casualties_temporal_analysis_HS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a6a3cfcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import json\n",
    "import geopandas as gpd\n",
    "from sklearn import linear_model\n",
    "import statsmodels.api as sm\n",
    "from sklearn.feature_selection import f_regression\n",
    "from sklearn.feature_selection import chi2\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "013bfdb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data exported successfully.\n"
     ]
    }
   ],
   "source": [
    "# URL of the JSON file\n",
    "url = \"https://cycling.data.tfl.gov.uk/CyclingInfrastructure/data/lines/advanced_stop_line.json\"\n",
    "\n",
    "# Fetch the content from the URL\n",
    "response = requests.get(url)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Parse the JSON data\n",
    "    data = response.json()\n",
    "    \n",
    "    # Convert JSON data to pandas DataFrame\n",
    "    df_adv_stop_line = pd.DataFrame(data)\n",
    "    \n",
    "    # Export DataFrame to CSV\n",
    "    df_adv_stop_line.to_csv('advanced_stop_line_data.csv', index=False)\n",
    "    \n",
    "    # Export DataFrame to Excel\n",
    "    # df.to_excel('signal_data.xlsx', index=False)\n",
    "    \n",
    "    print(\"Data exported successfully.\")\n",
    "else:\n",
    "    print(\"Failed to fetch data. Status code:\", response.status_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5fc05c1",
   "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>type</th>\n",
       "      <th>name</th>\n",
       "      <th>features</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>advanced_stop_line</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Line...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                type                name  \\\n",
       "0  FeatureCollection  advanced_stop_line   \n",
       "\n",
       "                                            features geometry  \n",
       "0  {'type': 'Feature', 'geometry': {'type': 'Line...     None  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in advanced stop line data\n",
    "rs = pd.read_csv(\"advanced_stop_line_data.csv\")\n",
    "rs.head()\n",
    "\n",
    "# Convert to geopandas\n",
    "grs = gpd.read_file(\"advanced_stop_line_data.csv\")\n",
    "grs.head(1)\n",
    "\n",
    "# Plot grs\n",
    "# rs.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b574d35",
   "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>_Collision Id</th>\n",
       "      <th>Borough Name</th>\n",
       "      <th>Borough Number</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>_Casualty Id</th>\n",
       "      <th>_Casualty Class</th>\n",
       "      <th>Casualty Gender</th>\n",
       "      <th>Casualty Age (5 Year Bands)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>_Casualty Count</th>\n",
       "      <th>_Casualty Severity</th>\n",
       "      <th>Pedestrian Location</th>\n",
       "      <th>Pedestrian Movement</th>\n",
       "      <th>Casualty Mode of Travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1210287148</td>\n",
       "      <td>Barnet</td>\n",
       "      <td>30</td>\n",
       "      <td>521508</td>\n",
       "      <td>193079</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>15-19</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1210287149</td>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>5</td>\n",
       "      <td>535379</td>\n",
       "      <td>180783</td>\n",
       "      <td>1</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>Male</td>\n",
       "      <td>25-29</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1210287149</td>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>5</td>\n",
       "      <td>535379</td>\n",
       "      <td>180783</td>\n",
       "      <td>2</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>20-24</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1210287149</td>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>5</td>\n",
       "      <td>535379</td>\n",
       "      <td>180783</td>\n",
       "      <td>3</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>Male</td>\n",
       "      <td>15-19</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Serious</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1210287151</td>\n",
       "      <td>Lambeth</td>\n",
       "      <td>9</td>\n",
       "      <td>529701</td>\n",
       "      <td>170398</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>25-29</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   _Collision Id   Borough Name  Borough Number  Easting  Northing  \\\n",
       "0     1210287148         Barnet              30   521508    193079   \n",
       "1     1210287149  Tower Hamlets               5   535379    180783   \n",
       "2     1210287149  Tower Hamlets               5   535379    180783   \n",
       "3     1210287149  Tower Hamlets               5   535379    180783   \n",
       "4     1210287151        Lambeth               9   529701    170398   \n",
       "\n",
       "   _Casualty Id _Casualty Class Casualty Gender Casualty Age (5 Year Bands)  \\\n",
       "0             1    Driver/Rider            Male                       15-19   \n",
       "1             1       Passenger            Male                       25-29   \n",
       "2             2    Driver/Rider            Male                       20-24   \n",
       "3             3       Passenger            Male                       15-19   \n",
       "4             1    Driver/Rider            Male                       25-29   \n",
       "\n",
       "   Casualty Age  _Casualty Count _Casualty Severity Pedestrian Location  \\\n",
       "0          19.0                1             Slight      Not Applicable   \n",
       "1          25.0                1             Slight      Not Applicable   \n",
       "2          21.0                1             Slight      Not Applicable   \n",
       "3          17.0                1            Serious      Not Applicable   \n",
       "4          27.0                1             Slight      Not Applicable   \n",
       "\n",
       "  Pedestrian Movement Casualty Mode of Travel  \n",
