Segmenting and Clustering Neighborhoods in Toronto Public.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <b>Segmenting and Clustering Neighborhoods in Toronto</b>\n",
    "<br>In this notebook I am working on the assignment for week 3 of the IBM's [Applied Data Science Capstone](https://www.coursera.org/learn/applied-data-science-capstone) Course on Coursera. <br>\n",
    "There are three major tasks to perform. <br>\n",
    "In [Task 1](#Task-1---Generating-the-Data-Frame) Toronto neighborhood data is imported into a pandas data frame from a wikipedia.<br>\n",
    "In [Task 2](#Task-2---Adding-Coordinates-to-Neighborhood-Data) we use geocoder to add geospacial coordinates to each neighborhood.<br>\n",
    "In [Task 3](#Task-3---Exploring-and-Clustering-Neighborhoods-in-Toronto) we use Foursquare to explore the neighborhoods and cluster them according to some characteristics. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <b>Task 1 - Generating the Data Frame</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am going to use the pandas to read the table into my dataframe. I looked up how to do it in this [video](https://www.youtube.com/watch?v=D8T1epVAx-4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "wikitable = pd.read_html('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M')\n",
    "len(wikitable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there were actually 3 tables imported from the html. We can easily see that the first one is the one we want:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code           Borough              Neighbourhood\n",
       "0         M1A      Not assigned               Not assigned\n",
       "1         M2A      Not assigned               Not assigned\n",
       "2         M3A        North York                  Parkwoods\n",
       "3         M4A        North York           Victoria Village\n",
       "4         M5A  Downtown Toronto  Regent Park, Harbourfront"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wikitable[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create pandas data frame called df_postal. We only use the rows with actual Boroughs asigned to the postal code.\n",
    "df_postal=wikitable[0][wikitable[0]['Borough']!='Not assigned']# we only use the rows which have assigned boroughs. Therefore we conditionally select the rows by using !='Not assigned'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code           Borough                                Neighbourhood\n",
       "2         M3A        North York                                    Parkwoods\n",
       "3         M4A        North York                             Victoria Village\n",
       "4         M5A  Downtown Toronto                    Regent Park, Harbourfront\n",
       "5         M6A        North York             Lawrence Manor, Lawrence Heights\n",
       "6         M7A  Downtown Toronto  Queen's Park, Ontario Provincial Government"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_postal.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we deleted some rows, the index is not correct anymore. Therefore we want to reset it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code           Borough                                Neighbourhood\n",
       "0         M3A        North York                                    Parkwoods\n",
       "1         M4A        North York                             Victoria Village\n",
       "2         M5A  Downtown Toronto                    Regent Park, Harbourfront\n",
       "3         M6A        North York             Lawrence Manor, Lawrence Heights\n",
       "4         M7A  Downtown Toronto  Queen's Park, Ontario Provincial Government"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_postal.reset_index(drop=True, inplace=True) #drop=True deletes the current index, False would leave it as a new row.\n",
    "df_postal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_postal.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <b>Task 2 - Adding Coordinates to Neighborhood Data<b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geocoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As stated in the discussion [here](https://www.coursera.org/learn/applied-data-science-capstone/discussions/forums/R0oydmJfEeiMwRKU7H4Mng/threads/mPXvayChEeuDNgpyJIMFiQ), geocoder.google does not work. However geocoder.arcgis does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we create two separate lists to store our Latitude an Longitude values.\n",
    "Latitude=[] \n",
    "Longitude=[]\n",
    "for i in range(len(df_postal)): #our dataframe consists of 103 rows. We iterate through all of them to geht our Postal Code.\n",
    "    g = geocoder.arcgis('{}, Toronto, Ontario'.format(df_postal.loc[i,'Postal Code']))\n",
    "    lat_lng_coords = g.latlng\n",
    "    Latitude.append(lat_lng_coords[0]) #and put the coordinates in lists.\n",
    "    Longitude.append(lat_lng_coords[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-84-fc5a9cbd9abe>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_postal['Latitude']=Latitude\n",
      "<ipython-input-84-fc5a9cbd9abe>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_postal['Longitude']=Longitude\n"
     ]
    },
    {
     "data": {
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       "    }\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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.75245</td>\n",
       "      <td>-79.32991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.73057</td>\n",
       "      <td>-79.31306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "      <td>43.65512</td>\n",
       "      <td>-79.36264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "      <td>43.72327</td>\n",
       "      <td>-79.45042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "      <td>43.66253</td>\n",
       "      <td>-79.39188</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>98</th>\n",
       "      <td>M8X</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>The Kingsway, Montgomery Road, Old Mill North</td>\n",
       "      <td>43.65319</td>\n",
       "      <td>-79.51113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M4Y</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Church and Wellesley</td>\n",
       "      <td>43.66659</td>\n",
       "      <td>-79.38133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>M7Y</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Business reply mail Processing Centre, South C...</td>\n",
       "      <td>43.64869</td>\n",
       "      <td>-79.38544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M8Y</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Old Mill South, King's Mill Park, Sunnylea, Hu...</td>\n",
       "      <td>43.63278</td>\n",
       "      <td>-79.48945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
       "      <td>43.62513</td>\n",
       "      <td>-79.52681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Postal Code           Borough  \\\n",
       "0           M3A        North York   \n",
       "1           M4A        North York   \n",
       "2           M5A  Downtown Toronto   \n",
       "3           M6A        North York   \n",
       "4           M7A  Downtown Toronto   \n",
       "..          ...               ...   \n",
       "98          M8X         Etobicoke   \n",
       "99          M4Y  Downtown Toronto   \n",
       "100         M7Y      East Toronto   \n",
       "101         M8Y         Etobicoke   \n",
       "102         M8Z         Etobicoke   \n",
       "\n",
       "                                         Neighbourhood  Latitude  Longitude  \n",
       "0                                            Parkwoods  43.75245  -79.32991  \n",
       "1                                     Victoria Village  43.73057  -79.31306  \n",
       "2                            Regent Park, Harbourfront  43.65512  -79.36264  \n",
       "3                     Lawrence Manor, Lawrence Heights  43.72327  -79.45042  \n",
       "4          Queen's Park, Ontario Provincial Government  43.66253  -79.39188  \n",
       "..                                                 ...       ...        ...  \n",
       "98       The Kingsway, Montgomery Road, Old Mill North  43.65319  -79.51113  \n",
       "99                                Church and Wellesley  43.66659  -79.38133  \n",
       "100  Business reply mail Processing Centre, South C...  43.64869  -79.38544  \n",
       "101  Old Mill South, King's Mill Park, Sunnylea, Hu...  43.63278  -79.48945  \n",
       "102  Mimico NW, The Queensway West, South of Bloor,...  43.62513  -79.52681  \n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we add the lists as columns to our dataframe\n",
    "df_postal['Latitude']=Latitude\n",
    "df_postal['Longitude']=Longitude\n",
    "df_postal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Important Note</b>: ARCGIS gives slightly different coordinates than goolge. But they are in a walking distance, so they represent the same neighborhoods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <b>Task 3 - Exploring and Clustering Neighborhoods in Toronto</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's import all dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "from pandas.io.json import json_normalize\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "from sklearn.cluster import KMeans\n",
    "import folium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use Foursquare. First our credientials, version and limit settings. The CLIENT_ID and CLIENT_SECRET should not be visiable in the public version of this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = 'YOURCLIENTID' \n",
    "CLIENT_SECRET = 'YOURCLIENTSECRET'\n",
    "VERSION = '20201212' # Foursquare API version\n",
    "LIMIT = 100 # A default Foursquare API limit value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the function provided in the [lab on clustering neighborhoods in New York](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/labs_v1/DS0701EN-3-3-2-Neighborhoods-New-York-py-v2.0.ipynb) to explore our neighborhoods. We want to extract the category of venues found in our neighborhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use the function getNearbyVenues() and put the results into the dataframe Toronto_venues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Toronto_venues = getNearbyVenues(names=df_postal['Neighbourhood'],\n",
    "                                   latitudes=df_postal['Latitude'],\n",
    "                                   longitudes=df_postal['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many venues we found overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2405, 7)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_venues.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2405 rows in the data frame, which means Foursquare found 2405 venues overall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering Neighborhoods in all of Toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we examine the venue categories, there is actually a category called \"Neighborhood\" - for example the venue \"upper beaches\" has the category \"Neighborhood\". I think this happens, because Forsquere treats whole interesting neighborhoods as venues. However we don't want that, so we drop all the rows which entail 'Neighborhood' in the 'Venue Category' column from Toronto_venues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2399, 7)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_venues= Toronto_venues.drop(Toronto_venues[Toronto_venues['Venue Category']=='Neighborhood'].index)\n",
    "Toronto_venues.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that 6 rows were dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the dummy variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Neighborhood</th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>Afghan Restaurant</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Antique Shop</th>\n",
       "      <th>Aquarium</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Art Museum</th>\n",
       "      <th>Arts &amp; Crafts Store</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>...</th>\n",
       "      <th>Veterinarian</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Video Store</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Wine Shop</th>\n",
       "      <th>Wings Joint</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Yoga Studio</th>\n",
       "      <th>Zoo Exhibit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 270 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighborhood  Accessories Store  Afghan Restaurant  \\\n",
       "0         Parkwoods                  0                  0   \n",
       "1         Parkwoods                  0                  0   \n",
       "2  Victoria Village                  0                  0   \n",
       "3  Victoria Village                  0                  0   \n",
       "4  Victoria Village                  0                  0   \n",
       "\n",
       "   American Restaurant  Antique Shop  Aquarium  Art Gallery  Art Museum  \\\n",
       "0                    0             0         0            0           0   \n",
       "1                    0             0         0            0           0   \n",
       "2                    0             0         0            0           0   \n",
       "3                    0             0         0            0           0   \n",
       "4                    0             0         0            0           0   \n",
       "\n",
       "   Arts & Crafts Store  Asian Restaurant  ...  Veterinarian  Video Game Store  \\\n",
       "0                    0                 0  ...             0                 0   \n",
       "1                    0                 0  ...             0                 0   \n",
       "2                    0                 0  ...             0                 0   \n",
       "3                    0                 0  ...             0                 0   \n",
       "4                    0                 0  ...             0                 0   \n",
       "\n",
       "   Video Store  Vietnamese Restaurant  Wine Bar  Wine Shop  Wings Joint  \\\n",
       "0            0                      0         0          0            0   \n",
       "1            0                      0         0          0            0   \n",
       "2            0                      0         0          0            0   \n",
       "3            0                      0         0          0            0   \n",
       "4            0                      0         0          0            0   \n",
       "\n",
       "   Women's Store  Yoga Studio  Zoo Exhibit  \n",
       "0              0            0            0  \n",
       "1              0            0            0  \n",
       "2              0            0            0  \n",
       "3              0            0            0  \n",
       "4              0            0            0  \n",
       "\n",
       "[5 rows x 270 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "Toronto_onehot = pd.get_dummies(Toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "Toronto_onehot['Neighborhood'] = Toronto_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [Toronto_onehot.columns[-1]] + list(Toronto_onehot.columns[:-1])\n",
    "Toronto_onehot = Toronto_onehot[fixed_columns]\n",
    "\n",
    "Toronto_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2399, 270)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_onehot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we have 269 different venue categories in our dataset. 270 columns including the Neighborhood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want each neighborhood to be one row, so we group them together and use the mean of each venue category as value for the category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Neighborhood</th>\n",
       "      <th>Accessories Store</th>\n",
       "      <th>Afghan Restaurant</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Antique Shop</th>\n",
       "      <th>Aquarium</th>\n",
       "      <th>Art Gallery</th>\n",
       "      <th>Art Museum</th>\n",
       "      <th>Arts &amp; Crafts Store</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>...</th>\n",
       "      <th>Veterinarian</th>\n",
       "      <th>Video Game Store</th>\n",
       "      <th>Video Store</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Wine Shop</th>\n",
       "      <th>Wings Joint</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Yoga Studio</th>\n",
       "      <th>Zoo Exhibit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agincourt</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alderwood, Long Branch</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bayview Village</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bedford Park, Lawrence Manor East</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Willowdale, Willowdale West</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Woodbine Heights</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>York Mills West</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>York Mills, Silver Hills</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 270 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Neighborhood  Accessories Store  Afghan Restaurant  \\\n",
       "0                           Agincourt                0.0                0.0   \n",
       "1              Alderwood, Long Branch                0.0                0.0   \n",
       "2                     Bayview Village                0.0                0.0   \n",
       "3   Bedford Park, Lawrence Manor East                0.0                0.0   \n",
       "4                         Berczy Park                0.0                0.0   \n",
       "..                                ...                ...                ...   \n",
       "91        Willowdale, Willowdale West                0.0                0.0   \n",
       "92                             Woburn                0.0                0.0   \n",
       "93                   Woodbine Heights                0.0                0.0   \n",
       "94                    York Mills West                0.0                0.0   \n",
       "95           York Mills, Silver Hills                0.0                0.0   \n",
       "\n",
       "    American Restaurant  Antique Shop  Aquarium  Art Gallery  Art Museum  \\\n",
       "0                   0.0      0.000000       0.0     0.000000         0.0   \n",
       "1                   0.0      0.000000       0.0     0.000000         0.0   \n",
       "2                   0.0      0.000000       0.0     0.000000         0.0   \n",
       "3                   0.0      0.000000       0.0     0.000000         0.0   \n",
       "4                   0.0      0.016667       0.0     0.016667         0.0   \n",
       "..                  ...           ...       ...          ...         ...   \n",
       "91                  0.0      0.000000       0.0     0.000000         0.0   \n",
       "92                  0.0      0.000000       0.0     0.000000         0.0   \n",
       "93                  0.0      0.000000       0.0     0.000000         0.0   \n",
       "94                  0.0      0.000000       0.0     0.000000         0.0   \n",
       "95                  0.0      0.000000       0.0     0.000000         0.0   \n",
       "\n",
       "    Arts & Crafts Store  Asian Restaurant  ...  Veterinarian  \\\n",
       "0              0.000000               0.0  ...           0.0   \n",
       "1              0.000000               0.0  ...           0.0   \n",
       "2              0.000000               0.0  ...           0.0   \n",
       "3              0.000000               0.0  ...           0.0   \n",
       "4              0.000000               0.0  ...           0.0   \n",
       "..                  ...               ...  ...           ...   \n",
       "91             0.000000               0.0  ...           0.0   \n",
       "92             0.000000               0.0  ...           0.0   \n",
       "93             0.055556               0.0  ...           0.0   \n",
       "94             0.000000               0.0  ...           0.0   \n",
       "95             0.000000               0.0  ...           0.0   \n",
       "\n",
       "    Video Game Store  Video Store  Vietnamese Restaurant  Wine Bar  Wine Shop  \\\n",
       "0                0.0          0.0                 0.0625       0.0        0.0   \n",
       "1                0.0          0.0                 0.0000       0.0        0.0   \n",
       "2                0.0          0.0                 0.0000       0.0        0.0   \n",
       "3                0.0          0.0                 0.0000       0.0        0.0   \n",
       "4                0.0          0.0                 0.0000       0.0        0.0   \n",
       "..               ...          ...                    ...       ...        ...   \n",
       "91               0.0          0.0                 0.0000       0.0        0.0   \n",
       "92               0.0          0.0                 0.0000       0.0        0.0   \n",
       "93               0.0          0.0                 0.0000       0.0        0.0   \n",
       "94               0.0          0.0                 0.0000       0.0        0.0   \n",
       "95               0.0          0.0                 0.0000       0.0        0.0   \n",
       "\n",
       "    Wings Joint  Women's Store  Yoga Studio  Zoo Exhibit  \n",
       "0           0.0            0.0     0.000000          0.0  \n",
       "1           0.0            0.0     0.000000          0.0  \n",
       "2           0.0            0.0     0.000000          0.0  \n",
       "3           0.0            0.0     0.000000          0.0  \n",
       "4           0.0            0.0     0.016667          0.0  \n",
       "..          ...            ...          ...          ...  \n",
       "91          0.0            0.0     0.000000          0.0  \n",
       "92          0.0            0.0     0.000000          0.0  \n",
       "93          0.0            0.0     0.000000          0.0  \n",
       "94          0.0            0.0     0.000000          0.0  \n",
       "95          0.0            0.0     0.000000          0.0  \n",
       "\n",
       "[96 rows x 270 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_grouped = Toronto_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "Toronto_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below prints out the top 5 venue-categories for each neighborhood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's put them in a pandas data frame, so we can apply clustering later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we take a function from the [New York lab](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/labs_v1/DS0701EN-3-3-2-Neighborhoods-New-York-py-v2.0.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the new dataframe and display the top 5 venues for each neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agincourt</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Hong Kong Restaurant</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Shopping Mall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alderwood, Long Branch</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Field</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bayview Village</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Golf Driving Range</td>\n",
       "      <td>Trail</td>\n",
       "      <td>Park</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bedford Park, Lawrence Manor East</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "      <td>Pub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berczy Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Cheese Shop</td>\n",
       "      <td>Cocktail Bar</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Beer Bar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Neighborhood       1st Most Common Venue  \\\n",
       "0                          Agincourt          Chinese Restaurant   \n",
       "1             Alderwood, Long Branch           Convenience Store   \n",
       "2                    Bayview Village  Construction & Landscaping   \n",
       "3  Bedford Park, Lawrence Manor East              Sandwich Place   \n",
       "4                        Berczy Park                 Coffee Shop   \n",
       "\n",
       "  2nd Most Common Venue  3rd Most Common Venue    4th Most Common Venue  \\\n",
       "0  Hong Kong Restaurant         Discount Store             Skating Rink   \n",
       "1                   Pub  Performing Arts Venue                      Gym   \n",
       "2    Golf Driving Range                  Trail                     Park   \n",
       "3    Italian Restaurant            Coffee Shop  Comfort Food Restaurant   \n",
       "4           Cheese Shop           Cocktail Bar           Breakfast Spot   \n",
       "\n",
       "  5th Most Common Venue  \n",
       "0         Shopping Mall  \n",
       "1                 Field  \n",
       "2  Ethiopian Restaurant  \n",
       "3                   Pub  \n",
       "4              Beer Bar  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = Toronto_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(Toronto_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(Toronto_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the moment we have been waiting for. We can apply machine learning to <b>cluster our neighborhoods</b>!\n",
    "<br>I will use all of Toronto for clustering. I want to try something that was not part of the labs. I want to use the elbow method as descibed in this [video](https://www.coursera.org/learn/machine-learning-with-python/lecture/rLcgP/more-on-k-means) to determine the number of clusters. So we are going to run the clustering algorithm multiple times and calculate the model accuracy each time. We are going to plot the accuracy levels against the number of clusters and finally look if there is an elbow point - the number of keys where the increase in the model accuracy does not increase much anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "Toronto_grouped_clustering = Toronto_grouped.drop('Neighborhood', 1)\n",
    "# set number of clusters\n",
    "inertia=[]\n",
    "ks=[]\n",
    "for k in range(1, 11): #we try 10 different values for k.\n",
    "    # run k-means clustering\n",
    "    k_means = KMeans(init = \"k-means++\", n_clusters = k, n_init = 20)\n",
    "    k_means.fit(Toronto_grouped_clustering)\n",
    "    # determine the model-accuracy\n",
    "    inertia.append( k_means.inertia_) #inertia is the Sum of squared distances of samples to their closest cluster center. We use this as a badness of fit meassure.\n",
    "    ks.append(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a simple line plot to visualize inertia as dependent of k. Inertia is a value that tells us how close a point is to its centroid. That means we want our inertia to be as small as possible. However since inertia always decreases with higher ks we also don't want too many clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(17, 10))\n",
    "plt.style.use('ggplot')\n",
    "plt.plot(ks, inertia)\n",
    "plt.xlabel('Numbuer of Clusters', size=16)\n",
    "plt.ylabel('Inertia', size=16)\n",
    "plt.title('Mean Distance of Neighborhoods to Clusters, Dependent on Number of Clusters', size=16)\n",
    "plt.xticks(np.arange(min(ks), max(ks)+1, 1.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that k=2 clusters reduce the inertia quite alot. Adding a third cluster only has half of the effect of the second cluster. Adding more clusters has the same effect. We will try clustering with only two clusters and then see what changes, when we increase the number of clusters later on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2, n_init=20)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using k=2.\n",
    "k=2\n",
    "k_means = KMeans(init = \"k-means++\", n_clusters = k, n_init = 20)\n",
    "k_means.fit(Toronto_grouped_clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check cluster labels generated for each row in the dataframe\n",
    "k_means.labels_[0:100] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\lib\\site-packages\\pandas\\core\\frame.py:4300: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().rename(\n"
     ]
    },
    {
     "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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.75245</td>\n",
       "      <td>-79.32991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Elementary School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.73057</td>\n",
       "      <td>-79.31306</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Park</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "      <td>43.65512</td>\n",
       "      <td>-79.36264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Distribution Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "      <td>43.72327</td>\n",
       "      <td>-79.45042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Women's Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "      <td>43.66253</td>\n",
       "      <td>-79.39188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Burrito Place</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code           Borough                                 Neighborhood  \\\n",
       "0         M3A        North York                                    Parkwoods   \n",
       "1         M4A        North York                             Victoria Village   \n",
       "2         M5A  Downtown Toronto                    Regent Park, Harbourfront   \n",
       "3         M6A        North York             Lawrence Manor, Lawrence Heights   \n",
       "4         M7A  Downtown Toronto  Queen's Park, Ontario Provincial Government   \n",
       "\n",
       "   Latitude  Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "0  43.75245  -79.32991             1.0     Food & Drink Shop   \n",
       "1  43.73057  -79.31306             1.0         Grocery Store   \n",
       "2  43.65512  -79.36264             0.0           Coffee Shop   \n",
       "3  43.72327  -79.45042             0.0        Clothing Store   \n",
       "4  43.66253  -79.39188             0.0           Coffee Shop   \n",
       "\n",
       "    2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0                    Park    Falafel Restaurant     Electronics Store   \n",
       "1                    Park              Pharmacy           Zoo Exhibit   \n",
       "2          Breakfast Spot                   Pub     Electronics Store   \n",
       "3  Furniture / Home Store           Coffee Shop      Toy / Game Store   \n",
       "4          Sandwich Place         Burrito Place    Falafel Restaurant   \n",
       "\n",
       "  5th Most Common Venue  \n",
       "0     Elementary School  \n",
       "1     Electronics Store  \n",
       "2   Distribution Center  \n",
       "3         Women's Store  \n",
       "4                  Bank  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', k_means.labels_)\n",
    "\n",
    "df_postal.rename(columns={\"Neighbourhood\": \"Neighborhood\"},inplace=True) #We have different spellings of neighborhood, so we have to adjust.\n",
    "Toronto_merged = df_postal\n",
    "\n",
    "# merge Toronto_grouped with Toronto_data to add latitude/longitude for each neighborhood\n",
    "Toronto_merged = Toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "Toronto_merged.head() # check the cloumn 'Cluster Labels'!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0., nan])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_merged['Cluster Labels'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we have nan values. We will have to deal with them when we create the map. Look at the comments in the code below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the New York lab, here the map of the clusters with folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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            var circle_marker_0b6ec65aa04d4707a461abde6767805e = L.circleMarker(
                [43.78574000000003, -79.15874999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_cf5267d195e64f6c888fe8597bad1275 = L.popup({"maxWidth": "100%"});

