excluded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9l/2lhc2hnd6px5hd0bxlsy1rh80000gn/T/ipykernel_18296/386649411.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_connectivity = df_connectivity[df_connectivity.start != \"Marl\"][df_connectivity.end != \"Marl\"]\n"
     ]
    }
   ],
   "source": [
    "df_connectivity = df_connectivity[df_connectivity.start != \"Marl\"][df_connectivity.end != \"Marl\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>1628</th>\n",
       "      <td>Duisburg</td>\n",
       "      <td>Moers</td>\n",
       "      <td>27</td>\n",
       "      <td>245.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7525</th>\n",
       "      <td>Recklinghausen</td>\n",
       "      <td>Remscheid</td>\n",
       "      <td>84</td>\n",
       "      <td>233.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7733</th>\n",
       "      <td>Remscheid</td>\n",
       "      <td>Velbert</td>\n",
       "      <td>57</td>\n",
       "      <td>228.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9152</th>\n",
       "      <td>Witten</td>\n",
       "      <td>Herne</td>\n",
       "      <td>31</td>\n",
       "      <td>221.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3816</th>\n",
       "      <td>Herne</td>\n",
       "      <td>Witten</td>\n",
       "      <td>31</td>\n",
       "      <td>221.428571</td>\n",
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       "    <tr>\n",
       "      <th>6190</th>\n",
       "      <td>Mülheim an der Ruhr</td>\n",
       "      <td>Duisburg</td>\n",
       "      <td>-10</td>\n",
       "      <td>-62.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6796</th>\n",
       "      <td>Offenbach am Main</td>\n",
       "      <td>Hanau</td>\n",
       "      <td>-14</td>\n",
       "      <td>-63.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2120</th>\n",
       "      <td>Essen</td>\n",
       "      <td>Mülheim an der Ruhr</td>\n",
       "      <td>-9</td>\n",
       "      <td>-64.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2683</th>\n",
       "      <td>Gelsenkirchen</td>\n",
       "      <td>Herne</td>\n",
       "      <td>-8</td>\n",
       "      <td>-66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3751</th>\n",
       "      <td>Herne</td>\n",
       "      <td>Gelsenkirchen</td>\n",
       "      <td>-9</td>\n",
       "      <td>-69.230769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9506 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    start                  end  absolute_deviation  \\\n",
       "1628             Duisburg                Moers                  27   \n",
       "7525       Recklinghausen            Remscheid                  84   \n",
       "7733            Remscheid              Velbert                  57   \n",
       "9152               Witten                Herne                  31   \n",
       "3816                Herne               Witten                  31   \n",
       "...                   ...                  ...                 ...   \n",
       "6190  Mülheim an der Ruhr             Duisburg                 -10   \n",
       "6796    Offenbach am Main                Hanau                 -14   \n",
       "2120                Essen  Mülheim an der Ruhr                  -9   \n",
       "2683        Gelsenkirchen                Herne                  -8   \n",
       "3751                Herne        Gelsenkirchen                  -9   \n",
       "\n",
       "      relative_deviation  \n",
       "1628          245.454545  \n",
       "7525          233.333333  \n",
       "7733          228.000000  \n",
       "9152          221.428571  \n",
       "3816          221.428571  \n",
       "...                  ...  \n",
       "6190          -62.500000  \n",
       "6796          -63.636364  \n",
       "2120          -64.285714  \n",
       "2683          -66.666667  \n",
       "3751          -69.230769  \n",
       "\n",
       "[9506 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_connectivity[[\"start\", \"end\", \"absolute_deviation\", \"relative_deviation\"]].sort_values(by=\"relative_deviation\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definitions:\n",
    "\n",
    "Absolute deviation: Travelling from Duisburg to Moers is 27 Minutes longer by train than by car.\n",
    "\n",
    "Relative deviation: Travelling from Recklinghausen to Remscheid takes 233% longer by train than by car."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7642541552703556"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_connectivity[df_connectivity.absolute_deviation > 0])/len(df_connectivity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more than 75% of the routes, taking the car is faster than taking the train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On average, travelling a route by train takes 30.03 minutes longer than by car.\n",
      "On average, travelling a route by train takes 19.25% longer than by car.\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"On average, travelling a route by train takes {round(df_connectivity[\"absolute_deviation\"].mean(),2)} minutes longer than by car.\"\"\")\n",
    "print(f\"\"\"On average, travelling a route by train takes {round(df_connectivity[\"relative_deviation\"].mean(),2)}% longer than by car.\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 20 routes with the highest % deviation between car travel and train travel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>relative_deviation</th>\n",
       "      <th>driving_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>Duisburg</td>\n",
       "      <td>Moers</td>\n",
       "      <td>245.454545</td>\n",
       "      <td>14.689648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7525</th>\n",
       "      <td>Recklinghausen</td>\n",
       "      <td>Remscheid</td>\n",
       "      <td>233.333333</td>\n",
       "      <td>59.918343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7733</th>\n",
       "      <td>Remscheid</td>\n",
       "      <td>Velbert</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>28.061773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9152</th>\n",
       "      <td>Witten</td>\n",
       "      <td>Herne</td>\n",
       "      <td>221.428571</td>\n",
       "      <td>17.756796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3816</th>\n",
       "      <td>Herne</td>\n",
       "      <td>Witten</td>\n",
       "      <td>221.428571</td>\n",
       "      <td>18.573968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               start        end  relative_deviation  driving_distance\n",
       "1628        Duisburg      Moers          245.454545         14.689648\n",
       "7525  Recklinghausen  Remscheid          233.333333         59.918343\n",
       "7733       Remscheid    Velbert          228.000000         28.061773\n",
