The battle of neighborhoods in the city of Toronto

by Tomas Stankevičius, 2021

Introduction / Business Problem

The company wants to start a restaurant business and is trying to decide which area in Toronto could be a right spot to do that. An area should have the biggest potentiality in developing restaurant business. Location should help company to reach as wide customers auditory as possible to make business profitable. As company does not possess a knowledge of Toronto districts and neighborhoods, it decided to hire a business consultant company which provides various consultant services for companies which want to establish new business in Toronto area. A consultant company utilizing Data Science methods is going to cluster Toronto city area and provide recommendations where is the best place to start a restaurant business.

Explanation about Data

Consultant company is going to use Foursquare location data services to build a data base of various venues in every neighborhood of Toronto city. Then this data base will enriched with additional data provided by Toronto Open Data portal.

Methodology

As mentioned, this project goal is to find area in Toronto city with high population and enough density for a newly established restaurant to be able to attract sufficient amount of clients from the very beginning.

Clustering Analysis

Now let’s prepare a final data frame for visualization and further analysis. We merge tow pandas data frames — Toronto population data with Toronto venues data, which includes clustering labels.

Results and Discussion

Our clustering analysis show that taking into consideration such factors as population density, total neighborhood population number, a distance to Toronto Downtown as well as top10 venues category popularity distribution in every district, majority of Cluster 2 districts falls under those criterions. As Cluster 4 contains only one district it can also be included into potential areas to establish a new restaurant.

Conclusion

The goal of this project was to identify and locate Toronto districts which are close to a Downtown and has high population numbers as well as high density. Using Foursquare API we took into consideration a most popular venues types in Toronto districts to narrow our results.

Studying Data Science