Hubway Data
Visualization Challenge

By Jason Gibbs

When presented with the Hubway data set, I initially began to explore the questions posed on the Visualization Challenge website.

Are all rides down hill? No, rides between stations were uphill as frequently as they were downhill. The most extreme elevation change between two stations is 29 meters. 7 rides took this elevation change downhill and 4 rides made it uphill. 50% of all station-to-station rides ended at a station within 1 meter difference in elevation from the point they started. Roughly 3% of rides ended 10 or more meters difference in elevation from the point where they started (7,205 rides downhill, 6,845 uphill).

Are all rentals after 2:00 AM by people under 25? No, between 2 am and 3:00 AM less than 20% of rides by registered users were taken by people under 25. In fact, after 2:00 AM more rides were taken by users over 40 than by users under 25.

These first two questions were easy enough to answer, but didn’t really lend themselves to an interesting visualization. The next two questions, which could not be answered with a yes or no, provided a more interesting challenge.

I began to explore patterns of usage on weekends and weekdays and found a few interesting trends. The share of casual rides is noticeably higher on weekends with only about 40% of rides being by registered users. Trips also tend to get longer on weekends with Saturday and Sunday being the only days of the week where more than 50% of rides last longer than 15 minutes and more than 20% lasting longer then 30 minutes. These were noteworthy facts, but I was more interested in usage over time and decided to look at how many trips started every hour of every day that Hubway was open for business. The resulting table showed a noticeable difference in usage by time of day from weekday to weekend and also revealed the increasing utilization of Hubway over time. It was also an opportunity to examine how major events such as Hurricane Irene, baseball games and my birthday affected Hubway ridership.

For the final question, “What makes for a good Hubway location?” I worked towards clarification rather than conclusion. I examined station-to-station rides and displayed them by how many trips had occurred between each of the stations. This revealed how much of an influence geography had on utilization. I then looked at each station and what factors might make it useful for commuters. This resulted in a map showing station utilization compared to the number of employers within ¼ mile. I also Identified stations that are within ¼ mile of MBTA stops and college campuses. These factors certainly don’t determine how good a station is, just how useful it might be for getting to and from work or school.

Jason Gibbs