       "9152          Witten      Herne          221.428571         17.756796\n",
       "3816           Herne     Witten          221.428571         18.573968"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_rel_dev = df_connectivity.sort_values(by=\"relative_deviation\", ascending=False)[:50]\n",
    "highest_rel_dev[[\"start\", \"end\", \"relative_deviation\", \"driving_distance\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_routes_on_germany_map(highest_rel_dev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The routes with the highest relative deviation between train travel and car travel concentrate in the State Nordrhein-Westfalen and around Frankfurt am Main. They are short- to medium-distance travel routes (< 65km)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>absolute_deviation</th>\n",
       "      <th>driving_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4616</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>225</td>\n",
       "      <td>612.608189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>Konstanz</td>\n",
       "      <td>224</td>\n",
       "      <td>612.714833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4617</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Cottbus</td>\n",
       "      <td>223</td>\n",
       "      <td>782.250092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>217</td>\n",
       "      <td>542.602797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>Cottbus</td>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>216</td>\n",
       "      <td>712.144679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         start                     end  absolute_deviation  driving_distance\n",
       "4616  Konstanz                Chemnitz                 225        612.608189\n",
       "1026  Chemnitz                Konstanz                 224        612.714833\n",
       "4617  Konstanz                 Cottbus                 223        782.250092\n",
       "1070  Chemnitz  Villingen-Schwenningen                 217        542.602797\n",
       "1168   Cottbus  Villingen-Schwenningen                 216        712.144679"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_abs_dev = df_connectivity.sort_values(by=\"absolute_deviation\", ascending=False)[:50]\n",
    "highest_abs_dev[[\"start\", \"end\", \"absolute_deviation\", \"driving_distance\"]][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_routes_on_germany_map(highest_abs_dev[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The routes with the highest absolute disadvantage of taking a train (instead of a car) are routes which stretch over all of Germany. They are long-distance routes (> 500km)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_routes_to_analyse = highest_abs_dev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the following analyses, only the routes with the highest absolute deviation are used, as they are spread well over all of Germany and allow to analyse a larger portion of the German ev chargin infrastructure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>operator</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>district</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>rated_capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albwerk GmbH &amp; Co. KG</td>\n",
       "      <td>Heroldstatt</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Landkreis Alb-Donau-Kreis</td>\n",
       "      <td>48.442398</td>\n",
       "      <td>9.659075</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>smopi</td>\n",
       "      <td>Heroldstatt</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Landkreis Alb-Donau-Kreis</td>\n",
       "      <td>48.449353</td>\n",
       "      <td>9.672201</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albwerk GmbH &amp; Co. KG</td>\n",
       "      <td>Westerheim</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Landkreis Alb-Donau-Kreis</td>\n",
       "      <td>48.510500</td>\n",
       "      <td>9.609000</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EnBW mobility+ AG und Co.KG</td>\n",
       "      <td>Amstetten</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Landkreis Alb-Donau-Kreis</td>\n",
       "      <td>48.578534</td>\n",
       "      <td>9.874840</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SWU Energie GmbH</td>\n",
       "      <td>Balzheim</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>Landkreis Alb-Donau-Kreis</td>\n",
       "      <td>48.179959</td>\n",
       "      <td>10.076977</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54218</th>\n",
       "      <td>TEAG Mobil GmbH</td>\n",
       "      <td>Obertrebra</td>\n",
       "      <td>Thüringen</td>\n",
       "      <td>Landkreis Weimarer Land</td>\n",
       "      <td>51.060798</td>\n",
       "      <td>11.565503</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54219</th>\n",
       "      <td>EnBW mobility+ AG und Co.KG</td>\n",
       "      <td>Apolda</td>\n",
       "      <td>Thüringen</td>\n",
       "      <td>Landkreis Weimarer Land</td>\n",
       "      <td>51.028940</td>\n",
       "      <td>11.517130</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54220</th>\n",
       "      <td>EnBW mobility+ AG und Co.KG</td>\n",
       "      <td>Apolda</td>\n",
       "      <td>Thüringen</td>\n",
       "      <td>Landkreis Weimarer Land</td>\n",
       "      <td>51.028940</td>\n",
       "      <td>11.517130</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54221</th>\n",
       "      <td>TEAG Mobil GmbH</td>\n",
       "      <td>Großheringen</td>\n",
       "      <td>Thüringen</td>\n",
       "      <td>Landkreis Weimarer Land</td>\n",
       "      <td>51.107188</td>\n",
       "      <td>11.659489</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54222</th>\n",
       "      <td>Stadtwerke Weimar Stadtversorgungs-Gmbh</td>\n",
       "      <td>Bad Sulza</td>\n",
       "      <td>Thüringen</td>\n",
       "      <td>Landkreis Weimarer Land</td>\n",
       "      <td>51.092527</td>\n",
       "      <td>11.628574</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54223 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      operator          city  \\\n",
       "0                        Albwerk GmbH & Co. KG   Heroldstatt   \n",
       "1                                        smopi   Heroldstatt   \n",
       "2                        Albwerk GmbH & Co. KG    Westerheim   \n",
       "3                 EnBW mobility+ AG und Co.KG      Amstetten   \n",
       "4                             SWU Energie GmbH      Balzheim   \n",
       "...                                        ...           ...   \n",
       "54218                          TEAG Mobil GmbH    Obertrebra   \n",
       "54219             EnBW mobility+ AG und Co.KG         Apolda   \n",
       "54220             EnBW mobility+ AG und Co.KG         Apolda   \n",
       "54221                          TEAG Mobil GmbH  Großheringen   \n",
