Capstone
Project
The Battle of Neighborhood
Segmenting
and Clustering Neighborhoods of San Francisco and Los Angeles
By,
Charles C Gomes
Contents
Introduction……………………………………………………………………………..3
Objective………………………………………………………………………………..4
Data……………………………………………………………………………………..5
Results…………………………………………………………………………………..6
Discussion………………………………………………………………………………7
Conclusion……………………………………………………………………………...8
Introduction
San Francisco and Los
Angeles are two major cities in California.
Brief
information about both cities:
- San Francisco: officially the
City and County of San Francisco, is a city in, and the cultural,
commercial, and financial center of, Northern California. San Francisco is
the 13th-most populous city in the United States, and the fourth-most
populous in California, with 883,305 residents as of 2018.
- Los Angeles: officially the
City of Los Angeles and often known by its initials L.A., is the most
populous city in California, the second most populous city in the United
States, after New York City, and the third most populous city in North
America. With an estimated population of nearly four million,[11] Los
Angeles is the cultural, financial, and commercial center of Southern
California.
Objective
In this project, we will
study in details the area classification using Foursquare data and machine
learning segmentation and clustering. The aim of this project is to segment
areas or neighborhood of San Francisco and Los Angeles based on the most common
places captured from Foursquare.
Using
segmentation and clustering, we hope we can determine:
- The similarity or dissimilarity
of both cities
- Classification of area located
inside the city whether it is residential, tourism places, or others
Data
The data for neighborhoods
is acquired from following sources
Additionally Foursquare
data api was used for getting different kind of venues for segmentation and
clustering.
Results
Cluster 1: San Francisco: Tourism
Cluster 2: San Francisco: Residental and Tourism
Cluster 3: San Francisco: Tourism
Cluster 1: Los Angeles: Tourism
Cluster 2: Los Angeles: Residential based on the Park, Convenience Store and Yoga Studio.
Cluster 3: Los Angeles: Mixed.
Discussion
Based on cluster for each cities above, we believe that classification for each cluster can be done
better with calculation of venues categories (most common) in each cities.
Referring to each clsuter, we can not deterimine clearly what represent in each cluster by using
Foursquare - Most Common Venue data. What is lacking at this point is a systematic, quantitative
way to identify and distinguish different district and to describe the correlation most common venues
as recorded in Foursquare.
The reality is however more complex: similar cities might have or might not have similar common venues.
A further step in this classification would be to find a method to extract these common venues and
integrate the spatial correlations between different of areas or district. We believe that the classification we propose is an encouraging step towards a quantitative and systematic comparison of the different cities.
Further studies are indeed needed in order to relate the data acquired, then observe it to more meaningful and objective results.
Conclusion
With the help of Foursquare API, we were able to capture the venue information and using venue information,
we can figure out the similarities or dissimilarities of San Francisco and Los Angeles.
We did classification of Neighbourhoods as Residential, tourism or Mixed.
In conclusion, both cities San Francisco and Los Angeles have tourism as similarity as well as there are some residential areas.
It is somewhat clear that in San Francisco, the residential and tourism neighborhoods are mixed compare to Los Angeles.
