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drdebian/Toronto_3.ipynb

Created August 20, 2019 08:53
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Toronto_3.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore, segment, and cluster the neighborhoods in the city of Toronto"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this assignment, you will be required to explore, segment, and cluster the neighborhoods in the city of Toronto. However, unlike New York, the neighborhood data is not readily available on the internet. What is interesting about the field of data science is that each project can be challenging in its unique way, so you need to learn to be agile and refine the skill to learn new libraries and tools quickly depending on the project.\n",
"\n",
"For the Toronto neighborhood data, a Wikipedia page exists that has all the information we need to explore and cluster the neighborhoods in Toronto. You will be required to scrape the Wikipedia page and wrangle the data, clean it, and then read it into a pandas dataframe so that it is in a structured format like the New York dataset.\n",
"\n",
"Once the data is in a structured format, you can replicate the analysis that we did to the New York City dataset to explore and cluster the neighborhoods in the city of Toronto.\n",
"\n",
"Your submission will be a link to your Jupyter Notebook on your Github repository."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get Toronto neighborhood data\n",
"\n",
"For this assignment, you will be required to explore and cluster the neighborhoods in Toronto.\n",
"\n",
"1. Start by creating a new Notebook for this assignment.\n",
"2. Use the Notebook to build the code to scrape the following Wikipedia page, https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M, in order to obtain the data that is in the table of postal codes and to transform the data into a pandas dataframe like the one shown below:\n",
"![](https://d3c33hcgiwev3.cloudfront.net/imageAssetProxy.v1/7JXaz3NNEeiMwApe4i-fLg_40e690ae0e927abda2d4bde7d94ed133_Screen-Shot-2018-06-18-at-7.17.57-PM.png?expiry=1566000000000&hmac=AN3CJ7qeqs6bod-Dt7oM7fnL3e5Hx_ERYwMV3M1TSyM)\n",
"\n",
"3. To create the above dataframe:\n",
"\n",
"- The dataframe will consist of three columns: PostalCode, Borough, and Neighborhood\n",
"- Only process the cells that have an assigned borough. Ignore cells with a borough that is Not assigned.\n",
"- More than one neighborhood can exist in one postal code area. For example, in the table on the Wikipedia page, you will notice that M5A is listed twice and has two neighborhoods: Harbourfront and Regent Park. These two rows will be combined into one row with the neighborhoods separated with a comma as shown in row 11 in the above table.\n",
"- If a cell has a borough but a Not assigned neighborhood, then the neighborhood will be the same as the borough. So for the 9th cell in the table on the Wikipedia page, the value of the Borough and the Neighborhood columns will be Queen's Park.\n",
"- Clean your Notebook and add Markdown cells to explain your work and any assumptions you are making.\n",
"- In the last cell of your notebook, use the .shape method to print the number of rows of your dataframe.\n",
"4. Submit a link to your Notebook on your Github repository."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pull the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's load the necessary libraries first..."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Solving environment: done\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.5.11\n",
" latest version: 4.7.11\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c defaults conda\n",
"\n",
"\n",
"\n",
"# All requested packages already installed.\n",
"\n"
]
}
],
"source": [
"### libraries\n",
"import requests\n",
"import pandas as pd\n",
"pd.set_option('display.max_columns', 100)\n",
"pd.set_option('display.max_rows', 100)\n",
"\n",
"!conda install -c conda-forge beautifulsoup4 lxml html5lib --yes\n",
"from bs4 import BeautifulSoup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's pull down the data from that Wiki page..."
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>Postcode</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>Harbourfront</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postcode 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 Harbourfront"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### data\n",
"wiki_url = \"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\"\n",
"\n",
"# request\n",
"myres = requests.get(wiki_url)\n",
"mysoup = BeautifulSoup(myres.content, 'html5lib')\n",
"\n",
"# read table into df\n",
"mytable = mysoup.find_all('table')[0] \n",
"mydf = pd.read_html(str(mytable))[0]\n",
"\n",
"# check it\n",
"mydf.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Process the data\n",
"\n",
"Time for some filtering and postprocessing..."
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>Postcode</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>Harbourfront</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>M5A</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>Regent Park</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>M6A</td>\n",
" <td>North York</td>\n",
" <td>Lawrence Heights</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postcode Borough Neighbourhood\n",
"2 M3A North York Parkwoods\n",
"3 M4A North York Victoria Village\n",
"4 M5A Downtown Toronto Harbourfront\n",
"5 M5A Downtown Toronto Regent Park\n",
"6 M6A North York Lawrence Heights"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get rid of the \"Not assigned\" boroughs\n",
"mydf_bor = mydf[~mydf.Borough.isin(['Not assigned'])]\n",
"mydf_bor.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Postcodes appearing multiple times should be combined into one line with the neighbourhoods concatenated..."
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>Postcode</th>\n",
" <th>Borough</th>\n",
" <th>Neighbourhood</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1B</td>\n",
" <td>Scarborough</td>\n",
" <td>Rouge, Malvern</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M1C</td>\n",
" <td>Scarborough</td>\n",
" <td>Highland Creek, Rouge Hill, Port Union</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M1E</td>\n",
" <td>Scarborough</td>\n",
" <td>Guildwood, Morningside, West Hill</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M1G</td>\n",
" <td>Scarborough</td>\n",
" <td>Woburn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M1H</td>\n",
" <td>Scarborough</td>\n",
" <td>Cedarbrae</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postcode Borough Neighbourhood\n",
"0 M1B Scarborough Rouge, Malvern\n",
"1 M1C Scarborough Highland Creek, Rouge Hill, Port Union\n",
"2 M1E Scarborough Guildwood, Morningside, West Hill\n",
"3 M1G Scarborough Woburn\n",
"4 M1H Scarborough Cedarbrae"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# make those postcodes unique\n",
"mydf_combined = mydf_bor.groupby(['Postcode','Borough'])['Neighbourhood'].apply(', '.join).reset_index()\n",
"mydf_combined.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let's fix those rows where the Neighbourhood is \"not assigned\" by filling it with the content of the Borough column..."
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>Postcode</th>\n",
" <th>Borough</th>\n",
" <th>Neighbourhood</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1B</td>\n",
" <td>Scarborough</td>\n",
" <td>Rouge, Malvern</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M1C</td>\n",
" <td>Scarborough</td>\n",
" <td>Highland Creek, Rouge Hill, Port Union</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M1E</td>\n",
" <td>Scarborough</td>\n",
" <td>Guildwood, Morningside, West Hill</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M1G</td>\n",
" <td>Scarborough</td>\n",
" <td>Woburn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M1H</td>\n",
" <td>Scarborough</td>\n",
" <td>Cedarbrae</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postcode Borough Neighbourhood\n",
"0 M1B Scarborough Rouge, Malvern\n",
"1 M1C Scarborough Highland Creek, Rouge Hill, Port Union\n",
"2 M1E Scarborough Guildwood, Morningside, West Hill\n",
"3 M1G Scarborough Woburn\n",
"4 M1H Scarborough Cedarbrae"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fix the neighbourhood column where it contains \"not assigned\"\n",
"mydf_combined.loc[mydf_combined['Neighbourhood']==\"Not assigned\", 'Neighbourhood'] = mydf_combined.loc[mydf_combined['Neighbourhood']==\"Not assigned\", 'Borough']\n",
"mydf_combined.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# save the Toronto data to CSV for later use\n",
"mydf_combined.to_csv('toronto_postcodes.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(103, 3)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mydf_combined.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Enrich the Data\n",
"\n",
"### Getting geospatial coordinates\n",
"\n",
"Since the routine for obtaining geo coordinates described in the task outline did indeed prove to be rather unreliable, I opted for proceeding with the provided CSV file.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>Postal Code</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1B</td>\n",
" <td>43.806686</td>\n",
" <td>-79.194353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M1C</td>\n",
" <td>43.784535</td>\n",
" <td>-79.160497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M1E</td>\n",
" <td>43.763573</td>\n",
" <td>-79.188711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M1G</td>\n",
" <td>43.770992</td>\n",
" <td>-79.216917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M1H</td>\n",
" <td>43.773136</td>\n",
" <td>-79.239476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postal Code Latitude Longitude\n",
"0 M1B 43.806686 -79.194353\n",
"1 M1C 43.784535 -79.160497\n",
"2 M1E 43.763573 -79.188711\n",
"3 M1G 43.770992 -79.216917\n",
"4 M1H 43.773136 -79.239476"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read geo coordinates from CSV\n",
"mydf_geo = pd.read_csv('Geospatial_Coordinates.csv')\n",
"mydf_geo.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's add the 2 new Latitude and Longitude columns to the existing data frame."
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>Postcode</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>M1B</td>\n",
" <td>Scarborough</td>\n",
" <td>Rouge, Malvern</td>\n",
" <td>43.806686</td>\n",
" <td>-79.194353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M1C</td>\n",
" <td>Scarborough</td>\n",
" <td>Highland Creek, Rouge Hill, Port Union</td>\n",
" <td>43.784535</td>\n",
" <td>-79.160497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M1E</td>\n",
" <td>Scarborough</td>\n",
" <td>Guildwood, Morningside, West Hill</td>\n",
" <td>43.763573</td>\n",
" <td>-79.188711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M1G</td>\n",
" <td>Scarborough</td>\n",
" <td>Woburn</td>\n",
" <td>43.770992</td>\n",
" <td>-79.216917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M1H</td>\n",
" <td>Scarborough</td>\n",
" <td>Cedarbrae</td>\n",
" <td>43.773136</td>\n",
" <td>-79.239476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postcode Borough Neighbourhood Latitude \\\n",
"0 M1B Scarborough Rouge, Malvern 43.806686 \n",
"1 M1C Scarborough Highland Creek, Rouge Hill, Port Union 43.784535 \n",
"2 M1E Scarborough Guildwood, Morningside, West Hill 43.763573 \n",
"3 M1G Scarborough Woburn 43.770992 \n",
"4 M1H Scarborough Cedarbrae 43.773136 \n",
"\n",
" Longitude \n",
"0 -79.194353 \n",
"1 -79.160497 \n",
"2 -79.188711 \n",
"3 -79.216917 \n",
"4 -79.239476 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# add geo columns to the existing data frame, remove redundant Postal Code column\n",
"mydf_post = pd.merge(mydf_combined, mydf_geo, how='left', left_on='Postcode', right_on='Postal Code')\n",
"mydf_post.drop('Postal Code', axis=1, inplace=True)\n",
"mydf_post.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(103, 5)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mydf_post.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore and cluster the neighborhoods in Toronto\n",
"\n",
"In the following, we want to separate Toronto's Neighbourhoods into different clusters as a guide to people thinking about moving to or within Toronto. To do so, we use the previously prepared data and enrich it with the venues present in Toronto using Foursquare as an external data source. With this data, we proceed to determine the top 10 venues found in each neighbourhood and use this data to actually find clusters. In the process, we will also make an informed decision about how many clusters make sense and proceed to visualize them using Folium, after which the clusters are inspected and a final conclusion will be drawn."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's download all the dependencies that we will need."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Solving environment: done\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.5.11\n",
" latest version: 4.7.11\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c defaults conda\n",
"\n",
"\n",
"\n",
"# All requested packages already installed.\n",
"\n",
"Solving environment: done\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.5.11\n",
" latest version: 4.7.11\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c defaults conda\n",
"\n",
"\n",
"\n",
"# All requested packages already installed.\n",
"\n",
"Libraries imported.\n"
]
}
],
"source": [
"### libraries\n",
"import numpy as np # library to handle data in a vectorized manner\n",
"\n",
"import json # library to handle JSON files\n",
"\n",
"!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
"from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
"\n",
"import requests # library to handle requests\n",
"from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
"\n",
"# Matplotlib and associated plotting modules\n",
"import matplotlib.cm as cm\n",
"import matplotlib.colors as colors\n",
"\n",
"# import k-means from clustering stage\n",
"from sklearn.cluster import KMeans\n",
"\n",
"!conda install -c conda-forge folium --yes \n",
"import folium # map rendering library\n",
"\n",
"print('Libraries imported.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a map of Toronto with neighborhoods superimposed on top.\n",
"\n",
"Since our analysis will focus on Toronto, let's determine the central coordinates using Nominatim."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The geograpical coordinate of Toronto City are 43.653963, -79.387207.\n"
]
}
],
"source": [
"address = 'Toronto, ON'\n",
"\n",
"geolocator = Nominatim(user_agent=\"toronto_explorer\")\n",
"location = geolocator.geocode(address)\n",
"latitude = location.latitude\n",
"longitude = location.longitude\n",
"print('The geograpical coordinate of Toronto City are {}, {}.'.format(latitude, longitude))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To verify that we're on the right track, let's quickly plot a map centered on those coordinates."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f89b7274eb8>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map of Toronto using latitude and longitude values\n",
"map_toronto = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
"\n",
"# add markers to map\n",
"for lat, lng, borough, neighborhood in zip(mydf_post['Latitude'], mydf_post['Longitude'], mydf_post['Borough'], mydf_post['Neighbourhood']):\n",
" label = '{}, {}'.format(neighborhood, borough)\n",
" label = folium.Popup(label, parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lng],\n",
" radius=9,\n",
" popup=label,\n",
" color='blue',\n",
" fill=True,\n",
" fill_color='#3287cd',\n",
" fill_opacity=0.5,\n",
" parse_html=False).add_to(map_toronto) \n",
" \n",
"map_toronto"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Foursquare data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First off, we need to enter our credentials in order to access the Foursquare API."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"jupyter": {
"source_hidden": true
}
},
"outputs": [],
"source": [
"CLIENT_ID = 'EOGS2ZA3IH1DZAGOT0G0MLFHMRLQSAV1TMGAIW4N2EJEGPFG' # your Foursquare ID\n",
"CLIENT_SECRET = 'M2QXMSBBAOTLRW5RCOSNJUGYM0SRZB3WMT3QMR4SB1HVOYIT' # your Foursquare Secret\n",
"VERSION = '20180605' # Foursquare API version\n",
"\n",
"#print('Your credentails:')\n",
"#print('CLIENT_ID: ' + CLIENT_ID)\n",
"#print('CLIENT_SECRET:' + CLIENT_SECRET)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's set some sensible defaults to limit the amount of data we are pulling from Foursquare."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"LIMIT = 100 # limit of number of venues returned by Foursquare API\n",