       "54222  Stadtwerke Weimar Stadtversorgungs-Gmbh     Bad Sulza   \n",
       "\n",
       "                   state                   district   latitude  longitude  \\\n",
       "0      Baden-Württemberg  Landkreis Alb-Donau-Kreis  48.442398   9.659075   \n",
       "1      Baden-Württemberg  Landkreis Alb-Donau-Kreis  48.449353   9.672201   \n",
       "2      Baden-Württemberg  Landkreis Alb-Donau-Kreis  48.510500   9.609000   \n",
       "3      Baden-Württemberg  Landkreis Alb-Donau-Kreis  48.578534   9.874840   \n",
       "4      Baden-Württemberg  Landkreis Alb-Donau-Kreis  48.179959  10.076977   \n",
       "...                  ...                        ...        ...        ...   \n",
       "54218          Thüringen    Landkreis Weimarer Land  51.060798  11.565503   \n",
       "54219          Thüringen    Landkreis Weimarer Land  51.028940  11.517130   \n",
       "54220          Thüringen    Landkreis Weimarer Land  51.028940  11.517130   \n",
       "54221          Thüringen    Landkreis Weimarer Land  51.107188  11.659489   \n",
       "54222          Thüringen    Landkreis Weimarer Land  51.092527  11.628574   \n",
       "\n",
       "       rated_capacity  \n",
       "0                22.0  \n",
       "1                22.0  \n",
       "2                22.0  \n",
       "3                98.0  \n",
       "4                22.0  \n",
       "...               ...  \n",
       "54218           300.0  \n",
       "54219           300.0  \n",
       "54220           300.0  \n",
       "54221            30.0  \n",
       "54222            44.0  \n",
       "\n",
       "[54223 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnx = sqlite3.connect('data/charging_points_germany.sqlite')\n",
    "df_chargers = pd.read_sql_query(\"SELECT * FROM chargers\", cnx)\n",
    "df_chargers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following calcualtes two metrics for the routes which have shown the greatest disadvatage of train travel. Firstly, the density of chargers along the route is calculated (chargers/length of route). Secondly, the average distance to the next charger is calculated. This is done by sampling points along the route, and then calculating the distance to the nearest charger within close proximity to the route."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:44<00:00,  1.12it/s]\n"
     ]
    }
   ],
   "source": [
    "densities, distances = compute_connectivity_metrics(df_chargers, df_routes_to_analyse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>driving_distance</th>\n",
       "      <th>absolute_deviation</th>\n",
       "      <th>relative_deviation</th>\n",
       "      <th>charger_density</th>\n",
       "      <th>avg_dist_charger</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7930</th>\n",
       "      <td>Rostock</td>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>927.829617</td>\n",
       "      <td>171</td>\n",
       "      <td>35.477178</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>2820.340949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7887</th>\n",
       "      <td>Rostock</td>\n",
       "      <td>Konstanz</td>\n",
       "      <td>990.636424</td>\n",
       "      <td>196</td>\n",
       "      <td>38.132296</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>2740.096567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4617</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Cottbus</td>\n",
       "      <td>782.250092</td>\n",
       "      <td>223</td>\n",
       "      <td>52.470588</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>2445.740567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5487</th>\n",
       "      <td>Lünen</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>469.720700</td>\n",
       "      <td>161</td>\n",
       "      <td>65.983607</td>\n",
       "      <td>0.000425</td>\n",
       "      <td>2406.597703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7545</th>\n",
       "      <td>Recklinghausen</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>496.218955</td>\n",
       "      <td>169</td>\n",
       "      <td>66.274510</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>2380.626331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7458</th>\n",
       "      <td>Recklinghausen</td>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>520.519639</td>\n",
       "      <td>171</td>\n",
       "      <td>63.805970</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>2371.440557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4028</th>\n",
       "      <td>Iserlohn</td>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>505.403187</td>\n",
       "      <td>176</td>\n",
       "      <td>67.432950</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>2345.937506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>Zwickau</td>\n",
       "      <td>Recklinghausen</td>\n",
       "      <td>491.175042</td>\n",
       "      <td>158</td>\n",
       "      <td>60.769231</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>2340.385242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8427</th>\n",
       "      <td>Sterkrade</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>521.880456</td>\n",
       "      <td>181</td>\n",
       "      <td>67.537313</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>2338.280964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9211</th>\n",
       "      <td>Witten</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>476.510255</td>\n",
       "      <td>160</td>\n",
       "      <td>64.257028</td>\n",
       "      <td>0.000426</td>\n",
       "      <td>2321.538367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9694</th>\n",
       "      <td>Zwickau</td>\n",
       "      <td>Velbert</td>\n",
       "      <td>511.720622</td>\n",
       "      <td>182</td>\n",
       "      <td>67.407407</td>\n",
       "      <td>0.000449</td>\n",
       "      <td>2320.840861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>Konstanz</td>\n",
       "      <td>612.714833</td>\n",
       "      <td>224</td>\n",
       "      <td>68.085106</td>\n",
       "      <td>0.000684</td>\n",
       "      <td>2311.846805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4115</th>\n",
       "      <td>Iserlohn</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>481.102502</td>\n",
       "      <td>159</td>\n",
       "      <td>63.855422</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>2309.826405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4703</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>580.937994</td>\n",
       "      <td>205</td>\n",
       "      <td>65.916399</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>2306.131089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8623</th>\n",
       "      <td>Trier</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>541.722508</td>\n",
