Parks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a0f4af7",
   "metadata": {},
   "source": [
    "# Does Brampton have enough parks?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02ee8bf",
   "metadata": {},
   "source": [
    "####  Background  \n",
    "Brampton is experiencing rapid population growth. As of the 2016 Census, \"Brampton had the 2nd highest growth rate among Canada's 25 largest cities.\" (City of Brampton, 2019).\n",
    "\n",
    "This population growth presents the need for more community parks and open recreational spaces as tools for mental health and physical activity opportunities. \n",
    "\n",
    "#### The Demand \n",
    "Obesity and living in the suburbs are linked (Oregon State University, 2005). Further, rates of being overweight and obese in the Peel Region are higher than in the rest of the Greater Toronto Area (Glazier et al., 2014).\n",
    "\n",
    "Parks seem to also be important for mental health. “Mental health is significantly related to residential distance from parks”. (Sturm & Cohen, 2014)\n",
    "\n",
    "#### Interest\n",
    "This project can be used to inform City of Brampton planners. The goal is to inform these planners about the need for more parks for the people of the city and where specifically the need is the greatest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "405858dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geojson\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf14687",
   "metadata": {},
   "source": [
    "There were two main datasets used in the analysis of this project. The parks dataset and the address points dataset were retrieved from Brampton's Open Data on GeoHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ba475ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "address_points_file_path = \"Data/Address_Points.geojson\" # (City of Brampton, 2021)\n",
    "parks_file_path = \"Data/Parks.geojson\" # (City of Brampton, 2021)\n",
    "# data retrieved from  Brampton's Open Data on geohub\n",
    "\n",
    "with open(address_points_file_path) as f:\n",
    "    address_points_data = geojson.load(f)\n",
    "\n",
    "with open(parks_file_path) as f:\n",
    "    parks_data = geojson.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e13c11",
   "metadata": {},
   "source": [
    "From the address points dataset, we are interested in LATITUDE, LONGITUDE, FULL_ADDRESS, and PERMIT_TYPE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "849c96d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_address_point_df = []\n",
    "\n",
    "for address_point_feature in address_points_data['features']:\n",
    "    value_list = []\n",
    "    value_list.append(address_point_feature[\"geometry\"][\"coordinates\"][1])\n",
    "    value_list.append(address_point_feature[\"geometry\"][\"coordinates\"][0])\n",
    "    value_list.append(address_point_feature[\"properties\"][\"FULL_ADDRESS\"])\n",
    "    value_list.append(address_point_feature[\"properties\"][\"PERMIT_TYPE\"])\n",
    "    pre_address_point_df.append(value_list)\n",
    "    \n",
    "address_point_df = pd.DataFrame(pre_address_point_df)\n",
    "address_point_df.columns = [\"LATITUDE\", \"LONGITUDE\", \"FULL_ADDRESS\", \"PERMIT_TYPE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "065afb57",
   "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>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FULL_ADDRESS</th>\n",
       "      <th>PERMIT_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43.711492</td>\n",
       "      <td>-79.823719</td>\n",
       "      <td>4 ANSBURY DR</td>\n",
       "      <td>Repeat Residential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.711533</td>\n",
       "      <td>-79.823863</td>\n",
       "      <td>6 ANSBURY DR</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43.711575</td>\n",
       "      <td>-79.824015</td>\n",
       "      <td>8 ANSBURY DR</td>\n",
       "      <td>Repeat Residential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.696703</td>\n",
       "      <td>-79.812413</td>\n",
       "      <td>142 BONISTEL CRES</td>\n",
       "      <td>Repeat Residential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.681749</td>\n",
       "      <td>-79.822541</td>\n",
       "      <td>21 LEAGATE ST</td>\n",
       "      <td>Repeat Residential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222543</th>\n",
       "      <td>43.798054</td>\n",