       "0      Not Applicable                     Car  \n",
       "1      Not Applicable                     Car  \n",
       "2      Not Applicable                     Car  \n",
       "3      Not Applicable                     Car  \n",
       "4      Not Applicable                     Car  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2021 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df2 = pd.read_csv(\"jan-dec-2021-gla-data-extract-casualty.csv\", header=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3d17b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot count of casualties per borough\n",
    "\n",
    "# Filter to only show Pedal Cycles casualties\n",
    "borough_casualties = df2[df2[\"Casualty Mode of Travel\"] == \"Pedal Cycle\"]\n",
    "\n",
    "# Count casualties per borough\n",
    "borough_casualties_count = borough_casualties[\"Borough Name\"].value_counts()\n",
    "\n",
    "# Sort borough casualties count in descending order\n",
    "borough_casualties_count = borough_casualties_count.sort_values(ascending=False)\n",
    "\n",
    "# Plot the count of casualties per borough\n",
    "borough_casualties_count.plot(kind=\"bar\")\n",
    "plt.xlabel(\"Borough\")\n",
    "plt.ylabel(\"Count of Pedal Cycle Casualties\")\n",
    "plt.title(\"Count of Pedal Cycle Casualties per London Borough\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef3c3d5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Casualty Mode of Travel\n",
      "Pedal Cycle          999\n",
      "Pedestrian           959\n",
      "Powered 2 Wheeler    929\n",
      "Car                  464\n",
      "Private Hire          11\n",
      "Taxi                   4\n",
      "dtype: int64\n",
      "3366\n"
     ]
    }
   ],
   "source": [
    "KSI_Options = ['Serious', 'Fatal']\n",
    "KSI_2021 = df2[df2[\"_Casualty Severity\"].isin(KSI_Options)]\n",
    "Mode_Options = ['Bus Or Coach', 'Goods Vehicle', 'Other Vehicle']\n",
    "KSI_2021 = KSI_2021[~KSI_2021[\"Casualty Mode of Travel\"].isin(Mode_Options)]\n",
    "casualties_per_mode_2021 = KSI_2021.groupby(\"Casualty Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2021)\n",
    "print(casualties_per_mode_2021.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5317c47",
   "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>Accident Ref.</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Boro</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>CREFNO</th>\n",
       "      <th>Casualty Class</th>\n",
       "      <th>Casualty Sex</th>\n",
       "      <th>Casualty Age (Banded)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>No. of Casualties</th>\n",
       "      <th>Casualty Severity</th>\n",
       "      <th>Ped. Location</th>\n",
       "      <th>Ped. Movement</th>\n",
       "      <th>Mode of Travel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1200240171</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531188</td>\n",
       "      <td>181609</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>1 MALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>3 POWERED 2 WHEELER</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1200246170</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533386</td>\n",
       "      <td>181909</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>1 MALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1200246170</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533386</td>\n",
       "      <td>181909</td>\n",
       "      <td>2</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>'-1 UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200248479</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533359</td>\n",
       "      <td>181760</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>1 MALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1200251644</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>532122</td>\n",
       "      <td>181853</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accident Ref.         Borough  Boro  Easting  Northing  CREFNO  \\\n",
       "0     1200240171  CITY OF LONDON     0   531188    181609       1   \n",
       "1     1200246170  CITY OF LONDON     0   533386    181909       1   \n",
       "2     1200246170  CITY OF LONDON     0   533386    181909       2   \n",
       "3     1200248479  CITY OF LONDON     0   533359    181760       1   \n",
       "4     1200251644  CITY OF LONDON     0   532122    181853       1   \n",
       "\n",
       "   Casualty Class Casualty Sex Casualty Age (Banded)  Casualty Age  \\\n",
       "0  1 DRIVER/RIDER       1 MALE                 25-59          30.0   \n",
       "1  1 DRIVER/RIDER       1 MALE                 25-59          29.0   \n",
       "2  1 DRIVER/RIDER  '-1 UNKNOWN               UNKNOWN           NaN   \n",
       "3  1 DRIVER/RIDER       1 MALE                 25-59          30.0   \n",
       "4  1 DRIVER/RIDER     2 FEMALE                 25-59          27.0   \n",
       "\n",
       "   No. of Casualties Casualty Severity       Ped. Location  \\\n",
       "0                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "1                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "2                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "3                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "4                  1          3 SLIGHT  '-2 NOT APPLICABLE   \n",
       "\n",
       "        Ped. Movement       Mode of Travel      \n",
       "0  '-2 NOT APPLICABLE  3 POWERED 2 WHEELER NaN  \n",
       "1  '-2 NOT APPLICABLE        2 PEDAL CYCLE NaN  \n",
       "2  '-2 NOT APPLICABLE        2 PEDAL CYCLE NaN  \n",
       "3  '-2 NOT APPLICABLE        2 PEDAL CYCLE NaN  \n",
       "4  '-2 NOT APPLICABLE        2 PEDAL CYCLE NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2020 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df3 = pd.read_csv(\"2020-gla-data-extract-casualty.csv\")\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2e691c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "1 PEDESTRIAN           868\n",
      "2 PEDAL CYCLE          868\n",
      "3 POWERED 2 WHEELER    768\n",
      "4 CAR                  416\n",