"radius = 500 # define radius"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make gathering the nearby venues more efficient, let's create a function to pull the relevant data for all the Boroughs that returns the data in a neat data frame."
]
},
{
"cell_type": "code",
"execution_count": 16,
"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 = ['Neighbourhood', \n",
" 'Neighbourhood Latitude', \n",
" 'Neighbourhood Longitude', \n",
" 'Venue', \n",
" 'Venue Latitude', \n",
" 'Venue Longitude', \n",
" 'Venue Category']\n",
" \n",
" return(nearby_venues)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's call the function with our data as parameter."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rouge, Malvern\n",
"Highland Creek, Rouge Hill, Port Union\n",
"Guildwood, Morningside, West Hill\n",
"Woburn\n",
"Cedarbrae\n",
"Scarborough Village\n",
"East Birchmount Park, Ionview, Kennedy Park\n",
"Clairlea, Golden Mile, Oakridge\n",
"Cliffcrest, Cliffside, Scarborough Village West\n",
"Birch Cliff, Cliffside West\n",
"Dorset Park, Scarborough Town Centre, Wexford Heights\n",
"Maryvale, Wexford\n",
"Agincourt\n",
"Clarks Corners, Sullivan, Tam O'Shanter\n",
"Agincourt North, L'Amoreaux East, Milliken, Steeles East\n",
"L'Amoreaux West\n",
"Upper Rouge\n",
"Hillcrest Village\n",
"Fairview, Henry Farm, Oriole\n",
"Bayview Village\n",
"Silver Hills, York Mills\n",
"Newtonbrook, Willowdale\n",
"Willowdale South\n",
"York Mills West\n",
"Willowdale West\n",
"Parkwoods\n",
"Don Mills North\n",
"Flemingdon Park, Don Mills South\n",
"Bathurst Manor, Downsview North, Wilson Heights\n",
"Northwood Park, York University\n",
"CFB Toronto, Downsview East\n",
"Downsview West\n",
"Downsview Central\n",
"Downsview Northwest\n",
"Victoria Village\n",
"Woodbine Gardens, Parkview Hill\n",
"Woodbine Heights\n",
"The Beaches\n",
"Leaside\n",
"Thorncliffe Park\n",
"East Toronto\n",
"The Danforth West, Riverdale\n",
"The Beaches West, India Bazaar\n",
"Studio District\n",
"Lawrence Park\n",
"Davisville North\n",
"North Toronto West\n",
"Davisville\n",
"Moore Park, Summerhill East\n",
"Deer Park, Forest Hill SE, Rathnelly, South Hill, Summerhill West\n",
"Rosedale\n",
"Cabbagetown, St. James Town\n",
"Church and Wellesley\n",
"Harbourfront, Regent Park\n",
"Ryerson, Garden District\n",
"St. James Town\n",
"Berczy Park\n",
"Central Bay Street\n",
"Adelaide, King, Richmond\n",
"Harbourfront East, Toronto Islands, Union Station\n",
"Design Exchange, Toronto Dominion Centre\n",
"Commerce Court, Victoria Hotel\n",
"Bedford Park, Lawrence Manor East\n",
"Roselawn\n",
"Forest Hill North, Forest Hill West\n",
"The Annex, North Midtown, Yorkville\n",
"Harbord, University of Toronto\n",
"Chinatown, Grange Park, Kensington Market\n",
"CN Tower, Bathurst Quay, Island airport, Harbourfront West, King and Spadina, Railway Lands, South Niagara\n",
"Stn A PO Boxes 25 The Esplanade\n",
"First Canadian Place, Underground city\n",
"Lawrence Heights, Lawrence Manor\n",
"Glencairn\n",
"Humewood-Cedarvale\n",
"Caledonia-Fairbanks\n",
"Christie\n",
"Dovercourt Village, Dufferin\n",
"Little Portugal, Trinity\n",
"Brockton, Exhibition Place, Parkdale Village\n",
"Downsview, North Park, Upwood Park\n",
"Del Ray, Keelesdale, Mount Dennis, Silverthorn\n",
"The Junction North, Runnymede\n",
"High Park, The Junction South\n",
"Parkdale, Roncesvalles\n",
"Runnymede, Swansea\n",
"Queen's Park\n",
"Canada Post Gateway Processing Centre\n",
"Business Reply Mail Processing Centre 969 Eastern\n",
"Humber Bay Shores, Mimico South, New Toronto\n",
"Alderwood, Long Branch\n",
"The Kingsway, Montgomery Road, Old Mill North\n",
"Humber Bay, King's Mill Park, Kingsway Park South East, Mimico NE, Old Mill South, The Queensway East, Royal York South East, Sunnylea\n",
"Kingsway Park South West, Mimico NW, The Queensway West, Royal York South West, South of Bloor\n",
"Islington Avenue\n",
"Cloverdale, Islington, Martin Grove, Princess Gardens, West Deane Park\n",
"Bloordale Gardens, Eringate, Markland Wood, Old Burnhamthorpe\n",
"Humber Summit\n",
"Emery, Humberlea\n",
"Weston\n",
"Westmount\n",
"Kingsview Village, Martin Grove Gardens, Richview Gardens, St. Phillips\n",
"Albion Gardens, Beaumond Heights, Humbergate, Jamestown, Mount Olive, Silverstone, South Steeles, Thistletown\n",
"Northwest\n"
]
}
],
"source": [
"toronto_venues = getNearbyVenues(names=mydf_post['Neighbourhood'],\n",
" latitudes=mydf_post['Latitude'],\n",
" longitudes=mydf_post['Longitude']\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's inspect how much data we got back."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2244, 7)\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>Neighbourhood</th>\n",
" <th>Neighbourhood Latitude</th>\n",
" <th>Neighbourhood Longitude</th>\n",
" <th>Venue</th>\n",
" <th>Venue Latitude</th>\n",
" <th>Venue Longitude</th>\n",
" <th>Venue Category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rouge, Malvern</td>\n",
" <td>43.806686</td>\n",
" <td>-79.194353</td>\n",
" <td>Wendy's</td>\n",
" <td>43.807448</td>\n",
" <td>-79.199056</td>\n",
" <td>Fast Food Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Highland Creek, Rouge Hill, Port Union</td>\n",
" <td>43.784535</td>\n",
" <td>-79.160497</td>\n",
" <td>Royal Canadian Legion</td>\n",
" <td>43.782533</td>\n",
" <td>-79.163085</td>\n",
" <td>Bar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Guildwood, Morningside, West Hill</td>\n",
" <td>43.763573</td>\n",
" <td>-79.188711</td>\n",
" <td>Swiss Chalet Rotisserie &amp; Grill</td>\n",
" <td>43.767697</td>\n",
" <td>-79.189914</td>\n",
" <td>Pizza Place</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Guildwood, Morningside, West Hill</td>\n",
" <td>43.763573</td>\n",
" <td>-79.188711</td>\n",
" <td>G &amp; G Electronics</td>\n",
" <td>43.765309</td>\n",
" <td>-79.191537</td>\n",
" <td>Electronics Store</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Guildwood, Morningside, West Hill</td>\n",
" <td>43.763573</td>\n",
" <td>-79.188711</td>\n",
" <td>Big Bite Burrito</td>\n",
" <td>43.766299</td>\n",
" <td>-79.190720</td>\n",
" <td>Mexican Restaurant</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighbourhood Neighbourhood Latitude \\\n",
"0 Rouge, Malvern 43.806686 \n",
"1 Highland Creek, Rouge Hill, Port Union 43.784535 \n",
"2 Guildwood, Morningside, West Hill 43.763573 \n",
"3 Guildwood, Morningside, West Hill 43.763573 \n",
"4 Guildwood, Morningside, West Hill 43.763573 \n",
"\n",
" Neighbourhood Longitude Venue Venue Latitude \\\n",
"0 -79.194353 Wendy's 43.807448 \n",
"1 -79.160497 Royal Canadian Legion 43.782533 \n",
"2 -79.188711 Swiss Chalet Rotisserie & Grill 43.767697 \n",
"3 -79.188711 G & G Electronics 43.765309 \n",
"4 -79.188711 Big Bite Burrito 43.766299 \n",
"\n",
" Venue Longitude Venue Category \n",
"0 -79.199056 Fast Food Restaurant \n",
"1 -79.163085 Bar \n",
"2 -79.189914 Pizza Place \n",
"3 -79.191537 Electronics Store \n",
"4 -79.190720 Mexican Restaurant "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(toronto_venues.shape)\n",
"toronto_venues.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analyze Neighbourhoods"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the venue data from Foursquare, we are now ready to analyze the neighborhoods. Since we are interested in the frequency of the venue categories in each neighbourhood, we'll first one-hot encode those features and proceed with calculating the relative frequencies."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Series.unique of 0 Rouge, Malvern\n",
"1 Highland Creek, Rouge Hill, Port Union\n",
"2 Guildwood, Morningside, West Hill\n",
"3 Guildwood, Morningside, West Hill\n",
"4 Guildwood, Morningside, West Hill\n",
" ... \n",
"2239 Albion Gardens, Beaumond Heights, Humbergate, ...\n",
"2240 Albion Gardens, Beaumond Heights, Humbergate, ...\n",
"2241 Northwest\n",
"2242 Northwest\n",
"2243 Northwest\n",
"Name: Neighbourhood, Length: 2244, dtype: object>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_venues['Neighbourhood'].unique"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2244, 280)\n",
"(2244, 281)\n"
]
},
{
"data": {
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" <th></th>\n",
" <th>Neighbourhood</th>\n",
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" <th>Airport</th>\n",
" <th>Airport Food Court</th>\n",
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" <th>Asian Restaurant</th>\n",
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" <th>Beach</th>\n",
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" <th>Bistro</th>\n",
" <th>Boat or Ferry</th>\n",
" <th>Bookstore</th>\n",
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" <th>Brazilian Restaurant</th>\n",
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" <th>Brewery</th>\n",
" <th>Bridal Shop</th>\n",
" <th>Bubble Tea Shop</th>\n",
" <th>Building</th>\n",
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" <th>...</th>\n",
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" <th>Sandwich Place</th>\n",
" <th>Scenic Lookout</th>\n",
" <th>Sculpture Garden</th>\n",
" <th>Seafood Restaurant</th>\n",
" <th>Shoe Store</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Shopping Plaza</th>\n",
" <th>Skate Park</th>\n",
" <th>Skating Rink</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Smoothie Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Soccer Field</th>\n",
" <th>Soup Place</th>\n",
" <th>Southern / Soul Food Restaurant</th>\n",
" <th>Spa</th>\n",
" <th>Speakeasy</th>\n",
" <th>Sporting Goods Shop</th>\n",
" <th>Sports Bar</th>\n",
" <th>Stadium</th>\n",
" <th>Stationery Store</th>\n",
" <th>Steakhouse</th>\n",
" <th>Strip Club</th>\n",
" <th>Supermarket</th>\n",
" <th>Supplement Shop</th>\n",
" <th>Sushi Restaurant</th>\n",
" <th>Swim School</th>\n",
" <th>Taco Place</th>\n",
" <th>Tailor Shop</th>\n",
" <th>Taiwanese Restaurant</th>\n",
" <th>Tanning Salon</th>\n",
" <th>Tapas Restaurant</th>\n",
" <th>Tea Room</th>\n",
" <th>Thai Restaurant</th>\n",
" <th>Theater</th>\n",
" <th>Theme Restaurant</th>\n",
" <th>Thrift / Vintage Store</th>\n",
" <th>Toy / Game Store</th>\n",
" <th>Trail</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Video Game Store</th>\n",
" <th>Video Store</th>\n",
" <th>Vietnamese Restaurant</th>\n",
" <th>Warehouse Store</th>\n",
" <th>Wine Bar</th>\n",
" <th>Wings Joint</th>\n",
" <th>Women's Store</th>\n",
" <th>Yoga Studio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Rouge, Malvern</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Guildwood, Morningside, West Hill</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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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>Guildwood, Morningside, West Hill</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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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 × 281 columns</p>\n",
"</div>"
],
"text/plain": [
" Neighbourhood Accessories Store \\\n",
"0 Rouge, Malvern 0 \n",
"1 Highland Creek, Rouge Hill, Port Union 0 \n",
"2 Guildwood, Morningside, West Hill 0 \n",
"3 Guildwood, Morningside, West Hill 0 \n",
"4 Guildwood, Morningside, West Hill 0 \n",
"\n",
" Afghan Restaurant Airport Airport Food Court Airport Gate \\\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",
" Airport Lounge Airport Service Airport Terminal American Restaurant \\\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",
" Antique Shop Aquarium Art Gallery Art Museum Arts & Crafts Store \\\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",
" Asian Restaurant Athletics & Sports Auto Garage Auto Workshop \\\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",
" BBQ Joint Baby Store Bagel Shop Bakery Bank Bar Baseball Field \\\n",
"0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 1 0 \n",
"2 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 0 \n",
"\n",
" Baseball Stadium Basketball Court Basketball Stadium Beach Beer Bar \\\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",
" Beer Store Bike Shop Bistro Boat or Ferry Bookstore Boutique \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" Brazilian Restaurant Breakfast Spot Brewery Bridal Shop \\\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",
" Bubble Tea Shop Building Burger Joint Burrito Place Bus Line \\\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",
" Bus Station Bus Stop Butcher Cafeteria ... Salon / Barbershop \\\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",
" Sandwich Place Scenic Lookout Sculpture Garden Seafood Restaurant \\\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",
" Shoe Store Shopping Mall Shopping Plaza Skate Park Skating Rink \\\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",
" Smoke Shop Smoothie Shop Snack Place Soccer Field Soup Place \\\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",
" Southern / Soul Food Restaurant Spa Speakeasy Sporting Goods Shop \\\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",
" Sports Bar Stadium Stationery Store Steakhouse Strip Club Supermarket \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" Supplement Shop Sushi Restaurant Swim School Taco Place Tailor Shop \\\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",
" Taiwanese Restaurant Tanning Salon Tapas Restaurant Tea Room \\\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",
" Thai Restaurant Theater Theme Restaurant Thrift / Vintage 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",
" Toy / Game Store Trail Train Station Vegetarian / Vegan Restaurant \\\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 Game Store Video Store Vietnamese Restaurant Warehouse 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",
" Wine Bar Wings Joint Women's Store Yoga Studio \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",
"[5 rows x 281 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# one hot encoding\n",
"toronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
"print(toronto_onehot.shape)\n",
"\n",
"# add neighborhood column back to dataframe\n",
"toronto_onehot['Neighbourhood'] = toronto_venues['Neighbourhood'] \n",
"print(toronto_onehot.shape)\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": "markdown",
"metadata": {},
"source": [
"Now let's group by Neighbourhood to determine the frequencies of the venue types."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(101, 281)\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>Neighbourhood</th>\n",
" <th>Accessories Store</th>\n",
" <th>Afghan Restaurant</th>\n",
" <th>Airport</th>\n",
" <th>Airport Food Court</th>\n",
" <th>Airport Gate</th>\n",
" <th>Airport Lounge</th>\n",
" <th>Airport Service</th>\n",
" <th>Airport Terminal</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>Athletics &amp; Sports</th>\n",
" <th>Auto Garage</th>\n",
" <th>Auto Workshop</th>\n",
" <th>BBQ Joint</th>\n",
" <th>Baby Store</th>\n",
" <th>Bagel Shop</th>\n",
" <th>Bakery</th>\n",
" <th>Bank</th>\n",
" <th>Bar</th>\n",
" <th>Baseball Field</th>\n",
" <th>Baseball Stadium</th>\n",
" <th>Basketball Court</th>\n",
" <th>Basketball Stadium</th>\n",
" <th>Beach</th>\n",
" <th>Beer Bar</th>\n",
" <th>Beer Store</th>\n",
" <th>Bike Shop</th>\n",
" <th>Bistro</th>\n",
" <th>Boat or Ferry</th>\n",
" <th>Bookstore</th>\n",
" <th>Boutique</th>\n",
" <th>Brazilian Restaurant</th>\n",
" <th>Breakfast Spot</th>\n",
" <th>Brewery</th>\n",
" <th>Bridal Shop</th>\n",
" <th>Bubble Tea Shop</th>\n",
" <th>Building</th>\n",
" <th>Burger Joint</th>\n",
" <th>Burrito Place</th>\n",
" <th>Bus Line</th>\n",
" <th>Bus Station</th>\n",
" <th>Bus Stop</th>\n",
" <th>Butcher</th>\n",
" <th>Cafeteria</th>\n",
" <th>...</th>\n",
" <th>Salon / Barbershop</th>\n",
" <th>Sandwich Place</th>\n",
" <th>Scenic Lookout</th>\n",
" <th>Sculpture Garden</th>\n",
" <th>Seafood Restaurant</th>\n",
" <th>Shoe Store</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Shopping Plaza</th>\n",
" <th>Skate Park</th>\n",
" <th>Skating Rink</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Smoothie Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Soccer Field</th>\n",
" <th>Soup Place</th>\n",
" <th>Southern / Soul Food Restaurant</th>\n",
" <th>Spa</th>\n",
" <th>Speakeasy</th>\n",
" <th>Sporting Goods Shop</th>\n",
" <th>Sports Bar</th>\n",
" <th>Stadium</th>\n",
" <th>Stationery Store</th>\n",
" <th>Steakhouse</th>\n",
" <th>Strip Club</th>\n",
" <th>Supermarket</th>\n",
" <th>Supplement Shop</th>\n",
" <th>Sushi Restaurant</th>\n",
" <th>Swim School</th>\n",
" <th>Taco Place</th>\n",
" <th>Tailor Shop</th>\n",
" <th>Taiwanese Restaurant</th>\n",
" <th>Tanning Salon</th>\n",
" <th>Tapas Restaurant</th>\n",
" <th>Tea Room</th>\n",
" <th>Thai Restaurant</th>\n",
" <th>Theater</th>\n",
" <th>Theme Restaurant</th>\n",
" <th>Thrift / Vintage Store</th>\n",
" <th>Toy / Game Store</th>\n",
" <th>Trail</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Video Game Store</th>\n",
" <th>Video Store</th>\n",
" <th>Vietnamese Restaurant</th>\n",
" <th>Warehouse Store</th>\n",
" <th>Wine Bar</th>\n",