       "      <td>-79.669785</td>\n",
       "      <td>2-4 BADDOW RD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222544</th>\n",
       "      <td>43.705644</td>\n",
       "      <td>-79.827955</td>\n",
       "      <td>2-19 EDENVALLEY RD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222545</th>\n",
       "      <td>43.711522</td>\n",
       "      <td>-79.758810</td>\n",
       "      <td>2-40 ROYAL SALISBURY WAY</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222546</th>\n",
       "      <td>43.770844</td>\n",
       "      <td>-79.682609</td>\n",
       "      <td>2-20 VINTONRIDGE DR</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222547</th>\n",
       "      <td>43.781615</td>\n",
       "      <td>-79.677452</td>\n",
       "      <td>2-106 SAINT GRACE CRT</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222548 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         LATITUDE  LONGITUDE              FULL_ADDRESS         PERMIT_TYPE\n",
       "0       43.711492 -79.823719              4 ANSBURY DR  Repeat Residential\n",
       "1       43.711533 -79.823863              6 ANSBURY DR                None\n",
       "2       43.711575 -79.824015              8 ANSBURY DR  Repeat Residential\n",
       "3       43.696703 -79.812413         142 BONISTEL CRES  Repeat Residential\n",
       "4       43.681749 -79.822541             21 LEAGATE ST  Repeat Residential\n",
       "...           ...        ...                       ...                 ...\n",
       "222543  43.798054 -79.669785             2-4 BADDOW RD                None\n",
       "222544  43.705644 -79.827955        2-19 EDENVALLEY RD                None\n",
       "222545  43.711522 -79.758810  2-40 ROYAL SALISBURY WAY                None\n",
       "222546  43.770844 -79.682609       2-20 VINTONRIDGE DR                None\n",
       "222547  43.781615 -79.677452     2-106 SAINT GRACE CRT                None\n",
       "\n",
       "[222548 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address_point_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb7d5e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN                                     137115\n",
       "Repeat Residential                       81120\n",
       "Commercial                                2830\n",
       "Industrial                                 567\n",
       "House                                      328\n",
       "Institutional                              232\n",
       "Residential                                218\n",
       "Commercial Finish Live Work                103\n",
       "Residential Finish Live Work                30\n",
       "Miscellaneous Industrial Plumbing            2\n",
       "House Miscellaneous Plumbing                 1\n",
       "Miscellaneous Residential                    1\n",
       "Miscellaneous Institutional Plumbing         1\n",
       "Name: PERMIT_TYPE, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address_point_df[\"PERMIT_TYPE\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a3ba1f",
   "metadata": {},
   "source": [
    "The PERMIT_TYPE field is used to remove address points that are not houses. Unfortunately, many of the address points have not been given a PERMIT_TYPE category. So the address points that are not houses, but not given a PERMIT_TYPE category are unfortunately considered in this analysis. Further, some address points did not have an address name and some addresses had street number 0. Both of these types of address points were excluded from the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2b97faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "address_point_df = address_point_df[(address_point_df[\"PERMIT_TYPE\"].isnull()) | \n",
    "                                   (address_point_df[\"PERMIT_TYPE\"] == \"Repeat Residential\") | \n",
    "                                   (address_point_df[\"PERMIT_TYPE\"] == \"House\") | \n",
    "                                   (address_point_df[\"PERMIT_TYPE\"] == \"Residential\")]\n",
    "\n",
    "# some addresses points did not have an address name. These were removed from the analysis\n",
    "address_point_df = address_point_df[address_point_df[\"FULL_ADDRESS\"] != \"UNASSIGNED\"]\n",
    "\n",
    "# there seem to be addresses with street number 0 that aren't houses\n",