        
            var html_028cbe6793424093a72042c270547e2d = $(`<div id="html_028cbe6793424093a72042c270547e2d" style="width: 100.0%; height: 100.0%;">Rouge Hill, Port Union, Highland Creek Cluster 0.0</div>`)[0];
            popup_cf5267d195e64f6c888fe8597bad1275.setContent(html_028cbe6793424093a72042c270547e2d);
        

        circle_marker_0b6ec65aa04d4707a461abde6767805e.bindPopup(popup_cf5267d195e64f6c888fe8597bad1275)
        ;

        
    
    
            var circle_marker_78d9af788a6c42ed885f502bbcd40e08 = L.circleMarker(
                [43.72168000000005, -79.34351999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_2cafc7031c4c4fbdbd1b931b658e7431 = L.popup({"maxWidth": "100%"});

        
            var html_3149b72c398b4cd39ccd3746ac538888 = $(`<div id="html_3149b72c398b4cd39ccd3746ac538888" style="width: 100.0%; height: 100.0%;">Don Mills Cluster 0.0</div>`)[0];
            popup_2cafc7031c4c4fbdbd1b931b658e7431.setContent(html_3149b72c398b4cd39ccd3746ac538888);
        

        circle_marker_78d9af788a6c42ed885f502bbcd40e08.bindPopup(popup_2cafc7031c4c4fbdbd1b931b658e7431)
        ;

        
    
    
            var circle_marker_f4701ea0d55947c9b701c8dd5359e439 = L.circleMarker(
                [43.68970000000007, -79.30681999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_6af5b091e38a4fc3933da0cb24a8f93f = L.popup({"maxWidth": "100%"});

        
            var html_d9f26e35ed4d45018cb3f1eab93eefa9 = $(`<div id="html_d9f26e35ed4d45018cb3f1eab93eefa9" style="width: 100.0%; height: 100.0%;">Woodbine Heights Cluster 0.0</div>`)[0];
            popup_6af5b091e38a4fc3933da0cb24a8f93f.setContent(html_d9f26e35ed4d45018cb3f1eab93eefa9);
        

        circle_marker_f4701ea0d55947c9b701c8dd5359e439.bindPopup(popup_6af5b091e38a4fc3933da0cb24a8f93f)
        ;

