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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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…)

I fractured my ankle in a hard snowboard crash a couple of weeks ago and of course I was data logging the accelerometer forces. I was using the iPhone app developed last fall for the seismi{c}ycling project; while riding the phone was in my jacket’s internal chest pocket.

A group from ITP was enjoying the bitter weather at Mount Snow, in West Dover, VT on our (now annual?) Snowbunnies trip. This crash was late in the day on a wide open trail. I accidentally disengaged my heelside edge for a moment, causing me to rotate slightly clockwise and slide laterally. Moments later, my heelside edge caught again, now on the downhill side, causing me to quickly flip backwards onto my head … thankfully I was wearing a helmet. After that I can’t recall what exactly happened, but I know that it involved a lot of tumbling which my right ankle just couldn’t weather. (more…)

[written for Media Economics & Participation at ITP]

Slashdot users are seeking karma. However, gaining positive karma at Slashdot is just a means to an end; Slashdot users are seeking (limited) power and status among their peers in the form of fleeting moderator access for the vibrant comments component of the highly active, technology-focused news aggregation site. Moderators are chosen from among the registered users using a somewhat obscure algorithm which incorporates each user’s karma rating (a scale of Terrible, Bad, Neutral, Positive, Good, and Excellent), length of membership and randomness. Selected moderators are given special status and 5 mod{eration} points with an expiration window of three days. The moderation status ends when the points have been used in the act of moderating comments or have expired.

The moderation system has been borne out of necessity as the Slashdot community has grown large, bringing the signal-to-noise ratio down and decreasing the satisfaction in reading the raw comment threads. “Flamebait” and “trolls” contribute little more than instigation for starting arguments and fights among the users with typically strong opinions on matters which usually appear on Slashdot. Rob Malda, founder of Slashdot, explains this phenomenon on the Slashdot FAQ: (more…)

after a couple of late nights, i have a proof-of-concept in the real-time cycling-related datalogging. the reasons behind the project are still getting sorted, but lately my interest in ubiquitous urban sensor networks has been piqued and this is a tentative exploration in that area. sensors don’t have to remain static as part of physical infrastructure…millions of people are carrying millions of sensors around with them daily. (more…)

For the 1-in-1 Project as part of ITP’s 30th anniversary celebration I over-engineering the simple bicycle blinky light. Why go for a simple, off-the-shelf blinky light when we can build a much more complicated one ourselves?!

I’m creating an animated display, which changes to solid red while braking.

Update: (5:26) It’s been a long night, but it all came together. I wish I could have finished at the floor, but sometimes you need to go to your cave. The bike blinky light is all I could have asked for…it’s obnoxious, it’s animated, it has a handlebar control to switch to a flashing warning mode. (more…)

(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…)

This is just a teaser for a project I’ve recently begun, but I had an “a-ha” moment when the visualization program finally came together just now and wanted to share…

This is geocoded accelerometer data from the handlebars of my bicycle on my commute from Greenpoint to ITP in the East Village. The sample at middle of the graph, and the red circle on the path coincide. I realized just now that this is a seam in the Williamsburg Bridge. Neat.

I’ll post proper documentation of the project as it becomes more robust…but I’m excited. Waiting for the heart rate monitor interface to arrive…looking to couple internal status with external events…

This is a rough collection of comments and notes on the assignment for week 2 of Site-Specific. (more…)