    "address_point_df = address_point_df[~address_point_df[\"FULL_ADDRESS\"].str.strip().str.startswith('0', na=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40bde304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "201645"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address_point_df = address_point_df.reset_index()\n",
    "address_point_df = address_point_df.drop(labels=\"index\", axis=1)\n",
    "len(address_point_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d71ce347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "827"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(parks_data[\"features\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1cffb8",
   "metadata": {},
   "source": [
    "From the parks dataset, we are interested in PARK_NAME, PARK_STATUS, PARK_TYPE, AREA_HECTARES, and coordinates. Seven of the parks had PARK_TYPE equal to OPERATIONS/ADMINISTRATION FACILITIES. These parks did not seem like actual parks and were excluded from the anylsis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31823c41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18                           CIVIC CENTRE\n",
      "115                             CITY HALL\n",
      "315    ARTS, CULTURE AND THEATRE DIVISION\n",
      "336                 SANDALWOOD WORKS YARD\n",
      "618                BRAMPTON FIRE TRAINING\n",
      "808    WILLIAMS PARKWAY OPERATIONS CENTRE\n",
      "811       BRAMPTON TRANSIT CLARK FACILITY\n",
      "Name: PARK_NAME, dtype: object\n"
     ]
    }
   ],
   "source": [
    "pre_parks_df = []\n",
    "\n",
    "\n",
    "for parks_feature in parks_data['features']:\n",
    "    value_list = []\n",
    "    value_list.append(parks_feature[\"properties\"][\"PARK_NAME\"])\n",
    "    value_list.append(parks_feature[\"properties\"][\"PARK_STATUS\"])\n",
    "    value_list.append(parks_feature[\"properties\"][\"PARK_TYPE\"])\n",
    "    value_list.append(parks_feature[\"properties\"][\"AREA_HECTARES\"])\n",
    "    value_list.append(parks_feature[\"geometry\"][\"coordinates\"][0])\n",
    "    pre_parks_df.append(value_list)\n",
    "    \n",
    "parks_df = pd.DataFrame(pre_parks_df)\n",
    "parks_df.columns = [\"PARK_NAME\", \"PARK_STATUS\", \"PARK_TYPE\", \"AREA_HECTARES\", \"COORDINATES_LIST\"]\n",
    "\n",
    "print(parks_df[parks_df[\"PARK_TYPE\"] == \"OPERATIONS/ADMINISTRATION FACILITIES\"][\"PARK_NAME\"])\n",
    "\n",
    "parks_df = parks_df[parks_df[\"PARK_TYPE\"] != \"OPERATIONS/ADMINISTRATION FACILITIES\"]\n",
    "parks_df = parks_df.reset_index()\n",
    "parks_df = parks_df.drop(labels=\"index\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6142f0c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEIGHBOURHOOD PARKS                                    352\n",
       "ENVIRONMENTAL PARKS                                    283\n",
       "STORMWATER MANAGEMENT PONDS                            109\n",
       "COMMUNITY PARKS                                         38\n",
       "CITY PARKS                                              17\n",
       "CONSERVATION AUTHORITY-OWNED LANDS(CITY MAINTAINED)     12\n",
       "CONSERVATION AUTHORITY-OWNED LANDS                       6\n",
       "LEASED RECREATION FACILITY LANDS                         3\n",
       "Name: PARK_TYPE, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parks_df[\"PARK_TYPE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b74ea441",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PARK_NAME</th>\n",
       "      <th>PARK_STATUS</th>\n",
       "      <th>PARK_TYPE</th>\n",
       "      <th>AREA_HECTARES</th>\n",
       "      <th>COORDINATES_LIST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>THATCHER PARKETTE</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.067</td>\n",
       "      <td>[[-79.798597, 43.731194], [-79.798323, 43.7310...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KINDLE PARKETTE</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.173</td>\n",
       "      <td>[[-79.801502, 43.727685], [-79.801124, 43.7275...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUNCREST PARKETTE</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.370</td>\n",
       "      <td>[[-79.790451, 43.693514], [-79.790178, 43.6933...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JORGEN JENSEN PARKETTE</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.091</td>\n",
       "      <td>[[-79.792607, 43.728372], [-79.792381, 43.7282...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RC CHARLTON PARK</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.791</td>\n",