" <th>Wings Joint</th>\n",
" <th>Women's Store</th>\n",
" <th>Yoga Studio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Adelaide, King, Richmond</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.03</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.0</td>\n",
" <td>0.03</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>Agincourt</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <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>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.20</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.00</td>\n",
" <td>0.200000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.200000</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Agincourt North, L'Amoreaux East, Milliken, St...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</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>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Albion Gardens, Beaumond Heights, Humbergate, ...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.111111</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</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>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alderwood, Long Branch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.00</td>\n",
" <td>0.111111</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.111111</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 281 columns</p>\n",
"</div>"
],
"text/plain": [
" Neighbourhood Accessories Store \\\n",
"0 Adelaide, King, Richmond 0.0 \n",
"1 Agincourt 0.0 \n",
"2 Agincourt North, L'Amoreaux East, Milliken, St... 0.0 \n",
"3 Albion Gardens, Beaumond Heights, Humbergate, ... 0.0 \n",
"4 Alderwood, Long Branch 0.0 \n",
"\n",
" Afghan Restaurant Airport Airport Food Court Airport Gate \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"\n",
" Airport Lounge Airport Service Airport Terminal American Restaurant \\\n",
"0 0.0 0.0 0.0 0.03 \n",
"1 0.0 0.0 0.0 0.00 \n",
"2 0.0 0.0 0.0 0.00 \n",
"3 0.0 0.0 0.0 0.00 \n",
"4 0.0 0.0 0.0 0.00 \n",
"\n",
" Antique Shop Aquarium Art Gallery Art Museum Arts & Crafts Store \\\n",
"0 0.0 0.0 0.01 0.01 0.0 \n",
"1 0.0 0.0 0.00 0.00 0.0 \n",
"2 0.0 0.0 0.00 0.00 0.0 \n",
"3 0.0 0.0 0.00 0.00 0.0 \n",
"4 0.0 0.0 0.00 0.00 0.0 \n",
"\n",
" Asian Restaurant Athletics & Sports Auto Garage Auto Workshop \\\n",
"0 0.03 0.0 0.0 0.0 \n",
"1 0.00 0.0 0.0 0.0 \n",
"2 0.00 0.0 0.0 0.0 \n",
"3 0.00 0.0 0.0 0.0 \n",
"4 0.00 0.0 0.0 0.0 \n",
"\n",
" BBQ Joint Baby Store Bagel Shop Bakery Bank Bar Baseball Field \\\n",
"0 0.0 0.0 0.0 0.02 0.0 0.04 0.0 \n",
"1 0.0 0.0 0.0 0.00 0.0 0.00 0.0 \n",
"2 0.0 0.0 0.0 0.00 0.0 0.00 0.0 \n",
"3 0.0 0.0 0.0 0.00 0.0 0.00 0.0 \n",
"4 0.0 0.0 0.0 0.00 0.0 0.00 0.0 \n",
"\n",
" Baseball Stadium Basketball Court Basketball Stadium Beach Beer Bar \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" Beer Store Bike Shop Bistro Boat or Ferry Bookstore Boutique \\\n",
"0 0.000000 0.0 0.0 0.0 0.01 0.0 \n",
"1 0.000000 0.0 0.0 0.0 0.00 0.0 \n",
"2 0.000000 0.0 0.0 0.0 0.00 0.0 \n",
"3 0.111111 0.0 0.0 0.0 0.00 0.0 \n",
"4 0.000000 0.0 0.0 0.0 0.00 0.0 \n",
"\n",
" Brazilian Restaurant Breakfast Spot Brewery Bridal Shop \\\n",
"0 0.01 0.03 0.0 0.0 \n",
"1 0.00 0.20 0.0 0.0 \n",
"2 0.00 0.00 0.0 0.0 \n",
"3 0.00 0.00 0.0 0.0 \n",
"4 0.00 0.00 0.0 0.0 \n",
"\n",
" Bubble Tea Shop Building Burger Joint Burrito Place Bus Line \\\n",
"0 0.0 0.01 0.02 0.01 0.0 \n",
"1 0.0 0.00 0.00 0.00 0.0 \n",
"2 0.0 0.00 0.00 0.00 0.0 \n",
"3 0.0 0.00 0.00 0.00 0.0 \n",
"4 0.0 0.00 0.00 0.00 0.0 \n",
"\n",
" Bus Station Bus Stop Butcher Cafeteria ... Salon / Barbershop \\\n",
"0 0.0 0.0 0.0 0.0 ... 0.01 \n",
"1 0.0 0.0 0.0 0.0 ... 0.00 \n",
"2 0.0 0.0 0.0 0.0 ... 0.00 \n",
"3 0.0 0.0 0.0 0.0 ... 0.00 \n",
"4 0.0 0.0 0.0 0.0 ... 0.00 \n",
"\n",
" Sandwich Place Scenic Lookout Sculpture Garden Seafood Restaurant \\\n",
"0 0.000000 0.0 0.0 0.01 \n",
"1 0.200000 0.0 0.0 0.00 \n",
"2 0.000000 0.0 0.0 0.00 \n",
"3 0.111111 0.0 0.0 0.00 \n",
"4 0.111111 0.0 0.0 0.00 \n",
"\n",
" Shoe Store Shopping Mall Shopping Plaza Skate Park Skating Rink \\\n",
"0 0.0 0.0 0.0 0.0 0.000000 \n",
"1 0.0 0.0 0.0 0.0 0.200000 \n",
"2 0.0 0.0 0.0 0.0 0.000000 \n",
"3 0.0 0.0 0.0 0.0 0.000000 \n",
"4 0.0 0.0 0.0 0.0 0.111111 \n",
"\n",
" Smoke Shop Smoothie Shop Snack Place Soccer Field Soup Place \\\n",
"0 0.01 0.0 0.0 0.0 0.0 \n",
"1 0.00 0.0 0.0 0.0 0.0 \n",
"2 0.00 0.0 0.0 0.0 0.0 \n",
"3 0.00 0.0 0.0 0.0 0.0 \n",
"4 0.00 0.0 0.0 0.0 0.0 \n",
"\n",
" Southern / Soul Food Restaurant Spa Speakeasy Sporting Goods Shop \\\n",
"0 0.0 0.0 0.01 0.0 \n",
"1 0.0 0.0 0.00 0.0 \n",
"2 0.0 0.0 0.00 0.0 \n",
"3 0.0 0.0 0.00 0.0 \n",
"4 0.0 0.0 0.00 0.0 \n",
"\n",
" Sports Bar Stadium Stationery Store Steakhouse Strip Club Supermarket \\\n",
"0 0.0 0.0 0.0 0.04 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.00 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.00 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.00 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.00 0.0 0.0 \n",
"\n",
" Supplement Shop Sushi Restaurant Swim School Taco Place Tailor Shop \\\n",
"0 0.0 0.02 0.0 0.0 0.0 \n",
"1 0.0 0.00 0.0 0.0 0.0 \n",
"2 0.0 0.00 0.0 0.0 0.0 \n",
"3 0.0 0.00 0.0 0.0 0.0 \n",
"4 0.0 0.00 0.0 0.0 0.0 \n",
"\n",
" Taiwanese Restaurant Tanning Salon Tapas Restaurant Tea Room \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"\n",
" Thai Restaurant Theater Theme Restaurant Thrift / Vintage Store \\\n",
"0 0.04 0.01 0.0 0.0 \n",
"1 0.00 0.00 0.0 0.0 \n",
"2 0.00 0.00 0.0 0.0 \n",
"3 0.00 0.00 0.0 0.0 \n",
"4 0.00 0.00 0.0 0.0 \n",
"\n",
" Toy / Game Store Trail Train Station Vegetarian / Vegan Restaurant \\\n",
"0 0.0 0.0 0.0 0.01 \n",
"1 0.0 0.0 0.0 0.00 \n",
"2 0.0 0.0 0.0 0.00 \n",
"3 0.0 0.0 0.0 0.00 \n",
"4 0.0 0.0 0.0 0.00 \n",
"\n",
" Video Game Store Video Store Vietnamese Restaurant Warehouse Store \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 \n",
"\n",
" Wine Bar Wings Joint Women's Store Yoga Studio \n",
"0 0.01 0.0 0.01 0.0 \n",
"1 0.00 0.0 0.00 0.0 \n",
"2 0.00 0.0 0.00 0.0 \n",
"3 0.00 0.0 0.00 0.0 \n",
"4 0.00 0.0 0.00 0.0 \n",
"\n",
"[5 rows x 281 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_grouped = toronto_onehot.groupby('Neighbourhood').mean().reset_index()\n",
"print(toronto_grouped.shape)\n",
"toronto_grouped.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obviously, we're dealing with a very sparse data set here, so let's determine the top 10 most frequent venue types per neighbourhood. First, let's create a function that sorts the venue frequencies in descending order."
]
},
{
"cell_type": "code",
"execution_count": 22,
"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 containing the top 10 venues for each neighborhood."
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>Neighbourhood</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",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Adelaide, King, Richmond</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Café</td>\n",
" <td>Thai Restaurant</td>\n",
" <td>Bar</td>\n",
" <td>Steakhouse</td>\n",
" <td>Gym</td>\n",
" <td>Restaurant</td>\n",
" <td>American Restaurant</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Hotel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Agincourt</td>\n",
" <td>Lounge</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Skating Rink</td>\n",
" <td>Chinese Restaurant</td>\n",
" <td>Sandwich Place</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Agincourt North, L'Amoreaux East, Milliken, St...</td>\n",
" <td>Park</td>\n",
" <td>Playground</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Dive Bar</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Albion Gardens, Beaumond Heights, Humbergate, ...</td>\n",
" <td>Grocery Store</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Pizza Place</td>\n",
" <td>Sandwich Place</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Beer Store</td>\n",
" <td>Pharmacy</td>\n",
" <td>Fried Chicken Joint</td>\n",
" <td>Empanada Restaurant</td>\n",
" <td>Electronics Store</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alderwood, Long Branch</td>\n",
" <td>Pizza Place</td>\n",
" <td>Gym</td>\n",
" <td>Pool</td>\n",
" <td>Skating Rink</td>\n",
" <td>Pharmacy</td>\n",
" <td>Pub</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Sandwich Place</td>\n",
" <td>Diner</td>\n",
" <td>Discount Store</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighbourhood 1st Most Common Venue \\\n",
"0 Adelaide, King, Richmond Coffee Shop \n",
"1 Agincourt Lounge \n",
"2 Agincourt North, L'Amoreaux East, Milliken, St... Park \n",
"3 Albion Gardens, Beaumond Heights, Humbergate, ... Grocery Store \n",
"4 Alderwood, Long Branch Pizza Place \n",
"\n",
" 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue \\\n",
"0 Café Thai Restaurant Bar \n",
"1 Breakfast Spot Skating Rink Chinese Restaurant \n",
"2 Playground Yoga Studio Eastern European Restaurant \n",
"3 Fast Food Restaurant Pizza Place Sandwich Place \n",
"4 Gym Pool Skating Rink \n",
"\n",
" 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue \\\n",
"0 Steakhouse Gym Restaurant \n",
"1 Sandwich Place Eastern European Restaurant Doner Restaurant \n",
"2 Dive Bar Dog Run Doner Restaurant \n",
"3 Coffee Shop Beer Store Pharmacy \n",
"4 Pharmacy Pub Coffee Shop \n",
"\n",
" 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue \n",
"0 American Restaurant Breakfast Spot Hotel \n",
"1 Donut Shop Drugstore Dumpling Restaurant \n",
"2 Donut Shop Drugstore Dumpling Restaurant \n",
"3 Fried Chicken Joint Empanada Restaurant Electronics Store \n",
"4 Sandwich Place Diner Discount Store "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_top_venues = 10\n",
"\n",
"indicators = ['st', 'nd', 'rd']\n",
"\n",
"# create columns according to number of top venues\n",
"columns = ['Neighbourhood']\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['Neighbourhood'] = toronto_grouped['Neighbourhood']\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": [
"## Neighbourhood clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're now ready to cluster the neighbourhoods based on the prevalence of venue types. \n",
"\n",
"### Finding k\n",
"\n",
"One question that always comes up is how to choose k for the clustering. We solve this by looking at the inertia of the clusters and applying the elbox rule."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# remove Neighbourhood column\n",
"toronto_grouped_clustering = toronto_grouped.drop('Neighbourhood', 1)\n",
"\n",
"\n",
"# set number of clusters to test\n",
"maxk = 8\n",
"cost = np.zeros((maxk-1))\n",
"\n",
"for n in range(1, maxk):\n",
" # run k-means clustering\n",
" kmeans = KMeans(n_clusters=n, random_state=0).fit(toronto_grouped_clustering)\n",
" cost[n-1] = kmeans.inertia_\n",
" #print(cost[n-1])\n",
"\n",
"# plot inertia to find best value for K\n",
"plt.plot(range(2, maxk), cost[1:maxk], 'g')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performing the clustering\n",
"\n",
"Judging from the plot above, it looks like setting k to 7 will be the best balance, so let's run the final clustering like this."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 6, 5, 5, 0, 3, 0, 0, 0], dtype=int32)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kclusters = 7\n",
"kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)\n",
"# check cluster labels generated for each row in the dataframe\n",
"kmeans.labels_[0:10] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood."
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>Postcode</th>\n",
" <th>Borough</th>\n",
" <th>Neighbourhood</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",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1B</td>\n",
" <td>Scarborough</td>\n",
" <td>Rouge, Malvern</td>\n",
" <td>43.806686</td>\n",
" <td>-79.194353</td>\n",
" <td>5</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Electronics Store</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Empanada Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M1C</td>\n",
" <td>Scarborough</td>\n",
" <td>Highland Creek, Rouge Hill, Port Union</td>\n",
" <td>43.784535</td>\n",
" <td>-79.160497</td>\n",
" <td>0</td>\n",
" <td>Bar</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Electronics Store</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Empanada Restaurant</td>\n",
" <td>Dive Bar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M1E</td>\n",
" <td>Scarborough</td>\n",
" <td>Guildwood, Morningside, West Hill</td>\n",
" <td>43.763573</td>\n",
" <td>-79.188711</td>\n",
" <td>0</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Rental Car Location</td>\n",
" <td>Intersection</td>\n",
" <td>Pizza Place</td>\n",
" <td>Electronics Store</td>\n",
" <td>Medical Center</td>\n",
" <td>Mexican Restaurant</td>\n",
" <td>Drugstore</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M1G</td>\n",
" <td>Scarborough</td>\n",
" <td>Woburn</td>\n",
" <td>43.770992</td>\n",
" <td>-79.216917</td>\n",
" <td>0</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Korean Restaurant</td>\n",
" <td>Electronics Store</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Yoga Studio</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M1H</td>\n",
" <td>Scarborough</td>\n",
" <td>Cedarbrae</td>\n",
" <td>43.773136</td>\n",
" <td>-79.239476</td>\n",
" <td>0</td>\n",
" <td>Hakka Restaurant</td>\n",
" <td>Thai Restaurant</td>\n",
" <td>Fried Chicken Joint</td>\n",
" <td>Bank</td>\n",
" <td>Bakery</td>\n",
" <td>Athletics &amp; Sports</td>\n",
" <td>Caribbean Restaurant</td>\n",
" <td>Cuban Restaurant</td>\n",
" <td>Costume Shop</td>\n",
" <td>Farmers Market</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postcode Borough Neighbourhood Latitude \\\n",
"0 M1B Scarborough Rouge, Malvern 43.806686 \n",
"1 M1C Scarborough Highland Creek, Rouge Hill, Port Union 43.784535 \n",
"2 M1E Scarborough Guildwood, Morningside, West Hill 43.763573 \n",
"3 M1G Scarborough Woburn 43.770992 \n",
"4 M1H Scarborough Cedarbrae 43.773136 \n",
"\n",
" Longitude Cluster Labels 1st Most Common Venue 2nd Most Common Venue \\\n",
"0 -79.194353 5 Fast Food Restaurant Yoga Studio \n",
"1 -79.160497 0 Bar Yoga Studio \n",
"2 -79.188711 0 Breakfast Spot Rental Car Location \n",
"3 -79.216917 0 Coffee Shop Korean Restaurant \n",
"4 -79.239476 0 Hakka Restaurant Thai Restaurant \n",
"\n",
" 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n",
"0 Electronics Store Dog Run Doner Restaurant \n",
"1 Electronics Store Doner Restaurant Donut Shop \n",
"2 Intersection Pizza Place Electronics Store \n",
"3 Electronics Store Dog Run Doner Restaurant \n",
"4 Fried Chicken Joint Bank Bakery \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"0 Donut Shop Drugstore Dumpling Restaurant \n",
"1 Drugstore Dumpling Restaurant Eastern European Restaurant \n",
"2 Medical Center Mexican Restaurant Drugstore \n",
"3 Donut Shop Drugstore Dumpling Restaurant \n",
"4 Athletics & Sports Caribbean Restaurant Cuban Restaurant \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"0 Eastern European Restaurant Empanada Restaurant \n",
"1 Empanada Restaurant Dive Bar \n",
"2 Dog Run Doner Restaurant \n",
"3 Eastern European Restaurant Yoga Studio \n",
"4 Costume Shop Farmers Market "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# add clustering labels\n",
"#neighborhoods_venues_sorted.drop('Cluster Labels', axis=1, inplace=True)\n",
"neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
"\n",
"toronto_merged = mydf_post\n",
"\n",
"# inner join toronto_merged with neighborhood_venues_sorted to add latitude/longitude for each neighbourhood\n",
"toronto_labeled = pd.merge(toronto_merged, neighborhoods_venues_sorted, how='inner', on='Neighbourhood')\n",
"\n",
"toronto_labeled.head() # check the last columns!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Clustering results\n",
"\n",
"Finally, let's visualize the resulting clusters of similar neighbourhoods. Let's start by counting the number of neighbourhoods in each cluster."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Cluster Labels\n",
"0 72\n",
"1 1\n",
"2 1\n",
"3 2\n",
"4 1\n",
"5 10\n",
"6 14\n",
"Name: Neighbourhood, dtype: int64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_labeled.groupby('Cluster Labels').count()['Neighbourhood']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is very interesting, as it appears that there are 3 main clusters accompanied by 4 outliers. \n",
"\n",
"## Mapping the clusters\n",
"\n",
"Let's see how this looks on the map using Folium."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
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            var html_dd8e7418eb0745a6b0f21a0c2af77e9f = $(`<div id="html_dd8e7418eb0745a6b0f21a0c2af77e9f" style="width: 100.0%; height: 100.0%;">Rouge, Malvern Cluster 5</div>`)[0];
            popup_83ddbc382feb41c3a0355e99df79bf56.setContent(html_dd8e7418eb0745a6b0f21a0c2af77e9f);
        