        
    
    
            var circle_marker_c3e5c7e66d564c17bd84870213e19ead = L.circleMarker(
                [43.65215000000006, -79.37586999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_f88ce7bc1d0c41d0b42a738ed3f11320 = L.popup({"maxWidth": "100%"});

        
            var html_2d703b7458b84eecbb6f08f1d83809c1 = $(`<div id="html_2d703b7458b84eecbb6f08f1d83809c1" style="width: 100.0%; height: 100.0%;">St. James Town Cluster 0.0</div>`)[0];
            popup_f88ce7bc1d0c41d0b42a738ed3f11320.setContent(html_2d703b7458b84eecbb6f08f1d83809c1);
        

        circle_marker_c3e5c7e66d564c17bd84870213e19ead.bindPopup(popup_f88ce7bc1d0c41d0b42a738ed3f11320)
        ;

        
    
    
            var circle_marker_d58cf9a164c74b56b117e618667e4fee = L.circleMarker(
                [43.69211000000007, -79.43035999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_f01d6eb340b644558a217478334af206 = L.popup({"maxWidth": "100%"});

        
            var html_bfdb303dbda441c6990a387f3a26d5dd = $(`<div id="html_bfdb303dbda441c6990a387f3a26d5dd" style="width: 100.0%; height: 100.0%;">Humewood-Cedarvale Cluster 0.0</div>`)[0];
            popup_f01d6eb340b644558a217478334af206.setContent(html_bfdb303dbda441c6990a387f3a26d5dd);
        

        circle_marker_d58cf9a164c74b56b117e618667e4fee.bindPopup(popup_f01d6eb340b644558a217478334af206)
        ;

        
    
    
            var circle_marker_2a2f2c525a29435c92f53406e44a9fe9 = L.circleMarker(
                [43.64857000000006, -79.57824999999997],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_6c15816b81b34b9c90575afedce46af0 = L.popup({"maxWidth": "100%"});

        
            var html_4b45e67f33b34c3280c6a070d096005c = $(`<div id="html_4b45e67f33b34c3280c6a070d096005c" style="width: 100.0%; height: 100.0%;">Eringate, Bloordale Gardens, Old Burnhamthorpe, Markland Wood Cluster 0.0</div>`)[0];
            popup_6c15816b81b34b9c90575afedce46af0.setContent(html_4b45e67f33b34c3280c6a070d096005c);
        

        circle_marker_2a2f2c525a29435c92f53406e44a9fe9.bindPopup(popup_6c15816b81b34b9c90575afedce46af0)
        ;

        
    
    
            var circle_marker_5c983d39cb0d4064922151a564f5b034 = L.circleMarker(
                [43.765750000000025, -79.17469999999997],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_8bbc5d4b674149a09706d7e3dcf5fe97 = L.popup({"maxWidth": "100%"});

        
            var html_ad10dbda5ded40a6ba01af4ecdd65d9e = $(`<div id="html_ad10dbda5ded40a6ba01af4ecdd65d9e" style="width: 100.0%; height: 100.0%;">Guildwood, Morningside, West Hill Cluster 1.0</div>`)[0];
            popup_8bbc5d4b674149a09706d7e3dcf5fe97.setContent(html_ad10dbda5ded40a6ba01af4ecdd65d9e);
        

        circle_marker_5c983d39cb0d4064922151a564f5b034.bindPopup(popup_8bbc5d4b674149a09706d7e3dcf5fe97)
        ;

        
    
    
            var circle_marker_6944d096924c4e76a05dc21da1570224 = L.circleMarker(
                [43.67709000000008, -79.29546999999997],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_cb9f20d372894c52ae6ab66c57bbd5d6 = L.popup({"maxWidth": "100%"});

        
            var html_953568e082d1451184bf8b8ae6cf4141 = $(`<div id="html_953568e082d1451184bf8b8ae6cf4141" style="width: 100.0%; height: 100.0%;">The Beaches Cluster 0.0</div>`)[0];
            popup_cb9f20d372894c52ae6ab66c57bbd5d6.setContent(html_953568e082d1451184bf8b8ae6cf4141);
        

        circle_marker_6944d096924c4e76a05dc21da1570224.bindPopup(popup_cb9f20d372894c52ae6ab66c57bbd5d6)
        ;

        
    
    
            var circle_marker_288bdb8c817a4ea692c95acd63d9fedc = L.circleMarker(
                [43.64536000000004, -79.37305999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_748a9afc57f34f7b9905234a563bc709 = L.popup({"maxWidth": "100%"});

        
            var html_002a5cf84e6a4c93b99424a1507ee65d = $(`<div id="html_002a5cf84e6a4c93b99424a1507ee65d" style="width: 100.0%; height: 100.0%;">Berczy Park Cluster 0.0</div>`)[0];
            popup_748a9afc57f34f7b9905234a563bc709.setContent(html_002a5cf84e6a4c93b99424a1507ee65d);
        

        circle_marker_288bdb8c817a4ea692c95acd63d9fedc.bindPopup(popup_748a9afc57f34f7b9905234a563bc709)
        ;

        
    
    
            var circle_marker_0b732b0a29ba4b04afddcd8b20c3ec0a = L.circleMarker(
                [43.68784000000005, -79.45045999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_dd8109a9b2b347b487b1d489660f0b54 = L.popup({"maxWidth": "100%"});

        
            var html_b9d640ea775c4ed691062aebec7fbf52 = $(`<div id="html_b9d640ea775c4ed691062aebec7fbf52" style="width: 100.0%; height: 100.0%;">Caledonia-Fairbanks Cluster 0.0</div>`)[0];
            popup_dd8109a9b2b347b487b1d489660f0b54.setContent(html_b9d640ea775c4ed691062aebec7fbf52);
        

        circle_marker_0b732b0a29ba4b04afddcd8b20c3ec0a.bindPopup(popup_dd8109a9b2b347b487b1d489660f0b54)
        ;

        
    
    
            var circle_marker_ce555d4ad32a4b818f5f94248ffde3f1 = L.circleMarker(
                [43.76812000000007, -79.21760999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_7a3d8d3a40ad44adb88bddf230139c7e = L.popup({"maxWidth": "100%"});

        
            var html_41a658a18d4f431ea4d128b48ae579fa = $(`<div id="html_41a658a18d4f431ea4d128b48ae579fa" style="width: 100.0%; height: 100.0%;">Woburn Cluster 0.0</div>`)[0];
            popup_7a3d8d3a40ad44adb88bddf230139c7e.setContent(html_41a658a18d4f431ea4d128b48ae579fa);
        

        circle_marker_ce555d4ad32a4b818f5f94248ffde3f1.bindPopup(popup_7a3d8d3a40ad44adb88bddf230139c7e)
        ;

        
    
    
            var circle_marker_47f9f4d53d2b4938a5cbd6e57715508d = L.circleMarker(
                [43.709020000000066, -79.36348999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_12f4d891ab7d4bbaba1ed80105fc8822 = L.popup({"maxWidth": "100%"});

        
            var html_11181d6b668b4573858b19d52b86dd55 = $(`<div id="html_11181d6b668b4573858b19d52b86dd55" style="width: 100.0%; height: 100.0%;">Leaside Cluster 0.0</div>`)[0];
            popup_12f4d891ab7d4bbaba1ed80105fc8822.setContent(html_11181d6b668b4573858b19d52b86dd55);
        

        circle_marker_47f9f4d53d2b4938a5cbd6e57715508d.bindPopup(popup_12f4d891ab7d4bbaba1ed80105fc8822)
        ;

        
    
    
            var circle_marker_4456ac3e95ff4fb9959382940308cb51 = L.circleMarker(
                [43.65609000000006, -79.38492999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_6dc7e9020f98494e8bade0f34dbc902b = L.popup({"maxWidth": "100%"});

        
            var html_a0d537cea22c4cab9535ea6e64d7f780 = $(`<div id="html_a0d537cea22c4cab9535ea6e64d7f780" style="width: 100.0%; height: 100.0%;">Central Bay Street Cluster 0.0</div>`)[0];
            popup_6dc7e9020f98494e8bade0f34dbc902b.setContent(html_a0d537cea22c4cab9535ea6e64d7f780);
        

        circle_marker_4456ac3e95ff4fb9959382940308cb51.bindPopup(popup_6dc7e9020f98494e8bade0f34dbc902b)
        ;

        
    
    
            var circle_marker_bd482e9f72444b02831618bdf6d9e7ed = L.circleMarker(
                [43.668690000000026, -79.42070999999999],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_0ede6ff7d1b14d30923d526c4ed044d0 = L.popup({"maxWidth": "100%"});

        
            var html_1623581a519444a1b9648d2470733e44 = $(`<div id="html_1623581a519444a1b9648d2470733e44" style="width: 100.0%; height: 100.0%;">Christie Cluster 0.0</div>`)[0];
            popup_0ede6ff7d1b14d30923d526c4ed044d0.setContent(html_1623581a519444a1b9648d2470733e44);
        

        circle_marker_bd482e9f72444b02831618bdf6d9e7ed.bindPopup(popup_0ede6ff7d1b14d30923d526c4ed044d0)
        ;

        
    
    
            var circle_marker_e68bc631cf48486fb920d593809bca29 = L.circleMarker(
                [43.76944000000003, -79.23891999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_37c1cba1dcc84b1eb6c6d3d6e182459d = L.popup({"maxWidth": "100%"});

        
            var html_24ac4b2d06014c8b9f9537b32fc2a03b = $(`<div id="html_24ac4b2d06014c8b9f9537b32fc2a03b" style="width: 100.0%; height: 100.0%;">Cedarbrae Cluster 0.0</div>`)[0];
            popup_37c1cba1dcc84b1eb6c6d3d6e182459d.setContent(html_24ac4b2d06014c8b9f9537b32fc2a03b);
        

        circle_marker_e68bc631cf48486fb920d593809bca29.bindPopup(popup_37c1cba1dcc84b1eb6c6d3d6e182459d)
        ;

        
    
    
            var circle_marker_75d256b2b9874475be52135937e314ac = L.circleMarker(
                [43.80225000000007, -79.35557999999997],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_4209902152e641b79ff2a62785779384 = L.popup({"maxWidth": "100%"});

        
            var html_245e3c99c9914cf0b7c58960f1cd9540 = $(`<div id="html_245e3c99c9914cf0b7c58960f1cd9540" style="width: 100.0%; height: 100.0%;">Hillcrest Village Cluster 1.0</div>`)[0];
            popup_4209902152e641b79ff2a62785779384.setContent(html_245e3c99c9914cf0b7c58960f1cd9540);
        

        circle_marker_75d256b2b9874475be52135937e314ac.bindPopup(popup_4209902152e641b79ff2a62785779384)
        ;

        
    
    
            var circle_marker_2147f6d0575d4025acb8c611ed5e2cff = L.circleMarker(
                [43.70142000000004, -79.34904999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_b05fa3d4c7d24f679a3d51b72b09d5da = L.popup({"maxWidth": "100%"});

        
            var html_9ec4bde7f7604f57afa3258c207bf18c = $(`<div id="html_9ec4bde7f7604f57afa3258c207bf18c" style="width: 100.0%; height: 100.0%;">Thorncliffe Park Cluster 0.0</div>`)[0];
            popup_b05fa3d4c7d24f679a3d51b72b09d5da.setContent(html_9ec4bde7f7604f57afa3258c207bf18c);
        

        circle_marker_2147f6d0575d4025acb8c611ed5e2cff.bindPopup(popup_b05fa3d4c7d24f679a3d51b72b09d5da)
        ;

        
    
    
            var circle_marker_52ee4158ec694b86994fb1ea21aef8b4 = L.circleMarker(
                [43.64970000000005, -79.38257999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_ca14d1a9ca2b4fb680791105fb358b54 = L.popup({"maxWidth": "100%"});

        
            var html_1c290fc0201344bfbb50050f5e9fcda9 = $(`<div id="html_1c290fc0201344bfbb50050f5e9fcda9" style="width: 100.0%; height: 100.0%;">Richmond, Adelaide, King Cluster 0.0</div>`)[0];
            popup_ca14d1a9ca2b4fb680791105fb358b54.setContent(html_1c290fc0201344bfbb50050f5e9fcda9);
        

        circle_marker_52ee4158ec694b86994fb1ea21aef8b4.bindPopup(popup_ca14d1a9ca2b4fb680791105fb358b54)
        ;

        
    
    
            var circle_marker_848be0a1ed404e24b6d8f935a3444ad9 = L.circleMarker(
                [43.665050000000065, -79.43890999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_addea51791664320a450bd25f91a0088 = L.popup({"maxWidth": "100%"});

        
            var html_375e1e7995c74cb0b8887bbf04454adb = $(`<div id="html_375e1e7995c74cb0b8887bbf04454adb" style="width: 100.0%; height: 100.0%;">Dufferin, Dovercourt Village Cluster 0.0</div>`)[0];
            popup_addea51791664320a450bd25f91a0088.setContent(html_375e1e7995c74cb0b8887bbf04454adb);
        

        circle_marker_848be0a1ed404e24b6d8f935a3444ad9.bindPopup(popup_addea51791664320a450bd25f91a0088)
        ;