       "      <td>207</td>\n",
       "      <td>69.932432</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>2304.141510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9682</th>\n",
       "      <td>Zwickau</td>\n",
       "      <td>Remscheid</td>\n",
       "      <td>503.232878</td>\n",
       "      <td>167</td>\n",
       "      <td>63.257576</td>\n",
       "      <td>0.000411</td>\n",
       "      <td>2292.672042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4605</th>\n",
       "      <td>Koblenz</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>423.449388</td>\n",
       "      <td>171</td>\n",
       "      <td>71.548117</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>2290.644267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7342</th>\n",
       "      <td>Potsdam</td>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>718.772419</td>\n",
       "      <td>159</td>\n",
       "      <td>42.513369</td>\n",
       "      <td>0.000681</td>\n",
       "      <td>2290.520017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4616</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>612.608189</td>\n",
       "      <td>225</td>\n",
       "      <td>69.444444</td>\n",
       "      <td>0.000676</td>\n",
       "      <td>2285.873811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4621</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Dresden</td>\n",
       "      <td>685.332219</td>\n",
       "      <td>161</td>\n",
       "      <td>44.109589</td>\n",
       "      <td>0.000664</td>\n",
       "      <td>2285.861083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7741</th>\n",
       "      <td>Remscheid</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>504.561067</td>\n",
       "      <td>167</td>\n",
       "      <td>63.018868</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>2245.088396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Rostock</td>\n",
       "      <td>1011.844474</td>\n",
       "      <td>177</td>\n",
       "      <td>32.777778</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>2241.844881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9691</th>\n",
       "      <td>Zwickau</td>\n",
       "      <td>Trier</td>\n",
       "      <td>544.299111</td>\n",
       "      <td>202</td>\n",
       "      <td>67.109635</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>2235.043537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4679</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Potsdam</td>\n",
       "      <td>724.635032</td>\n",
       "      <td>167</td>\n",
       "      <td>40.144231</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>2234.946887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>Cottbus</td>\n",
       "      <td>Trier</td>\n",
       "      <td>730.427596</td>\n",
       "      <td>159</td>\n",
       "      <td>39.066339</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>2232.444463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8917</th>\n",
       "      <td>Velbert</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>511.977577</td>\n",
       "      <td>167</td>\n",
       "      <td>62.546816</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>2229.586365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>Cottbus</td>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>712.144679</td>\n",
       "      <td>216</td>\n",
       "      <td>55.670103</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>2228.361097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>Bottrop</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>502.984958</td>\n",
       "      <td>161</td>\n",
       "      <td>59.851301</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>2217.879412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>Trier</td>\n",
       "      <td>570.408154</td>\n",
       "      <td>162</td>\n",
       "      <td>51.592357</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>2198.411932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8929</th>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>Cottbus</td>\n",
       "      <td>713.223949</td>\n",
       "      <td>167</td>\n",
       "      <td>43.041237</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>2184.816377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8536</th>\n",
       "      <td>Trier</td>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>567.305535</td>\n",
       "      <td>162</td>\n",
       "      <td>52.258065</td>\n",
       "      <td>0.000493</td>\n",
       "      <td>2173.891472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Flensburg</td>\n",
       "      <td>995.802853</td>\n",
       "      <td>197</td>\n",
       "      <td>36.481481</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>2171.114225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9000</th>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>Salzgitter</td>\n",
       "      <td>600.126039</td>\n",
       "      <td>167</td>\n",
       "      <td>52.681388</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>2164.285042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7654</th>\n",
       "      <td>Remscheid</td>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>528.861751</td>\n",
       "      <td>163</td>\n",
       "      <td>58.633094</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>2155.889501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8928</th>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>543.617972</td>\n",
       "      <td>178</td>\n",
       "      <td>62.020906</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>2127.575895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9695</th>\n",
       "      <td>Zwickau</td>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>508.603368</td>\n",
       "      <td>198</td>\n",
       "      <td>72.527473</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>2110.687205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9015</th>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>Zwickau</td>\n",
       "      <td>511.947777</td>\n",
       "      <td>197</td>\n",
       "      <td>72.161172</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>2097.863853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>Flensburg</td>\n",
       "      <td>Konstanz</td>\n",
       "      <td>991.054759</td>\n",
       "      <td>185</td>\n",
       "      <td>33.575318</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>2059.850776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>Chemnitz</td>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>542.602797</td>\n",
       "      <td>217</td>\n",
       "      <td>75.086505</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>2034.965936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9014</th>\n",