        circle_marker_4c44fc68f49f47c19036d3453f826572.bindPopup(popup_83ddbc382feb41c3a0355e99df79bf56)
        ;

        
    
    
            var circle_marker_73863c7d17e64c6894d9c2b568e0d162 = L.circleMarker(
                [43.7845351, -79.16049709999999],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_8ac2d574753d4effa81c96ca6202c32b = L.popup({"maxWidth": "100%"});

        
            var html_1e46b4ebe2104d4a8c68ad6e8b8d992d = $(`<div id="html_1e46b4ebe2104d4a8c68ad6e8b8d992d" style="width: 100.0%; height: 100.0%;">Highland Creek, Rouge Hill, Port Union Cluster 0</div>`)[0];
            popup_8ac2d574753d4effa81c96ca6202c32b.setContent(html_1e46b4ebe2104d4a8c68ad6e8b8d992d);
        

        circle_marker_73863c7d17e64c6894d9c2b568e0d162.bindPopup(popup_8ac2d574753d4effa81c96ca6202c32b)
        ;

        
    
    
            var circle_marker_4a09be2387834f85ac79d8085351e449 = L.circleMarker(
                [43.7635726, -79.1887115],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_a9cd9f49026845de919bf7f6ae1bb63d = L.popup({"maxWidth": "100%"});

        
            var html_7052b6b1f5d14bf1b97dd08d80b9cfa9 = $(`<div id="html_7052b6b1f5d14bf1b97dd08d80b9cfa9" style="width: 100.0%; height: 100.0%;">Guildwood, Morningside, West Hill Cluster 0</div>`)[0];
            popup_a9cd9f49026845de919bf7f6ae1bb63d.setContent(html_7052b6b1f5d14bf1b97dd08d80b9cfa9);
        

        circle_marker_4a09be2387834f85ac79d8085351e449.bindPopup(popup_a9cd9f49026845de919bf7f6ae1bb63d)
        ;

        
    
    
            var circle_marker_75789c1b9401490991d838b9ca437b83 = L.circleMarker(
                [43.7709921, -79.21691740000001],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_5a096cf424594795b9fd826803f08af7 = L.popup({"maxWidth": "100%"});

        
            var html_96a40ba1558d4037ae9a4281c8c22be0 = $(`<div id="html_96a40ba1558d4037ae9a4281c8c22be0" style="width: 100.0%; height: 100.0%;">Woburn Cluster 0</div>`)[0];
            popup_5a096cf424594795b9fd826803f08af7.setContent(html_96a40ba1558d4037ae9a4281c8c22be0);
        

        circle_marker_75789c1b9401490991d838b9ca437b83.bindPopup(popup_5a096cf424594795b9fd826803f08af7)
        ;

        
    
    
            var circle_marker_8a59da6bf91e494bb4b52f6d76a4a474 = L.circleMarker(
                [43.773136, -79.23947609999999],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_46d6619bf26b4b4caa79d1494608b960 = L.popup({"maxWidth": "100%"});

        
            var html_b0c5fb13e1fe46a09ce4ce463f0c6fe9 = $(`<div id="html_b0c5fb13e1fe46a09ce4ce463f0c6fe9" style="width: 100.0%; height: 100.0%;">Cedarbrae Cluster 0</div>`)[0];
            popup_46d6619bf26b4b4caa79d1494608b960.setContent(html_b0c5fb13e1fe46a09ce4ce463f0c6fe9);
        

        circle_marker_8a59da6bf91e494bb4b52f6d76a4a474.bindPopup(popup_46d6619bf26b4b4caa79d1494608b960)
        ;

        
    
    
            var circle_marker_95e32bc7eabc49b38c425356c7239ab1 = L.circleMarker(
                [43.7447342, -79.23947609999999],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_e6cf07ad62f64564b8cb683f321b193b = L.popup({"maxWidth": "100%"});

        
            var html_4ffd86af4df847e0a9a7b8572fcb4802 = $(`<div id="html_4ffd86af4df847e0a9a7b8572fcb4802" style="width: 100.0%; height: 100.0%;">Scarborough Village Cluster 0</div>`)[0];
            popup_e6cf07ad62f64564b8cb683f321b193b.setContent(html_4ffd86af4df847e0a9a7b8572fcb4802);
        

        circle_marker_95e32bc7eabc49b38c425356c7239ab1.bindPopup(popup_e6cf07ad62f64564b8cb683f321b193b)
        ;

        
    
    
            var circle_marker_3cb3ee7710824130995901d75ef6f85d = L.circleMarker(
                [43.7279292, -79.26202940000002],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_67b9c49c470e430d8563dd38f0b23fda = L.popup({"maxWidth": "100%"});

        
            var html_492f5c86439046e3a361686e8a518a0d = $(`<div id="html_492f5c86439046e3a361686e8a518a0d" style="width: 100.0%; height: 100.0%;">East Birchmount Park, Ionview, Kennedy Park Cluster 0</div>`)[0];
            popup_67b9c49c470e430d8563dd38f0b23fda.setContent(html_492f5c86439046e3a361686e8a518a0d);
        

        circle_marker_3cb3ee7710824130995901d75ef6f85d.bindPopup(popup_67b9c49c470e430d8563dd38f0b23fda)
        ;

        
    
    
            var circle_marker_a59bf2f7144843b38909bb27db772acb = L.circleMarker(
                [43.711111700000004, -79.2845772],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_87618c19f1dd49eabfff662af8409651 = L.popup({"maxWidth": "100%"});

        
            var html_436c002674a3412eb120d28cfad32948 = $(`<div id="html_436c002674a3412eb120d28cfad32948" style="width: 100.0%; height: 100.0%;">Clairlea, Golden Mile, Oakridge Cluster 0</div>`)[0];
            popup_87618c19f1dd49eabfff662af8409651.setContent(html_436c002674a3412eb120d28cfad32948);
        

        circle_marker_a59bf2f7144843b38909bb27db772acb.bindPopup(popup_87618c19f1dd49eabfff662af8409651)
        ;

        
    
    
            var circle_marker_99672932e10c4675be42bbdb88aebd38 = L.circleMarker(
                [43.716316, -79.23947609999999],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_2130726f87f84eacade6b7dae668afcd = L.popup({"maxWidth": "100%"});

        
            var html_1144be305e5b4627ad0a96de4590d653 = $(`<div id="html_1144be305e5b4627ad0a96de4590d653" style="width: 100.0%; height: 100.0%;">Cliffcrest, Cliffside, Scarborough Village West Cluster 0</div>`)[0];
            popup_2130726f87f84eacade6b7dae668afcd.setContent(html_1144be305e5b4627ad0a96de4590d653);
        

        circle_marker_99672932e10c4675be42bbdb88aebd38.bindPopup(popup_2130726f87f84eacade6b7dae668afcd)
        ;

        
    
    
            var circle_marker_d7379db33d5c45e8a1fc2a0af4fd8b3b = L.circleMarker(
                [43.692657000000004, -79.2648481],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_828e19c1a0dc469b80e810b69a6195a1 = L.popup({"maxWidth": "100%"});

        
            var html_0fa14a85bf4e4afa866d89c447fc7015 = $(`<div id="html_0fa14a85bf4e4afa866d89c447fc7015" style="width: 100.0%; height: 100.0%;">Birch Cliff, Cliffside West Cluster 0</div>`)[0];
            popup_828e19c1a0dc469b80e810b69a6195a1.setContent(html_0fa14a85bf4e4afa866d89c447fc7015);
        

        circle_marker_d7379db33d5c45e8a1fc2a0af4fd8b3b.bindPopup(popup_828e19c1a0dc469b80e810b69a6195a1)
        ;

        
    
    
            var circle_marker_acb589d523eb45f2859c009255464398 = L.circleMarker(
                [43.7574096, -79.27330400000001],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_d38a2e37ce1143c6ab0be3a585746b1c = L.popup({"maxWidth": "100%"});

        
            var html_dcefe868044d440ebae84893350bc2e7 = $(`<div id="html_dcefe868044d440ebae84893350bc2e7" style="width: 100.0%; height: 100.0%;">Dorset Park, Scarborough Town Centre, Wexford Heights Cluster 0</div>`)[0];
            popup_d38a2e37ce1143c6ab0be3a585746b1c.setContent(html_dcefe868044d440ebae84893350bc2e7);
        

        circle_marker_acb589d523eb45f2859c009255464398.bindPopup(popup_d38a2e37ce1143c6ab0be3a585746b1c)
        ;

        
    
    
            var circle_marker_c458cc40e81446878d26737ebf5591f3 = L.circleMarker(
                [43.750071500000004, -79.2958491],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_48066827cdd945c88c32e85e568d9a5c = L.popup({"maxWidth": "100%"});

        
            var html_2864370508b54c6d93dc4bdbfcf27ae1 = $(`<div id="html_2864370508b54c6d93dc4bdbfcf27ae1" style="width: 100.0%; height: 100.0%;">Maryvale, Wexford Cluster 0</div>`)[0];
            popup_48066827cdd945c88c32e85e568d9a5c.setContent(html_2864370508b54c6d93dc4bdbfcf27ae1);
        

        circle_marker_c458cc40e81446878d26737ebf5591f3.bindPopup(popup_48066827cdd945c88c32e85e568d9a5c)
        ;

        
    
    
            var circle_marker_f10f8c43567b44b992526520f048e25e = L.circleMarker(
                [43.7942003, -79.26202940000002],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_de7bd2b46fe548588dc0cacd91d1e7fa = L.popup({"maxWidth": "100%"});

        
            var html_7cc86789cca84243b6bb9d5ea2dca8ca = $(`<div id="html_7cc86789cca84243b6bb9d5ea2dca8ca" style="width: 100.0%; height: 100.0%;">Agincourt Cluster 0</div>`)[0];
            popup_de7bd2b46fe548588dc0cacd91d1e7fa.setContent(html_7cc86789cca84243b6bb9d5ea2dca8ca);
        

        circle_marker_f10f8c43567b44b992526520f048e25e.bindPopup(popup_de7bd2b46fe548588dc0cacd91d1e7fa)
        ;

        
    
    
            var circle_marker_58d3d9c11cf3474490a1011dd37018c2 = L.circleMarker(
                [43.7816375, -79.3043021],
                {"bubblingMouseEvents": true, "color": "#008fff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#008fff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_55207b0d883649b7bdd4c8b0b3a4abab = L.popup({"maxWidth": "100%"});

        
            var html_8720685ef47b43f6ae26494a0010e7bd = $(`<div id="html_8720685ef47b43f6ae26494a0010e7bd" style="width: 100.0%; height: 100.0%;">Clarks Corners, Sullivan, Tam O&#39;Shanter Cluster 5</div>`)[0];
            popup_55207b0d883649b7bdd4c8b0b3a4abab.setContent(html_8720685ef47b43f6ae26494a0010e7bd);
        

        circle_marker_58d3d9c11cf3474490a1011dd37018c2.bindPopup(popup_55207b0d883649b7bdd4c8b0b3a4abab)
        ;

