— ITPG-GT 2621 001 CLOUDCOMMUTING

Mid-term project, A STUDY OF CITIBIKE USER DEMOGRAPHICS

Team: Daniel Melancon, David Tracy, and Saki Hayashi

Who uses the citiBike system and how might changes to its user base broadly affect its use?

These questions served as points of entry for our research, analysis and simulation of the citiBike system. We began by analyzing system users by three factors:

  • Their gender.
  • Their age.
  • Where they go.

These three data points are readily available from CitiBike. Age was subdivided into ten year chunks (ie. under thirty, thirty to forty, forty to fifty, and over fifty).

We took Citi Bike system data and organized it by station; giving each station its own unique demographic profile. Python was used to parse this data. Git repo here. (https://github.com/davidptracy/CitiBikeParse)

Diagram01-01

Next, we analysed and visualised the data what kinds of people go to which community districts.
Exploration of Demographic for City Bike Stations in NYC copy

Lastly, we applied the extracted data into our simulation model. Each circle, station has different capacity to allow bikes to be in and the turtles have different variables, gender and age. This simulation model eventually shows how they fill each group of stations and behave when they cannot fit in their desired stations.

Screen Shot 2014-10-01 at 9.11.48 PM Screen Shot 2014-10-01 at 9.12.39 PM

What we learned:

  1. The age and gender of the users getting to all the community districts are significantly similar. Male users are predominant. Especially, under 40 years old males comprises almost the half whereas, only one quarter of the users are females.
  2. As the capacity of the each station is similar, changing one parameter, proportion of the users affected all the stations.
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