dataset_curation/germany.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Germany"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Source of original dataset:** https://unfallatlas.statistikportal.de/\n",
    "\n",
    "**Location of accidents:** Latitude, Longitude\n",
    "\n",
    "**Date of accidents:** Date\n",
    "\n",
    "**Outcome of accidents:** Fatality, Serious Injury, Minor Injury, PDO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('max_columns', None)\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "import numpy as np\n",
    "from plotly import graph_objects as go\n",
    "import plotly.express as px\n",
    "from itertools import chain\n",
    "import matplotlib.pyplot as plt\n",
    "import pyproj\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"../data/germany/\"\n",
    "\n",
    "accident_files = [\"Shapefile_2016/Unfaelle_2016_LinRef.shp\",\n",
    "                  \"Shapefile_2017/Unfallorte2017_LinRef.shp\",\n",
    "                  \"Shapefile_2018/Unfallorte2018_LinRef.shp\",\n",
    "                  \"Shapefile_2019/Unfallorte2019_LinRef.shp\"]\n",
    "accidents_data_files = [data_dir + s for s in accident_files]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.io.parsers import ParserError\n",
    "data_aux = []\n",
    "\n",
    "for i, accidents_data in enumerate(accidents_data_files):\n",
    "    data_aux.append(gpd.read_file(accidents_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_dfs = data_aux\n",
    "list_of_dicts = [cur_df.T.to_dict().values() for cur_df in list_of_dfs]    \n",
    "data = pd.DataFrame(list(chain(*list_of_dicts)))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Datetime column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Date'] = data['UJAHR'].astype('str') +'-'+ data['UMONAT'].astype('str') +'-01 '+ data['USTUNDE'].astype('str') + ':00'\n",
    "\n",
    "data['Date'] = pd.to_datetime(data['Date'], format='%Y-%m-%d %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['fatal'] = 0\n",
    "data['serious_injury'] = 0\n",
    "data['minor_injury'] = 0\n",
    "\n",
    "data.loc[data['UKATEGORIE'] == '1', 'fatal'] = 1\n",
    "data.loc[data['UKATEGORIE'] == '2', 'serious_injury'] = 1\n",
    "data.loc[data['UKATEGORIE'] == '3', 'minor_injury'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup bicycles column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Bicycles'] = 0\n",
    "\n",
    "data.loc[data['IstRad'] == '1', 'Bicycles'] = 1\n",
    "\n",
    "data['Bicycles'] = data['Bicycles'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup latitude & longitude column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Latitude'] = data['YGCSWGS84']\n",
    "data['Longitude'] = data['XGCSWGS84']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some key statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Accidents between '+str(data['Date'].min())+' and '+str(data['Date'].max()))\n",
    "\n",
    "total_accidents = data.shape[0]\n",
    "print(\"There are a total of \"+str(total_accidents)+\" accidents.\")\n",
    "\n",
    "fatalities = data['fatal'].sum()\n",
    "print(\"There are a total of \"+str(fatalities)+\" fatalities.\")\n",
    "\n",
    "serious_injuries = data['serious_injury'].sum()\n",
    "print(\"There are a total of \"+str(serious_injuries)+\" serious injury.\")\n",
    "\n",
    "minor_injuries = data['minor_injury'].sum()\n",
    "print(\"There are a total of \"+str(minor_injuries)+\" minor injury.\")\n",
    "\n",
    "bicycles = data['Bicycles'].sum()\n",
    "print(\"There are a total of \"+str(bicycles)+\" bicycles involved in all the accidents.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slice all bicycle accidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_bicycles = data[data['Bicycles']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_bicycles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data_bicycles.shape)\n",
    "data_bicycles.to_csv('cycling_safety_germany.csv')\n",
    "print('Wrote file to: cycling_safety_germany.csv')"
   ]
  }
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