        
    
    
            var circle_marker_fa5019473ffe43cb82c4c013fb6398f8 = L.circleMarker(
                [43.8152522, -79.2845772],
                {"bubblingMouseEvents": true, "color": "#5a00ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5a00ff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_d1d28ffae8fc4db993b7da94844af353 = L.popup({"maxWidth": "100%"});

        
            var html_38628a02247c44beb92d70f2ea18fe28 = $(`<div id="html_38628a02247c44beb92d70f2ea18fe28" style="width: 100.0%; height: 100.0%;">Agincourt North, L&#39;Amoreaux East, Milliken, Steeles East Cluster 6</div>`)[0];
            popup_d1d28ffae8fc4db993b7da94844af353.setContent(html_38628a02247c44beb92d70f2ea18fe28);
        

        circle_marker_fa5019473ffe43cb82c4c013fb6398f8.bindPopup(popup_d1d28ffae8fc4db993b7da94844af353)
        ;

        
    
    
            var circle_marker_7bc7b703f3b74f19ae902caa3e707543 = L.circleMarker(
                [43.799525200000005, -79.3183887],
                {"bubblingMouseEvents": true, "color": "#008fff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#008fff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_74293894c1c5427794872344063eb4e5 = L.popup({"maxWidth": "100%"});

        
            var html_f27a1792c1bf4b01b54033c10fd733a6 = $(`<div id="html_f27a1792c1bf4b01b54033c10fd733a6" style="width: 100.0%; height: 100.0%;">L&#39;Amoreaux West Cluster 5</div>`)[0];
            popup_74293894c1c5427794872344063eb4e5.setContent(html_f27a1792c1bf4b01b54033c10fd733a6);
        

        circle_marker_7bc7b703f3b74f19ae902caa3e707543.bindPopup(popup_74293894c1c5427794872344063eb4e5)
        ;

        
    
    
            var circle_marker_4347613b98d949a982aaf2def824ac85 = L.circleMarker(
                [43.8037622, -79.3634517],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_dfbae37fac9347a5935edc23202e6448 = L.popup({"maxWidth": "100%"});

        
            var html_325414ab5504452b9a8a7a53891b54d6 = $(`<div id="html_325414ab5504452b9a8a7a53891b54d6" style="width: 100.0%; height: 100.0%;">Hillcrest Village Cluster 0</div>`)[0];
            popup_dfbae37fac9347a5935edc23202e6448.setContent(html_325414ab5504452b9a8a7a53891b54d6);
        

        circle_marker_4347613b98d949a982aaf2def824ac85.bindPopup(popup_dfbae37fac9347a5935edc23202e6448)
        ;

        
    
    
            var circle_marker_1364e09038394ee0a4a5615ae162c234 = L.circleMarker(
                [43.7785175, -79.3465557],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_d79c98ba4f774adba6bdf01c41702900 = L.popup({"maxWidth": "100%"});

        
            var html_ad680e8d753242c7ae0e69d5303a97ff = $(`<div id="html_ad680e8d753242c7ae0e69d5303a97ff" style="width: 100.0%; height: 100.0%;">Fairview, Henry Farm, Oriole Cluster 0</div>`)[0];
            popup_d79c98ba4f774adba6bdf01c41702900.setContent(html_ad680e8d753242c7ae0e69d5303a97ff);
        

        circle_marker_1364e09038394ee0a4a5615ae162c234.bindPopup(popup_d79c98ba4f774adba6bdf01c41702900)
        ;

        
    
    
            var circle_marker_b24151c48f0e49f39510460703abb5cd = L.circleMarker(
                [43.7869473, -79.385975],
                {"bubblingMouseEvents": true, "color": "#5cff00", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5cff00", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_8647b5a0737e4339b12c3500e0e8bd51 = L.popup({"maxWidth": "100%"});

        
            var html_8e1be16d1aff4881b1bd813b51f93179 = $(`<div id="html_8e1be16d1aff4881b1bd813b51f93179" style="width: 100.0%; height: 100.0%;">Bayview Village Cluster 3</div>`)[0];
            popup_8647b5a0737e4339b12c3500e0e8bd51.setContent(html_8e1be16d1aff4881b1bd813b51f93179);
        

        circle_marker_b24151c48f0e49f39510460703abb5cd.bindPopup(popup_8647b5a0737e4339b12c3500e0e8bd51)
        ;

        
    
    
            var circle_marker_971f4aed54a34983a97407d1218171fb = L.circleMarker(
                [43.7574902, -79.37471409999999],
                {"bubblingMouseEvents": true, "color": "#00ff8c", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#00ff8c", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_999abc42375b408bb7ca3b55a1d4e239 = L.popup({"maxWidth": "100%"});

        
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        circle_marker_ea76f8d1a6374889adc8d785cc875d2a.bindPopup(popup_1a3b5d93a46d4d99bd14c5c7a15f38ee)
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        circle_marker_ca873b5dd817470a99b186da8b2802a3.bindPopup(popup_43bca3565de14fec9faa798ab614be50)
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        var popup_1cc9f86c72094f5ab0132fc7563fde29 = L.popup({"maxWidth": "100%"});

        
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        circle_marker_f84fa14df84c47a084ae8c6be3539017.bindPopup(popup_1cc9f86c72094f5ab0132fc7563fde29)
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        circle_marker_1c0d0b2edace4ca99acd61f7365d2fff.bindPopup(popup_30e8e523534a4f59a63100eed391bac2)
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        var popup_526dca8b48a740d3911ce00e4aee83f2 = L.popup({"maxWidth": "100%"});

        
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        var popup_8797e1aff6f745398f048fca1082b131 = L.popup({"maxWidth": "100%"});

        
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        var popup_32a05e969f4945ebb32f3d5738c280bf = L.popup({"maxWidth": "100%"});

        
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        circle_marker_240a0ae4801e4034b2b51b2c8f5b0c8b.bindPopup(popup_32a05e969f4945ebb32f3d5738c280bf)
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        var popup_aa96bdc9187e43759e2860fc0e69dbeb = L.popup({"maxWidth": "100%"});

        
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        circle_marker_996c943bbfba4976bb9fc0ca650ace1a.bindPopup(popup_aa96bdc9187e43759e2860fc0e69dbeb)
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            popup_8370f2fd4fa542db8759fa00cfa66783.setContent(html_e4c347d2f67f48b8801cc8085a90efc5);
        

        circle_marker_32659c31a9174915975d96b73e328ce3.bindPopup(popup_8370f2fd4fa542db8759fa00cfa66783)
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        var popup_ddecedfaac3c4369a404b9a8b44ce33c = L.popup({"maxWidth": "100%"});

        
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            popup_ddecedfaac3c4369a404b9a8b44ce33c.setContent(html_f3c5cd5cb4cc43b9a6bc7274faabfd78);
        

        circle_marker_99e72f58f4ca4df9b62d05af90020c74.bindPopup(popup_ddecedfaac3c4369a404b9a8b44ce33c)
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        var popup_6dcdd3a0380a45cb8499ca227dee643f = L.popup({"maxWidth": "100%"});

        
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            popup_6dcdd3a0380a45cb8499ca227dee643f.setContent(html_2142a612ddfd44fca15f0a7622a60e93);
        

        circle_marker_8c19004c58c24d338159b8904edd033e.bindPopup(popup_6dcdd3a0380a45cb8499ca227dee643f)
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        var popup_a480ab93d5444a97859d42c1ca040efa = L.popup({"maxWidth": "100%"});

        
            var html_8b2eb7cedaee407b8530d69968bea5df = $(`<div id="html_8b2eb7cedaee407b8530d69968bea5df" style="width: 100.0%; height: 100.0%;">Victoria Village Cluster 0</div>`)[0];
            popup_a480ab93d5444a97859d42c1ca040efa.setContent(html_8b2eb7cedaee407b8530d69968bea5df);
        

        circle_marker_09e062ec2fde4d6cac98c959f068b833.bindPopup(popup_a480ab93d5444a97859d42c1ca040efa)
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            var circle_marker_df92fc0263c74bf48ae6e1e78990bad5 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#008fff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#008fff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
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            var html_8a07365360c6431684bfc1ae5f4e42e3 = $(`<div id="html_8a07365360c6431684bfc1ae5f4e42e3" style="width: 100.0%; height: 100.0%;">Woodbine Gardens, Parkview Hill Cluster 5</div>`)[0];
            popup_68b7cc3016a7460da7b20f9d25f5ce58.setContent(html_8a07365360c6431684bfc1ae5f4e42e3);
        

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        var popup_9c10a6d510754fc9ad5c9a70f75b8d04 = L.popup({"maxWidth": "100%"});

        
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            popup_9c10a6d510754fc9ad5c9a70f75b8d04.setContent(html_462b1d89fd194d9094bdb77c3c2ed24c);
        

        circle_marker_1d821f9f44ac4a01bc67d7b8b3fe030e.bindPopup(popup_9c10a6d510754fc9ad5c9a70f75b8d04)
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            var circle_marker_4e366d3cc9f3491491f5ed0e2fa25ae0 = L.circleMarker(
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        var popup_7856173bf64e48dd81dd310078e5959f = L.popup({"maxWidth": "100%"});

        
            var html_4c92a26dc6684de3a4c02ec62ebb1ef8 = $(`<div id="html_4c92a26dc6684de3a4c02ec62ebb1ef8" style="width: 100.0%; height: 100.0%;">The Beaches Cluster 0</div>`)[0];
            popup_7856173bf64e48dd81dd310078e5959f.setContent(html_4c92a26dc6684de3a4c02ec62ebb1ef8);
        

        circle_marker_4e366d3cc9f3491491f5ed0e2fa25ae0.bindPopup(popup_7856173bf64e48dd81dd310078e5959f)
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            var circle_marker_9cf8832c208c4d7c8fa539c34d1ffeeb = L.circleMarker(
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            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_16dcadcf6de0431a858fbb38472b444b = L.popup({"maxWidth": "100%"});

        
            var html_7260d21570e84c36bf270d72fd11afe4 = $(`<div id="html_7260d21570e84c36bf270d72fd11afe4" style="width: 100.0%; height: 100.0%;">Leaside Cluster 0</div>`)[0];
            popup_16dcadcf6de0431a858fbb38472b444b.setContent(html_7260d21570e84c36bf270d72fd11afe4);
        

        circle_marker_9cf8832c208c4d7c8fa539c34d1ffeeb.bindPopup(popup_16dcadcf6de0431a858fbb38472b444b)
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            var circle_marker_fbb46b30013a41478fccca6482e0b434 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_7d7c4530cb5e4c4ead3752fd60d52a5a = L.popup({"maxWidth": "100%"});

        
            var html_529ce9bf4d694eb3a95d1a17a7652eb8 = $(`<div id="html_529ce9bf4d694eb3a95d1a17a7652eb8" style="width: 100.0%; height: 100.0%;">Thorncliffe Park Cluster 0</div>`)[0];
            popup_7d7c4530cb5e4c4ead3752fd60d52a5a.setContent(html_529ce9bf4d694eb3a95d1a17a7652eb8);
        

        circle_marker_fbb46b30013a41478fccca6482e0b434.bindPopup(popup_7d7c4530cb5e4c4ead3752fd60d52a5a)
        ;

        
    
    
            var circle_marker_2a1d7fe2d9474fe39f34b2ad0f502dd7 = L.circleMarker(
                [43.685347, -79.3381065],
                {"bubblingMouseEvents": true, "color": "#5a00ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5a00ff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_5490161796c14cefa54a5fc479464a73 = L.popup({"maxWidth": "100%"});

        
            var html_10cec84e62084f67a222a714f87e912d = $(`<div id="html_10cec84e62084f67a222a714f87e912d" style="width: 100.0%; height: 100.0%;">East Toronto Cluster 6</div>`)[0];
            popup_5490161796c14cefa54a5fc479464a73.setContent(html_10cec84e62084f67a222a714f87e912d);
        

        circle_marker_2a1d7fe2d9474fe39f34b2ad0f502dd7.bindPopup(popup_5490161796c14cefa54a5fc479464a73)
        ;

        
    
    
            var circle_marker_761d08b9e3f643dd96bba150b2d4d565 = L.circleMarker(
                [43.6795571, -79.352188],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_6e50d243d625422bb5b17e234ebc8663 = L.popup({"maxWidth": "100%"});

        
            var html_13e9e7f80c914ef8a66d3fd7f09e389e = $(`<div id="html_13e9e7f80c914ef8a66d3fd7f09e389e" style="width: 100.0%; height: 100.0%;">The Danforth West, Riverdale Cluster 0</div>`)[0];
            popup_6e50d243d625422bb5b17e234ebc8663.setContent(html_13e9e7f80c914ef8a66d3fd7f09e389e);
        

        circle_marker_761d08b9e3f643dd96bba150b2d4d565.bindPopup(popup_6e50d243d625422bb5b17e234ebc8663)
        ;

        
    
    
            var circle_marker_64f779d5cfc74993960495185719ff93 = L.circleMarker(
                [43.6689985, -79.31557159999998],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_9a69a07cc5a042c5af0a2312f1129bf5 = L.popup({"maxWidth": "100%"});

        
            var html_f0870da694d94718809d6051dc99d15c = $(`<div id="html_f0870da694d94718809d6051dc99d15c" style="width: 100.0%; height: 100.0%;">The Beaches West, India Bazaar Cluster 0</div>`)[0];
            popup_9a69a07cc5a042c5af0a2312f1129bf5.setContent(html_f0870da694d94718809d6051dc99d15c);
        

        circle_marker_64f779d5cfc74993960495185719ff93.bindPopup(popup_9a69a07cc5a042c5af0a2312f1129bf5)
        ;

        
    
    
            var circle_marker_e347f34227264e8099355fada2a02da2 = L.circleMarker(
                [43.6595255, -79.340923],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_700699ecb5ac4c2e96c24e38d02ecc18 = L.popup({"maxWidth": "100%"});

        
            var html_4da9c84a522f4881a6044fe724623dd3 = $(`<div id="html_4da9c84a522f4881a6044fe724623dd3" style="width: 100.0%; height: 100.0%;">Studio District Cluster 0</div>`)[0];
            popup_700699ecb5ac4c2e96c24e38d02ecc18.setContent(html_4da9c84a522f4881a6044fe724623dd3);
        

        circle_marker_e347f34227264e8099355fada2a02da2.bindPopup(popup_700699ecb5ac4c2e96c24e38d02ecc18)
        ;

        
    
    
            var circle_marker_1157fb2e2744419cb8ff117c392c68f6 = L.circleMarker(
                [43.7280205, -79.3887901],
                {"bubblingMouseEvents": true, "color": "#5a00ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5a00ff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_5e469cf828dd4269b549778a6e8df4a0 = L.popup({"maxWidth": "100%"});

        
            var html_ff82fe68f5a44eab99505d3eb57bd82a = $(`<div id="html_ff82fe68f5a44eab99505d3eb57bd82a" style="width: 100.0%; height: 100.0%;">Lawrence Park Cluster 6</div>`)[0];
            popup_5e469cf828dd4269b549778a6e8df4a0.setContent(html_ff82fe68f5a44eab99505d3eb57bd82a);
        

        circle_marker_1157fb2e2744419cb8ff117c392c68f6.bindPopup(popup_5e469cf828dd4269b549778a6e8df4a0)
        ;

        
    
    
            var circle_marker_dfdbd7c4086b472b9b9869e85d20cbf3 = L.circleMarker(
                [43.7127511, -79.3901975],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_766fbd3acf454373b673fa0d71b3114b = L.popup({"maxWidth": "100%"});

        
            var html_9290e8ddebad498494cbe1ddf79f1b6b = $(`<div id="html_9290e8ddebad498494cbe1ddf79f1b6b" style="width: 100.0%; height: 100.0%;">Davisville North Cluster 0</div>`)[0];
            popup_766fbd3acf454373b673fa0d71b3114b.setContent(html_9290e8ddebad498494cbe1ddf79f1b6b);
        

        circle_marker_dfdbd7c4086b472b9b9869e85d20cbf3.bindPopup(popup_766fbd3acf454373b673fa0d71b3114b)
        ;

