Public Transit in the Pandemic
Project Scope:
In this project, I investigated the impact of COVID-19 on Citi Bike usage in NYC and the equitable distribution of Citi Bike docking stations across the city, utilizing publicly available Citi Bike data. With Python, I conducted a comprehensive analysis and visualization of Citi Bike usage patterns before the pandemic, during the peak of the initial COVID wave, and as the city began to lift restrictions. The Python packages I utilized for this research included Pandas, Geopandas, Plotly, NetworkX, Matplotlib, and Seaborn.
My findings revealed a correlation between the decline in subway ridership in Manhattan and an upswing in Citi Bike usage, particularly in commercial areas like Midtown. However, this relationship was not observed in more residential neighborhoods such as the East Village. These insights shed light on the nuanced dynamics of transportation shifts during the various phases of the pandemic in different urban contexts.
![](https://freight.cargo.site/t/original/i/116be2431ffd78c80f78cb0873c74a8cf8b672fd92ed3cb907865e8d618e2c88/Urban-Informatics.png)
A network analysis map indicating the trips between Citi bike docking stations
![](https://freight.cargo.site/t/original/i/f9bd278ca2e33022bccec4d7386f4a1a2acb3c2bef7a0c6437cb4c9e40e85946/Screen-Shot-2023-05-15-at-5.46.32-AM.png)
September– December 2021