        
    
    
            var circle_marker_78b3aefe4a544432b1a4b462dc41cd1f = L.circleMarker(
                [43.74446000000006, -79.23116999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_cbb68272515c45229639a15799c933e1 = L.popup({"maxWidth": "100%"});

        
            var html_7610cea997c1480c838915fa44968d33 = $(`<div id="html_7610cea997c1480c838915fa44968d33" style="width: 100.0%; height: 100.0%;">Scarborough Village Cluster 0.0</div>`)[0];
            popup_cbb68272515c45229639a15799c933e1.setContent(html_7610cea997c1480c838915fa44968d33);
        

        circle_marker_78b3aefe4a544432b1a4b462dc41cd1f.bindPopup(popup_cbb68272515c45229639a15799c933e1)
        ;

        
    
    
            var circle_marker_71522c68decc420ebf3106e9e0e2a3a5 = L.circleMarker(
                [43.780970000000025, -79.34780999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_83a7385624734b89a02a86c360815633 = L.popup({"maxWidth": "100%"});

        
            var html_48806992784548e88a95e68ebc3b9fe2 = $(`<div id="html_48806992784548e88a95e68ebc3b9fe2" style="width: 100.0%; height: 100.0%;">Fairview, Henry Farm, Oriole Cluster 0.0</div>`)[0];
            popup_83a7385624734b89a02a86c360815633.setContent(html_48806992784548e88a95e68ebc3b9fe2);
        

        circle_marker_71522c68decc420ebf3106e9e0e2a3a5.bindPopup(popup_83a7385624734b89a02a86c360815633)
        ;

        
    
    
            var circle_marker_85a43e61bdf7408a9dff0bc506be1e34 = L.circleMarker(
                [43.76476000000008, -79.48797999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_e428ba8ab4f7412a97aabb5d3d6a9ad7 = L.popup({"maxWidth": "100%"});

        
            var html_aaadb61e4e624c6f8d36e71fae16a509 = $(`<div id="html_aaadb61e4e624c6f8d36e71fae16a509" style="width: 100.0%; height: 100.0%;">Northwood Park, York University Cluster 0.0</div>`)[0];
            popup_e428ba8ab4f7412a97aabb5d3d6a9ad7.setContent(html_aaadb61e4e624c6f8d36e71fae16a509);
        

        circle_marker_85a43e61bdf7408a9dff0bc506be1e34.bindPopup(popup_e428ba8ab4f7412a97aabb5d3d6a9ad7)
        ;

        
    
    
            var circle_marker_29f67a2a904d448fbd7334f5c4da4b90 = L.circleMarker(
                [43.68811000000005, -79.33417999999995],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_a61cb932c5b84a708025b27330eb0503 = L.popup({"maxWidth": "100%"});

        
            var html_8ffd1c65757b4cb08cc3658a02fe12fa = $(`<div id="html_8ffd1c65757b4cb08cc3658a02fe12fa" style="width: 100.0%; height: 100.0%;">East Toronto, Broadview North (Old East York) Cluster 1.0</div>`)[0];
            popup_a61cb932c5b84a708025b27330eb0503.setContent(html_8ffd1c65757b4cb08cc3658a02fe12fa);
        

        circle_marker_29f67a2a904d448fbd7334f5c4da4b90.bindPopup(popup_a61cb932c5b84a708025b27330eb0503)
        ;

        
    
    
            var circle_marker_4cbacd18ea0c4473a2de7249ef608baf = L.circleMarker(
                [43.64285000000007, -79.38075999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_4f38df25d4224898829168152652f722 = L.popup({"maxWidth": "100%"});

        
            var html_3513d43928bb415482e48b1a5a66ae6e = $(`<div id="html_3513d43928bb415482e48b1a5a66ae6e" style="width: 100.0%; height: 100.0%;">Harbourfront East, Union Station, Toronto Islands Cluster 0.0</div>`)[0];
            popup_4f38df25d4224898829168152652f722.setContent(html_3513d43928bb415482e48b1a5a66ae6e);
        

        circle_marker_4cbacd18ea0c4473a2de7249ef608baf.bindPopup(popup_4f38df25d4224898829168152652f722)
        ;

        
    
    
            var circle_marker_a70f606d942e41bda654df73e6d876e1 = L.circleMarker(
                [43.64848000000006, -79.41773999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_679d7794d22e4c639013ae20943d24c4 = L.popup({"maxWidth": "100%"});

        
            var html_bead6b22afcc46cd9d441cc9c7c06b4e = $(`<div id="html_bead6b22afcc46cd9d441cc9c7c06b4e" style="width: 100.0%; height: 100.0%;">Little Portugal, Trinity Cluster 0.0</div>`)[0];
            popup_679d7794d22e4c639013ae20943d24c4.setContent(html_bead6b22afcc46cd9d441cc9c7c06b4e);
        

        circle_marker_a70f606d942e41bda654df73e6d876e1.bindPopup(popup_679d7794d22e4c639013ae20943d24c4)
        ;

        
    
    
            var circle_marker_dcbb4ec6ca5349e9b97cffab11a4f64c = L.circleMarker(
                [43.725820000000056, -79.26460999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_a1b118f653a540a3a10564d338e24467 = L.popup({"maxWidth": "100%"});

        
            var html_59a3fa110baa406aa3fd476ec12f7e58 = $(`<div id="html_59a3fa110baa406aa3fd476ec12f7e58" style="width: 100.0%; height: 100.0%;">Kennedy Park, Ionview, East Birchmount Park Cluster 0.0</div>`)[0];
            popup_a1b118f653a540a3a10564d338e24467.setContent(html_59a3fa110baa406aa3fd476ec12f7e58);
        

        circle_marker_dcbb4ec6ca5349e9b97cffab11a4f64c.bindPopup(popup_a1b118f653a540a3a10564d338e24467)
        ;

        
    
    
            var circle_marker_bb08c7e1f889419a96ee96b3cf11de52 = L.circleMarker(
                [43.781120000000044, -79.38059999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_6b3e0886f8a94015a0fed0954c4782a6 = L.popup({"maxWidth": "100%"});

        
            var html_cf14a03aa26d48a6b769ea6cf7bd0d82 = $(`<div id="html_cf14a03aa26d48a6b769ea6cf7bd0d82" style="width: 100.0%; height: 100.0%;">Bayview Village Cluster 0.0</div>`)[0];
            popup_6b3e0886f8a94015a0fed0954c4782a6.setContent(html_cf14a03aa26d48a6b769ea6cf7bd0d82);
        

        circle_marker_bb08c7e1f889419a96ee96b3cf11de52.bindPopup(popup_6b3e0886f8a94015a0fed0954c4782a6)
        ;

        
    
    
            var circle_marker_ecaeae5fbf5a435694dde9750254bf8b = L.circleMarker(
                [43.73384000000004, -79.46827999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_f283bfcd6c62438a9ba01e692dedde15 = L.popup({"maxWidth": "100%"});

        
            var html_a8cfb88fbc024a9ea879693c56f170c8 = $(`<div id="html_a8cfb88fbc024a9ea879693c56f170c8" style="width: 100.0%; height: 100.0%;">Downsview Cluster 0.0</div>`)[0];
            popup_f283bfcd6c62438a9ba01e692dedde15.setContent(html_a8cfb88fbc024a9ea879693c56f170c8);
        

        circle_marker_ecaeae5fbf5a435694dde9750254bf8b.bindPopup(popup_f283bfcd6c62438a9ba01e692dedde15)
        ;

        
    
    
            var circle_marker_3fb87019fc0d4ce1acd7b5dff95909d1 = L.circleMarker(
                [43.68375000000003, -79.35511999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_4c7e90b34b62459992fa37e4a3870cb5 = L.popup({"maxWidth": "100%"});

        
            var html_14502d0c7f8d4d67ab1d4634c33db2f9 = $(`<div id="html_14502d0c7f8d4d67ab1d4634c33db2f9" style="width: 100.0%; height: 100.0%;">The Danforth West, Riverdale Cluster 0.0</div>`)[0];
            popup_4c7e90b34b62459992fa37e4a3870cb5.setContent(html_14502d0c7f8d4d67ab1d4634c33db2f9);
        

        circle_marker_3fb87019fc0d4ce1acd7b5dff95909d1.bindPopup(popup_4c7e90b34b62459992fa37e4a3870cb5)
        ;

        
    
    
            var circle_marker_7cb86b5d6fa24e4fb8cea64381a1def9 = L.circleMarker(
                [43.64710000000008, -79.38152999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_a9036687cdc1421185124f6d51b41d53 = L.popup({"maxWidth": "100%"});

        
            var html_df851440e88e4ba2a9622afbb65dbeab = $(`<div id="html_df851440e88e4ba2a9622afbb65dbeab" style="width: 100.0%; height: 100.0%;">Toronto Dominion Centre, Design Exchange Cluster 0.0</div>`)[0];
            popup_a9036687cdc1421185124f6d51b41d53.setContent(html_df851440e88e4ba2a9622afbb65dbeab);
        

        circle_marker_7cb86b5d6fa24e4fb8cea64381a1def9.bindPopup(popup_a9036687cdc1421185124f6d51b41d53)
        ;

        
    
    
            var circle_marker_a20faca525584e07afe68db1dec0785e = L.circleMarker(
                [43.639410000000055, -79.42675999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_8718e2b252874b209d862a1545a46ef7 = L.popup({"maxWidth": "100%"});

        
            var html_46740fb938004cfc848ac39654343405 = $(`<div id="html_46740fb938004cfc848ac39654343405" style="width: 100.0%; height: 100.0%;">Brockton, Parkdale Village, Exhibition Place Cluster 0.0</div>`)[0];
            popup_8718e2b252874b209d862a1545a46ef7.setContent(html_46740fb938004cfc848ac39654343405);
        

        circle_marker_a20faca525584e07afe68db1dec0785e.bindPopup(popup_8718e2b252874b209d862a1545a46ef7)
        ;

        
    
    
            var circle_marker_cc2f8081a94c4fc8af6c4caf94d12241 = L.circleMarker(
                [43.71289000000007, -79.28505999999999],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_44e3c8ccbe3f46879109baaf3ef6aba0 = L.popup({"maxWidth": "100%"});

        
            var html_d932a6f2c3a34949ad09f9b87eb8da83 = $(`<div id="html_d932a6f2c3a34949ad09f9b87eb8da83" style="width: 100.0%; height: 100.0%;">Golden Mile, Clairlea, Oakridge Cluster 0.0</div>`)[0];
            popup_44e3c8ccbe3f46879109baaf3ef6aba0.setContent(html_d932a6f2c3a34949ad09f9b87eb8da83);
        

        circle_marker_cc2f8081a94c4fc8af6c4caf94d12241.bindPopup(popup_44e3c8ccbe3f46879109baaf3ef6aba0)
        ;

        
    
    
            var circle_marker_58f31f101717481f9c07202d05eee697 = L.circleMarker(
                [43.756980000000055, -79.38059999999996],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_514a0869b29d45afaae885e7247a0ec3 = L.popup({"maxWidth": "100%"});

        
            var html_63d4665e472449268c850d3fc2b8f63c = $(`<div id="html_63d4665e472449268c850d3fc2b8f63c" style="width: 100.0%; height: 100.0%;">York Mills, Silver Hills Cluster 1.0</div>`)[0];
            popup_514a0869b29d45afaae885e7247a0ec3.setContent(html_63d4665e472449268c850d3fc2b8f63c);
        

        circle_marker_58f31f101717481f9c07202d05eee697.bindPopup(popup_514a0869b29d45afaae885e7247a0ec3)
        ;

        
    
    
            var circle_marker_01e0d6cc15c5457aae0fe3c20e443e70 = L.circleMarker(
                [43.720710000000054, -79.51700999999997],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_842bdb12290b4574b8351aa7dfa642e9 = L.popup({"maxWidth": "100%"});

        
            var html_b9eea7069ae2437b9f0ec2f5eb3b96f3 = $(`<div id="html_b9eea7069ae2437b9f0ec2f5eb3b96f3" style="width: 100.0%; height: 100.0%;">Downsview Cluster 0.0</div>`)[0];
            popup_842bdb12290b4574b8351aa7dfa642e9.setContent(html_b9eea7069ae2437b9f0ec2f5eb3b96f3);
        

        circle_marker_01e0d6cc15c5457aae0fe3c20e443e70.bindPopup(popup_842bdb12290b4574b8351aa7dfa642e9)
        ;