        
    
    
            var circle_marker_ae08687cea104b129da337c2cf4932ef = L.circleMarker(
                [43.7153834, -79.40567840000001],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_5acb78cb8fcd4b02bb3a15b4ab5135f8 = L.popup({"maxWidth": "100%"});

        
            var html_5e603f95eeab41ceac2cceed72b1c1e0 = $(`<div id="html_5e603f95eeab41ceac2cceed72b1c1e0" style="width: 100.0%; height: 100.0%;">North Toronto West Cluster 0</div>`)[0];
            popup_5acb78cb8fcd4b02bb3a15b4ab5135f8.setContent(html_5e603f95eeab41ceac2cceed72b1c1e0);
        

        circle_marker_ae08687cea104b129da337c2cf4932ef.bindPopup(popup_5acb78cb8fcd4b02bb3a15b4ab5135f8)
        ;

        
    
    
            var circle_marker_cb9bd51009d84178a0fc2b4289f4b989 = L.circleMarker(
                [43.7043244, -79.3887901],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_7a15d2717da34850806c42efbae270be = L.popup({"maxWidth": "100%"});

        
            var html_b8d51af12466479e9eeeb93dc5900e1d = $(`<div id="html_b8d51af12466479e9eeeb93dc5900e1d" style="width: 100.0%; height: 100.0%;">Davisville Cluster 0</div>`)[0];
            popup_7a15d2717da34850806c42efbae270be.setContent(html_b8d51af12466479e9eeeb93dc5900e1d);
        

        circle_marker_cb9bd51009d84178a0fc2b4289f4b989.bindPopup(popup_7a15d2717da34850806c42efbae270be)
        ;

        
    
    
            var circle_marker_d462487065a248c9840305da0c356495 = L.circleMarker(
                [43.6895743, -79.38315990000001],
                {"bubblingMouseEvents": true, "color": "#5a00ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5a00ff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_3d70f06b377649a9a2dde40aaa8f3806 = L.popup({"maxWidth": "100%"});

        
            var html_51fac704afb746029d3de5387818aad5 = $(`<div id="html_51fac704afb746029d3de5387818aad5" style="width: 100.0%; height: 100.0%;">Moore Park, Summerhill East Cluster 6</div>`)[0];
            popup_3d70f06b377649a9a2dde40aaa8f3806.setContent(html_51fac704afb746029d3de5387818aad5);
        

        circle_marker_d462487065a248c9840305da0c356495.bindPopup(popup_3d70f06b377649a9a2dde40aaa8f3806)
        ;

        
    
    
            var circle_marker_3634953dedba4e72ab09c6c11a27731d = L.circleMarker(
                [43.68641229999999, -79.4000493],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_92f12ef833f745b088c80bbb1912a4dd = L.popup({"maxWidth": "100%"});

        
            var html_55f6ea03adc14c04b5bb1dacc331e9b3 = $(`<div id="html_55f6ea03adc14c04b5bb1dacc331e9b3" style="width: 100.0%; height: 100.0%;">Deer Park, Forest Hill SE, Rathnelly, South Hill, Summerhill West Cluster 0</div>`)[0];
            popup_92f12ef833f745b088c80bbb1912a4dd.setContent(html_55f6ea03adc14c04b5bb1dacc331e9b3);
        

        circle_marker_3634953dedba4e72ab09c6c11a27731d.bindPopup(popup_92f12ef833f745b088c80bbb1912a4dd)
        ;

        
    
    
            var circle_marker_9e2dc9df4e8a4ee9b703715ca2d95221 = L.circleMarker(
                [43.6795626, -79.37752940000001],
                {"bubblingMouseEvents": true, "color": "#5a00ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5a00ff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_0cdcbc6b403d44e7bd41c9b209c6214a = L.popup({"maxWidth": "100%"});

        
            var html_a47d33fbfaf04646acc5771515e831db = $(`<div id="html_a47d33fbfaf04646acc5771515e831db" style="width: 100.0%; height: 100.0%;">Rosedale Cluster 6</div>`)[0];
            popup_0cdcbc6b403d44e7bd41c9b209c6214a.setContent(html_a47d33fbfaf04646acc5771515e831db);
        

        circle_marker_9e2dc9df4e8a4ee9b703715ca2d95221.bindPopup(popup_0cdcbc6b403d44e7bd41c9b209c6214a)
        ;

        
    
    
            var circle_marker_e40c1d93a56d4fb4bdc47039b0b6777f = L.circleMarker(
                [43.667967, -79.3676753],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_73be708771d44e59b20993f0ab17405c = L.popup({"maxWidth": "100%"});

        
            var html_f6a6d87873de45f88db1cf47381170c9 = $(`<div id="html_f6a6d87873de45f88db1cf47381170c9" style="width: 100.0%; height: 100.0%;">Cabbagetown, St. James Town Cluster 0</div>`)[0];
            popup_73be708771d44e59b20993f0ab17405c.setContent(html_f6a6d87873de45f88db1cf47381170c9);
        

        circle_marker_e40c1d93a56d4fb4bdc47039b0b6777f.bindPopup(popup_73be708771d44e59b20993f0ab17405c)
        ;

        
    
    
            var circle_marker_02961e2a843148c1b61200600c34767f = L.circleMarker(
                [43.6658599, -79.38315990000001],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_3cca700be1654f619301da48b33f5502 = L.popup({"maxWidth": "100%"});

        
            var html_bf1860513cea49959f1f7b36431b6f44 = $(`<div id="html_bf1860513cea49959f1f7b36431b6f44" style="width: 100.0%; height: 100.0%;">Church and Wellesley Cluster 0</div>`)[0];
            popup_3cca700be1654f619301da48b33f5502.setContent(html_bf1860513cea49959f1f7b36431b6f44);
        

        circle_marker_02961e2a843148c1b61200600c34767f.bindPopup(popup_3cca700be1654f619301da48b33f5502)
        ;

        
    
    
            var circle_marker_b6d44d87e2254e7d81c79d51cda3e424 = L.circleMarker(
                [43.6542599, -79.3606359],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_60d1a17fecea41bd95975b29a8a0c1fa = L.popup({"maxWidth": "100%"});

        
            var html_80458c5afe804e65af47e8ec267b5f07 = $(`<div id="html_80458c5afe804e65af47e8ec267b5f07" style="width: 100.0%; height: 100.0%;">Harbourfront, Regent Park Cluster 0</div>`)[0];
            popup_60d1a17fecea41bd95975b29a8a0c1fa.setContent(html_80458c5afe804e65af47e8ec267b5f07);
        

        circle_marker_b6d44d87e2254e7d81c79d51cda3e424.bindPopup(popup_60d1a17fecea41bd95975b29a8a0c1fa)
        ;

        
    
    
            var circle_marker_1357561c3f144bfa95cac289569c55c9 = L.circleMarker(
                [43.6571618, -79.37893709999999],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_e9054ec46a93469681dc9d5b70416c28 = L.popup({"maxWidth": "100%"});

        
            var html_98bd1841664b4a5a894c730a3c771516 = $(`<div id="html_98bd1841664b4a5a894c730a3c771516" style="width: 100.0%; height: 100.0%;">Ryerson, Garden District Cluster 0</div>`)[0];
            popup_e9054ec46a93469681dc9d5b70416c28.setContent(html_98bd1841664b4a5a894c730a3c771516);
        

        circle_marker_1357561c3f144bfa95cac289569c55c9.bindPopup(popup_e9054ec46a93469681dc9d5b70416c28)
        ;

        
    
    
            var circle_marker_3642ddc4cda84a708638192a59a86c9d = L.circleMarker(
                [43.6514939, -79.3754179],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_0cbc6bac25244ed68529bc194a321750 = L.popup({"maxWidth": "100%"});

        
            var html_c0d8e2b6cbeb46caa1ede275d106f083 = $(`<div id="html_c0d8e2b6cbeb46caa1ede275d106f083" style="width: 100.0%; height: 100.0%;">St. James Town Cluster 0</div>`)[0];
            popup_0cbc6bac25244ed68529bc194a321750.setContent(html_c0d8e2b6cbeb46caa1ede275d106f083);
        

        circle_marker_3642ddc4cda84a708638192a59a86c9d.bindPopup(popup_0cbc6bac25244ed68529bc194a321750)
        ;

        
    
    
            var circle_marker_47ee168027724155b0af141538e9cd38 = L.circleMarker(
                [43.644770799999996, -79.3733064],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_7ed6306875344b798319df5d04e5aa13 = L.popup({"maxWidth": "100%"});

        
            var html_c01699eaf4ea4c779d81e88203fd6430 = $(`<div id="html_c01699eaf4ea4c779d81e88203fd6430" style="width: 100.0%; height: 100.0%;">Berczy Park Cluster 0</div>`)[0];
            popup_7ed6306875344b798319df5d04e5aa13.setContent(html_c01699eaf4ea4c779d81e88203fd6430);
        

        circle_marker_47ee168027724155b0af141538e9cd38.bindPopup(popup_7ed6306875344b798319df5d04e5aa13)
        ;

        
    
    
            var circle_marker_0ad192fae5e14346b180925d6bbc904f = L.circleMarker(
                [43.6579524, -79.3873826],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_4bcd571db2ff4d919a96780bd0405386 = L.popup({"maxWidth": "100%"});

        
            var html_ce2d554fd44c466886d35fce6f84241e = $(`<div id="html_ce2d554fd44c466886d35fce6f84241e" style="width: 100.0%; height: 100.0%;">Central Bay Street Cluster 0</div>`)[0];
            popup_4bcd571db2ff4d919a96780bd0405386.setContent(html_ce2d554fd44c466886d35fce6f84241e);
        

        circle_marker_0ad192fae5e14346b180925d6bbc904f.bindPopup(popup_4bcd571db2ff4d919a96780bd0405386)
        ;

        
    
    
            var circle_marker_c6a763eba9f047c8bc84cea76af760bc = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
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        var popup_e792d2d0bc384db980d9c8287470b34f = L.popup({"maxWidth": "100%"});

        
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        circle_marker_c6a763eba9f047c8bc84cea76af760bc.bindPopup(popup_e792d2d0bc384db980d9c8287470b34f)
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        var popup_a135914ef36f4ee0a0926cca7e675e01 = L.popup({"maxWidth": "100%"});

        
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        circle_marker_491b1b545bbd43ca85bd9bc6cbf3c86b.bindPopup(popup_a135914ef36f4ee0a0926cca7e675e01)
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        var popup_afbf5b2d477c455fa7544d603cf6cfb3 = L.popup({"maxWidth": "100%"});

        
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        circle_marker_29d7ba84d2914c3193ecd6da0e5e7caf.bindPopup(popup_297a6124ffe74413b76dbc33179d2e9f)
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        var popup_3a411314df2443f4adb85e2f9bc118ed = L.popup({"maxWidth": "100%"});

        
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        circle_marker_63ad43fb9eef4fca9761fdf4dd2ef6a7.bindPopup(popup_9468ffaabc144fffb0c365369a2659f2)
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        circle_marker_69a1c6a4b8a343198a0b7650250e3db1.bindPopup(popup_7e150965a0304c758ee147af4ae34626)
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            var circle_marker_6855fe5a2cbe416c9f14002c27995880 = L.circleMarker(
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        var popup_c65322e98a1e4fd0984f0afb949685c5 = L.popup({"maxWidth": "100%"});

        
            var html_78819e0646fd449bbd42f7e994722230 = $(`<div id="html_78819e0646fd449bbd42f7e994722230" style="width: 100.0%; height: 100.0%;">Christie Cluster 0</div>`)[0];
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        circle_marker_6855fe5a2cbe416c9f14002c27995880.bindPopup(popup_c65322e98a1e4fd0984f0afb949685c5)
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            var circle_marker_7e5a205f065d4e29956fe5bb3225af0d = L.circleMarker(
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        var popup_8c332be3a13d45efbf0d982448588697 = L.popup({"maxWidth": "100%"});

        
            var html_60e503bb219343dc8b160e70ff4ec40b = $(`<div id="html_60e503bb219343dc8b160e70ff4ec40b" style="width: 100.0%; height: 100.0%;">Dovercourt Village, Dufferin Cluster 0</div>`)[0];
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        circle_marker_7e5a205f065d4e29956fe5bb3225af0d.bindPopup(popup_8c332be3a13d45efbf0d982448588697)
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            var circle_marker_ede88f5b33bb4ab19cbc7f0e6ad75257 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_bd947417a8004b16b83aa5d3b4f5e182 = L.popup({"maxWidth": "100%"});

        
            var html_44ea6b9b047b4d2fb2558f3a4212ce31 = $(`<div id="html_44ea6b9b047b4d2fb2558f3a4212ce31" style="width: 100.0%; height: 100.0%;">Little Portugal, Trinity Cluster 0</div>`)[0];
            popup_bd947417a8004b16b83aa5d3b4f5e182.setContent(html_44ea6b9b047b4d2fb2558f3a4212ce31);
        

        circle_marker_ede88f5b33bb4ab19cbc7f0e6ad75257.bindPopup(popup_bd947417a8004b16b83aa5d3b4f5e182)
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            var circle_marker_dcaa91ad3ffb4544a61274bcb923aa8b = L.circleMarker(
                [43.6368472, -79.42819140000002],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_331e3e8296f4476db176c2c3b6dbf9b7 = L.popup({"maxWidth": "100%"});

        
            var html_d0c46d3e54ce47fd9b81f1ab2759e3c4 = $(`<div id="html_d0c46d3e54ce47fd9b81f1ab2759e3c4" style="width: 100.0%; height: 100.0%;">Brockton, Exhibition Place, Parkdale Village Cluster 0</div>`)[0];
            popup_331e3e8296f4476db176c2c3b6dbf9b7.setContent(html_d0c46d3e54ce47fd9b81f1ab2759e3c4);
        

        circle_marker_dcaa91ad3ffb4544a61274bcb923aa8b.bindPopup(popup_331e3e8296f4476db176c2c3b6dbf9b7)
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            var circle_marker_9801d4f80cc8473b9f73f66870d0e50f = L.circleMarker(
                [43.713756200000006, -79.4900738],
                {"bubblingMouseEvents": true, "color": "#5a00ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5a00ff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_0eef84028ae7464e8924465b08e865a8 = L.popup({"maxWidth": "100%"});

        
            var html_b948dc1bf2164533ab577a1d6817082c = $(`<div id="html_b948dc1bf2164533ab577a1d6817082c" style="width: 100.0%; height: 100.0%;">Downsview, North Park, Upwood Park Cluster 6</div>`)[0];
            popup_0eef84028ae7464e8924465b08e865a8.setContent(html_b948dc1bf2164533ab577a1d6817082c);
        

        circle_marker_9801d4f80cc8473b9f73f66870d0e50f.bindPopup(popup_0eef84028ae7464e8924465b08e865a8)
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            var circle_marker_0ed755783dfe40de86b366ea093af956 = L.circleMarker(
                [43.6911158, -79.47601329999999],
                {"bubblingMouseEvents": true, "color": "#008fff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#008fff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_fa571991631847b8abb560859f25829d = L.popup({"maxWidth": "100%"});

        
            var html_1ae3ef959bfe4afea4745d9cc480f538 = $(`<div id="html_1ae3ef959bfe4afea4745d9cc480f538" style="width: 100.0%; height: 100.0%;">Del Ray, Keelesdale, Mount Dennis, Silverthorn Cluster 5</div>`)[0];
            popup_fa571991631847b8abb560859f25829d.setContent(html_1ae3ef959bfe4afea4745d9cc480f538);
        

        circle_marker_0ed755783dfe40de86b366ea093af956.bindPopup(popup_fa571991631847b8abb560859f25829d)
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            var circle_marker_567a7bd6ed194eda90e7c052b5a655db = L.circleMarker(
                [43.67318529999999, -79.48726190000001],
                {"bubblingMouseEvents": true, "color": "#008fff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#008fff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
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        var popup_46d137d59d0b4edabea47c092684564d = L.popup({"maxWidth": "100%"});

