(Here is the documentation for my thesis project at NYU’s Interactive Telecommunications Program. PDF version here.)

Riding through Mountains of Data:
Visualizations of Cycling

Robert Carlsen
Interactive Telecommunications Program
Tisch School of the Arts
New York University

Abstract

This project attempts to describe the cycling experiences of several riders in New York City through a series of visualizations. Specifically, I am interested to discover if riders similar to myself share a common experience through which a sense of connection could be derived.

Cyclists were encouraged to record their travels using their personal mobile devices running Mobile Logger, a custom iPhone application.
Log data was uploaded by the application to an online database in near real-time during each ride. This data was analyzed and filtered to provide source material for the resulting visualizations and system “dashboard” at http://mobilelogger.robertcarlsen.net.

Keywords

Cycling, New York City, sensors, iPhone, visualization, mapping, tracking, logging, mobile, application, bicycle

(more…)

Thursday, April 22nd is Earth Day. The weather is looking to be sunny and 65 degrees in New York City. Sounds like a perfect day to ride your bike (or walk, run or whatever you like to do outside). Since you’re already going to be out there, why not log the trip, help me with my thesis, and have your data made into some visualizations I’m preparing for the project?

It’s pretty simple…download Mobile Logger from the App Store (iPhone 3G/3GS), open it, then tap Logging switch to begin. Put the phone in your pocket, bag, mounted to handlebars, or wherever is convenient and go. You can double-tap the screen to disable the display, but shouldn’t lock the phone.

When you’ve reached your destination, tap the logging switch again to stop and you’re done! The log data is automatically uploaded to the Mobile Logger server and will be included in my research (this uploading can be disabled if you’d like to use the app without contributing to the project, too).

What I’m really interested in exploring is a sense of connection between us by sharing our experiences. I ride a bike daily through NYC, and encounter many other cyclists, walkers and drivers. We pass each other in a moment, or perhaps share a lane for a bit and then continue on our separate ways. How does my 5 mile, 25 minute ride from Greenpoint to the East Village compare to someone riding from Queens? What does a ride around Prospect Park share with one in Central Park? What’s the loudest part of the city for a cyclist? Where are the most frequently ridden routes?

I’ll be working with the contributed data to create visualizations which attempt to answer these questions. The “dashboard” of the system will be present at mobilelogger.robertcarlsen.net. More info about the app is available on it’s documentation page.

Times UP! is also organizing a ride at 7pm from Union Square if you still need another excuse to get on a bike, skates or a board. It would be neat to see a bunch of riders converge on a location, then ride together in a group. I really want to see what that visualization would look like…

Thanks, and enjoy the ride!

(note: I’m awaiting the HR sensor, this is mostly outward forces)

I’m logging the acceleration forces at the handlebars of my bicycle while riding through New York City. The body has roughly three contact points with a bicycle, the hands at the handlebars, the “seat” at the saddle, and the feet at the pedals. The downward force of the rider’s weight and pedaling force and the upward forces of the bicycle rolling over uneven ground are distributed over these three points. I was interested to see just what kind of forces are “pushing back” that I may not be aware of, myself lost in the act of simply keeping the bicycle upright and safely navigating through traffic.

To contextualize the raw accelerometer data I also tracking GPS location and eventually geocoding the raw data in software. The bicycle sensors are being transmitted via Bluetooth to a mobile phone and the data is logged with a custom written (but now open-source!) python script. Below is the first draft of the visualization. (more…)