      "9 PRIVATE HIRE          12\n",
      "5 TAXI                   6\n",
      "dtype: int64\n",
      "2938\n"
     ]
    }
   ],
   "source": [
    "KSI_Options = ['2 SERIOUS', '1 FATAL']\n",
    "KSI_2020 = df3[df3[\"Casualty Severity\"].isin(KSI_Options)]\n",
    "Mode_Options = ['6 BUS OR COACH', '7 GOODS VEHICLE', '8 OTHER VEHICLE']\n",
    "KSI_2020 = KSI_2020[~KSI_2020[\"Mode of Travel\"].isin(Mode_Options)]\n",
    "casualties_per_mode_2020 = KSI_2020.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2020)\n",
    "print(casualties_per_mode_2020.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4da8f01",
   "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>AREFNO</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Boro</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>CREFNO</th>\n",
       "      <th>Casualty Class</th>\n",
       "      <th>Casualty Sex</th>\n",
       "      <th>Casualty Age (Banded)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>No. of Casualties</th>\n",
       "      <th>Casualty Severity</th>\n",
       "      <th>Ped Location</th>\n",
       "      <th>Ped Movement</th>\n",
       "      <th>Mode of Travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1190128300</td>\n",
       "      <td>WESTMINSTER</td>\n",
       "      <td>1</td>\n",
       "      <td>528218</td>\n",
       "      <td>180407</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>1 MALE</td>\n",
       "      <td>25-59</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>4 CAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1190128300</td>\n",
       "      <td>WESTMINSTER</td>\n",
       "      <td>1</td>\n",
       "      <td>528218</td>\n",
       "      <td>180407</td>\n",
       "      <td>2</td>\n",
       "      <td>2 PASSENGER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>4 CAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1190128300</td>\n",
       "      <td>WESTMINSTER</td>\n",
       "      <td>1</td>\n",
       "      <td>528218</td>\n",
       "      <td>180407</td>\n",
       "      <td>3</td>\n",
       "      <td>2 PASSENGER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>4 CAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1190152270</td>\n",
       "      <td>LAMBETH</td>\n",
       "      <td>9</td>\n",
       "      <td>530219</td>\n",
       "      <td>172463</td>\n",
       "      <td>1</td>\n",
       "      <td>1 DRIVER/RIDER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>16-24</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>4 CAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1190155191</td>\n",
       "      <td>CAMDEN</td>\n",
       "      <td>2</td>\n",
       "      <td>530222</td>\n",
       "      <td>182543</td>\n",
       "      <td>1</td>\n",
       "      <td>2 PASSENGER</td>\n",
       "      <td>2 FEMALE</td>\n",
       "      <td>16-24</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 SLIGHT</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>'-2 NOT APPLICABLE</td>\n",
       "      <td>2 PEDAL CYCLE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AREFNO      Borough  Boro  Easting  Northing  CREFNO  Casualty Class  \\\n",
       "0  1190128300  WESTMINSTER     1   528218    180407       1  1 DRIVER/RIDER   \n",
       "1  1190128300  WESTMINSTER     1   528218    180407       2     2 PASSENGER   \n",
       "2  1190128300  WESTMINSTER     1   528218    180407       3     2 PASSENGER   \n",
       "3  1190152270      LAMBETH     9   530219    172463       1  1 DRIVER/RIDER   \n",
       "4  1190155191       CAMDEN     2   530222    182543       1     2 PASSENGER   \n",
       "\n",
       "  Casualty Sex Casualty Age (Banded)  Casualty Age  No. of Casualties  \\\n",
       "0       1 MALE                 25-59          58.0                  3   \n",
       "1     2 FEMALE                     U           NaN                  3   \n",
       "2     2 FEMALE                     U           NaN                  3   \n",
       "3     2 FEMALE                 16-24          24.0                  1   \n",
       "4     2 FEMALE                 16-24          21.0                  1   \n",
       "\n",
       "  Casualty Severity        Ped Location        Ped Movement Mode of Travel  \n",
       "0          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE          4 CAR  \n",
       "1          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE          4 CAR  \n",
       "2          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE          4 CAR  \n",
       "3          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE          4 CAR  \n",
       "4          3 SLIGHT  '-2 NOT APPLICABLE  '-2 NOT APPLICABLE  2 PEDAL CYCLE  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2019 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df4 = pd.read_csv(\"2019-gla-data-extract-casualty.csv\")\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3c0300eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "1 PEDESTRIAN           1350\n",
      "3 POWERED 2 WHEELER    1019\n",
      "2 PEDAL CYCLE           778\n",
      "4 CAR                   574\n",
      "5 TAXI                   16\n",
      "9 PRIVATE HIRE           14\n",
      "dtype: int64\n",
      "3751\n"
     ]
    }
   ],
   "source": [
    "KSI_Options = ['2 SERIOUS', '1 FATAL']\n",
    "KSI_2019 = df4[df4[\"Casualty Severity\"].isin(KSI_Options)]\n",
    "Mode_Options = ['6 BUS OR COACH', '7 GOODS VEHICLE', '8 OTHER VEHICLE']\n",
    "KSI_2019 = KSI_2019[~KSI_2019[\"Mode of Travel\"].isin(Mode_Options)]\n",
    "casualties_per_mode_2019 = KSI_2019.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2019)\n",
    "print(casualties_per_mode_2019.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f182aa0",
   "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>Accident Ref.</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Boro</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>CREFNO</th>\n",
       "      <th>Casualty Class</th>\n",
       "      <th>Casualty Sex</th>\n",
       "      <th>Casualty Age (Banded)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>No. of Casualties</th>\n",
       "      <th>Casualty Severity</th>\n",