        
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            popup_46d137d59d0b4edabea47c092684564d.setContent(html_3f357beea8e24ca1832defc643880abb);
        

        circle_marker_567a7bd6ed194eda90e7c052b5a655db.bindPopup(popup_46d137d59d0b4edabea47c092684564d)
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            var circle_marker_16725b0c6551430ca208105af36e6d91 = L.circleMarker(
                [43.6616083, -79.46476329999999],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
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        var popup_ca830fa9f89d4d88935ba42eacb8c649 = L.popup({"maxWidth": "100%"});

        
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            popup_ca830fa9f89d4d88935ba42eacb8c649.setContent(html_de671bf526a44a76bbe41ae1bfe334a9);
        

        circle_marker_16725b0c6551430ca208105af36e6d91.bindPopup(popup_ca830fa9f89d4d88935ba42eacb8c649)
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            var circle_marker_ecd65086645e4c78a0a600a275a12586 = L.circleMarker(
                [43.6489597, -79.456325],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_33c332fa89d6403b8cfcca5a06f5bd7f = L.popup({"maxWidth": "100%"});

        
            var html_00f1aed20d8346c48578ae9bd6f8e68b = $(`<div id="html_00f1aed20d8346c48578ae9bd6f8e68b" style="width: 100.0%; height: 100.0%;">Parkdale, Roncesvalles Cluster 0</div>`)[0];
            popup_33c332fa89d6403b8cfcca5a06f5bd7f.setContent(html_00f1aed20d8346c48578ae9bd6f8e68b);
        

        circle_marker_ecd65086645e4c78a0a600a275a12586.bindPopup(popup_33c332fa89d6403b8cfcca5a06f5bd7f)
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            var circle_marker_5f65d5a22cd444b788aa744d4434b999 = L.circleMarker(
                [43.6515706, -79.4844499],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_9e28527eef324bd1a35a5ce11db68d11 = L.popup({"maxWidth": "100%"});

        
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            popup_9e28527eef324bd1a35a5ce11db68d11.setContent(html_609c9e7a71b5403c97c8968d001be226);
        

        circle_marker_5f65d5a22cd444b788aa744d4434b999.bindPopup(popup_9e28527eef324bd1a35a5ce11db68d11)
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            var circle_marker_8bc8aaa966614eb4a22acd69607c9e62 = L.circleMarker(
                [43.6623015, -79.3894938],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_46871fc068074a59810f0d56eefc5ac4 = L.popup({"maxWidth": "100%"});

        
            var html_1e20f2824466474aaa9100c107f5022c = $(`<div id="html_1e20f2824466474aaa9100c107f5022c" style="width: 100.0%; height: 100.0%;">Queen&#39;s Park Cluster 0</div>`)[0];
            popup_46871fc068074a59810f0d56eefc5ac4.setContent(html_1e20f2824466474aaa9100c107f5022c);
        

        circle_marker_8bc8aaa966614eb4a22acd69607c9e62.bindPopup(popup_46871fc068074a59810f0d56eefc5ac4)
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            var circle_marker_e7d4425a9ff346ce8f784790d08a2e3a = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_92fcff07e7514fe1a620418bdf4f9dbb = L.popup({"maxWidth": "100%"});

        
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            popup_92fcff07e7514fe1a620418bdf4f9dbb.setContent(html_ae317372e973416c87184b71e8c95b01);
        

        circle_marker_e7d4425a9ff346ce8f784790d08a2e3a.bindPopup(popup_92fcff07e7514fe1a620418bdf4f9dbb)
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            var circle_marker_72806e5ac1094e119b80975b6218f991 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_5b1581ed0d9a44bbbcdee0be4632cb78 = L.popup({"maxWidth": "100%"});

        
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            popup_5b1581ed0d9a44bbbcdee0be4632cb78.setContent(html_301275d1e50e435084202000d349ca2a);
        

        circle_marker_72806e5ac1094e119b80975b6218f991.bindPopup(popup_5b1581ed0d9a44bbbcdee0be4632cb78)
        ;

        
    
    
            var circle_marker_fd5c5ac93ca1452c9367369fdfc7cde7 = L.circleMarker(
                [43.6056466, -79.50132070000001],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_51a5c4e0ade8440991bdabcd4ea30c46 = L.popup({"maxWidth": "100%"});

        
            var html_9150696996e74e39a0c40451c2c916e4 = $(`<div id="html_9150696996e74e39a0c40451c2c916e4" style="width: 100.0%; height: 100.0%;">Humber Bay Shores, Mimico South, New Toronto Cluster 0</div>`)[0];
            popup_51a5c4e0ade8440991bdabcd4ea30c46.setContent(html_9150696996e74e39a0c40451c2c916e4);
        

        circle_marker_fd5c5ac93ca1452c9367369fdfc7cde7.bindPopup(popup_51a5c4e0ade8440991bdabcd4ea30c46)
        ;

        
    
    
            var circle_marker_c5a96b6471f0414082da02ef3c20de14 = L.circleMarker(
                [43.60241370000001, -79.54348409999999],
                {"bubblingMouseEvents": true, "color": "#008fff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#008fff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_f2f3ab5ad8724b9e89bc555094c5fcbd = L.popup({"maxWidth": "100%"});

        
            var html_4354efe389dd4b6fad6be765710bb8a2 = $(`<div id="html_4354efe389dd4b6fad6be765710bb8a2" style="width: 100.0%; height: 100.0%;">Alderwood, Long Branch Cluster 5</div>`)[0];
            popup_f2f3ab5ad8724b9e89bc555094c5fcbd.setContent(html_4354efe389dd4b6fad6be765710bb8a2);
        

        circle_marker_c5a96b6471f0414082da02ef3c20de14.bindPopup(popup_f2f3ab5ad8724b9e89bc555094c5fcbd)
        ;

        
    
    
            var circle_marker_b28dbfe3055b4a78b8b5d5867ac7b6ec = L.circleMarker(
                [43.653653600000005, -79.5069436],
                {"bubblingMouseEvents": true, "color": "#5a00ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5a00ff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_24249e9dc7f64684ae94f81af27e6d6a = L.popup({"maxWidth": "100%"});

        
            var html_36368067746143ad93f26ea7198e1b5b = $(`<div id="html_36368067746143ad93f26ea7198e1b5b" style="width: 100.0%; height: 100.0%;">The Kingsway, Montgomery Road, Old Mill North Cluster 6</div>`)[0];
            popup_24249e9dc7f64684ae94f81af27e6d6a.setContent(html_36368067746143ad93f26ea7198e1b5b);
        

        circle_marker_b28dbfe3055b4a78b8b5d5867ac7b6ec.bindPopup(popup_24249e9dc7f64684ae94f81af27e6d6a)
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            var circle_marker_bfceca1b71d243a0925a5d648603a272 = L.circleMarker(
                [43.6362579, -79.49850909999999],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_079978df76c54a279bea7534dc8bd606 = L.popup({"maxWidth": "100%"});

        
            var html_ffed431a23b04094b3c1f52c3c45177d = $(`<div id="html_ffed431a23b04094b3c1f52c3c45177d" style="width: 100.0%; height: 100.0%;">Humber Bay, King&#39;s Mill Park, Kingsway Park South East, Mimico NE, Old Mill South, The Queensway East, Royal York South East, Sunnylea Cluster 0</div>`)[0];
            popup_079978df76c54a279bea7534dc8bd606.setContent(html_ffed431a23b04094b3c1f52c3c45177d);
        

        circle_marker_bfceca1b71d243a0925a5d648603a272.bindPopup(popup_079978df76c54a279bea7534dc8bd606)
        ;

        
    
    
            var circle_marker_1126cf9ccd97421fa057de539f5ee2ca = L.circleMarker(
                [43.6288408, -79.52099940000001],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_92482b2a83d74d11b6ddf9dd96408559 = L.popup({"maxWidth": "100%"});

        
            var html_fb3959f656714f5a9e5a0368a4b85127 = $(`<div id="html_fb3959f656714f5a9e5a0368a4b85127" style="width: 100.0%; height: 100.0%;">Kingsway Park South West, Mimico NW, The Queensway West, Royal York South West, South of Bloor Cluster 0</div>`)[0];
            popup_92482b2a83d74d11b6ddf9dd96408559.setContent(html_fb3959f656714f5a9e5a0368a4b85127);
        

        circle_marker_1126cf9ccd97421fa057de539f5ee2ca.bindPopup(popup_92482b2a83d74d11b6ddf9dd96408559)
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            var circle_marker_56d1e3ab315c45b7ab8f7b5025b81891 = L.circleMarker(
                [43.6509432, -79.55472440000001],
                {"bubblingMouseEvents": true, "color": "#5cff00", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5cff00", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_1f3d39cc98d74a96beac80373b3f8719 = L.popup({"maxWidth": "100%"});

        
            var html_f8fe985ce62c4dc0b1d3bb6f620eea22 = $(`<div id="html_f8fe985ce62c4dc0b1d3bb6f620eea22" style="width: 100.0%; height: 100.0%;">Cloverdale, Islington, Martin Grove, Princess Gardens, West Deane Park Cluster 3</div>`)[0];
            popup_1f3d39cc98d74a96beac80373b3f8719.setContent(html_f8fe985ce62c4dc0b1d3bb6f620eea22);
        

        circle_marker_56d1e3ab315c45b7ab8f7b5025b81891.bindPopup(popup_1f3d39cc98d74a96beac80373b3f8719)
        ;

        
    
    
            var circle_marker_f2c2fe550c634039ae3059b539c37982 = L.circleMarker(
                [43.6435152, -79.57720079999999],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_d54497ec1e3546b4895e21e10009a022 = L.popup({"maxWidth": "100%"});

        
            var html_f0b7fd8f106646fb9e898bbb4c2968b6 = $(`<div id="html_f0b7fd8f106646fb9e898bbb4c2968b6" style="width: 100.0%; height: 100.0%;">Bloordale Gardens, Eringate, Markland Wood, Old Burnhamthorpe Cluster 0</div>`)[0];
            popup_d54497ec1e3546b4895e21e10009a022.setContent(html_f0b7fd8f106646fb9e898bbb4c2968b6);
        

        circle_marker_f2c2fe550c634039ae3059b539c37982.bindPopup(popup_d54497ec1e3546b4895e21e10009a022)
        ;

        
    
    
            var circle_marker_2857dbf88d9f4e2faa591632c8ae9ff5 = L.circleMarker(
                [43.7563033, -79.56596329999999],
                {"bubblingMouseEvents": true, "color": "#008fff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#008fff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_a1d7ffa336ea4942bb54b8454ba41017 = L.popup({"maxWidth": "100%"});

        
            var html_707dfb7eedae40ffb510ee82e390db56 = $(`<div id="html_707dfb7eedae40ffb510ee82e390db56" style="width: 100.0%; height: 100.0%;">Humber Summit Cluster 5</div>`)[0];
            popup_a1d7ffa336ea4942bb54b8454ba41017.setContent(html_707dfb7eedae40ffb510ee82e390db56);
        

        circle_marker_2857dbf88d9f4e2faa591632c8ae9ff5.bindPopup(popup_a1d7ffa336ea4942bb54b8454ba41017)
        ;

        
    
    
            var circle_marker_357ccc3b38b04a0191cf6d8d038a6a67 = L.circleMarker(
                [43.7247659, -79.53224240000002],
                {"bubblingMouseEvents": true, "color": "#ffba00", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ffba00", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_f0422c4b136e4998852c8a9b52457b1d = L.popup({"maxWidth": "100%"});

        
            var html_667d8e13e29b4745bcac744eb3f238a4 = $(`<div id="html_667d8e13e29b4745bcac744eb3f238a4" style="width: 100.0%; height: 100.0%;">Emery, Humberlea Cluster 2</div>`)[0];
            popup_f0422c4b136e4998852c8a9b52457b1d.setContent(html_667d8e13e29b4745bcac744eb3f238a4);
        

        circle_marker_357ccc3b38b04a0191cf6d8d038a6a67.bindPopup(popup_f0422c4b136e4998852c8a9b52457b1d)
        ;

        
    
    
            var circle_marker_42dfd893150f4e1195467cf440f33c64 = L.circleMarker(
                [43.706876, -79.51818840000001],
                {"bubblingMouseEvents": true, "color": "#5a00ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5a00ff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_18e12dd36ebe42379eb310bb50b9dd52 = L.popup({"maxWidth": "100%"});

        
            var html_a6133c85ca9a4533b0bd70f4cee5bd25 = $(`<div id="html_a6133c85ca9a4533b0bd70f4cee5bd25" style="width: 100.0%; height: 100.0%;">Weston Cluster 6</div>`)[0];
            popup_18e12dd36ebe42379eb310bb50b9dd52.setContent(html_a6133c85ca9a4533b0bd70f4cee5bd25);
        

        circle_marker_42dfd893150f4e1195467cf440f33c64.bindPopup(popup_18e12dd36ebe42379eb310bb50b9dd52)
        ;

        
    
    
            var circle_marker_c5f72fd79e91446985b568e4f233c049 = L.circleMarker(
                [43.696319, -79.53224240000002],
                {"bubblingMouseEvents": true, "color": "#008fff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#008fff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_ee22cebbd4d24d0ab92dfe627824f6ab = L.popup({"maxWidth": "100%"});

        
            var html_da16c492a4bc40a2b4038b4f156504bb = $(`<div id="html_da16c492a4bc40a2b4038b4f156504bb" style="width: 100.0%; height: 100.0%;">Westmount Cluster 5</div>`)[0];
            popup_ee22cebbd4d24d0ab92dfe627824f6ab.setContent(html_da16c492a4bc40a2b4038b4f156504bb);
        

        circle_marker_c5f72fd79e91446985b568e4f233c049.bindPopup(popup_ee22cebbd4d24d0ab92dfe627824f6ab)
        ;

        
    
    
            var circle_marker_37d6d854298a43bdb469191cd5a701e5 = L.circleMarker(
                [43.6889054, -79.55472440000001],
                {"bubblingMouseEvents": true, "color": "#5a00ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#5a00ff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_27eedc865b774654a8514d481dc104d1 = L.popup({"maxWidth": "100%"});

        
            var html_8100c19504464fc9b15ac277b5cef01d = $(`<div id="html_8100c19504464fc9b15ac277b5cef01d" style="width: 100.0%; height: 100.0%;">Kingsview Village, Martin Grove Gardens, Richview Gardens, St. Phillips Cluster 6</div>`)[0];
            popup_27eedc865b774654a8514d481dc104d1.setContent(html_8100c19504464fc9b15ac277b5cef01d);
        

        circle_marker_37d6d854298a43bdb469191cd5a701e5.bindPopup(popup_27eedc865b774654a8514d481dc104d1)
        ;

        
    
    
            var circle_marker_c07488428f174addbc847e4e5047118c = L.circleMarker(
                [43.739416399999996, -79.5884369],
                {"bubblingMouseEvents": true, "color": "#008fff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#008fff", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_80fe08f9fe7f4ddf8bb81fb3ee88f290 = L.popup({"maxWidth": "100%"});

        
            var html_4c3b73eb1c5443349f123b45d9c25c12 = $(`<div id="html_4c3b73eb1c5443349f123b45d9c25c12" style="width: 100.0%; height: 100.0%;">Albion Gardens, Beaumond Heights, Humbergate, Jamestown, Mount Olive, Silverstone, South Steeles, Thistletown Cluster 5</div>`)[0];
            popup_80fe08f9fe7f4ddf8bb81fb3ee88f290.setContent(html_4c3b73eb1c5443349f123b45d9c25c12);
        

        circle_marker_c07488428f174addbc847e4e5047118c.bindPopup(popup_80fe08f9fe7f4ddf8bb81fb3ee88f290)
        ;

