Last week, Jennifer Sta. Ines came to speak to our class about her work with Citi Bikes and the collection and analysis of big data. Her presentation was something I would have been looking forward to had the identity of our secret guest lecturer been shared earlier, so needless to say I was excited when she began to speak.
No, she did not explain how her middle name acquired its period. She did, however, break down how she and her team collect data from the wildly successful young program, and how they assess the most effective locations in which to add additional Citi bike stations in the future.
I found it interesting that they not only use data to find the location throughout the five boroughs to add new stations, but also conduct focus groups with local neighborhood populations to get their feedback. Small factors like essential parking spaces are not necessarily something they could glean from data, but can be crucial to the success of the program after implementation.
After her presentation, she walked us through a tutorial of QGIS, an open-source geographic information application that lets users effectively (and beautifully) display and parse geographic data.
The flexibility that QGIS gives users to display maps by datapoint left me feeling like I could use my midterm project for something more ambitious than the idea that is currently at the top of my list. I'd like to collect datapoints that I could creatively display using QGIS, now the only question is what data can I collect? I'd like all the points to tell a compelling visual story, which I could use to assess some part of my behavior. What do you think of this idea?
Using a sensor and a GPS shield, create an "anxiety map" of my day. It would graphically display where my anxiety levels are highest, and lowest, and give me a sense of how I can prepare for consistent spikes. Perhaps it could be developed as a smart-watch app in the future.
I'll add more idea as they come. Please comment below if you have any feedback!