       "      <th>Ped. Location</th>\n",
       "      <th>Ped. Movement</th>\n",
       "      <th>Mode of Travel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1180087514</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533090</td>\n",
       "      <td>181120</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Pedestrian</td>\n",
       "      <td>2 Female</td>\n",
       "      <td>16-24</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>06 On Footpath - Verge</td>\n",
       "      <td>9 Unknown Or Other</td>\n",
       "      <td>1 Pedestrian</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1180104854</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533650</td>\n",
       "      <td>180200</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>2 Female</td>\n",
       "      <td>16-24</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>4 Car</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1180104878</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531860</td>\n",
       "      <td>181830</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1180108670</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531109</td>\n",
       "      <td>180810</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1180118053</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533834</td>\n",
       "      <td>180877</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accident Ref.         Borough  Boro  Easting  Northing  CREFNO  \\\n",
       "0     1180087514  CITY OF LONDON     0   533090    181120       1   \n",
       "1     1180104854  CITY OF LONDON     0   533650    180200       1   \n",
       "2     1180104878  CITY OF LONDON     0   531860    181830       1   \n",
       "3     1180108670  CITY OF LONDON     0   531109    180810       1   \n",
       "4     1180118053  CITY OF LONDON     0   533834    180877       1   \n",
       "\n",
       "   Casualty Class Casualty Sex Casualty Age (Banded)  Casualty Age  \\\n",
       "0    3 Pedestrian     2 Female                 16-24            24   \n",
       "1  1 Driver/Rider     2 Female                 16-24            21   \n",
       "2  1 Driver/Rider       1 Male                 25-59            32   \n",
       "3  1 Driver/Rider       1 Male               Unknown             0   \n",
       "4  1 Driver/Rider       1 Male                 25-59            57   \n",
       "\n",
       "   No. of Casualties Casualty Severity           Ped. Location  \\\n",
       "0                  1          3 Slight  06 On Footpath - Verge   \n",
       "1                  1          3 Slight              -2 Unknown   \n",
       "2                  1          3 Slight              -2 Unknown   \n",
       "3                  1          3 Slight              -2 Unknown   \n",
       "4                  1          3 Slight              -2 Unknown   \n",
       "\n",
       "        Ped. Movement Mode of Travel     \n",
       "0  9 Unknown Or Other   1 Pedestrian     \n",
       "1              -2 N/A          4 Car     \n",
       "2              -2 N/A  2 Pedal Cycle     \n",
       "3              -2 N/A  2 Pedal Cycle     \n",
       "4              -2 N/A  2 Pedal Cycle     "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2018 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df5 = pd.read_csv(\"2018-data-files-casualty.csv\")\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ece4c92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "1 Pedestrian           1366\n",
      "3 Powered 2 Wheeler    1080\n",
      "2 Pedal Cycle           782\n",
      "4 Car                   623\n",
      "5 Taxi                   46\n",
      "dtype: int64\n",
      "3897\n"
     ]
    }
   ],
   "source": [
    "KSI_Options = ['2 Serious', '1 Fatal']\n",
    "KSI_2018 = df5[df5[\"Casualty Severity\"].isin(KSI_Options)]\n",
    "Mode_Options = ['6 Bus Or Coach', '7 Goods Vehicle', '8 Other Vehicle']\n",
    "KSI_2018 = KSI_2018[~KSI_2018[\"Mode of Travel\"].isin(Mode_Options)]\n",
    "casualties_per_mode_2018 = KSI_2018.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2018)\n",
    "print(casualties_per_mode_2018.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "139820bd",
   "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>Accident Ref.</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Boro</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>CREFNO</th>\n",
       "      <th>Casualty Class</th>\n",
       "      <th>Casualty Sex</th>\n",
       "      <th>Casualty Age (Banded)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>No. of Casualties</th>\n",
       "      <th>Casualty Severity</th>\n",
       "      <th>Ped. Location</th>\n",
       "      <th>Ped. Movement</th>\n",
       "      <th>Mode of Travel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1170014045</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>532780</td>\n",
       "      <td>180350</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1170014113</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533841</td>\n",
       "      <td>180787</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>2 Female</td>\n",
       "      <td>25-59</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1170017232</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>533630</td>\n",
       "      <td>181200</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Pedestrian</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>01 Crossing Road On Ped Xing</td>\n",
       "      <td>9 Unknown Or Other</td>\n",
       "      <td>1 Pedestrian</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1170019554</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531130</td>\n",
       "      <td>181110</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Pedestrian</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2 Serious</td>\n",
       "      <td>01 Crossing Road On Ped Xing</td>\n",
       "      <td>3 From Drivers O/Side</td>\n",
       "      <td>1 Pedestrian</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1170020053</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531900</td>\n",
       "      <td>181840</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Pedestrian</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>2 Serious</td>\n",