       "      <td>[[-79.760236, 43.648314], [-79.759948, 43.6481...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>ANDREW McCANDLESS VALLEY</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>ENVIRONMENTAL PARKS</td>\n",
       "      <td>14.980</td>\n",
       "      <td>[[-79.819136, 43.665396], [-79.818812, 43.6653...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>SAM RAYSON VALLEY</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>ENVIRONMENTAL PARKS</td>\n",
       "      <td>16.571</td>\n",
       "      <td>[[-79.706476, 43.674182], [-79.706443, 43.6739...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>ORNSTOCK POND EAST</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>STORMWATER MANAGEMENT PONDS</td>\n",
       "      <td>4.653</td>\n",
       "      <td>[[-79.776888, 43.626901], [-79.776914, 43.6268...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>ALLAN KERBEL POND</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>STORMWATER MANAGEMENT PONDS</td>\n",
       "      <td>1.066</td>\n",
       "      <td>[[-79.777856, 43.705068], [-79.777843, 43.7050...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>ALLAN KERBEL PARK</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>3.332</td>\n",
       "      <td>[[-79.777697, 43.705215], [-79.777856, 43.7050...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>820 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    PARK_NAME       PARK_STATUS                    PARK_TYPE  \\\n",
       "0           THATCHER PARKETTE  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "1             KINDLE PARKETTE  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "2           SUNCREST PARKETTE  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "3      JORGEN JENSEN PARKETTE  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "4            RC CHARLTON PARK  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "..                        ...               ...                          ...   \n",
       "815  ANDREW McCANDLESS VALLEY  FINAL ACCEPTANCE          ENVIRONMENTAL PARKS   \n",
       "816         SAM RAYSON VALLEY  FINAL ACCEPTANCE          ENVIRONMENTAL PARKS   \n",
       "817        ORNSTOCK POND EAST  FINAL ACCEPTANCE  STORMWATER MANAGEMENT PONDS   \n",
       "818         ALLAN KERBEL POND  FINAL ACCEPTANCE  STORMWATER MANAGEMENT PONDS   \n",
       "819         ALLAN KERBEL PARK  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "\n",
       "     AREA_HECTARES                                   COORDINATES_LIST  \n",
       "0            0.067  [[-79.798597, 43.731194], [-79.798323, 43.7310...  \n",
       "1            0.173  [[-79.801502, 43.727685], [-79.801124, 43.7275...  \n",
       "2            0.370  [[-79.790451, 43.693514], [-79.790178, 43.6933...  \n",
       "3            0.091  [[-79.792607, 43.728372], [-79.792381, 43.7282...  \n",
       "4            0.791  [[-79.760236, 43.648314], [-79.759948, 43.6481...  \n",
       "..             ...                                                ...  \n",
       "815         14.980  [[-79.819136, 43.665396], [-79.818812, 43.6653...  \n",
       "816         16.571  [[-79.706476, 43.674182], [-79.706443, 43.6739...  \n",
       "817          4.653  [[-79.776888, 43.626901], [-79.776914, 43.6268...  \n",
       "818          1.066  [[-79.777856, 43.705068], [-79.777843, 43.7050...  \n",
       "819          3.332  [[-79.777697, 43.705215], [-79.777856, 43.7050...  \n",
       "\n",
       "[820 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parks_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f8ee83",
   "metadata": {},
   "source": [
    "All of the parks in the dataset have PARK_STATUS as FINAL ACCEPTANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "71678f2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FINAL ACCEPTANCE    820\n",
       "Name: PARK_STATUS, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parks_df[\"PARK_STATUS\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf2a17a",
   "metadata": {},
   "source": [
    "The haversine formula was used to calculate the distance between 2 latitude/longitude points in km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21332864",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using haversine formula\n",