       "      <td>Villingen-Schwenningen</td>\n",
       "      <td>Würzburg</td>\n",
       "      <td>257.337766</td>\n",
       "      <td>180</td>\n",
       "      <td>127.659574</td>\n",
       "      <td>0.001105</td>\n",
       "      <td>2028.721665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>Gütersloh</td>\n",
       "      <td>Regensburg</td>\n",
       "      <td>522.436715</td>\n",
       "      <td>179</td>\n",
       "      <td>62.587413</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>2013.916529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>Bielefeld</td>\n",
       "      <td>Regensburg</td>\n",
       "      <td>531.137753</td>\n",
       "      <td>165</td>\n",
       "      <td>58.928571</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>1986.122944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4619</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Dessau-Roßlau</td>\n",
       "      <td>628.547080</td>\n",
       "      <td>163</td>\n",
       "      <td>44.535519</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>1973.105316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8603</th>\n",
       "      <td>Trier</td>\n",
       "      <td>Regensburg</td>\n",
       "      <td>500.934559</td>\n",
       "      <td>184</td>\n",
       "      <td>68.401487</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>1755.753910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7632</th>\n",
       "      <td>Regensburg</td>\n",
       "      <td>Trier</td>\n",
       "      <td>506.940259</td>\n",
       "      <td>194</td>\n",
       "      <td>72.659176</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>1745.596861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8546</th>\n",
       "      <td>Trier</td>\n",
       "      <td>Erlangen</td>\n",
       "      <td>445.563028</td>\n",
       "      <td>166</td>\n",
       "      <td>69.747899</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>1703.122311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8552</th>\n",
       "      <td>Trier</td>\n",
       "      <td>Fürth</td>\n",
       "      <td>421.944878</td>\n",
       "      <td>162</td>\n",
       "      <td>71.365639</td>\n",
       "      <td>0.000675</td>\n",
       "      <td>1701.364033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4675</th>\n",
       "      <td>Konstanz</td>\n",
       "      <td>Oldenburg</td>\n",
       "      <td>787.956399</td>\n",
       "      <td>178</td>\n",
       "      <td>42.180095</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>1662.952013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7005</th>\n",
       "      <td>Osnabrück</td>\n",
       "      <td>Konstanz</td>\n",
       "      <td>678.987853</td>\n",
       "      <td>171</td>\n",
       "      <td>46.594005</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>1576.207541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6907</th>\n",
       "      <td>Oldenburg</td>\n",
       "      <td>Konstanz</td>\n",
       "      <td>779.913267</td>\n",
       "      <td>163</td>\n",
       "      <td>39.182692</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>1448.313634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       start                     end  driving_distance  \\\n",
       "7930                 Rostock  Villingen-Schwenningen        927.829617   \n",
       "7887                 Rostock                Konstanz        990.636424   \n",
       "4617                Konstanz                 Cottbus        782.250092   \n",
       "5487                   Lünen                 Zwickau        469.720700   \n",
       "7545          Recklinghausen                 Zwickau        496.218955   \n",
       "7458          Recklinghausen                Chemnitz        520.519639   \n",
       "4028                Iserlohn                Chemnitz        505.403187   \n",
       "9680                 Zwickau          Recklinghausen        491.175042   \n",
       "8427               Sterkrade                 Zwickau        521.880456   \n",
       "9211                  Witten                 Zwickau        476.510255   \n",
       "9694                 Zwickau                 Velbert        511.720622   \n",
       "1026                Chemnitz                Konstanz        612.714833   \n",
       "4115                Iserlohn                 Zwickau        481.102502   \n",
       "4703                Konstanz                 Zwickau        580.937994   \n",
       "8623                   Trier                 Zwickau        541.722508   \n",
       "9682                 Zwickau               Remscheid        503.232878   \n",
       "4605                 Koblenz                 Zwickau        423.449388   \n",
       "7342                 Potsdam  Villingen-Schwenningen        718.772419   \n",
       "4616                Konstanz                Chemnitz        612.608189   \n",
       "4621                Konstanz                 Dresden        685.332219   \n",
       "7741               Remscheid                 Zwickau        504.561067   \n",
       "4685                Konstanz                 Rostock       1011.844474   \n",
       "9691                 Zwickau                   Trier        544.299111   \n",
       "4679                Konstanz                 Potsdam        724.635032   \n",
       "1164                 Cottbus                   Trier        730.427596   \n",
       "8917                 Velbert                 Zwickau        511.977577   \n",
       "1168                 Cottbus  Villingen-Schwenningen        712.144679   \n",
       "685                  Bottrop                 Zwickau        502.984958   \n",
       "1066                Chemnitz                   Trier        570.408154   \n",
       "8929  Villingen-Schwenningen                 Cottbus        713.223949   \n",
       "8536                   Trier                Chemnitz        567.305535   \n",
       "4629                Konstanz               Flensburg        995.802853   \n",
       "9000  Villingen-Schwenningen              Salzgitter        600.126039   \n",
       "7654               Remscheid                Chemnitz        528.861751   \n",
       "8928  Villingen-Schwenningen                Chemnitz        543.617972   \n",
       "9695                 Zwickau  Villingen-Schwenningen        508.603368   \n",
       "9015  Villingen-Schwenningen                 Zwickau        511.947777   \n",
       "2300               Flensburg                Konstanz        991.054759   \n",
       "1070                Chemnitz  Villingen-Schwenningen        542.602797   \n",
       "9014  Villingen-Schwenningen                Würzburg        257.337766   \n",
       "2918               Gütersloh              Regensburg        522.436715   \n",
       "370                Bielefeld              Regensburg        531.137753   \n",
       "4619                Konstanz           Dessau-Roßlau        628.547080   \n",