       "      <td>09 In Road - Not Crossing</td>\n",
       "      <td>7 In Rd Facing Traffic</td>\n",
       "      <td>1 Pedestrian</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accident Ref.         Borough  Boro  Easting  Northing  CREFNO  \\\n",
       "0     1170014045  CITY OF LONDON     0   532780    180350       1   \n",
       "1     1170014113  CITY OF LONDON     0   533841    180787       1   \n",
       "2     1170017232  CITY OF LONDON     0   533630    181200       1   \n",
       "3     1170019554  CITY OF LONDON     0   531130    181110       1   \n",
       "4     1170020053  CITY OF LONDON     0   531900    181840       1   \n",
       "\n",
       "   Casualty Class Casualty Sex Casualty Age (Banded)  Casualty Age  \\\n",
       "0  1 Driver/Rider       1 Male                 25-59            34   \n",
       "1  1 Driver/Rider     2 Female                 25-59            35   \n",
       "2    3 Pedestrian       1 Male                 25-59            37   \n",
       "3    3 Pedestrian       1 Male                 25-59            29   \n",
       "4    3 Pedestrian       1 Male                 25-59            36   \n",
       "\n",
       "   No. of Casualties Casualty Severity                 Ped. Location  \\\n",
       "0                  1          3 Slight                    -2 Unknown   \n",
       "1                  1          3 Slight                    -2 Unknown   \n",
       "2                  1          3 Slight  01 Crossing Road On Ped Xing   \n",
       "3                  1         2 Serious  01 Crossing Road On Ped Xing   \n",
       "4                  1         2 Serious     09 In Road - Not Crossing   \n",
       "\n",
       "            Ped. Movement Mode of Travel     \n",
       "0                  -2 N/A  2 Pedal Cycle     \n",
       "1                  -2 N/A  2 Pedal Cycle     \n",
       "2      9 Unknown Or Other   1 Pedestrian     \n",
       "3   3 From Drivers O/Side   1 Pedestrian     \n",
       "4  7 In Rd Facing Traffic   1 Pedestrian     "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2017 collision dataset\n",
    "\n",
    "# Read the xlsx file into a DataFrame\n",
    "df6 = pd.read_csv(\"2017-data-casualty.csv\")\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c3f6bd5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "1 Pedestrian           1412\n",
      "3 Powered 2 Wheeler    1099\n",
      "2 Pedal Cycle           685\n",
      "4 Car                   490\n",
      "5 Taxi                   45\n",
      "dtype: int64\n",
      "3731\n"
     ]
    }
   ],
   "source": [
    "KSI_Options = ['2 Serious', '1 Fatal']\n",
    "KSI_2017 = df6[df6[\"Casualty Severity\"].isin(KSI_Options)]\n",
    "Mode_Options = ['6 Bus Or Coach', '7 Goods Vehicle', '8 Other Vehicle']\n",
    "KSI_2017 = KSI_2017[~KSI_2017[\"Mode of Travel\"].isin(Mode_Options)]\n",
    "casualties_per_mode_2017 = KSI_2017.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2017)\n",
    "print(casualties_per_mode_2017.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7806b225",
   "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>_Collision Id</th>\n",
       "      <th>Borough Name</th>\n",
       "      <th>Borough Number</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>_Casualty Id</th>\n",
       "      <th>_Casualty Class</th>\n",
       "      <th>Casualty Gender</th>\n",
       "      <th>Casualty Age (5 Year Bands)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>_Casualty Count</th>\n",
       "      <th>_Casualty Severity</th>\n",
       "      <th>Pedestrian Location</th>\n",
       "      <th>Pedestrian Movement</th>\n",
       "      <th>Casualty Mode of Travel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1220352073</td>\n",
       "      <td>Hammersmith &amp; Fulham</td>\n",
       "      <td>11</td>\n",
       "      <td>525199</td>\n",
       "      <td>177928</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>15-19</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Powered 2 Wheeler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1220352573</td>\n",
       "      <td>Greenwich</td>\n",
       "      <td>6</td>\n",
       "      <td>546214</td>\n",
       "      <td>179866</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1220352575</td>\n",
       "      <td>Bexley</td>\n",
       "      <td>18</td>\n",
       "      <td>551119</td>\n",
       "      <td>174789</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>20-24</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1220352578</td>\n",
       "      <td>Enfield</td>\n",
       "      <td>32</td>\n",
       "      <td>528889</td>\n",
       "      <td>192230</td>\n",
       "      <td>1</td>\n",
       "      <td>Driver/Rider</td>\n",
       "      <td>Male</td>\n",
       "      <td>45-49</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1220352578</td>\n",
       "      <td>Enfield</td>\n",
       "      <td>32</td>\n",
       "      <td>528889</td>\n",
       "      <td>192230</td>\n",
       "      <td>2</td>\n",
       "      <td>Passenger</td>\n",
       "      <td>Male</td>\n",
       "      <td>45-49</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Slight</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   _Collision Id          Borough Name  Borough Number  Easting  Northing  \\\n",
       "0     1220352073  Hammersmith & Fulham              11   525199    177928   \n",
       "1     1220352573             Greenwich               6   546214    179866   \n",
       "2     1220352575                Bexley              18   551119    174789   \n",
       "3     1220352578               Enfield              32   528889    192230   \n",
       "4     1220352578               Enfield              32   528889    192230   \n",
       "\n",
       "   _Casualty Id _Casualty Class Casualty Gender Casualty Age (5 Year Bands)  \\\n",
       "0             1    Driver/Rider            Male                       15-19   \n",
       "1             1    Driver/Rider          Female                       40-44   \n",
       "2             1    Driver/Rider            Male                       20-24   \n",