    "def calculate_nearest_distance_park(coordinate, coordinates_list, park_names):\n",
    "    distances = haversine(coordinate[0], coordinate[1], np.array(coordinates_list)[:,1], np.array(coordinates_list)[:,0])\n",
    "    closest_coord_index = np.argmin(distances)\n",
    "    return np.amin(distances), park_names[closest_coord_index]\n",
    "    \n",
    "# in kilometers \n",
    "def haversine(lat_A, lon_A, lat_B, lon_B):\n",
    "\n",
    "   # Earth radius in km, https://en.wikipedia.org/wiki/Earth_radius, exact value doesn't really matter, just need calculated distances to be accurately comparable\n",
    "    R = 6371\n",
    "\n",
    "    lat_A = np.deg2rad(lat_A)                      \n",
    "    lon_A = np.deg2rad(lon_A)     \n",
    "    lat_B = np.deg2rad(lat_B)                       \n",
    "    lon_B = np.deg2rad(lon_B)\n",
    "    \n",
    "    lat_diff = lat_B - lat_A\n",
    "    lon_diff = lon_B - lon_A \n",
    "\n",
    "    d = np.sin(lat_diff/2)**2 + np.cos(lat_A)*np.cos(lat_B) * np.sin(lon_diff/2)**2\n",
    "\n",
    "    return 2 * R * np.arcsin(np.sqrt(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a98c8bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of every coordinate for every park\n",
    "all_parks_coordinates_list = np.empty(2)\n",
    "# parallel list of park names, to keep track of which coordinate belongs to which park\n",
    "park_names = []\n",
    "\n",
    "for i, row in parks_df.iterrows():\n",
    "    x = row[\"COORDINATES_LIST\"]\n",
    "    if len(x) == 1:\n",
    "        x = x[0]\n",
    "    for y in x:\n",
    "        park_names.append(row[\"PARK_NAME\"])\n",
    "        all_parks_coordinates_list = np.vstack((all_parks_coordinates_list,y))      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a0a83566",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_nearest_distance_address_points(row):\n",
    "    return calculate_nearest_distance_park([row[\"LATITUDE\"], row[\"LONGITUDE\"]], all_parks_coordinates_list, park_names)\n",
    "    \n",
    "result = address_point_df.apply(calculate_nearest_distance_address_points, axis=1)\n",
    "address_point_df[\"Nearest_park_distance\"] = pd.DataFrame(result.tolist())[0].values\n",
    "address_point_df[\"Nearest_park_name\"] = pd.DataFrame(result.tolist())[1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "614a738e",
   "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>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FULL_ADDRESS</th>\n",
       "      <th>PERMIT_TYPE</th>\n",
       "      <th>Nearest_park_distance</th>\n",
       "      <th>Nearest_park_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43.711492</td>\n",
       "      <td>-79.823719</td>\n",
       "      <td>4 ANSBURY DR</td>\n",
       "      <td>Repeat Residential</td>\n",
       "      <td>0.125074</td>\n",
       "      <td>JACOB SHOOK PARK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.711533</td>\n",
       "      <td>-79.823863</td>\n",
       "      <td>6 ANSBURY DR</td>\n",
       "      <td>None</td>\n",
       "      <td>0.114713</td>\n",
       "      <td>JACOB SHOOK PARK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43.711575</td>\n",
       "      <td>-79.824015</td>\n",
       "      <td>8 ANSBURY DR</td>\n",
       "      <td>Repeat Residential</td>\n",
       "      <td>0.104394</td>\n",
       "      <td>JACOB SHOOK PARK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.696703</td>\n",
       "      <td>-79.812413</td>\n",
       "      <td>142 BONISTEL CRES</td>\n",
       "      <td>Repeat Residential</td>\n",
       "      <td>0.021863</td>\n",
       "      <td>MCMICKING PARK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.681749</td>\n",
       "      <td>-79.822541</td>\n",
       "      <td>21 LEAGATE ST</td>\n",
       "      <td>Repeat Residential</td>\n",
       "      <td>0.185206</td>\n",
       "      <td>FLETCHERS PARKETTE</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201640</th>\n",
       "      <td>43.798054</td>\n",
       "      <td>-79.669785</td>\n",
       "      <td>2-4 BADDOW RD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.041007</td>\n",
       "      <td>HERZIG VALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201641</th>\n",
       "      <td>43.705644</td>\n",
       "      <td>-79.827955</td>\n",
       "      <td>2-19 EDENVALLEY RD</td>\n",
       "      <td>None</td>\n",
       "      <td>0.073532</td>\n",
