Exploring Citi Bike and Subway Ridership Data with Python
September–December 2021 | New York
Project Members: Hilary Ho, Kit Nga Chou
How did COVID affect Citi Bike use in NYC? Are Citi Bike docking stations equitably distributed across NYC? These were some of the questions I explored using publicly available Citi Bike data. In this project, I used Python to analyze and, more importantly, visualize Citi Bike data from before the pandemic, during the height of the first COVID wave, and right as the city was starting to lift COVID restrictions.
Stay updated with my work combining urbanism, data analysis and storytelling by following me on Medium @hilaryho22!
Methods
Python packages I used for my research include: Pandas, Geopandas, Plotly, NetworkX, Matplotlib, and Seaborn.
Findings
My key findings showed that the overall drop in subway ridership in Manhattan was related to an increase in Citi Bike usage only in commercial neighborhoods like Midtown; this relationship was not seen in more residential neighborhoods like the East Village.