        
    
    
            var circle_marker_24b76a39b6c844d0bb6bae3284b30785 = L.circleMarker(
                [43.667970000000025, -79.31466999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_223725bc61ce496e94792575b398cbcf = L.popup({"maxWidth": "100%"});

        
            var html_ee950c83d5af472ea23d9c3fed87911b = $(`<div id="html_ee950c83d5af472ea23d9c3fed87911b" style="width: 100.0%; height: 100.0%;">India Bazaar, The Beaches West Cluster 0.0</div>`)[0];
            popup_223725bc61ce496e94792575b398cbcf.setContent(html_ee950c83d5af472ea23d9c3fed87911b);
        

        circle_marker_24b76a39b6c844d0bb6bae3284b30785.bindPopup(popup_223725bc61ce496e94792575b398cbcf)
        ;

        
    
    
            var circle_marker_ffffbbd00f6042c49d074773f3efd065 = L.circleMarker(
                [43.64840000000004, -79.37913999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_cde718808f3049c0b01bd772f95f7b3b = L.popup({"maxWidth": "100%"});

        
            var html_c6b82271feb443aaba44b5e4a516795c = $(`<div id="html_c6b82271feb443aaba44b5e4a516795c" style="width: 100.0%; height: 100.0%;">Commerce Court, Victoria Hotel Cluster 0.0</div>`)[0];
            popup_cde718808f3049c0b01bd772f95f7b3b.setContent(html_c6b82271feb443aaba44b5e4a516795c);
        

        circle_marker_ffffbbd00f6042c49d074773f3efd065.bindPopup(popup_cde718808f3049c0b01bd772f95f7b3b)
        ;

        
    
    
            var circle_marker_420a7bc5b1ce407fac3fdf0310c79a0c = L.circleMarker(
                [43.71381000000008, -79.48873999999995],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_dd581220e1f042fea2742e0be352f3e6 = L.popup({"maxWidth": "100%"});

        
            var html_41d21f7fbb0e44a29f180d9841a53615 = $(`<div id="html_41d21f7fbb0e44a29f180d9841a53615" style="width: 100.0%; height: 100.0%;">North Park, Maple Leaf Park, Upwood Park Cluster 1.0</div>`)[0];
            popup_dd581220e1f042fea2742e0be352f3e6.setContent(html_41d21f7fbb0e44a29f180d9841a53615);
        

        circle_marker_420a7bc5b1ce407fac3fdf0310c79a0c.bindPopup(popup_dd581220e1f042fea2742e0be352f3e6)
        ;

        
    
    
            var circle_marker_fd8821b6a5bd48099d960c472377ef5b = L.circleMarker(
                [43.75948000000005, -79.55706999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_6c43f77af32549ff945fdcc3f0b428f8 = L.popup({"maxWidth": "100%"});

        
            var html_849c5f37d24149058c7f2aa7bcfff65a = $(`<div id="html_849c5f37d24149058c7f2aa7bcfff65a" style="width: 100.0%; height: 100.0%;">Humber Summit Cluster 0.0</div>`)[0];
            popup_6c43f77af32549ff945fdcc3f0b428f8.setContent(html_849c5f37d24149058c7f2aa7bcfff65a);
        

        circle_marker_fd8821b6a5bd48099d960c472377ef5b.bindPopup(popup_6c43f77af32549ff945fdcc3f0b428f8)
        ;

        
    
    
            var circle_marker_9154814d3156479d9526debd504548ff = L.circleMarker(
                [43.72360000000003, -79.23495999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_7368eb45dec64e0d86fc9e41f950a838 = L.popup({"maxWidth": "100%"});

        
            var html_396d46b95a274debb486573b61f7149c = $(`<div id="html_396d46b95a274debb486573b61f7149c" style="width: 100.0%; height: 100.0%;">Cliffside, Cliffcrest, Scarborough Village West Cluster 0.0</div>`)[0];
            popup_7368eb45dec64e0d86fc9e41f950a838.setContent(html_396d46b95a274debb486573b61f7149c);
        

        circle_marker_9154814d3156479d9526debd504548ff.bindPopup(popup_7368eb45dec64e0d86fc9e41f950a838)
        ;

        
    
    
            var circle_marker_283c28547553455eac8f99e8ab39c08c = L.circleMarker(
                [43.79182000000003, -79.41371999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_f630c914985d45d595236d992b4d41c7 = L.popup({"maxWidth": "100%"});

        
            var html_9410a44c8176468998e923a01b1e4529 = $(`<div id="html_9410a44c8176468998e923a01b1e4529" style="width: 100.0%; height: 100.0%;">Willowdale, Newtonbrook Cluster 0.0</div>`)[0];
            popup_f630c914985d45d595236d992b4d41c7.setContent(html_9410a44c8176468998e923a01b1e4529);
        

        circle_marker_283c28547553455eac8f99e8ab39c08c.bindPopup(popup_f630c914985d45d595236d992b4d41c7)
        ;

        
    
    
            var circle_marker_53a030474e14405baaa1737f77f6fe1c = L.circleMarker(
                [43.73224000000005, -79.50177999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_28f139ce6ceb4a3aba5940c4d595074e = L.popup({"maxWidth": "100%"});

        
            var html_8880ac5922224c87bf34101665b29549 = $(`<div id="html_8880ac5922224c87bf34101665b29549" style="width: 100.0%; height: 100.0%;">Downsview Cluster 0.0</div>`)[0];
            popup_28f139ce6ceb4a3aba5940c4d595074e.setContent(html_8880ac5922224c87bf34101665b29549);
        

        circle_marker_53a030474e14405baaa1737f77f6fe1c.bindPopup(popup_28f139ce6ceb4a3aba5940c4d595074e)
        ;

        
    
    
            var circle_marker_8bb5d7770c4c4e29ad2e4568d8107c8b = L.circleMarker(
                [43.66213000000005, -79.33496999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_8a9fbf341308435d9d320370709daa2f = L.popup({"maxWidth": "100%"});

        
            var html_023aa37e71fe498db5641f854852f9d8 = $(`<div id="html_023aa37e71fe498db5641f854852f9d8" style="width: 100.0%; height: 100.0%;">Studio District Cluster 0.0</div>`)[0];
            popup_8a9fbf341308435d9d320370709daa2f.setContent(html_023aa37e71fe498db5641f854852f9d8);
        

        circle_marker_8bb5d7770c4c4e29ad2e4568d8107c8b.bindPopup(popup_8a9fbf341308435d9d320370709daa2f)
        ;

        
    
    
            var circle_marker_a4c23daeba8148dc9a70b6cb43f2d630 = L.circleMarker(
                [43.73545000000007, -79.41915999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_3bf1daf54df7481dabf9a3886492ba78 = L.popup({"maxWidth": "100%"});

        
            var html_181c9d5a53e548c6a1c2bb564d3a42dc = $(`<div id="html_181c9d5a53e548c6a1c2bb564d3a42dc" style="width: 100.0%; height: 100.0%;">Bedford Park, Lawrence Manor East Cluster 0.0</div>`)[0];
            popup_3bf1daf54df7481dabf9a3886492ba78.setContent(html_181c9d5a53e548c6a1c2bb564d3a42dc);
        

        circle_marker_a4c23daeba8148dc9a70b6cb43f2d630.bindPopup(popup_3bf1daf54df7481dabf9a3886492ba78)
        ;

        
    
    
            var circle_marker_cebbcf1afaec4304b65eb0d5a312c14f = L.circleMarker(
                [43.695170000000076, -79.48396999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_5f964586613247f9a01c4b2eae75d357 = L.popup({"maxWidth": "100%"});

        
            var html_6c92542d80b74786bad714290d5b8954 = $(`<div id="html_6c92542d80b74786bad714290d5b8954" style="width: 100.0%; height: 100.0%;">Del Ray, Mount Dennis, Keelsdale and Silverthorn Cluster 0.0</div>`)[0];
            popup_5f964586613247f9a01c4b2eae75d357.setContent(html_6c92542d80b74786bad714290d5b8954);
        

        circle_marker_cebbcf1afaec4304b65eb0d5a312c14f.bindPopup(popup_5f964586613247f9a01c4b2eae75d357)
        ;

        
    
    
            var circle_marker_36adc2521b224c2493640482ca1a1538 = L.circleMarker(
                [43.733670000000075, -79.53768999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_819111516f564312b5d7e036d191f42a = L.popup({"maxWidth": "100%"});

        
            var html_6d72b6f5b96347ae9eaf69d639847324 = $(`<div id="html_6d72b6f5b96347ae9eaf69d639847324" style="width: 100.0%; height: 100.0%;">Humberlea, Emery Cluster 0.0</div>`)[0];
            popup_819111516f564312b5d7e036d191f42a.setContent(html_6d72b6f5b96347ae9eaf69d639847324);
        

        circle_marker_36adc2521b224c2493640482ca1a1538.bindPopup(popup_819111516f564312b5d7e036d191f42a)
        ;

        
    
    
            var circle_marker_a0d19dc337f844879cd0ee4cf0a007ed = L.circleMarker(
                [43.695100000000025, -79.26465999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_56df1c083aae46b3ad916f3424fabd95 = L.popup({"maxWidth": "100%"});

        
            var html_f3088aba2cb44416b2d8de803d177810 = $(`<div id="html_f3088aba2cb44416b2d8de803d177810" style="width: 100.0%; height: 100.0%;">Birch Cliff, Cliffside West Cluster 0.0</div>`)[0];
            popup_56df1c083aae46b3ad916f3424fabd95.setContent(html_f3088aba2cb44416b2d8de803d177810);
        

        circle_marker_a0d19dc337f844879cd0ee4cf0a007ed.bindPopup(popup_56df1c083aae46b3ad916f3424fabd95)
        ;

        
    
    
            var circle_marker_ec88d9c7579b49c08127a19036f08907 = L.circleMarker(
                [43.76774000000006, -79.40727999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_68d4ce49d0fa4d18969ebdf8dad781f8 = L.popup({"maxWidth": "100%"});

        
            var html_f8a4b17a0c044150b140b9d52e3b95b9 = $(`<div id="html_f8a4b17a0c044150b140b9d52e3b95b9" style="width: 100.0%; height: 100.0%;">Willowdale, Willowdale East Cluster 0.0</div>`)[0];
            popup_68d4ce49d0fa4d18969ebdf8dad781f8.setContent(html_f8a4b17a0c044150b140b9d52e3b95b9);
        

        circle_marker_ec88d9c7579b49c08127a19036f08907.bindPopup(popup_68d4ce49d0fa4d18969ebdf8dad781f8)
        ;

        
    
    
            var circle_marker_12f6bba5a1ee4ec6bc9846da74871c01 = L.circleMarker(
                [43.75478000000004, -79.51958999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_6e1c9631b9d04ae48b6ee97687be85c7 = L.popup({"maxWidth": "100%"});

        
            var html_61725b6e724944d6ab11d4688f632637 = $(`<div id="html_61725b6e724944d6ab11d4688f632637" style="width: 100.0%; height: 100.0%;">Downsview Cluster 0.0</div>`)[0];
            popup_6e1c9631b9d04ae48b6ee97687be85c7.setContent(html_61725b6e724944d6ab11d4688f632637);
        

        circle_marker_12f6bba5a1ee4ec6bc9846da74871c01.bindPopup(popup_6e1c9631b9d04ae48b6ee97687be85c7)
        ;

        
    
    
            var circle_marker_4dfcd145d262494aa919609556dbfad6 = L.circleMarker(
                [43.72843000000006, -79.38712999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_80b7382d8e5c49adb851a7233bce6023 = L.popup({"maxWidth": "100%"});

        
            var html_1bbdbd9c827642c8bb231b05d7a6e7e3 = $(`<div id="html_1bbdbd9c827642c8bb231b05d7a6e7e3" style="width: 100.0%; height: 100.0%;">Lawrence Park Cluster 0.0</div>`)[0];
            popup_80b7382d8e5c49adb851a7233bce6023.setContent(html_1bbdbd9c827642c8bb231b05d7a6e7e3);
        

        circle_marker_4dfcd145d262494aa919609556dbfad6.bindPopup(popup_80b7382d8e5c49adb851a7233bce6023)
        ;

        
    
    
            var circle_marker_c46a49761e39464d8c13ddf9ef385179 = L.circleMarker(
                [43.67646000000008, -79.48271999999997],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_eb9dbef74d7a4b91ac61a0248669f26f = L.popup({"maxWidth": "100%"});

        
            var html_2b74739b1b284a61b518a9e91d32b0b3 = $(`<div id="html_2b74739b1b284a61b518a9e91d32b0b3" style="width: 100.0%; height: 100.0%;">Runnymede, The Junction North Cluster 0.0</div>`)[0];
            popup_eb9dbef74d7a4b91ac61a0248669f26f.setContent(html_2b74739b1b284a61b518a9e91d32b0b3);
        

        circle_marker_c46a49761e39464d8c13ddf9ef385179.bindPopup(popup_eb9dbef74d7a4b91ac61a0248669f26f)
        ;

