[Project] Graphing MTA data

[Yuanqi-Hong]

Pitch

Three years ago the Wall Street Journal did a story titled New York City Subway Ridership Jumps, exploring data provided by MTA and pointing out that average daily ridership went up, especially in growing neighborhoods. This project of mine is basically a followup on that story. I want to see how the subway ridership changed from then to now.

Summary

In this project I plan to be using WSJ's data for the above-mentioned story and current MTA data to explore the ridership changes of NYC subway.

Details

Possible headline(s): New York City Subway Ridership Trends

Data set(s): WSJ's data and MTA turnstile data

Code repository: To be updated.

Possible problems/fears/questions: To be updated.

Work so far

To be updated.

Checklist

• [ ] I have already spent time with my data set, opening it, exploring it, etc
• [ ] I have created a "DIARY.md" file to save links and list all of the terrible, no good problems I come across
• [ ] My issue links to my data set(s)
• [ ] My issue links to my code repository
• [ ] My issue explains what I'd like to explore in the data set
• [ ] My issue includes images - either inspiration or what I've done so far

[vpenney]

Nice story idea! It looks like you have some good data to work with. It's tough that the MTA data is broken down by each individual day, but you should be able to scrape that with selenium and append it to a dataframe pretty easily. It might be best to append each row as it gets added, instead of appending everything together at the end. That way, if you have an error, you won't have to start over from scratch.

I think that adding information about population growth is helpful context, but if you have time, it may also be interesting to look at socioeconomic factors. Does income affect ridership in some neighborhoods?

I'm looking forward to seeing your project and the visualizations that you choose!

[maxarvid]

This is excellent. If you want to go in a particular direction, I wonder if it could be worthwhile to look at service disruptions, or attempt to identify service disruptions by looking at the data you are scraping.

Another possible (and more focused) direction could be to see the impact of the extended Q train on the upper east side. Did the turnstiles on the parallel 6 train see a drop in usage? I.e. look at 96th, 86th, and 77th street stations.

Also looking forward to see what you end up doing!