Thesis Proposal Title
Where do we go now?
Thesis Statement
I will create a series of visualizations attempting to decipher the experiences of a large group of cyclists in New York City. The project has two principle components: data collection and analysis / visualization.
Personal Statement
Riding a bicycle provides me with a sense of self-reliance. It can provide transportation, fitness, employment and enjoyment. It’s faster than walking and more maneuverable than driving. In dense city congestion it can be faster than mass transit. However, we’re generally more exposed to the elements and danger than other traffic. What does this experience look like? How could it be recorded? Mobile sensors reflect a personal experience in a way that fixed sensors can only infer. Focusing on personal mobile devices as nodes in this network provides a priority on the experience of individuals.
What could we learn about ourselves and our world if there was a ubiquitous network of sensors collecting data about the environment as we experience it? Would analysis and visualization reveal trends and patterns in the aggregate behavior of participants in the network?
Research
Personal Environmental Impact Report (UCLA Cens) http://peir.cens.ucla.edu/
Flight Patterns, http://www.aaronkoblin.com/work/flightpatterns/
Cabspotting, http://cabspotting.org/
Copenhagen Wheel (MIT Sensible City)
CitySense (Sense Networks) http://www.citysense.com
Beautiful Data. Segaran & Hammerbacher. O’Reilly Media. 2009.
Open Data Consortium Project, http://www.opendataconsortium.org
Work Description
GPS-enabled mobile devices are becoming prevalent enough to use them for large-scale personal data collection. The data collection portion of this project will utilize a mobile logging application (initially iPhone and Android ) to record each rider’s experience. The application will upload data to a server for storage and later analysis. To facilitate ride data, I will organize a one-day event (“Log your ride to work day”?) or piggy-back on an existing event (critical mass, charity ride). Alternatively, I may organize a proof-of-concept event with a smaller group over a longer time, perhaps a week.
Post collection, I will analyze the data looking for relationships and trends among riders. This analysis will be critical for the eventual visualizations. I have an initial set of questions which I’m looking to answer: How many other riders are are near another rider? How far apart are they? How fast are they traveling? Respectively? How smooth is the ride? Are they rocking? Do they lean to one side? Do several riders experience similar conditions at the same place and time? Where do riders go? Where do they originate? Where do they congregate?
Visualizations of this data derive from further questions. What does a group ride look like? What if location was stretched along a time axis like a ribbon? My overarching goal with this project is to make these possibly abstract images be meaningful to uninitiated viewers.
The end product will primarily be this series of static and interactive visualizations using the collected data. Additionally, I’m aiming to publish the project’s process in the spirit of open source. This includes publishing the collected data for other analysis, releasing the logger application source code, documenting collection methods and describing the visualization process. Hopefully, this will enable other people to extend and augment the work in ways I haven’t envisioned.
Thanks to a (very flattering) mention of my thesis project on Gizmodo after the ITP Spring Show, the use of Mobile Logger has quadrupled in the past two days. I had been watching the number of unique users rise on the Dashboard page, currently near 800…but then wondered what that would look like animated over time.