       "3             1    Driver/Rider            Male                       45-49   \n",
       "4             2       Passenger            Male                       45-49   \n",
       "\n",
       "   Casualty Age  _Casualty Count _Casualty Severity Pedestrian Location  \\\n",
       "0          17.0                1             Slight      Not Applicable   \n",
       "1          42.0                1             Slight      Not Applicable   \n",
       "2          20.0                1             Slight      Not Applicable   \n",
       "3          46.0                1             Slight      Not Applicable   \n",
       "4          45.0                1             Slight      Not Applicable   \n",
       "\n",
       "  Pedestrian Movement Casualty Mode of Travel  \n",
       "0      Not Applicable       Powered 2 Wheeler  \n",
       "1      Not Applicable                     Car  \n",
       "2      Not Applicable                     Car  \n",
       "3      Not Applicable                     Car  \n",
       "4      Not Applicable                     Car  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2022 collision dataset\n",
    "\n",
    "# Read the file into a DataFrame\n",
    "df7 = pd.read_csv(\"jan-dec-2022-gla-data-extract-casualties.csv\")\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ac1d5d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Casualty Mode of Travel\n",
      "Pedestrian           1235\n",
      "Pedal Cycle          1027\n",
      "Powered 2 Wheeler     894\n",
      "Car                   526\n",
      "Private Hire           13\n",
      "Taxi                   12\n",
      "dtype: int64\n",
      "3707\n"
     ]
    }
   ],
   "source": [
    "KSI_Options = ['Serious', 'Fatal']\n",
    "KSI_2022 = df7[df7[\"_Casualty Severity\"].isin(KSI_Options)]\n",
    "Mode_Options = ['Bus Or Coach', 'Goods Vehicle', 'Other Vehicle']\n",
    "KSI_2022 = KSI_2022[~KSI_2022[\"Casualty Mode of Travel\"].isin(Mode_Options)]\n",
    "\n",
    "casualties_per_mode_2022 = KSI_2022.groupby(\"Casualty Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2022)\n",
    "print(casualties_per_mode_2022.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "52c6e0b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "20    1750\n",
      "30    1669\n",
      "40     164\n",
      "50     101\n",
      "70      11\n",
      "60       6\n",
      "10       5\n",
      "15       1\n",
      "dtype: int64\n",
      "3707\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2022 = pd.read_csv(\"jan-dec-2022-gla-data-extract-attendant.csv\")\n",
    "df_attendant_2022.head()\n",
    "\n",
    "# Join 2022 datasets\n",
    "df2022 = pd.merge(KSI_2022, df_attendant_2022, on='_Collision Id')\n",
    "df2022.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2022 = df2022.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2022)\n",
    "print(sl_casualties2022.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "19975780",
   "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>Accident Ref.</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Boro</th>\n",
       "      <th>Easting</th>\n",
       "      <th>Northing</th>\n",
       "      <th>CREFNO</th>\n",
       "      <th>Casualty Class</th>\n",
       "      <th>Casualty Sex</th>\n",
       "      <th>Casualty Age (Banded)</th>\n",
       "      <th>Casualty Age</th>\n",
       "      <th>No. of Casualties</th>\n",
       "      <th>Casualty Severity</th>\n",
       "      <th>Ped. Location</th>\n",
       "      <th>Ped. Movement</th>\n",
       "      <th>Mode of Travel</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1160001090</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531350</td>\n",
       "      <td>181580</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Pedestrian</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>09 In Road - Not Crossing</td>\n",
       "      <td>7 In Rd Facing Traffic</td>\n",
       "      <td>1 Pedestrian</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1160002980</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>532780</td>\n",
       "      <td>180410</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>25-59</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>2 Serious</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>2 Pedal Cycle</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1160006347</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531400</td>\n",
       "      <td>181570</td>\n",
       "      <td>3</td>\n",
       "      <td>2 Passenger</td>\n",
       "      <td>2 Female</td>\n",
       "      <td>60+</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>4 Car</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1160006347</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531400</td>\n",
       "      <td>181570</td>\n",
       "      <td>2</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>60+</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>4 Car</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1160006347</td>\n",
       "      <td>CITY OF LONDON</td>\n",
       "      <td>0</td>\n",
       "      <td>531400</td>\n",
       "      <td>181570</td>\n",
       "      <td>1</td>\n",
       "      <td>1 Driver/Rider</td>\n",
       "      <td>1 Male</td>\n",
       "      <td>16-24</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>3 Slight</td>\n",
       "      <td>-2 Unknown</td>\n",
       "      <td>-2 N/A</td>\n",
       "      <td>4 Car</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accident Ref.         Borough  Boro  Easting  Northing  CREFNO  \\\n",
       "0    1160001090  CITY OF LONDON     0   531350    181580       1   \n",
       "1    1160002980  CITY OF LONDON     0   532780    180410       1   \n",
       "2    1160006347  CITY OF LONDON     0   531400    181570       3   \n",
       "3    1160006347  CITY OF LONDON     0   531400    181570       2   \n",
       "4    1160006347  CITY OF LONDON     0   531400    181570       1   \n",
       "\n",
       "   Casualty Class Casualty Sex Casualty Age (Banded)  Casualty Age  \\\n",
       "0    3 Pedestrian       1 Male                 25-59            54   \n",