        
    
    
            var circle_marker_05ea147b73db44a6974ed7f438f2f3e5 = L.circleMarker(
                [43.705070000000035, -79.51803999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_7b68a06b36b44c22845db281e000411b = L.popup({"maxWidth": "100%"});

        
            var html_a223117b48b547059c3cae8b4ed77dd9 = $(`<div id="html_a223117b48b547059c3cae8b4ed77dd9" style="width: 100.0%; height: 100.0%;">Weston Cluster 0.0</div>`)[0];
            popup_7b68a06b36b44c22845db281e000411b.setContent(html_a223117b48b547059c3cae8b4ed77dd9);
        

        circle_marker_05ea147b73db44a6974ed7f438f2f3e5.bindPopup(popup_7b68a06b36b44c22845db281e000411b)
        ;

        
    
    
            var circle_marker_df9c6e7a813e49c887d7b5b654eacebb = L.circleMarker(
                [43.75998000000004, -79.26939999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_3a594c72da1d45039207f495b153da9d = L.popup({"maxWidth": "100%"});

        
            var html_e10aa205825c40549463da3ba1b37b7b = $(`<div id="html_e10aa205825c40549463da3ba1b37b7b" style="width: 100.0%; height: 100.0%;">Dorset Park, Wexford Heights, Scarborough Town Centre Cluster 0.0</div>`)[0];
            popup_3a594c72da1d45039207f495b153da9d.setContent(html_e10aa205825c40549463da3ba1b37b7b);
        

        circle_marker_df9c6e7a813e49c887d7b5b654eacebb.bindPopup(popup_3a594c72da1d45039207f495b153da9d)
        ;

        
    
    
            var circle_marker_195251b3a5dc40f584a2bcb76b88ad0d = L.circleMarker(
                [43.747780000000034, -79.40032999999994],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_b4fab9d0a2964a809901996ecf737ef7 = L.popup({"maxWidth": "100%"});

        
            var html_8e164d95024746368f878f7e9cb5151e = $(`<div id="html_8e164d95024746368f878f7e9cb5151e" style="width: 100.0%; height: 100.0%;">York Mills West Cluster 1.0</div>`)[0];
            popup_b4fab9d0a2964a809901996ecf737ef7.setContent(html_8e164d95024746368f878f7e9cb5151e);
        

        circle_marker_195251b3a5dc40f584a2bcb76b88ad0d.bindPopup(popup_b4fab9d0a2964a809901996ecf737ef7)
        ;

        
    
    
            var circle_marker_b2b622f87dc24a8a917c11e725a1e314 = L.circleMarker(
                [43.71276000000006, -79.38850999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_a9e97ecc40934f509f53064a36a10525 = L.popup({"maxWidth": "100%"});

        
            var html_859c766c683a4f52954fe84949a53136 = $(`<div id="html_859c766c683a4f52954fe84949a53136" style="width: 100.0%; height: 100.0%;">Davisville North Cluster 0.0</div>`)[0];
            popup_a9e97ecc40934f509f53064a36a10525.setContent(html_859c766c683a4f52954fe84949a53136);
        

        circle_marker_b2b622f87dc24a8a917c11e725a1e314.bindPopup(popup_a9e97ecc40934f509f53064a36a10525)
        ;

        
    
    
            var circle_marker_65e5725fd0c94e7b87ecfedd4384befe = L.circleMarker(
                [43.69479000000007, -79.41439999999994],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_a95a8de86ba04c0081addf23d739ebc7 = L.popup({"maxWidth": "100%"});

        
            var html_f42ad6c0a4c54ba78a848ac281c41d51 = $(`<div id="html_f42ad6c0a4c54ba78a848ac281c41d51" style="width: 100.0%; height: 100.0%;">Forest Hill North &amp; West, Forest Hill Road Park Cluster 1.0</div>`)[0];
            popup_a95a8de86ba04c0081addf23d739ebc7.setContent(html_f42ad6c0a4c54ba78a848ac281c41d51);
        

        circle_marker_65e5725fd0c94e7b87ecfedd4384befe.bindPopup(popup_a95a8de86ba04c0081addf23d739ebc7)
        ;

        
    
    
            var circle_marker_040d1f182d464f4681e5d4100004d552 = L.circleMarker(
                [43.659730000000025, -79.46280999999993],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_9eab2a849eea4b83a1cff999dbd12a52 = L.popup({"maxWidth": "100%"});

        
            var html_5e550d037d434b889bc3ebd0c4521b12 = $(`<div id="html_5e550d037d434b889bc3ebd0c4521b12" style="width: 100.0%; height: 100.0%;">High Park, The Junction South Cluster 1.0</div>`)[0];
            popup_9eab2a849eea4b83a1cff999dbd12a52.setContent(html_5e550d037d434b889bc3ebd0c4521b12);
        

        circle_marker_040d1f182d464f4681e5d4100004d552.bindPopup(popup_9eab2a849eea4b83a1cff999dbd12a52)
        ;

        
    
    
            var circle_marker_251e25b1e12942d4ac65e8df63ccaf69 = L.circleMarker(
                [43.696300000000065, -79.52925999999997],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_0c5e30b0b50b4cc4aa3ba8f756d1032b = L.popup({"maxWidth": "100%"});

        
            var html_2d6df8eaa9264e1d993f97c172ec7694 = $(`<div id="html_2d6df8eaa9264e1d993f97c172ec7694" style="width: 100.0%; height: 100.0%;">Westmount Cluster 0.0</div>`)[0];
            popup_0c5e30b0b50b4cc4aa3ba8f756d1032b.setContent(html_2d6df8eaa9264e1d993f97c172ec7694);
        

        circle_marker_251e25b1e12942d4ac65e8df63ccaf69.bindPopup(popup_0c5e30b0b50b4cc4aa3ba8f756d1032b)
        ;

        
    
    
            var circle_marker_05f35282ae4d47329e24834f103472a5 = L.circleMarker(
                [43.75075000000004, -79.30053999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_6d014c589ce344839061d9df2560af13 = L.popup({"maxWidth": "100%"});

        
            var html_0fddaa58fc7c46019c8200b6da301c32 = $(`<div id="html_0fddaa58fc7c46019c8200b6da301c32" style="width: 100.0%; height: 100.0%;">Wexford, Maryvale Cluster 0.0</div>`)[0];
            popup_6d014c589ce344839061d9df2560af13.setContent(html_0fddaa58fc7c46019c8200b6da301c32);
        

        circle_marker_05f35282ae4d47329e24834f103472a5.bindPopup(popup_6d014c589ce344839061d9df2560af13)
        ;

        
    
    
            var circle_marker_e1e59598cbd64bc6beca545d4f485074 = L.circleMarker(
                [43.77989000000008, -79.44677999999999],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_86e11975a2aa4d86abaad34c338d36c9 = L.popup({"maxWidth": "100%"});

        
            var html_76066f1a4126486d827dd2d34fcd55cb = $(`<div id="html_76066f1a4126486d827dd2d34fcd55cb" style="width: 100.0%; height: 100.0%;">Willowdale, Willowdale West Cluster 0.0</div>`)[0];
            popup_86e11975a2aa4d86abaad34c338d36c9.setContent(html_76066f1a4126486d827dd2d34fcd55cb);
        

        circle_marker_e1e59598cbd64bc6beca545d4f485074.bindPopup(popup_86e11975a2aa4d86abaad34c338d36c9)
        ;

        
    
    
            var circle_marker_9226f53890ab4bb488cee0ffaae2838a = L.circleMarker(
                [43.71458000000007, -79.40667999999994],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_1d1d13bc42394214a967881aa2e0d1e0 = L.popup({"maxWidth": "100%"});

        
            var html_bfe20731066240cabb8f8554e3dda266 = $(`<div id="html_bfe20731066240cabb8f8554e3dda266" style="width: 100.0%; height: 100.0%;">North Toronto West, Lawrence Park Cluster 1.0</div>`)[0];
            popup_1d1d13bc42394214a967881aa2e0d1e0.setContent(html_bfe20731066240cabb8f8554e3dda266);
        

        circle_marker_9226f53890ab4bb488cee0ffaae2838a.bindPopup(popup_1d1d13bc42394214a967881aa2e0d1e0)
        ;

        
    
    
            var circle_marker_0ff3598fc1934d7882d35e4c9530ddb9 = L.circleMarker(
                [43.674840000000074, -79.40451999999993],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_41b8a483b8b746d19067d22640a69136 = L.popup({"maxWidth": "100%"});

        
            var html_6d5c100688084a71960ab806246a534c = $(`<div id="html_6d5c100688084a71960ab806246a534c" style="width: 100.0%; height: 100.0%;">The Annex, North Midtown, Yorkville Cluster 0.0</div>`)[0];
            popup_41b8a483b8b746d19067d22640a69136.setContent(html_6d5c100688084a71960ab806246a534c);
        

        circle_marker_0ff3598fc1934d7882d35e4c9530ddb9.bindPopup(popup_41b8a483b8b746d19067d22640a69136)
        ;

        
    
    
            var circle_marker_3139e5af44734acbaf2407ff23c14d3a = L.circleMarker(
                [43.64777000000004, -79.44988999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_24cf0e5f798e4d8294eb672cdffadd7f = L.popup({"maxWidth": "100%"});

        
            var html_700bdf8aec014f59bbe98478454f93e7 = $(`<div id="html_700bdf8aec014f59bbe98478454f93e7" style="width: 100.0%; height: 100.0%;">Parkdale, Roncesvalles Cluster 0.0</div>`)[0];
            popup_24cf0e5f798e4d8294eb672cdffadd7f.setContent(html_700bdf8aec014f59bbe98478454f93e7);
        

        circle_marker_3139e5af44734acbaf2407ff23c14d3a.bindPopup(popup_24cf0e5f798e4d8294eb672cdffadd7f)
        ;

        
    
    
            var circle_marker_a4b53301b5e045aaa759e856c981c925 = L.circleMarker(
                [43.648690000000045, -79.38543999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_61ef979fd00849d0944350a243c75b5c = L.popup({"maxWidth": "100%"});

        
            var html_d3d90227b8ef4859a84530d057eb8189 = $(`<div id="html_d3d90227b8ef4859a84530d057eb8189" style="width: 100.0%; height: 100.0%;">Canada Post Gateway Processing Centre Cluster 0.0</div>`)[0];
            popup_61ef979fd00849d0944350a243c75b5c.setContent(html_d3d90227b8ef4859a84530d057eb8189);
        

        circle_marker_a4b53301b5e045aaa759e856c981c925.bindPopup(popup_61ef979fd00849d0944350a243c75b5c)
        ;

        
    
    
            var circle_marker_956de67a780b4835b3896f8cdcbff779 = L.circleMarker(
                [43.68681000000004, -79.55727999999993],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_06db77e3b8bd438cb6a2db437e67855a = L.popup({"maxWidth": "100%"});

        
            var html_52e067aad29546c895cbeed8bbedd94d = $(`<div id="html_52e067aad29546c895cbeed8bbedd94d" style="width: 100.0%; height: 100.0%;">Kingsview Village, St. Phillips, Martin Grove Gardens, Richview Gardens Cluster 0.0</div>`)[0];
            popup_06db77e3b8bd438cb6a2db437e67855a.setContent(html_52e067aad29546c895cbeed8bbedd94d);
        

        circle_marker_956de67a780b4835b3896f8cdcbff779.bindPopup(popup_06db77e3b8bd438cb6a2db437e67855a)
        ;

        
    
    
            var circle_marker_b7e3f4eb64a94fd3aab8e9120020a4ba = L.circleMarker(
                [43.794520000000034, -79.26707999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_2c631cc5555e49e6807064d9d61b6f46 = L.popup({"maxWidth": "100%"});

        
            var html_8d22e6456c9243b0a899aba34ef84b5d = $(`<div id="html_8d22e6456c9243b0a899aba34ef84b5d" style="width: 100.0%; height: 100.0%;">Agincourt Cluster 0.0</div>`)[0];
            popup_2c631cc5555e49e6807064d9d61b6f46.setContent(html_8d22e6456c9243b0a899aba34ef84b5d);
        

        circle_marker_b7e3f4eb64a94fd3aab8e9120020a4ba.bindPopup(popup_2c631cc5555e49e6807064d9d61b6f46)
        ;

        
    
    
            var circle_marker_60e86878212d4543a860d660751e174b = L.circleMarker(
                [43.703400000000045, -79.38658999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_6dc864724db9412090e8628a5f5241e5 = L.popup({"maxWidth": "100%"});

        
            var html_fd9e561edaf04d7ba90e15a8a61a128c = $(`<div id="html_fd9e561edaf04d7ba90e15a8a61a128c" style="width: 100.0%; height: 100.0%;">Davisville Cluster 0.0</div>`)[0];
            popup_6dc864724db9412090e8628a5f5241e5.setContent(html_fd9e561edaf04d7ba90e15a8a61a128c);
        

        circle_marker_60e86878212d4543a860d660751e174b.bindPopup(popup_6dc864724db9412090e8628a5f5241e5)
        ;