       "      <td>MATTHEW CATION PARK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201642</th>\n",
       "      <td>43.711522</td>\n",
       "      <td>-79.758810</td>\n",
       "      <td>2-40 ROYAL SALISBURY WAY</td>\n",
       "      <td>None</td>\n",
       "      <td>0.046871</td>\n",
       "      <td>ROYAL SALISBURY PARKETTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201643</th>\n",
       "      <td>43.770844</td>\n",
       "      <td>-79.682609</td>\n",
       "      <td>2-20 VINTONRIDGE DR</td>\n",
       "      <td>None</td>\n",
       "      <td>0.085403</td>\n",
       "      <td>UPPERLINKS PARKETTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201644</th>\n",
       "      <td>43.781615</td>\n",
       "      <td>-79.677452</td>\n",
       "      <td>2-106 SAINT GRACE CRT</td>\n",
       "      <td>None</td>\n",
       "      <td>0.023135</td>\n",
       "      <td>RIVER HEIGHTS VALLEY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201645 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         LATITUDE  LONGITUDE              FULL_ADDRESS         PERMIT_TYPE  \\\n",
       "0       43.711492 -79.823719              4 ANSBURY DR  Repeat Residential   \n",
       "1       43.711533 -79.823863              6 ANSBURY DR                None   \n",
       "2       43.711575 -79.824015              8 ANSBURY DR  Repeat Residential   \n",
       "3       43.696703 -79.812413         142 BONISTEL CRES  Repeat Residential   \n",
       "4       43.681749 -79.822541             21 LEAGATE ST  Repeat Residential   \n",
       "...           ...        ...                       ...                 ...   \n",
       "201640  43.798054 -79.669785             2-4 BADDOW RD                None   \n",
       "201641  43.705644 -79.827955        2-19 EDENVALLEY RD                None   \n",
       "201642  43.711522 -79.758810  2-40 ROYAL SALISBURY WAY                None   \n",
       "201643  43.770844 -79.682609       2-20 VINTONRIDGE DR                None   \n",
       "201644  43.781615 -79.677452     2-106 SAINT GRACE CRT                None   \n",
       "\n",
       "        Nearest_park_distance         Nearest_park_name  \n",
       "0                    0.125074          JACOB SHOOK PARK  \n",
       "1                    0.114713          JACOB SHOOK PARK  \n",
       "2                    0.104394          JACOB SHOOK PARK  \n",
       "3                    0.021863            MCMICKING PARK  \n",
       "4                    0.185206        FLETCHERS PARKETTE  \n",
       "...                       ...                       ...  \n",
       "201640               0.041007             HERZIG VALLEY  \n",
       "201641               0.073532       MATTHEW CATION PARK  \n",
       "201642               0.046871  ROYAL SALISBURY PARKETTE  \n",
       "201643               0.085403       UPPERLINKS PARKETTE  \n",
       "201644               0.023135      RIVER HEIGHTS VALLEY  \n",
       "\n",
       "[201645 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address_point_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "994ea9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    201645.000000\n",
       "mean          0.145422\n",
       "std           0.136764\n",
       "min           0.000137\n",
       "25%           0.062656\n",
       "50%           0.113938\n",
       "75%           0.185928\n",
       "max           3.667022\n",
       "Name: Nearest_park_distance, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address_point_df['Nearest_park_distance'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d257375",
   "metadata": {},
   "source": [
    "There are 3 major sources of error.\n",
    "\n",
    "1. Distance numbers are misleading because although a park might be some distance away, this is a point-to-point distance and does not represent the distance that a citizen would have to walk to get to a park from their house (sidewalks do not exist as straight paths from any house to any park).\n",
    "2. Using coordinates to calculate distances means that both parks and houses are treated as points, rather than large areas. This was ameliorated somewhat by treating parks as a set of points (polygons) and calculating the distance from a house to the nearest park as the distance from a house to the nearest point of a park polygon.\n",
    "3. As mentioned earlier, many of the address points used are not necessarily residential address points, but they nonetheless are assumed to represent houses.\n",