       "1  1 Driver/Rider       1 Male                 25-59            46   \n",
       "2     2 Passenger     2 Female                   60+            61   \n",
       "3  1 Driver/Rider       1 Male                   60+            69   \n",
       "4  1 Driver/Rider       1 Male                 16-24            22   \n",
       "\n",
       "   No. of Casualties Casualty Severity              Ped. Location  \\\n",
       "0                  1          3 Slight  09 In Road - Not Crossing   \n",
       "1                  1         2 Serious                 -2 Unknown   \n",
       "2                  1          3 Slight                 -2 Unknown   \n",
       "3                  1          3 Slight                 -2 Unknown   \n",
       "4                  1          3 Slight                 -2 Unknown   \n",
       "\n",
       "            Ped. Movement Mode of Travel     \n",
       "0  7 In Rd Facing Traffic   1 Pedestrian     \n",
       "1                  -2 N/A  2 Pedal Cycle     \n",
       "2                  -2 N/A          4 Car     \n",
       "3                  -2 N/A          4 Car     \n",
       "4                  -2 N/A          4 Car     "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = pd.read_csv(\"2016-gla-data-extract-casualty.csv\")\n",
    "df8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Travel\n",
      "1 Pedestrian           875\n",
      "3 Powered 2 Wheeler    681\n",
      "2 Pedal Cycle          454\n",
      "4 Car                  368\n",
      "6 Bus Or Coach          70\n",
      "5 Taxi                  27\n",
      "7 Goods Vehicle         20\n",
      "8 Other Vehicle          6\n",
      "dtype: int64\n",
      "2501\n"
     ]
    }
   ],
   "source": [
    "KSI_Options = ['2 Serious', '1 Fatal']\n",
    "KSI_2016 = df8[df8[\"Casualty Severity\"].isin(KSI_Options)]\n",
    "Mode_Options = ['Bus Or Coach', 'Goods Vehicle', 'Other Vehicle']\n",
    "KSI_2016 = KSI_2016[~KSI_2016[\"Mode of Travel\"].isin(Mode_Options)]\n",
    "\n",
    "casualties_per_mode_2016 = KSI_2016.groupby(\"Mode of Travel\").size().sort_values(ascending = False)\n",
    "print(casualties_per_mode_2016)\n",
    "print(casualties_per_mode_2016.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "74d3d96b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "2 30 MPH       2031\n",
      "1 <= 20 MPH     262\n",
      "3 40 MPH        120\n",
      "4 50 MPH         65\n",
      "6 70 MPH         22\n",
      "5 60 MPH          1\n",
      "dtype: int64\n",
      "2501\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2016 = pd.read_csv(\"2016-gla-data-extract-attendant.csv\")\n",
    "df_attendant_2016.head()\n",
    "\n",
    "# Join 2016 datasets\n",
    "df2016 = pd.merge(KSI_2016, df_attendant_2016, on='Accident Ref.')\n",
    "df2016.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2016 = df2016.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2016)\n",
    "print(sl_casualties2016.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e5591654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a DataFrame\n",
    "total_casualties = pd.DataFrame({\n",
    "    \"Year\": [\"2017\", \"2018\", \"2019\", \"2020\", \"2021\",\"2022\"],\n",
    "    \"KSI Casualties\": [3731, 3897, 3751, 2938, 3366, 3707],\n",
    "    \"Total KM Road Travel\": [20055359109, 19992861706, 20264369690, 16613638242, 18453291284, 19089347093],\n",
    "    \"KSI Casualties - Speed Limit <= 20 MPH\": [750/0.3, 948/0.34, 1024/0.38, 1007/0.42, 1451/0.475, 1750/0.52],\n",
    "    \"Enforcement Actions\":[298535,  291956,  349354,  403245,  542737, 791172],\n",
    "    \"Cycle Infrastructure Proxy\": [10446044, 10567540, 10168936, 10434167, 10941264, 11505872]\n",
    "})\n",
    "\n",
    "# Calculate Total Road Accidents per Million Km Road Travel\n",
    "total_casualties[\"KSI per Billion Km Road Travel\"] = (total_casualties[\"KSI Casualties\"] / total_casualties[\"Total KM Road Travel\"]) * 1000000000\n",
    "total_casualties[\"KSI <= 20\"] = (total_casualties[\"KSI Casualties - Speed Limit <= 20 MPH\"] / total_casualties[\"Total KM Road Travel\"]) * 1000000\n",
    "total_casualties[\"KSI <= 20, Bus/HGV/Other Removed Per Billion KM Travel\"] = total_casualties[\"KSI Casualties - Speed Limit <= 20 MPH\"] / (total_casualties[\"Total KM Road Travel\"] * total_casualties[\"Enforcement Actions\"]) * 1000000000\n",
    "# Plot the DataFrame\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(total_casualties[\"Year\"], total_casualties[\"KSI per Billion Km Road Travel\"], color=\"g\", marker=\"x\")\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(\"KSI per Billion Km Road Travel by Year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"KSI per Billion Km Road Travel\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6abb8114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Total Casualties - Speed Limit\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(total_casualties[\"Year\"], total_casualties[\"KSI <= 20, Bus/HGV/Other Removed Per Billion KM Travel Per Enforcement Action\"], color=\"r\", marker=\".\")\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title(\"KSI Incidents where the speed limit is <= 20 MPH\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"KSI Casualties - Speed Limit <= 20 MPH\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f47561d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.366 0.653]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.417\n",
      "Model:                            OLS   Adj. R-squared:                  0.028\n",
      "Method:                 Least Squares   F-statistic:                     1.072\n",
      "Date:                Thu, 22 Feb 2024   Prob (F-statistic):              0.445\n",
      "Time:                        17:32:20   Log-Likelihood:                -17.990\n",
      "No. Observations:                   6   AIC:                             41.98\n",
      "Df Residuals:                       3   BIC:                             41.36\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                        -31.9164    157.709     -0.202      0.853    -533.817     469.984\n",
      "Cycle Infrastructure Proxy  2.222e-05   1.63e-05      1.363      0.266   -2.97e-05    7.41e-05\n",