        
    
    
            var circle_marker_e034bd1827654686b9fb9afd2672f64d = L.circleMarker(
                [43.663110000000074, -79.40179999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_44240f13b50d4240b9fa81bb18ad8ae5 = L.popup({"maxWidth": "100%"});

        
            var html_9cb75263145941839efbc74c5153ed21 = $(`<div id="html_9cb75263145941839efbc74c5153ed21" style="width: 100.0%; height: 100.0%;">University of Toronto, Harbord Cluster 0.0</div>`)[0];
            popup_44240f13b50d4240b9fa81bb18ad8ae5.setContent(html_9cb75263145941839efbc74c5153ed21);
        

        circle_marker_e034bd1827654686b9fb9afd2672f64d.bindPopup(popup_44240f13b50d4240b9fa81bb18ad8ae5)
        ;

        
    
    
            var circle_marker_658ba61b7fbe4c5f8873d03aeb5e26cf = L.circleMarker(
                [43.649820000000034, -79.47547999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_50980477daca41778d1f21f408ab763e = L.popup({"maxWidth": "100%"});

        
            var html_d2c5ae085fcf429986a293858efa3044 = $(`<div id="html_d2c5ae085fcf429986a293858efa3044" style="width: 100.0%; height: 100.0%;">Runnymede, Swansea Cluster 0.0</div>`)[0];
            popup_50980477daca41778d1f21f408ab763e.setContent(html_d2c5ae085fcf429986a293858efa3044);
        

        circle_marker_658ba61b7fbe4c5f8873d03aeb5e26cf.bindPopup(popup_50980477daca41778d1f21f408ab763e)
        ;

        
    
    
            var circle_marker_5ab74a1108424f229d94ab9387f66206 = L.circleMarker(
                [43.784910000000025, -79.29721999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_d5a7184ccbbe453d87813d79263fe0d4 = L.popup({"maxWidth": "100%"});

        
            var html_6c8159aa7bee4c8988872280aeb39a98 = $(`<div id="html_6c8159aa7bee4c8988872280aeb39a98" style="width: 100.0%; height: 100.0%;">Clarks Corners, Tam O&#39;Shanter, Sullivan Cluster 0.0</div>`)[0];
            popup_d5a7184ccbbe453d87813d79263fe0d4.setContent(html_6c8159aa7bee4c8988872280aeb39a98);
        

        circle_marker_5ab74a1108424f229d94ab9387f66206.bindPopup(popup_d5a7184ccbbe453d87813d79263fe0d4)
        ;

        
    
    
            var circle_marker_fb7ab429bb254cca9c2a5c6473cdc17b = L.circleMarker(
                [43.690480000000036, -79.38317999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_986a5835383b409584d894d0b608fa5c = L.popup({"maxWidth": "100%"});

        
            var html_dcf928bc3baf41bf8674c165c3319a04 = $(`<div id="html_dcf928bc3baf41bf8674c165c3319a04" style="width: 100.0%; height: 100.0%;">Moore Park, Summerhill East Cluster 0.0</div>`)[0];
            popup_986a5835383b409584d894d0b608fa5c.setContent(html_dcf928bc3baf41bf8674c165c3319a04);
        

        circle_marker_fb7ab429bb254cca9c2a5c6473cdc17b.bindPopup(popup_986a5835383b409584d894d0b608fa5c)
        ;

        
    
    
            var circle_marker_54ec4f62ea584b859344d6e2f325cb91 = L.circleMarker(
                [43.65351000000004, -79.39721999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_ae621e1e52934496910a54aa3108c555 = L.popup({"maxWidth": "100%"});

        
            var html_a7af933043874e1ca6ea15a7e8f5c120 = $(`<div id="html_a7af933043874e1ca6ea15a7e8f5c120" style="width: 100.0%; height: 100.0%;">Kensington Market, Chinatown, Grange Park Cluster 0.0</div>`)[0];
            popup_ae621e1e52934496910a54aa3108c555.setContent(html_a7af933043874e1ca6ea15a7e8f5c120);
        

        circle_marker_54ec4f62ea584b859344d6e2f325cb91.bindPopup(popup_ae621e1e52934496910a54aa3108c555)
        ;

        
    
    
            var circle_marker_1f35c0791c3543e68fe3ae07bceaeb46 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_ac8bd8438b1e4ab4b889938ec9ad9c8c = L.popup({"maxWidth": "100%"});

        
            var html_236417b314b44d62936af75788f23461 = $(`<div id="html_236417b314b44d62936af75788f23461" style="width: 100.0%; height: 100.0%;">Milliken, Agincourt North, Steeles East, L&#39;Amoreaux East Cluster 0.0</div>`)[0];
            popup_ac8bd8438b1e4ab4b889938ec9ad9c8c.setContent(html_236417b314b44d62936af75788f23461);
        

        circle_marker_1f35c0791c3543e68fe3ae07bceaeb46.bindPopup(popup_ac8bd8438b1e4ab4b889938ec9ad9c8c)
        ;

        
    
    
            var circle_marker_ce0f9b22d9234d36b90ee038343b4fd7 = L.circleMarker(
                [43.68568000000005, -79.40236999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_be37d806d3934c3ab225a60a3fdec3a6 = L.popup({"maxWidth": "100%"});

        
            var html_78198ed46faa4d01b084f7890a7bbded = $(`<div id="html_78198ed46faa4d01b084f7890a7bbded" style="width: 100.0%; height: 100.0%;">Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park Cluster 0.0</div>`)[0];
            popup_be37d806d3934c3ab225a60a3fdec3a6.setContent(html_78198ed46faa4d01b084f7890a7bbded);
        

        circle_marker_ce0f9b22d9234d36b90ee038343b4fd7.bindPopup(popup_be37d806d3934c3ab225a60a3fdec3a6)
        ;

        
    
    
            var circle_marker_fd2bc195853f4365bd9f6b2a07a69066 = L.circleMarker(
                [43.640820000000076, -79.39817999999997],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_c1396c2be20b4f6e81d7b0a36589a45a = L.popup({"maxWidth": "100%"});

        
            var html_776998f4ec5f49b484243b60baec832b = $(`<div id="html_776998f4ec5f49b484243b60baec832b" style="width: 100.0%; height: 100.0%;">CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport Cluster 0.0</div>`)[0];
            popup_c1396c2be20b4f6e81d7b0a36589a45a.setContent(html_776998f4ec5f49b484243b60baec832b);
        

        circle_marker_fd2bc195853f4365bd9f6b2a07a69066.bindPopup(popup_c1396c2be20b4f6e81d7b0a36589a45a)
        ;

        
    
    
            var circle_marker_ec801b22c26e4fc58a18d6a7e19baf6b = L.circleMarker(
                [43.60987000000006, -79.49808999999993],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_05b049d0217a4566948416a085e173f2 = L.popup({"maxWidth": "100%"});

        
            var html_7a23205a7b724b68afddbbd99a3d71d8 = $(`<div id="html_7a23205a7b724b68afddbbd99a3d71d8" style="width: 100.0%; height: 100.0%;">New Toronto, Mimico South, Humber Bay Shores Cluster 0.0</div>`)[0];
            popup_05b049d0217a4566948416a085e173f2.setContent(html_7a23205a7b724b68afddbbd99a3d71d8);
        

        circle_marker_ec801b22c26e4fc58a18d6a7e19baf6b.bindPopup(popup_05b049d0217a4566948416a085e173f2)
        ;

        
    
    
            var circle_marker_369f983c33c641c7baa06e5eca88bbcc = L.circleMarker(
                [43.744530000000054, -79.58623999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_9c1516d5defb47a2a9916cf80d83af72 = L.popup({"maxWidth": "100%"});

        
            var html_5ca33ef1de5740c8a03b608609a2c1f7 = $(`<div id="html_5ca33ef1de5740c8a03b608609a2c1f7" style="width: 100.0%; height: 100.0%;">South Steeles, Silverstone, Humbergate, Jamestown, Mount Olive, Beaumond Heights, Thistletown, Albion Gardens Cluster 0.0</div>`)[0];
            popup_9c1516d5defb47a2a9916cf80d83af72.setContent(html_5ca33ef1de5740c8a03b608609a2c1f7);
        

        circle_marker_369f983c33c641c7baa06e5eca88bbcc.bindPopup(popup_9c1516d5defb47a2a9916cf80d83af72)
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        circle_marker_2b6c59ae19da49228cce6b84b97910b8.bindPopup(popup_178d29fcde024d858ad4bea34599f91a)
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        circle_marker_889ebf897c964647be701fba48a97bd1.bindPopup(popup_f77bd6a7b9e54cdabbd6d0c1a9272d3e)
        ;

        
    
    
            var circle_marker_3d31d8a32867423aa89a181ad22de292 = L.circleMarker(
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        circle_marker_3d31d8a32867423aa89a181ad22de292.bindPopup(popup_229076350391475489213b2a5efeeb10)
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            var circle_marker_db6e8e043f4c451abd6361c782417b6c = L.circleMarker(
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        circle_marker_db6e8e043f4c451abd6361c782417b6c.bindPopup(popup_5ecc08eaa3a54498834102877300690f)
        ;

        
    
    
            var circle_marker_6aaa0c1abe5141c98daf3f346e0541ea = L.circleMarker(
                [43.667880000000025, -79.36648999999994],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        circle_marker_6aaa0c1abe5141c98daf3f346e0541ea.bindPopup(popup_e35e0ee0c4d24c98a41393781103de89)
        ;

        
    
    
            var circle_marker_8bb999bbecf14e33912b94aca1c34182 = L.circleMarker(
                [43.64828000000006, -79.38145999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_eb88d752e93f4e8696ede730c3978650 = L.popup({"maxWidth": "100%"});

        
            var html_1befbe5294dd421bb50dabcd9a57db4a = $(`<div id="html_1befbe5294dd421bb50dabcd9a57db4a" style="width: 100.0%; height: 100.0%;">First Canadian Place, Underground city Cluster 0.0</div>`)[0];
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        circle_marker_8bb999bbecf14e33912b94aca1c34182.bindPopup(popup_eb88d752e93f4e8696ede730c3978650)
        ;

        
    
    
            var circle_marker_2681c631bbab4cd5a809abf53cb26cfa = L.circleMarker(
                [43.65319000000005, -79.51112999999998],
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            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_4cfc55e335c74481bac759d846c90c43 = L.popup({"maxWidth": "100%"});

        
            var html_5efafa293e7c4fb78dcb92080d814d34 = $(`<div id="html_5efafa293e7c4fb78dcb92080d814d34" style="width: 100.0%; height: 100.0%;">The Kingsway, Montgomery Road, Old Mill North Cluster 0.0</div>`)[0];
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        circle_marker_2681c631bbab4cd5a809abf53cb26cfa.bindPopup(popup_4cfc55e335c74481bac759d846c90c43)
        ;

        
    
    
            var circle_marker_cc67acec532a4649aad6babb286a3a29 = L.circleMarker(
                [43.66659000000004, -79.38132999999993],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_7c0c1fd8935c49f3a53cf1bf53fca105 = L.popup({"maxWidth": "100%"});

        
            var html_124c1c81e14540bb88d38837a6009808 = $(`<div id="html_124c1c81e14540bb88d38837a6009808" style="width: 100.0%; height: 100.0%;">Church and Wellesley Cluster 0.0</div>`)[0];
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        circle_marker_cc67acec532a4649aad6babb286a3a29.bindPopup(popup_7c0c1fd8935c49f3a53cf1bf53fca105)
        ;

        
    
    
            var circle_marker_7d70922c72e14c37b30eb63100b3ed9c = L.circleMarker(
                [43.648690000000045, -79.38543999999996],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_8f3cc9d69c8b4307b5c19743e6d587f1 = L.popup({"maxWidth": "100%"});

        
            var html_af5344ba09144914b51ae9804f73dd4c = $(`<div id="html_af5344ba09144914b51ae9804f73dd4c" style="width: 100.0%; height: 100.0%;">Business reply mail Processing Centre, South Central Letter Processing Plant Toronto Cluster 0.0</div>`)[0];
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        ;

        
    
    
            var circle_marker_6d97caed027f4d92a88634ecc7affc3c = L.circleMarker(
                [43.632780000000025, -79.48944999999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_57ae582333a94ff98ee5fa21fe9487ec = L.popup({"maxWidth": "100%"});

        
            var html_aa138a8c1f9344bdab1b42361898083c = $(`<div id="html_aa138a8c1f9344bdab1b42361898083c" style="width: 100.0%; height: 100.0%;">Old Mill South, King&#39;s Mill Park, Sunnylea, Humber Bay, Mimico NE, The Queensway East, Royal York South East, Kingsway Park South East Cluster 0.0</div>`)[0];
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        circle_marker_6d97caed027f4d92a88634ecc7affc3c.bindPopup(popup_57ae582333a94ff98ee5fa21fe9487ec)
        ;

