1. Business Problem
Client seeks to establish a franchised Asian restaurant in a Toronto neighborhood. Which neighborhood would appear to be the optimal and most strategic location for the business.
A client seeks to establish a franchised Asian restaurant in a Toronto neighborhood. Which neighbourhood would appear to be the optimal and most strategic location for the business operations? The objective of this capstone project is to locate the optimal neighborhood for operation. Our foundation of reasoning would be based off of spending power, distribution of ethnic group, and competition, across each neighbourhood. We will mainly be utilizing the Foursquare API and the extensive geographical and census data from Toronto's Open Data Portal.
2. Data
We are going to extract Datafrom multiple sources mentioned below along the data fields being extract from each source
Neighbourhood names, alongside their corresponding boroughs and postal codes, scraped from Wikipedia: (https://en.wikipedia.org/w/index.php?title=List_of_postal_codes_of_Canada:_M&oldid=945633050)
- Neighborhood Name
- Postal Code
The Toronto's census data for its social demographic characteristics will be distilled from Toronto's Neighborhood Profile (https://bit.ly/2ZivgPg).
- Total Population
- Southeast Asian Population
- Income
Geographical coordinates of each neighbourhood: (https://cocl.us/Geospatial_data)
- Latitude
- Longitude
The Foursquare API will be used to explore neighborhoods in Toronto, more specifically, we will be using the explore function to get the most common venue categories in each neighborhood.
- Venue
- Venue Category
- Venue Latitude
- Venue Longitude
1. Number of Asian Restaurants per neighbourhood
The number of existing Asian Restaurants per neighbourhood will let us know the competition in the market. An ideal neighbourhood for openining a restaurant will be one where there are average number of restaurants.
2. Number of Southeast Asians per neighborhood
4. Results
After that using the K-Means clustering, we have divided the neighborhood into 6 clusters as shown in the map below with different colors
- Get link
- X
- Other Apps
- Get link
- X
- Other Apps
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