dataset_curation/helsinki.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helsinki, Finland"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Source of original dataset:** https://hri.fi/data/en_GB/dataset/liikenneonnettomuudet-helsingissa\n",
    "\n",
    "**Location of accidents:** Latitude, Longitude\n",
    "\n",
    "**Date of accidents:** Date\n",
    "\n",
    "**Outcome of accidents:** Fatality, 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"../data/helsinki/\"\n",
    "\n",
    "data_file = data_dir + 'liikenneonnettomuudet_Helsingissa.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(data_file, encoding = \"ANSI\", sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Datetime column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Date'] = data['VV'].astype('str') + '/01/01 00:00'\n",
    "data['Date'] = pd.to_datetime(data['Date'])"
   ]
  },
  {
   "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.LAJI == 'PP', 'bicycles'] = 1\n",
    "\n",
    "data['fatalities'] = 0\n",
    "data['injury'] = 0\n",
    "data['property_damage'] = 0\n",
    "\n",
    "data.loc[data.VAKAV_A == 3, 'fatalities'] = 1\n",
    "data.loc[data.VAKAV_A == 2, 'injury'] = 1\n",
    "data.loc[data.VAKAV_A == 1, 'property_damage'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup latitude & longitude column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyproj import Proj, transform\n",
    "\n",
    "inProj = Proj('epsg:3879')\n",
    "outProj = Proj('epsg:4326')\n",
    "i=0\n",
    "for index, row in data.iterrows():\n",
    "    x = row['pohj_etrs']\n",
    "    y = row['ita_etrs']\n",
    "    \n",
    "    lat, lon = transform(inProj, outProj, x, y)\n",
    "    data.at[index, 'LAT'] = lat\n",
    "    data.at[index, 'LON'] = lon\n",
    "\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Latitude'] = data['LAT']\n",
    "data['Longitude'] = data['LON']"
   ]
  },
  {
   "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[\"fatalities\"].sum()\n",
    "print(\"There are a total of \"+str(fatalities)+\" fatalities.\")\n",
    "\n",
    "injuries = data[\"injury\"].sum()\n",
    "print(\"There are a total of \"+str(injuries)+\" injured.\")\n",
    "\n",
    "property_damage = data[\"property_damage\"].sum()\n",
    "print(\"There are a total of \"+str(property_damage)+\" propertiy damage accidents.\")\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_helsinki.csv')\n",
    "print('Wrote file to: cycling_safety_helsinki.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}