The MBTA, working with the Central Transportation Planning Staff, has just completed a systemwide passenger survey to collect necessary passenger demographic data for bus routes and rail stations. This project updates the 2008-2009 dataset and will be used for service planning, ridership analysis, and Title VI equity analyses.
The MBTA knows this data is useful for many other research projects, so we are releasing an interactive tool that allows you to compare the results for stations and bus routes.
In collaboration with the Boston Area Research Initiative, the MBTA is holding a data challenge to see how students and researchers can creatively use the survey data to answer research questions. The winners of the data challenge will be invited to present their work at the BARI Spring 2018 conference on April 27th, 2018.
Data Challenge Logistics
The first rule is read all the data caveats! After that you are free to do whatever analysis interests you. To get you started we have created a list of potential research questions (below). Feel free to combine this data with other datasets about Boston.
You may work on your submission as individuals or teams. Submissions are due at midnight at the end of April 16th, 2018. Please e-mail them along with your contact information to firstname.lastname@example.org. You may also contact us at this address with data questions you have as you work on the challenge.
Winners will be notified on April 20th, 2018 and invited to attend the BARI Spring 2018 conference and present their results. Winning submissions will also be featured on this very prestigious data blog.
Your submission can be a map, written analysis, an interactive tool, or whatever you think best conveys the analysis you did.
Data Challenge Criteria
Submissions will be judged on the following criteria:
- Accuracy of the analysis: Did you use the data correctly? Were your analyses methodologically sound and well-documented? Did you account for the caveats?
- How compelling the research question is: Does the analysis reveal something that was not apparent at first glance? Does it confirm something we believed but weren’;t sure about? The results do not have to be surprising to be compelling.
- Presentation of the analysis: Is the deliverable easy to understand? Are the graphics clear? Are the graphics and tables helpful in understanding the results?
Potential Research Questions
To get you started, one of our interns did an analysis of how the minority usage on our bus routes compares to demographics of the tracts the route passes through.
- Where do MBTA rider demographics match (or not match) resident populations? Extend the above analysis to other demographics or look at it another way.
- Which route/station has the most representative demographics of Boston? (It’;s up to you how you want to define Boston geographically and how you want to define representative.)
- Do the access modes to the stations reflect land use around those stations? Also think about parking availability and transfers (available in the dataset).
- How does household vehicle availability match (or not) with usage? Are there spatial or demographic explanations for any mismatch?