       "8603                   Trier              Regensburg        500.934559   \n",
       "7632              Regensburg                   Trier        506.940259   \n",
       "8546                   Trier                Erlangen        445.563028   \n",
       "8552                   Trier                   Fürth        421.944878   \n",
       "4675                Konstanz               Oldenburg        787.956399   \n",
       "7005               Osnabrück                Konstanz        678.987853   \n",
       "6907               Oldenburg                Konstanz        779.913267   \n",
       "\n",
       "      absolute_deviation  relative_deviation  charger_density  \\\n",
       "7930                 171           35.477178         0.000564   \n",
       "7887                 196           38.132296         0.000568   \n",
       "4617                 223           52.470588         0.000556   \n",
       "5487                 161           65.983607         0.000425   \n",
       "7545                 169           66.274510         0.000415   \n",
       "7458                 171           63.805970         0.000388   \n",
       "4028                 176           67.432950         0.000433   \n",
       "9680                 158           60.769231         0.000429   \n",
       "8427                 181           67.537313         0.000406   \n",
       "9211                 160           64.257028         0.000426   \n",
       "9694                 182           67.407407         0.000449   \n",
       "1026                 224           68.085106         0.000684   \n",
       "4115                 159           63.855422         0.000463   \n",
       "4703                 205           65.916399         0.000632   \n",
       "8623                 207           69.932432         0.000526   \n",
       "9682                 167           63.257576         0.000411   \n",
       "4605                 171           71.548117         0.000595   \n",
       "7342                 159           42.513369         0.000681   \n",
       "4616                 225           69.444444         0.000676   \n",
       "4621                 161           44.109589         0.000664   \n",
       "7741                 167           63.018868         0.000418   \n",
       "4685                 177           32.777778         0.000595   \n",
       "9691                 202           67.109635         0.000501   \n",
       "4679                 167           40.144231         0.000442   \n",
       "1164                 159           39.066339         0.000402   \n",
       "8917                 167           62.546816         0.000462   \n",
       "1168                 216           55.670103         0.000619   \n",
       "685                  161           59.851301         0.000653   \n",
       "1066                 162           51.592357         0.000485   \n",
       "8929                 167           43.041237         0.000619   \n",
       "8536                 162           52.258065         0.000493   \n",
       "4629                 197           36.481481         0.000699   \n",
       "9000                 167           52.681388         0.000699   \n",
       "7654                 163           58.633094         0.000391   \n",
       "8928                 178           62.020906         0.000774   \n",
       "9695                 198           72.527473         0.000743   \n",
       "9015                 197           72.161172         0.000731   \n",
       "2300                 185           33.575318         0.000761   \n",
       "1070                 217           75.086505         0.000779   \n",
       "9014                 180          127.659574         0.001105   \n",
       "2918                 179           62.587413         0.000440   \n",
       "370                  165           58.928571         0.000454   \n",
       "4619                 163           44.535519         0.000455   \n",
       "8603                 184           68.401487         0.000535   \n",
       "7632                 194           72.659176         0.000544   \n",
       "8546                 166           69.747899         0.000511   \n",
       "8552                 162           71.365639         0.000675   \n",
       "4675                 178           42.180095         0.000457   \n",
       "7005                 171           46.594005         0.000446   \n",
       "6907                 163           39.182692         0.000427   \n",
       "\n",
       "      avg_dist_charger  \n",
       "7930       2820.340949  \n",
       "7887       2740.096567  \n",
       "4617       2445.740567  \n",
       "5487       2406.597703  \n",
       "7545       2380.626331  \n",
       "7458       2371.440557  \n",
       "4028       2345.937506  \n",
       "9680       2340.385242  \n",
       "8427       2338.280964  \n",
       "9211       2321.538367  \n",
       "9694       2320.840861  \n",
       "1026       2311.846805  \n",
       "4115       2309.826405  \n",
       "4703       2306.131089  \n",
       "8623       2304.141510  \n",
       "9682       2292.672042  \n",
       "4605       2290.644267  \n",
       "7342       2290.520017  \n",
       "4616       2285.873811  \n",
       "4621       2285.861083  \n",
       "7741       2245.088396  \n",
       "4685       2241.844881  \n",
       "9691       2235.043537  \n",
       "4679       2234.946887  \n",
       "1164       2232.444463  \n",
       "8917       2229.586365  \n",
       "1168       2228.361097  \n",
       "685        2217.879412  \n",
       "1066       2198.411932  \n",
       "8929       2184.816377  \n",
       "8536       2173.891472  \n",
       "4629       2171.114225  \n",
       "9000       2164.285042  \n",
       "7654       2155.889501  \n",
       "8928       2127.575895  \n",
       "9695       2110.687205  \n",
       "9015       2097.863853  \n",
       "2300       2059.850776  \n",
       "1070       2034.965936  \n",
       "9014       2028.721665  \n",
       "2918       2013.916529  \n",
       "370        1986.122944  \n",
       "4619       1973.105316  \n",
       "8603       1755.753910  \n",
       "7632       1745.596861  \n",
       "8546       1703.122311  \n",
       "8552       1701.364033  \n",
       "4675       1662.952013  \n",
       "7005       1576.207541  \n",
       "6907       1448.313634  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_routes_to_analyse['charger_density'] = densities\n",
    "df_routes_to_analyse['avg_dist_charger'] = distances\n",
