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.
Here’s the world map, showing events pop up chronologically. There was the initial spread on April 12th from the public release in the app store…but just wait..wait…wait…for May 12th. Fun!
(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
The Mobile Logger application has been public for a couple of weeks and has (surprisingly, to me) been used in every continent, save Antarctica. I first noticed several events in the database from Australia, then the UK. I was mostly catching these events by coincidence when I was looking over my own data and wondered just where (in the world) these other users were logging from.
For Earth Day, I generated a map of the global users of Mobile Logger and put it on the status page. While the historical data is really neat, and humbling to know that people all over have tried this app, the real-time data is captivating. I added the city of the most recent event and a pulsing marker to the map. Now, the location of the newest log is marked when the status page is updated. Next, I’d like to show it when several events have been logged at the same time.
That’s it for now…working on the next iteration of the visualizations. I’m thinking of some Feltron-inspired summary charts, then a more detailed array of specific data. Who knows?!
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…
After several rounds of rejection, Mobile Logger has been accepted and is available on the App Store! Feel free to try it out; hopefully some folks will find it useful. The source code for the application has been released under the GPL and is available on github.
I’m still actively recruiting participants for my ongoing thesis project, which involves visualizing cyclists in New York City. If you’d like your riding to become incorporated in some pretty pictures to be presented in May, then by all means start logging (and thank you in advance)!
Be warned, it’s a battery hog. Feel free to let me know if it gives you any trouble.
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.
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?
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.
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).
This assignment was to develop a concept around a two-screen, live event experience; specifically, using TV and computer via internet. Our group’s concept was a variety-style, Gong Show inspired show with binary voting from viewers to determine via aggregate whether a performance was Fly (a rousing success) or Pie (a miserable failure) which received a whipped cream pie in the face. (We’re a classy outfit here..) (more…)
We’re using Foursquare as a data logger for one of our assignments in the Telling Stories with Sensors, Data and Humans class at ITP. As an aid to begin understanding the relationships between venues for our tracks, it’s helpful to munge the KML into CSV so it can be plotted and played with in a spreadsheet, Illustrator, R, Processing or whatever…
Below is a short python script to parse a Foursquare KML file into a simple CSV file. It outputs the check-in name, description, timestamp and location (as lat, lon). The Foursquare KML feed is available at the Feeds page on their site. (more…)
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.
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?