        
    
    
            var circle_marker_12aceb4d0fcf44e0bc32169ab105c935 = L.circleMarker(
                [43.706748299999994, -79.5940544],
                {"bubblingMouseEvents": true, "color": "#ff00bf", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff00bf", "fillOpacity": 0.5, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 11, "stroke": true, "weight": 3}
            ).addTo(map_4a11ecdcf5dc4b1cb627db92e1e6563f);
        
    
        var popup_79d39c6a34ce468aae53aa73bdbdd5da = L.popup({"maxWidth": "100%"});

        
            var html_2865fb08d555407981d0aad9be1e284b = $(`<div id="html_2865fb08d555407981d0aad9be1e284b" style="width: 100.0%; height: 100.0%;">Northwest Cluster 0</div>`)[0];
            popup_79d39c6a34ce468aae53aa73bdbdd5da.setContent(html_2865fb08d555407981d0aad9be1e284b);
        

        circle_marker_12aceb4d0fcf44e0bc32169ab105c935.bindPopup(popup_79d39c6a34ce468aae53aa73bdbdd5da)
        ;

        
    
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f89b40225f8>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map\n",
"map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
"\n",
"# set color scheme for the clusters\n",
"x = np.arange(kclusters)\n",
"ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
"#colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
"colors_array = cm.gist_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",
"markers_colors = []\n",
"for lat, lon, poi, cluster in zip(toronto_labeled['Latitude'], toronto_labeled['Longitude'], toronto_labeled['Neighbourhood'], toronto_labeled['Cluster Labels']):\n",
" label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lon],\n",
" radius=11,\n",
" popup=label,\n",
" color=rainbow[cluster-1],\n",
" fill=True,\n",
" fill_color=rainbow[cluster-1],\n",
" fill_opacity=0.5).add_to(map_clusters)\n",
" \n",
"map_clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis of the clusters\n",
"\n",
"Now, we are ready to examine each cluster and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories, we can assign a name to each cluster."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 1: Coffee to go"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>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",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>North York</td>\n",
" <td>0</td>\n",
" <td>Clothing Store</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Shoe Store</td>\n",
" <td>Cosmetics Shop</td>\n",
" <td>Japanese Restaurant</td>\n",
" <td>Bakery</td>\n",
" <td>Chinese Restaurant</td>\n",
" <td>Pharmacy</td>\n",
" <td>Sporting Goods Shop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Scarborough</td>\n",
" <td>0</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Rental Car Location</td>\n",
" <td>Intersection</td>\n",
" <td>Pizza Place</td>\n",
" <td>Electronics Store</td>\n",
" <td>Medical Center</td>\n",
" <td>Mexican Restaurant</td>\n",
" <td>Drugstore</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>West Toronto</td>\n",
" <td>0</td>\n",
" <td>Café</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Italian Restaurant</td>\n",
" <td>Bar</td>\n",
" <td>Intersection</td>\n",
" <td>Bakery</td>\n",
" <td>Stadium</td>\n",
" <td>Restaurant</td>\n",
" <td>Climbing Gym</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>0</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Café</td>\n",
" <td>Thai Restaurant</td>\n",
" <td>Bar</td>\n",
" <td>Steakhouse</td>\n",
" <td>Gym</td>\n",
" <td>Restaurant</td>\n",
" <td>American Restaurant</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Hotel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>North York</td>\n",
" <td>0</td>\n",
" <td>Gym / Fitness Center</td>\n",
" <td>Grocery Store</td>\n",
" <td>Liquor Store</td>\n",
" <td>Athletics &amp; Sports</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Cluster Labels 1st Most Common Venue \\\n",
"17 North York 0 Clothing Store \n",
"2 Scarborough 0 Breakfast Spot \n",
"77 West Toronto 0 Café \n",
"57 Downtown Toronto 0 Coffee Shop \n",
"32 North York 0 Gym / Fitness Center \n",
"\n",
" 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue \\\n",
"17 Fast Food Restaurant Coffee Shop Shoe Store \n",
"2 Rental Car Location Intersection Pizza Place \n",
"77 Breakfast Spot Coffee Shop Italian Restaurant \n",
"57 Café Thai Restaurant Bar \n",
"32 Grocery Store Liquor Store Athletics & Sports \n",
"\n",
" 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue \\\n",
"17 Cosmetics Shop Japanese Restaurant Bakery \n",
"2 Electronics Store Medical Center Mexican Restaurant \n",
"77 Bar Intersection Bakery \n",
"57 Steakhouse Gym Restaurant \n",
"32 Yoga Studio Eastern European Restaurant Dog Run \n",
"\n",
" 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue \n",
"17 Chinese Restaurant Pharmacy Sporting Goods Shop \n",
"2 Drugstore Dog Run Doner Restaurant \n",
"77 Stadium Restaurant Climbing Gym \n",
"57 American Restaurant Breakfast Spot Hotel \n",
"32 Doner Restaurant Donut Shop Drugstore "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_labeled.loc[toronto_labeled['Cluster Labels'] == 0, toronto_labeled.columns[[1] + list(range(5, toronto_labeled.shape[1]))]].sample(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 2: Mixed bag"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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>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",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>North York</td>\n",
" <td>1</td>\n",
" <td>Park</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Dive Bar</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Electronics Store</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Cluster Labels 1st Most Common Venue 2nd Most Common Venue \\\n",
"20 North York 1 Park Yoga Studio \n",
"\n",
" 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n",
"20 Eastern European Restaurant Dive Bar Dog Run \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"20 Doner Restaurant Donut Shop Drugstore \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"20 Dumpling Restaurant Electronics Store "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_labeled.loc[toronto_labeled['Cluster Labels'] == 1, toronto_labeled.columns[[1] + list(range(5, toronto_labeled.shape[1]))]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 3: Sports"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>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",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>North York</td>\n",
" <td>2</td>\n",
" <td>Baseball Field</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Electronics Store</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Empanada Restaurant</td>\n",
" <td>Dive Bar</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Cluster Labels 1st Most Common Venue 2nd Most Common Venue \\\n",
"95 North York 2 Baseball Field Yoga Studio \n",
"\n",
" 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n",
"95 Electronics Store Doner Restaurant Donut Shop \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"95 Drugstore Dumpling Restaurant Eastern European Restaurant \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"95 Empanada Restaurant Dive Bar "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_labeled.loc[toronto_labeled['Cluster Labels'] == 2, toronto_labeled.columns[[1] + list(range(5, toronto_labeled.shape[1]))]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 4: Banking"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>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",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>North York</td>\n",
" <td>3</td>\n",
" <td>Café</td>\n",
" <td>Chinese Restaurant</td>\n",
" <td>Japanese Restaurant</td>\n",
" <td>Bank</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Dog Run</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Eastern European Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>Etobicoke</td>\n",
" <td>3</td>\n",
" <td>Bank</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Electronics Store</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Empanada Restaurant</td>\n",
" <td>Dive Bar</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Cluster Labels 1st Most Common Venue 2nd Most Common Venue \\\n",
"18 North York 3 Café Chinese Restaurant \n",
"92 Etobicoke 3 Bank Yoga Studio \n",
"\n",
" 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n",
"18 Japanese Restaurant Bank Yoga Studio \n",
"92 Electronics Store Doner Restaurant Donut Shop \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"18 Dog Run Donut Shop Drugstore \n",
"92 Drugstore Dumpling Restaurant Eastern European Restaurant \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"18 Dumpling Restaurant Eastern European Restaurant \n",
"92 Empanada Restaurant Dive Bar "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_labeled.loc[toronto_labeled['Cluster Labels'] == 3, toronto_labeled.columns[[1] + list(range(5, toronto_labeled.shape[1]))]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 5: Oddball"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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>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",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>North York</td>\n",
" <td>4</td>\n",
" <td>Cafeteria</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Electronics Store</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Empanada Restaurant</td>\n",
" <td>Dive Bar</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Cluster Labels 1st Most Common Venue 2nd Most Common Venue \\\n",
"19 North York 4 Cafeteria Yoga Studio \n",
"\n",
" 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n",
"19 Electronics Store Doner Restaurant Donut Shop \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"19 Drugstore Dumpling Restaurant Eastern European Restaurant \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"19 Empanada Restaurant Dive Bar "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_labeled.loc[toronto_labeled['Cluster Labels'] == 4, toronto_labeled.columns[[1] + list(range(5, toronto_labeled.shape[1]))]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 6: Food & Shopping"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>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",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>East York</td>\n",
" <td>5</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Pizza Place</td>\n",
" <td>Gastropub</td>\n",
" <td>Bank</td>\n",
" <td>Intersection</td>\n",
" <td>Athletics &amp; Sports</td>\n",
" <td>Gym / Fitness Center</td>\n",
" <td>Pharmacy</td>\n",
" <td>Pet Store</td>\n",
" <td>Cosmetics Shop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Scarborough</td>\n",
" <td>5</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Chinese Restaurant</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Nail Salon</td>\n",
" <td>Grocery Store</td>\n",
" <td>Pharmacy</td>\n",
" <td>Pizza Place</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Sandwich Place</td>\n",
" <td>Thrift / Vintage Store</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>Etobicoke</td>\n",
" <td>5</td>\n",
" <td>Pizza Place</td>\n",
" <td>Gym</td>\n",
" <td>Pool</td>\n",
" <td>Skating Rink</td>\n",
" <td>Pharmacy</td>\n",
" <td>Pub</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Sandwich Place</td>\n",
" <td>Diner</td>\n",
" <td>Discount Store</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>Etobicoke</td>\n",
" <td>5</td>\n",
" <td>Pizza Place</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Discount Store</td>\n",
" <td>Chinese Restaurant</td>\n",
" <td>Middle Eastern Restaurant</td>\n",
" <td>Sandwich Place</td>\n",
" <td>Intersection</td>\n",
" <td>Electronics Store</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Dumpling Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Scarborough</td>\n",
" <td>5</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Electronics Store</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Empanada Restaurant</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Cluster Labels 1st Most Common Venue 2nd Most Common Venue \\\n",
"34 East York 5 Fast Food Restaurant Pizza Place \n",
"15 Scarborough 5 Fast Food Restaurant Chinese Restaurant \n",
"88 Etobicoke 5 Pizza Place Gym \n",
"97 Etobicoke 5 Pizza Place Coffee Shop \n",
"0 Scarborough 5 Fast Food Restaurant Yoga Studio \n",
"\n",
" 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n",
"34 Gastropub Bank Intersection \n",
"15 Coffee Shop Nail Salon Grocery Store \n",
"88 Pool Skating Rink Pharmacy \n",
"97 Discount Store Chinese Restaurant Middle Eastern Restaurant \n",
"0 Electronics Store Dog Run Doner Restaurant \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"34 Athletics & Sports Gym / Fitness Center Pharmacy \n",
"15 Pharmacy Pizza Place Breakfast Spot \n",
"88 Pub Coffee Shop Sandwich Place \n",
"97 Sandwich Place Intersection Electronics Store \n",
"0 Donut Shop Drugstore Dumpling Restaurant \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"34 Pet Store Cosmetics Shop \n",
"15 Sandwich Place Thrift / Vintage Store \n",
"88 Diner Discount Store \n",
"97 Eastern European Restaurant Dumpling Restaurant \n",
"0 Eastern European Restaurant Empanada Restaurant "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_labeled.loc[toronto_labeled['Cluster Labels'] == 5, toronto_labeled.columns[[1] + list(range(5, toronto_labeled.shape[1]))]].sample(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster 7: Rest & Relaxation"
]
},
{
"cell_type": "code",
"execution_count": 35,
"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>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",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Central Toronto</td>\n",
" <td>6</td>\n",
" <td>Park</td>\n",
" <td>Playground</td>\n",
" <td>Restaurant</td>\n",
" <td>Gym</td>\n",
" <td>Comic Shop</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Discount Store</td>\n",
" <td>Dive Bar</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>Etobicoke</td>\n",
" <td>6</td>\n",
" <td>Park</td>\n",
" <td>Bus Line</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Electronics Store</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>North York</td>\n",
" <td>6</td>\n",
" <td>Park</td>\n",
" <td>Convenience Store</td>\n",
" <td>Bank</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Electronics Store</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" <td>Eastern European Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Scarborough</td>\n",
" <td>6</td>\n",
" <td>Park</td>\n",
" <td>Playground</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Eastern European Restaurant</td>\n",
" <td>Dive Bar</td>\n",
" <td>Dog Run</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Donut Shop</td>\n",
" <td>Drugstore</td>\n",
" <td>Dumpling Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>East York</td>\n",
" <td>6</td>\n",
" <td>Park</td>\n",
" <td>Pizza Place</td>\n",
" <td>Convenience Store</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Falafel Restaurant</td>\n",
" <td>Farm</td>\n",
" <td>Event Space</td>\n",
" <td>Ethiopian Restaurant</td>\n",
" <td>Empanada Restaurant</td>\n",
" <td>Diner</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Cluster Labels 1st Most Common Venue \\\n",
"47 Central Toronto 6 Park \n",
"98 Etobicoke 6 Park \n",
"22 North York 6 Park \n",
"14 Scarborough 6 Park \n",
"39 East York 6 Park \n",
"\n",
" 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue \\\n",
"47 Playground Restaurant Gym \n",
"98 Bus Line Yoga Studio Eastern European Restaurant \n",
"22 Convenience Store Bank Yoga Studio \n",
"14 Playground Yoga Studio Eastern European Restaurant \n",
"39 Pizza Place Convenience Store Coffee Shop \n",
"\n",
" 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue \\\n",
"47 Comic Shop Dumpling Restaurant Discount Store \n",
"98 Dog Run Doner Restaurant Donut Shop \n",
"22 Electronics Store Doner Restaurant Donut Shop \n",
"14 Dive Bar Dog Run Doner Restaurant \n",
"39 Falafel Restaurant Farm Event Space \n",
"\n",
" 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue \n",
"47 Dive Bar Dog Run Doner Restaurant \n",
"98 Drugstore Dumpling Restaurant Electronics Store \n",
"22 Drugstore Dumpling Restaurant Eastern European Restaurant \n",
"14 Donut Shop Drugstore Dumpling Restaurant \n",
"39 Ethiopian Restaurant Empanada Restaurant Diner "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_labeled.loc[toronto_labeled['Cluster Labels'] == 6, toronto_labeled.columns[[1] + list(range(5, toronto_labeled.shape[1]))]].sample(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conclusion\n",
"\n",
"As we already suspected, the map is dominated by the majority cluster 0, while the other two big clusters (5 & 6) only start appearing once you leave the center of Toronto city. As for the remaining 4 outlier clusters, they appear on the outskirts of the Toronto city area and only have one or two members in them. \n",
"\n",
"Looking at the clusters more closely, there seems to be an abundance of fast food & beverage venues found in the biggest cluster. The 2nd and 3rd biggest clusters seem to address the needs of people in search of places to have lunch and/or dinner, or places to get outdoors and/or relax respectively. The rest of the clusters seem to be in neighbourhoods that cater to more specific needs, like electronics shopping or banking, which explains the low number of member neighbourhoods in them.\n",
"\n",
"Overall, the visualization on the map accompanied by the inspection of the data reveals that there are mainly to types of areas in Toronto. The first is the downtown city life while the other appears to be more work related on the outskirts of the city. This is important information for people considering moving to or within Toronto."
]
}
],
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"kernelspec": {
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"language": "python",
"name": "conda-env-python-py"
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"version": 3
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