    "df_routes_to_analyse[[\"start\", \"end\", \"driving_distance\", \"absolute_deviation\", \"relative_deviation\",\"charger_density\", \"avg_dist_charger\"]].sort_values(by=\"avg_dist_charger\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0005541390133131204"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_routes_to_analyse[\"charger_density\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2168.981333061286"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_routes_to_analyse[\"avg_dist_charger\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(197, 2700, 'Rostock->Konstanz')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_routes_to_analyse[\"absolute_deviation\"], df_routes_to_analyse[\"avg_dist_charger\"], marker=\"x\")\n",
    "plt.xlabel(\"Absolute disadvatage of taking the train (vs. car) [minutes]\")\n",
    "plt.ylabel(\"Average distance to the next EV charger along route [m]\")\n",
    "plt.title(\"Electrification of the Routes strongly in Favor of Car Travel\")\n",
    "plt.annotate(\"Rostock->Villingen-Schwenningen\", (172, 2800), color=\"red\")\n",
    "plt.annotate(\"Rostock->Konstanz\", (197, 2700), color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Least electrified and most disadvantageous train routes: Rostock/Villingen-Schwenningen (next charger in 2.8km) and Rostock/Konstanz (next charger in 2.7km):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_routes_on_germany_map(df_routes_to_analyse[df_routes_to_analyse[\"avg_dist_charger\"]>2700], color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EV Chargers in Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnx = sqlite3.connect('data/charging_points_development.sqlite')\n",
    "df_development = pd.read_sql_query(\"SELECT * FROM districts\", cnx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>chargers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bayern</th>\n",
       "      <td>17535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nordrhein-Westfalen</th>\n",
       "      <td>15776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baden-Württemberg</th>\n",
       "      <td>15531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niedersachsen</th>\n",
       "      <td>8479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hessen</th>\n",
       "      <td>6604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schleswig-Holstein</th>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sachsen</th>\n",
       "      <td>3449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rheinland-Pfalz</th>\n",
       "      <td>3266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Berlin</th>\n",
       "      <td>2414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hamburg</th>\n",
       "      <td>2139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brandenburg</th>\n",
       "      <td>1739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thüringen</th>\n",
       "      <td>1693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sachsen-Anhalt</th>\n",
       "      <td>1395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mecklenburg-Vorpommern</th>\n",
       "      <td>1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saarland</th>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bremen</th>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        chargers\n",
       "state                           \n",
       "Bayern                     17535\n",
       "Nordrhein-Westfalen        15776\n",
       "Baden-Württemberg          15531\n",
       "Niedersachsen               8479\n",
       "Hessen                      6604\n",
       "Schleswig-Holstein          3500\n",
       "Sachsen                     3449\n",
       "Rheinland-Pfalz             3266\n",
       "Berlin                      2414\n",
       "Hamburg                     2139\n",
       "Brandenburg                 1739\n",
       "Thüringen                   1693\n",
       "Sachsen-Anhalt              1395\n",
       "Mecklenburg-Vorpommern      1030\n",
       "Saarland                     718\n",
       "Bremen                       588"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_chargers_states = df_development[[\"state\", \"2023\"]]\n",
    "df_chargers_states = df_chargers_states.rename(columns={\"2023\":\"chargers\"})\n",
    "df_chargers_states = df_chargers_states.groupby(\"state\")[\"chargers\"].sum()\n",
    "\n",
    "import geopandas as gpd\n",
    "df_states = gpd.read_file('./GER_shape/DEU_adm1.shp')\n",
    "df_states = df_states.set_index('NAME_1')\n",
    "df_states[\"chargers-2023\"] = df_chargers_states\n",
    "\n",
    "df_states.plot(column=\"chargers-2023\", kind=\"geo\", legend=True)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['left'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_visible(False)\n",
    "plt.gca().yaxis.set_visible(False)\n",
    "plt.gca().xaxis.set_visible(False)\n",
    "\n",
    "pd.DataFrame(df_chargers_states).sort_values(by=\"chargers\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Development of EV charging stations overall in Germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = [str(d) for d in range(2017,2024)]\n",
    "values = df_development.sum(axis=0)[2:]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "bars = plt.bar(years, values, color='skyblue')  # Use a professional color\n",
    "\n",
    "plt.title('EV Charging Points in Germany show a CAGR of 53%', fontsize=16)\n",
    "plt.xlabel('Year', fontsize=12)\n",
    "plt.ylabel('Number of Points', fontsize=12)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['left'].set_visible(False)\n",
    "plt.gca().yaxis.set_visible(False)\n",
    "\n",
    "for bar in bars:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x() + bar.get_width()/2, yval, str(int(round(yval/1000, 0)))+\"K\", \n",
    "             verticalalignment='bottom', horizontalalignment='center', fontsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Development of charging stations per district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>location</th>\n",
       "      <th>state</th>\n",
       "      <th>CAGR-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Regen</td>\n",
       "      <td>Bayern</td>\n",
       "      <td>-5.940289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Straubing-Bogen</td>\n",
       "      <td>Bayern</td>\n",
       "      <td>6.917811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Friesland</td>\n",
       "      <td>Niedersachsen</td>\n",
       "      <td>9.204330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Bremerhaven</td>\n",
       "      <td>Bremen</td>\n",
       "      <td>10.064242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Uckermark</td>\n",
       "      <td>Brandenburg</td>\n",
       "      <td>11.804676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            location          state     CAGR-3\n",
       "123            Regen         Bayern  -5.940289\n",
       "132  Straubing-Bogen         Bayern   6.917811\n",
       "211        Friesland  Niedersachsen   9.204330\n",
       "160      Bremerhaven         Bremen  10.064242\n",
       "158        Uckermark    Brandenburg  11.804676"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cagr = ((df_development[\"2023\"] / df_development[\"2020\"]) ** (1 / 3) - 1) * 100\n",