        
    
    
            var circle_marker_f82d1f127c094f8a99ed6238323cd82f = L.circleMarker(
                [43.62513000000007, -79.52680999999995],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_8b7e3d13b05d47878871b36881e4876f);
        
    
        var popup_635a7b6702e9451ea4736a010f3f69b7 = L.popup({"maxWidth": "100%"});

        
            var html_f9f42ac0491a48caa72979fddd66b85b = $(`<div id="html_f9f42ac0491a48caa72979fddd66b85b" style="width: 100.0%; height: 100.0%;">Mimico NW, The Queensway West, South of Bloor, Kingsway Park South West, Royal York South West Cluster 0.0</div>`)[0];
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        circle_marker_f82d1f127c094f8a99ed6238323cd82f.bindPopup(popup_635a7b6702e9451ea4736a010f3f69b7)
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x2a99db91e20>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "# create map\n",
    "Toronto_coord=geocoder.arcgis('Toronto, Canada').latlng\n",
    "map_clusters = folium.Map(location=[Toronto_coord[0]+0.05,Toronto_coord[1]], zoom_start=11)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(k)\n",
    "ys = [i + x + (i*x)**2 for i in range(k)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "x=0\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(Toronto_merged['Latitude'], Toronto_merged['Longitude'], Toronto_merged['Neighborhood'], Toronto_merged['Cluster Labels']):\n",
    "    if math.isnan(cluster)==False: #as mentioned abvoe we have three missing values for Cluster. So we only create labels for those cases which do have a cluster label\n",
    "         label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "         folium.CircleMarker(\n",
    "             [lat, lon],\n",
    "             radius=5,\n",
    "             popup=label,\n",
    "             color=rainbow[int(cluster)-1],\n",
    "             fill=True,\n",
    "             fill_color=rainbow[int(cluster)-1],\n",
    "             fill_opacity=0.7).add_to(map_clusters)\n",
    "         x=x+1\n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The map will not display on github. You can find an image of the map [here](https://github.com/cromicron/Coursera_Capstone/blob/main/TorontoMap.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine Clusters\n",
    "When we simply look at the map we can see that there is a high density of red neighborhoods in downtown Toronto. The red neighborhoods seem to be quite evenly distributed in the north and east. Let's check out what venues are typical for the clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neighborhoods in Cluster 0: 89\n",
      "Neighborhoods in Cluster 1: 11\n"
     ]
    }
   ],
   "source": [
    "print('Neighborhoods in Cluster 0:', len(Toronto_merged[Toronto_merged['Cluster Labels']==0]))\n",
    "print('Neighborhoods in Cluster 1:', len(Toronto_merged[Toronto_merged['Cluster Labels']==1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we look at our map, we see that the red clusters, with high denisty in downtown are cluster 0, and the red ones are cluster 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Distribution Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North York</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Women's Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Burrito Place</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Café</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Skating Rink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Fish Market</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pool</td>\n",
       "      <td>Farm</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Elementary School</td>\n",
       "      <td>Escape Room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Gay Bar</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>East Toronto</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Café</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Flower Shop</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mattress Store</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Eastern European Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Borough  Cluster Labels 1st Most Common Venue  \\\n",
       "2    Downtown Toronto             0.0           Coffee Shop   \n",
       "3          North York             0.0        Clothing Store   \n",
       "4    Downtown Toronto             0.0           Coffee Shop   \n",
       "5           Etobicoke             0.0              Pharmacy   \n",
       "6         Scarborough             0.0           Zoo Exhibit   \n",
       "..                ...             ...                   ...   \n",
       "98          Etobicoke             0.0                  Pool   \n",
       "99   Downtown Toronto             0.0           Coffee Shop   \n",
       "100      East Toronto             0.0           Coffee Shop   \n",
       "101         Etobicoke             0.0                  Bank   \n",
       "102         Etobicoke             0.0        Mattress Store   \n",
       "\n",
       "      2nd Most Common Venue        3rd Most Common Venue  \\\n",
       "2            Breakfast Spot                          Pub   \n",
       "3    Furniture / Home Store                  Coffee Shop   \n",
       "4            Sandwich Place                Burrito Place   \n",
       "5             Grocery Store                         Café   \n",
       "6      Fast Food Restaurant       Furniture / Home Store   \n",
       "..                      ...                          ...   \n",
       "98                     Farm            Electronics Store   \n",
       "99                  Gay Bar          Japanese Restaurant   \n",
       "100                   Hotel                   Restaurant   \n",
       "101      Italian Restaurant           Chinese Restaurant   \n",
       "102    Gym / Fitness Center  Eastern European Restaurant   \n",
       "\n",
       "    4th Most Common Venue 5th Most Common Venue  \n",
       "2       Electronics Store   Distribution Center  \n",
       "3        Toy / Game Store         Women's Store  \n",
       "4      Falafel Restaurant                  Bank  \n",
       "5           Shopping Mall          Skating Rink  \n",
       "6             Fish Market     Fish & Chips Shop  \n",
       "..                    ...                   ...  \n",
       "98      Elementary School           Escape Room  \n",
       "99             Restaurant      Sushi Restaurant  \n",
       "100                  Café                   Bar  \n",
       "101           Flower Shop  Fast Food Restaurant  \n",
       "102       Thai Restaurant      Sushi Restaurant  \n",
       "\n",
       "[89 rows x 7 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_merged.loc[Toronto_merged['Cluster Labels'] == 0, Toronto_merged.columns[[1] + list(range(5, Toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that cluster 0 consists of neighborhoods with many coffee shops, Restaurants and shopping malls. This is typical for city centers. We saw in the map, that there is a high density of this cluster in downtown Torotno."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North York</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Elementary School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North York</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Park</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Park</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "      <td>Fish Market</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>North York</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Residential Building (Apartment / Condo)</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Event Space</td>\n",
       "      <td>Eastern European Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>East York</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Park</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Fish Market</td>\n",
       "      <td>Fish &amp; Chips Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>North York</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Elementary School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>North York</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Park</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Elementary School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>North York</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Speakeasy</td>\n",
       "      <td>Park</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>1.0</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Farm</td>\n",
       "      <td>Elementary School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>West Toronto</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Residential Building (Apartment / Condo)</td>\n",
       "      <td>Park</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Creperie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Central Toronto</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Gym Pool</td>\n",
       "      <td>Park</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Borough  Cluster Labels       1st Most Common Venue  \\\n",
       "0        North York             1.0           Food & Drink Shop   \n",
       "1        North York             1.0               Grocery Store   \n",
       "18      Scarborough             1.0  Construction & Landscaping   \n",
       "27       North York             1.0                        Park   \n",
       "35        East York             1.0           Convenience Store   \n",
       "45       North York             1.0                        Park   \n",
       "49       North York             1.0                      Bakery   \n",
       "66       North York             1.0           Convenience Store   \n",
       "68  Central Toronto             1.0           French Restaurant   \n",
       "69     West Toronto             1.0           Convenience Store   \n",
       "73  Central Toronto             1.0                  Playground   \n",
       "\n",
       "                       2nd Most Common Venue 3rd Most Common Venue  \\\n",
       "0                                       Park    Falafel Restaurant   \n",
       "1                                       Park              Pharmacy   \n",
       "18                                      Park  Gym / Fitness Center   \n",
       "27  Residential Building (Apartment / Condo)           Zoo Exhibit   \n",
       "35                                      Park          Intersection   \n",
       "45                               Zoo Exhibit    Falafel Restaurant   \n",
       "49                                      Park    Falafel Restaurant   \n",
       "66                                 Speakeasy                  Park   \n",
       "68                                      Park           Zoo Exhibit   \n",
       "69  Residential Building (Apartment / Condo)                  Park   \n",
       "73                                  Gym Pool                  Park   \n",
       "\n",
       "   4th Most Common Venue        5th Most Common Venue  \n",
       "0      Electronics Store            Elementary School  \n",
       "1            Zoo Exhibit            Electronics Store  \n",
       "18           Fish Market            Fish & Chips Shop  \n",
       "27           Event Space  Eastern European Restaurant  \n",
       "35           Fish Market            Fish & Chips Shop  \n",
       "45     Electronics Store            Elementary School  \n",
       "49     Electronics Store            Elementary School  \n",
       "66    Falafel Restaurant            Electronics Store  \n",
       "68                  Farm            Elementary School  \n",
       "69        Sandwich Place                     Creperie  \n",
       "73           Zoo Exhibit           Falafel Restaurant  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Toronto_merged.loc[Toronto_merged['Cluster Labels'] == 1, Toronto_merged.columns[[1] + list(range(5, Toronto_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared to cluster 0, we see that cluster 1 consists of many grocery stores, construction and landscaping values and parks.\n",
    "It makes sense - since downtown Toronto is your typical city center with venues to hang out and shop, while the neighborhoods in cluster 1 are probably more regions where families live, so they have many grocery stores and parks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a thrid cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we gain any useful information by adding a third cluster. We run the same code as above with an increased k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, n_init=20)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using k=3.\n",
    "k=3\n",
    "k_means = KMeans(init = \"k-means++\", n_clusters = k, n_init = 20)\n",
    "k_means.fit(Toronto_grouped_clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\lib\\site-packages\\pandas\\core\\frame.py:4300: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().rename(\n"
     ]
    },
    {
     "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",
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       "    }\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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.75245</td>\n",
       "      <td>-79.32991</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Elementary School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.73057</td>\n",
       "      <td>-79.31306</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Park</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Zoo Exhibit</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "      <td>43.65512</td>\n",
       "      <td>-79.36264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Distribution Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "      <td>43.72327</td>\n",
       "      <td>-79.45042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Furniture / Home Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Toy / Game Store</td>\n",
       "      <td>Women's Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "      <td>43.66253</td>\n",
       "      <td>-79.39188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Burrito Place</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code           Borough                                 Neighborhood  \\\n",
       "0         M3A        North York                                    Parkwoods   \n",
       "1         M4A        North York                             Victoria Village   \n",
       "2         M5A  Downtown Toronto                    Regent Park, Harbourfront   \n",
       "3         M6A        North York             Lawrence Manor, Lawrence Heights   \n",
       "4         M7A  Downtown Toronto  Queen's Park, Ontario Provincial Government   \n",
       "\n",
       "   Latitude  Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "0  43.75245  -79.32991             2.0     Food & Drink Shop   \n",
       "1  43.73057  -79.31306             2.0         Grocery Store   \n",
       "2  43.65512  -79.36264             0.0           Coffee Shop   \n",
       "3  43.72327  -79.45042             0.0        Clothing Store   \n",
       "4  43.66253  -79.39188             0.0           Coffee Shop   \n",
       "\n",
       "    2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0                    Park    Falafel Restaurant     Electronics Store   \n",
       "1                    Park              Pharmacy           Zoo Exhibit   \n",
       "2          Breakfast Spot                   Pub     Electronics Store   \n",
       "3  Furniture / Home Store           Coffee Shop      Toy / Game Store   \n",
       "4          Sandwich Place         Burrito Place    Falafel Restaurant   \n",
       "\n",
       "  5th Most Common Venue  \n",
       "0     Elementary School  \n",
       "1     Electronics Store  \n",
       "2   Distribution Center  \n",
       "3         Women's Store  \n",
       "4                  Bank  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted['Cluster Labels']= k_means.labels_\n",
    "\n",
    "df_postal.rename(columns={\"Neighbourhood\": \"Neighborhood\"},inplace=True) #We have different spellings of neighborhood, so we have to adjust.\n",
    "Toronto_merged = df_postal\n",
    "\n",
    "# merge Toronto_grouped with Toronto_data to add latitude/longitude for each neighborhood\n",
    "Toronto_merged = Toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "Toronto_merged.head() # check the cloumn 'Cluster Labels'!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neighborhoods in Cluster 0: 88\n",
      "Neighborhoods in Cluster 1: 1\n",
      "Neighborhoods in Cluster 1: 11\n"
     ]
    }
   ],
   "source": [
    "#Let's see how many rows fall into each of the three categories:\n",
    "print('Neighborhoods in Cluster 0:', len(Toronto_merged[Toronto_merged['Cluster Labels']==0]))\n",
    "print('Neighborhoods in Cluster 1:', len(Toronto_merged[Toronto_merged['Cluster Labels']==1]))\n",
    "print('Neighborhoods in Cluster 1:', len(Toronto_merged[Toronto_merged['Cluster Labels']==2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see a new cluster was created, which only consists of 1 neighborhood. We can see that this does not help us very much, so we stop the analysis here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "We found two clusters of neighborhoods in Toronto. Roughly we can sort them into neighborhoods with potentiality for going-out ,with many coffee shops and restaurants (high density in Downtown Toronto), and a second cluster of neighborhoods mostly for living, with grocery stores and parks. We can expect families to live there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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