    "\n",
    "On average, I found the address points considered are about 145 meters away from a park. And 75% of address points considered are less than 186 meters away from a park. Of course, the smaller the numbers the better, but these numbers seem reasonable. Again point #1 needs to be considered. Taking a look at a histogram of how far the address points considered are from their closest park:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2657566e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Houses')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('xtick', labelsize=12)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=12)    # fontsize of the tick labels\n",
    "\n",
    "bin_edges = np.linspace(0.0, 1.8, 37)\n",
    "address_point_df['Nearest_park_distance'].plot(kind = 'hist', figsize = (18, 8), xticks=bin_edges, bins = bin_edges, xlim=(0, 1.2),\n",
    "                                        alpha=0.75, rot=30)\n",
    "\n",
    "plt.title('How Far are Houses from their Closest Park?', fontsize = 16)\n",
    "plt.xlabel(\"Distance from Closest Park (km)\", fontsize = 16)\n",
    "plt.ylabel(\"Number of Houses\", fontsize = 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc2a94f4",
   "metadata": {},
   "source": [
    "There seem to be many address points that are a sufficiently small distance away from a park (again though, the smaller the better). However, one thing to note in the histogram is that it is right-skewed with a steady decline in frequency after the peak. There are many address points in the analysis that are disproportionately far from a park and this needs to be further looked into in the map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7744474e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# makes it easier to plot parks as polygons\n",
    "\n",
    "from shapely.geometry import Polygon\n",
    "def make_polygon(row):\n",
    "    if len(row[\"COORDINATES_LIST\"]) == 1:\n",
    "        return Polygon([tuple(l) for l in row[\"COORDINATES_LIST\"][0]])\n",
    "    return Polygon([tuple(l) for l in row[\"COORDINATES_LIST\"]])\n",
    "parks_df[\"GEOMETRY\"] = parks_df.apply(make_polygon, axis=1)\n",
    "parks_df = parks_df.drop(\"COORDINATES_LIST\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a1e72380",
   "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>PARK_NAME</th>\n",
       "      <th>PARK_STATUS</th>\n",
       "      <th>PARK_TYPE</th>\n",
       "      <th>AREA_HECTARES</th>\n",
       "      <th>GEOMETRY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>THATCHER PARKETTE</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.067</td>\n",
       "      <td>POLYGON ((-79.798597 43.731194, -79.798323 43....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KINDLE PARKETTE</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.173</td>\n",
       "      <td>POLYGON ((-79.801502 43.727685, -79.801124 43....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUNCREST PARKETTE</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.370</td>\n",
       "      <td>POLYGON ((-79.790451 43.693514, -79.790178 43....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JORGEN JENSEN PARKETTE</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.091</td>\n",
       "      <td>POLYGON ((-79.792607 43.728372, -79.7923810000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RC CHARLTON PARK</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>0.791</td>\n",
       "      <td>POLYGON ((-79.76023600000001 43.648314, -79.75...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>ANDREW McCANDLESS VALLEY</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>ENVIRONMENTAL PARKS</td>\n",
       "      <td>14.980</td>\n",
       "      <td>POLYGON ((-79.819136 43.665396, -79.8188119999...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>SAM RAYSON VALLEY</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>ENVIRONMENTAL PARKS</td>\n",
       "      <td>16.571</td>\n",
       "      <td>POLYGON ((-79.70647599999999 43.674182, -79.70...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>ORNSTOCK POND EAST</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>STORMWATER MANAGEMENT PONDS</td>\n",
       "      <td>4.653</td>\n",