    "df_development[\"CAGR-3\"] = cagr\n",
    "df_development.sort_values(by=\"CAGR-3\")[[\"location\", \"state\", \"CAGR-3\"]][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>state</th>\n",
       "      <th>CAGR-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>131.090655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Groß-Gerau</td>\n",
       "      <td>Hessen</td>\n",
       "      <td>132.587532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Cochem-Zell</td>\n",
       "      <td>Rheinland-Pfalz</td>\n",
       "      <td>149.331548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Prignitz</td>\n",
       "      <td>Brandenburg</td>\n",
       "      <td>151.984210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Schwerin</td>\n",
       "      <td>Mecklenburg-Vorpommern</td>\n",
       "      <td>233.222185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        location                   state      CAGR-3\n",
       "42     Stuttgart       Baden-Württemberg  131.090655\n",
       "171   Groß-Gerau                  Hessen  132.587532\n",
       "313  Cochem-Zell         Rheinland-Pfalz  149.331548\n",
       "155     Prignitz             Brandenburg  151.984210\n",
       "189     Schwerin  Mecklenburg-Vorpommern  233.222185"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_development.sort_values(by=\"CAGR-3\")[[\"location\", \"state\", \"CAGR-3\"]][-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME_1\n",
       "Saarland                  70.064454\n",
       "Mecklenburg-Vorpommern    69.500122\n",
       "Brandenburg               54.972203\n",
       "Baden-Württemberg         46.882587\n",
       "Hessen                    45.541177\n",
       "Sachsen                   44.116820\n",
       "Rheinland-Pfalz           42.932372\n",
       "Bayern                    42.505295\n",
       "Nordrhein-Westfalen       41.795339\n",
       "Niedersachsen             40.807948\n",
       "Schleswig-Holstein        38.877050\n",
       "Sachsen-Anhalt            38.403632\n",
       "Thüringen                 35.770980\n",
       "Berlin                    33.482318\n",
       "Hamburg                   22.102430\n",
       "Bremen                    20.678124\n",
       "Name: CAGR-3, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_states[\"CAGR-3\"] = df_development.groupby(\"state\")[\"CAGR-3\"].mean()\n",
    "\n",
    "df_states.plot(column=\"CAGR-3\", kind=\"geo\", legend=True)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['left'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_visible(False)\n",
    "plt.gca().yaxis.set_visible(False)\n",
    "plt.gca().xaxis.set_visible(False)\n",
    "\n",
    "pd.DataFrame(df_states).sort_values(by=\"CAGR-3\", ascending=False)[\"CAGR-3\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation\n",
    "This report has given an overview over the competitiveness of train travel versus car travel in terms of travel time.\n",
    "On average, taking the train is 30 minutes or 19% longer than taking the car. Additionally, taking the car is faster for more than 76% of analysed routes.\n",
    "This paints a dull picture of the German railway system. When making the decision of always driving by car, on average, a person will be faster than taking the train. This gives little incentive to reflect on one's travelling decisions.\n",
    "\n",
    "The largest relative deviations have shown up in the state of Nordrhein-Westfalen, with a striking 250% longer travel time between Duisburg and Moers when using a train connection. The shorter travel routes pose a higher risk of having large deviations, as even one minute difference can alter the ratio significantly.\n",
    "\n",
    "The analysis has shown that the routes with the largest absolute deviation are routes which span over the whole map area of Germany (\"most disadvantageous routes\"). This is not surprising, as longer routes have more potential to accumulate delays when there is even only a small difference in travel speed. The route with the largest absolute advatage of car travel is the route Konstanz-Chemnitz (and also the reverse route) with more than 3.5 hours (225 minutes) difference.\n",
    "\n",
    "For the most disadvantageous routes, the degree of electrification is assessed. The analysis has shown, that for these routes, there are 5.5 chargers per 10km of travel distance, and on average, the next charger is 2.1km away.\n",
    "\n",
    "Two routes have shown to be below-average in terms of electrification: Rostock/Villingen-Schwenningen (next charger every 2.8km) and Rostock/Konstanz (next charger every 2.7km).\n",
    "While their charger density is slightly above average, there seem to be regions which are charging deserts.\n",
    "\n",
    "The further analysis of existing ev chargers has shown that the eastern-German states and the state Rheinland-Pfalz as well as Saarland have the least chargers.\n",
    "\n",
    "The development of the charging infrastructure form 2017 to 2023 has shown an impressive cumulative average growth rate (CAGR) of 53% yearly, with the district of Stuttgart showing even a CAGR of 130%.\n",
    "\n",
    "Interestingly, there was one district showing a negative growth rate since 2020, which was Regen in Bayern with -5.9% growth.\n",
    "\n",
    "Overall, the states Mecklenburg-Vorpommern and Saarland have shown the largest growth over the last six years, with about 70% CAGR for both.\n",
    "\n",
    "<b>Answering the Main Question</b>\n",
    "\n",
    "With the analysis of the train-system and the electrification of the routes, this report recommends two routes for priority in electrification: Rostock/Villingen-Schwenningen and Rostock/Konstanz.\n",
    "\n",
    "### Limitations\n",
    "While this report assesses the degree of electrification of a whole route, it does not investigate the different parts of a route. The sampling of points along the routes only gives an average, and not insight into specific areas in Germany.\n",
    "\n",
    "Additionally, the most disadvantageous routes (by train) are only compared to each other w.r.t. electrification, and not the whole German average.\n",
    "\n",
    "Further, the recommendation of priority in electrification is made only on the basis of two pieces of data: the disadvatage of taking a train, and the degree of electrification.\n",
    "\n",
    "### Outlook\n",
    "For future analyses, it would be interesting to see which parts of the routes are not electrified well, to align this to specific districts in Germany.\n",
    "To make a more cost-efficient recommendation, a further analysis should also look at the number of passengers travelling along each route, to find the routes for whom an electrification would save the most total emissions."
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