Each Tuesday, Eurry Kim, a student in our class, picks one example of data visualization to share with us. This week is a little different. Eurry didn’t pick it- she created it! I asked if we could feature it. Eurry and Kaz Sakamoto, also a student in the class, submitted a visualization to the Hubway Challenge. Here is their submission. You can view the public leaderboard here, and vote for Kaz and Eurry’s submission! I’m excited to see students in our class collaborating in this way. Below I asked them to describe their collaborative process:

http://hubwaydatachallenge.org/submission/56/

This was our first time working with each other. Up until then, we only had a short geeky conversation about Adobe Illustrator. So we knew that we at least had that in common! Later that week, in an underground lab in Fayerweather, we started on our submission. Kaz is an adept GIS-er, so it went without saying that we would center the visualization around a map of the metro-Boston area. He got to gathering the spatial data and mapping the Hubway stations. Meanwhile, I plugged away at R and conducted some exploratory data analysis. Many ddplys and ggplots later, we developed a good sense of the data. After finding the large discrepancy in Hubway usage between males and females, Kaz integrated some 2010 Census tract-level gender data into the map.

As for the larger visualization… what to say… hmm, what to say? This part probably caused the biggest headache. Then, we considered our audience. The winning visualization would be featured on MAPC’s calendar and the Hubway website. OK, so our audience is Boston! Well, so then we need to appeal to them! We wanted Boston to see themselves in the visualization. After coming up with the cutesy theme of a relationship — Hubway in pursuit for the hand of metro-Boston — we put a draft visualization together (hunkered down with Hurricane Sandy).

We stepped back and considered the visualization’s underlying message. What was this thing about? What was this visualization trying to say? We needed another set of eyes. I now think that this is a crucial step in creating a visualization for a wide audience. Something completely obvious to you may not be as explicit in your visual communication of it. Anyway, we were able to have our own professor, Rachel Schutt, and one of our guest speakers, Mark Hansen, to comment on it. How lucky we were! Nay — how lucky we are!

We went back to the drawing board. Kaz refined the map by sizing the Hubway stations by their popularity and refined the color gradient of the majority gender-shaded Census blocks. I pulled some of the timestamped data and plotted some spark lines into an existing graph. We punched up some of our mini-title headings to play up our “relationship” theme. Voila — it’s in Hubway’s hands now!