A project by HaoChe Hung
Skills
Python
Intro to Urban Data and Informatics Course Proejct
GSAPP, Columbia U
2021 Fall
New York City
Medium Post
GSAPP, Columbia U
2021 Fall
New York City
Medium Post
Citi Bike Commute Ridership Difference in NYC
In this project, I conducted a data analysis of bike commuting behavior in New York City, using open data from the Citibike bike-sharing program, Neighborhood Tabulation Areas (NTAs) provided by NYC Open Data, and median household income data from the American Community Survey. The goal of this analysis was to identify any correlations between income level and bike commuting behavior in different neighborhoods.
To accomplish this, I utilized Python to perform ridership duration analysis and network analysis on the data. These methods allowed me to gain insights into the length of time riders were using bikes for commuting purposes, as well as the network of bike routes in each NTA. By analyzing this data, I was able to identify trends and correlations between income level and bike commuting behavior.
The results of this analysis can be used to inform urban planners and policymakers about the potential impact of income level on bike commuting behavior in different neighborhoods. By understanding these correlations, steps can be taken to improve bike infrastructure and encourage more equitable and sustainable transportation systems in urban areas.
Research Question
Would people living in different income level areas have different Citi Bike riding patterns during morning commute time on weekdays?
