Just submitted MobileLogger to the AppStore. Hoping this goes smoothly given my tight schedule for thesis.

For reference, when there are multiple versions of Xcode / iPhone SDK, specifically a beta version alongside the release version, and using xcodebuild command…explicitly set the xcode path to the release version or the application may be built against the beta SDK and get rejected by Apple:

sudo /usr/bin/xcode-select -switch /Developer

Yeah, knowing that ahead of time would have saved hours.

I’m looking for some beta testers for my iPhone data logger application. I’m specifically soliciting bicycle riders to record their rides around New York City in support of my thesis research involving visualizing the cyclist experience. This is a proof-of-concept exploration in what a ubiquitous mobile sensor network could possibly look like, using existing technology that we already carry to learn about ourselves and our world.

I’ve chosen to focus on cycling in the city, but the concept is far-reaching (and I’m certainly not the first to approach this). Recently, The New York Times published an article [1] revealing findings from a year of GPS logged taxi cab data, summarizing average traffic speeds in Manhattan by day. Similarly, Cabspotting [2] visualized the taxi routes in San Francisco. Flight Patterns [3] reveals the air traffic over the United States throughout a typical day.

Projects involving using the bicycle as a sensing platform have emerged as well. The Copenhagen Wheel [4] is a dense array of motion and environmental sensors packed into an electric-assist rear hub. While not cycling-specific, the Personal Environmental Impact Report [5] uses GPS-enabled mobile phones to infer mode of travel from speed and calculate your carbon footprint and exposure to air pollution.

I’m primarily looking to see if there are correlations in rider travel patterns. Are there commonalities in routes, sound levels, bumps? How are riders navigating to similar locations? What are typical trip durations and speeds? Do different types of riders (commuter, enthusiast, courier, racer, delivery rider) behave differently? When are riders on the roads? For all of this, what could it look like as visualization?

This application is the data collection mechanism I’ve chosen to employ for this exploration. It records location, heading, speed, altitude, accelerometer, sound level, trip duration and distance to storage on the device. Each log can be viewed on a map and individual samples inspected. Export logs via e-mail in CSV, JSON or Golden Cheetah format. Data can be automatically uploaded while recording as well.

This application will be released as open source software under the GPLv3. Source code will be available at: http://github.com/rcarlsen/Mobile-Logger

If you’d like to participate in this beta test, please e-mail me the UUID for your iPhone (3G or 3GS, OS 3.1+) device. This can be retrieved in iTunes by connecting the iPhone via USB cable, and clicking on the Serial Number field in the device summary. After displaying the UUID, go to Edit > Copy to copy it to the clipboard.

The basic functions of the application are on the project page. If you’re simply interested in recording your trips and not specifically interested in contributing to the project then I ask you to wait for the public release of the free app in the App Store. An Android version of the logger is also forthcoming.

[1] http://www.nytimes.com/2010/03/24/nyregion/24traffic.html?ref=nyregion
[2] http://cabspotting.org/
[3] http://www.aaronkoblin.com/work/flightpatterns/
[4] http://senseable.mit.edu/copenhagenwheel/
[5] http://peir.cens.ucla.edu/

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.

Golden Cheetah has gotten a really nice write-up on the “bike racing, news and events” site nyvelocity.com. The article was featured on the front page for a time, sporting the brand new icon from Dan Schmalz.

A couple of weeks ago I sat down with Andy Shen over lunch and discussed the project, it’s developer community and my thoughts on open source software. He did a really nice job parsing my semi-coherent babel and combining it with Sean and Justin’s perspectives into a thorough history of Golden Cheetah, brief outline of how the program grows through user contributions and a great outline of the major features (and he even built some hype for the forthcoming mapping and long-term metrics features).

Great to see! Read the article at:
http://nyvelocity.com/content/coachingfitness/2010/golden-cheetah-open-source-goodness

Our assignment last week was to use Foursquare to log our daily travels. This week, we were asked to use a classmate’s Foursquare check-in history as the source of our visualizations. I was given Bryan Lence’s data and set off to see what was there.

Over the past few weeks I’ve been teaching myself the R “environment for statistical computing and graphics“. It’s an open source project and has a doubly steep learning curve (for me, at least) of an unfamiliar syntax and medium (statistics). I can see it’s power for visualizations, however, when used to reveal interesting associations which can be further refined in other graphics software (in this case, Illustrator).

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