       "      <td>POLYGON ((-79.776888 43.626901, -79.776914 43....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>ALLAN KERBEL POND</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>STORMWATER MANAGEMENT PONDS</td>\n",
       "      <td>1.066</td>\n",
       "      <td>POLYGON ((-79.777856 43.705068, -79.777843 43....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>ALLAN KERBEL PARK</td>\n",
       "      <td>FINAL ACCEPTANCE</td>\n",
       "      <td>NEIGHBOURHOOD PARKS</td>\n",
       "      <td>3.332</td>\n",
       "      <td>POLYGON ((-79.777697 43.705215, -79.777856 43....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>820 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    PARK_NAME       PARK_STATUS                    PARK_TYPE  \\\n",
       "0           THATCHER PARKETTE  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "1             KINDLE PARKETTE  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "2           SUNCREST PARKETTE  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "3      JORGEN JENSEN PARKETTE  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "4            RC CHARLTON PARK  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "..                        ...               ...                          ...   \n",
       "815  ANDREW McCANDLESS VALLEY  FINAL ACCEPTANCE          ENVIRONMENTAL PARKS   \n",
       "816         SAM RAYSON VALLEY  FINAL ACCEPTANCE          ENVIRONMENTAL PARKS   \n",
       "817        ORNSTOCK POND EAST  FINAL ACCEPTANCE  STORMWATER MANAGEMENT PONDS   \n",
       "818         ALLAN KERBEL POND  FINAL ACCEPTANCE  STORMWATER MANAGEMENT PONDS   \n",
       "819         ALLAN KERBEL PARK  FINAL ACCEPTANCE          NEIGHBOURHOOD PARKS   \n",
       "\n",
       "     AREA_HECTARES                                           GEOMETRY  \n",
       "0            0.067  POLYGON ((-79.798597 43.731194, -79.798323 43....  \n",
       "1            0.173  POLYGON ((-79.801502 43.727685, -79.801124 43....  \n",
       "2            0.370  POLYGON ((-79.790451 43.693514, -79.790178 43....  \n",
       "3            0.091  POLYGON ((-79.792607 43.728372, -79.7923810000...  \n",
       "4            0.791  POLYGON ((-79.76023600000001 43.648314, -79.75...  \n",
       "..             ...                                                ...  \n",
       "815         14.980  POLYGON ((-79.819136 43.665396, -79.8188119999...  \n",
       "816         16.571  POLYGON ((-79.70647599999999 43.674182, -79.70...  \n",
       "817          4.653  POLYGON ((-79.776888 43.626901, -79.776914 43....  \n",
       "818          1.066  POLYGON ((-79.777856 43.705068, -79.777843 43....  \n",
       "819          3.332  POLYGON ((-79.777697 43.705215, -79.777856 43....  \n",
       "\n",
       "[820 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parks_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3f92ec2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# exporting dataframes to generate map\n",
    "address_point_df.to_pickle(\"address_point_df.pkl\")\n",
    "parks_df.to_pickle(\"parks_df.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6478559e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4462084f",
   "metadata": {},
   "source": [
    "## Citations\n",
    "   \n",
    "City of Brampton. (2021). *Address Points*. Retrieved October 3, 2021 from https://geohub.brampton.ca/datasets/address-points   \n",
    "  \n",
    "City of Brampton. (2019). *Census profile: Population and dwelling*. Brampton's GeoHub. Retrieved October 11, 2021, from https://geohub.brampton.ca/pages/profile-pop-dwelling   \n",
    "   \n",
    "City of Brampton. (2021). *Parks*. Retrieved October 3, 2021 from https://geohub.brampton.ca/datasets/parks\n",
    "\n",
    "Glazier, R.H., Booth, G. L., Dunn, J. R., Polsky, J. Y., Weyman, J. T., Tynan, A. M., Creatore, M. I., Gozdyra, P. (2014). *Diabetes Atlas for the Region of Peel*. Peel Public Health. Retrieved from https://www.peelregion.ca/health/resources/diabetes-atlas/full-report.pdf\n",
    "\n",
    "Oregon State University. (2005). Study Links Obesity, Urban Sprawl. *ScienceDaily*. Retrieved from www.sciencedaily.com/releases/2005/09/050909220354.htm\n",
    "    \n",
    "Sturm, R., & Cohen, D. (2014). Proximity to urban parks and mental health. *The journal of mental health policy and economics*, 17(1), 19–24."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eacde78",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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