      "Enforcement Actions        -4.214e-05   4.05e-05     -1.042      0.374      -0.000    8.66e-05\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   1.929\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.760\n",
      "Skew:                          -0.190   Prob(JB):                        0.684\n",
      "Kurtosis:                       1.299   Cond. No.                     6.02e+08\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 6.02e+08. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hardiksangwan/Library/Python/3.9/lib/python/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "X = total_casualties[['Cycle Infrastructure Proxy','Enforcement Actions']]\n",
    "y = total_casualties[['KSI per Billion Km Road Travel']]\n",
    "y = y.values.ravel()\n",
    "\n",
    "#logit_model=sm.Logit(y,X)\n",
    "#result=logit_model.fit()\n",
    "#print(result.summary())\n",
    "\n",
    "freg=f_regression(X,y)\n",
    "\n",
    "p=freg[1]\n",
    "\n",
    "print(p.round(3))\n",
    "\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())\n",
    "\n",
    "#scores, pvalues = chi2(X, y)\n",
    "\n",
    "#print(pvalues)\n",
    "\n",
    "#regr = linear_model.LinearRegression()\n",
    "#regr.fit(X, y)\n",
    "\n",
    "#print(regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5cd0717b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "2 30 MPH       2629\n",
      "1 <= 20 MPH     948\n",
      "3 40 MPH        168\n",
      "4 50 MPH        107\n",
      "6 70 MPH         36\n",
      "5 60 MPH          9\n",
      "dtype: int64\n",
      "3897\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2018 = pd.read_csv(\"2018-data-files-attendant.csv\")\n",
    "df_attendant_2018.head()\n",
    "\n",
    "# Join 2018 datasets\n",
    "df2018 = pd.merge(KSI_2018, df_attendant_2018, on='Accident Ref.')\n",
    "df2018.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "# print(df2018.groupby(\"Speed Limit\").sum())\n",
    "\n",
    "sl_casualties2018 = df2018.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2018)\n",
    "print(sl_casualties2018.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "73e5721b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "30    2428\n",
      "20    1024\n",
      "40     178\n",
      "50      76\n",
      "70      32\n",
      "60       8\n",
      "10       3\n",
      "5        2\n",
      "dtype: int64\n",
      "3751\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2019 = pd.read_csv(\"2019-gla-data-extract-attendant.csv\")\n",
    "df_attendant_2019.head()\n",
    "\n",
    "# Rename column\n",
    "df_attendant_2019 = df_attendant_2019.rename(columns={'Accident Ref': 'AREFNO'})\n",
    "\n",
    "# Join 2019 datasets\n",
    "df2019 = pd.merge(KSI_2019, df_attendant_2019, on='AREFNO')\n",
    "df2019.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2019 = df2019.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2019)\n",
    "print(sl_casualties2019.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "aa5d4b4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "30    1653\n",
      "20    1007\n",
      "40     175\n",
      "50      64\n",
      "70      19\n",
      "60       9\n",
      "10       6\n",
      "5        3\n",
      "2        1\n",
      "25       1\n",
      "dtype: int64\n",
      "2938\n"
     ]
    }
   ],
   "source": [
    "df_attendant_2020 = pd.read_csv(\"2020-gla-data-extract-attendant.csv\")\n",
    "df_attendant_2020.head()\n",
    "\n",
    "# Rename column\n",
    "df_attendant_2020 = df_attendant_2020.rename(columns={'Accident Ref': 'Accident Ref.'})\n",
    "\n",
    "# Join 2020 datasets\n",
    "df2020 = pd.merge(KSI_2020, df_attendant_2020, on='Accident Ref.')\n",
    "df2020.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2020 = df2020.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2020)\n",
    "print(sl_casualties2020.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c24badc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_attendant_2021 = pd.read_csv(\"jan-dec-2021-gla-data-extract-attendant.csv\", header=1)\n",
    "df_attendant_2021.head()\n",
    "\n",
    "# Drop the last row = \n",
    "df_attendant_2021 = df_attendant_2021.iloc[:-1]\n",
    "\n",
    "df_attendant_2021[\"_Collision Id\"] = df_attendant_2021[\"_Collision Id\"].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c1c27082",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "30.0    1655\n",
      "20.0    1451\n",
      "40.0     156\n",
      "50.0      65\n",
      "70.0      18\n",
      "10.0       9\n",
      "60.0       5\n",
      "5.0        3\n",
      "1.0        2\n",
      "15.0       2\n",
      "dtype: int64\n",
      "3366\n"
     ]
    }
   ],
   "source": [
    "# Join 2021 datasets\n",
    "df2021 = pd.merge(KSI_2021, df_attendant_2021, on=\"_Collision Id\")\n",
    "df2021.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2021 = df2021.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2021)\n",
    "print(sl_casualties2021.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2b8496c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speed Limit\n",
      "2 30 MPH       2700\n",
      "1 <= 20 MPH     750\n",
      "3 40 MPH        184\n",
      "4 50 MPH         70\n",
      "6 70 MPH         19\n",
      "5 60 MPH          8\n",
      "dtype: int64\n",
      "3731\n"
     ]
    }
   ],
   "source": [
    "file_encoding = 'utf8'        # set file_encoding to the file encoding (utf8, latin1, etc.)\n",
    "input_fd = open(\"2017-data-attendant.csv\", encoding=file_encoding, errors = 'backslashreplace')\n",
    "df_attendant_2017 = pd.read_csv(input_fd)\n",
    "df_attendant_2017.head()\n",
    "\n",
    "# Join 2017 datasets\n",
    "df2017 = pd.merge(KSI_2017, df_attendant_2017, on=\"Accident Ref.\")\n",
    "df2017.head()\n",
    "\n",
    "# Group number of casualties by speed limit\n",
    "\n",
    "sl_casualties2017 = df2017.groupby(\"Speed Limit\").size().sort_values(ascending = False)\n",
    "print(sl_casualties2017)\n",
    "print(sl_casualties2017.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91e791ab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}