The Quantified Self vs The Communified Us
Last year, in our first thesis class with Liz Danzico, we were asked to prepare a “thesis fiction”, a product that’s out in the world already that we present as if its our own. The point of the assignment was to get us thinking about the world before and after the introduction of this product. I chose to present Nike+.
As a former high school track and cross country athlete, I would return from my runs with a stopwatch and instantly climb in the car with my dad, watching the odometer as we miticulously retraced my route. When I returned home, we’d open Microsoft Excel, input my distance and time, and be given an output of pace. Over time we evolved this spreadsheet to create workout plans and more advanced statistics. There wasn’t a run that I didn’t capture the data for, whether it was a 10 mile LD, or 400 meter intervals on hills. So when Nike+ was announced, the benefit needed no explanation.
In its brief five year history, I’ve enjoyed running with the evolution of Nike+. Last summer, I had my dream internship, joining the Digital Sport team in Beaverton, Oregon to help work on the future of the Nike+ platform. In that time I saw the future of sports analytics for athletes, especially in the recently announced Nike+ FuelBand, Nike+ Basketball, and Nike+ Training. I also had the opportunity to work on some game mechanics and social mechanics within the Nike+ ecosystem to help motivate the casual athletes. It was an incredible experience, and I was thrilled to see my education at SVAixd be put to use, from cybernetics and feedback loops to physical computing around sensors.
The work being done at Nike Digital Sport is part of a bigger movement known as the Quantified Self, which aims to bring a greater self awareness to our daily lives, from exercise to sleep patterns, through personal data. I’m a huge fan of this movement, as I wear and interact with countless smart sensors throughout my day. I’ve seen these devices not only provide valuable insights into my behavior, but also, powerful motivation to correct my course of action and make improvements to various aspects of my life. I’m losing weight, sleeping better, and running further.
Nike follows the Bill Bowerman credo of “if you have a body, you are an athlete”, a sentiment I fully agree with. However, not every athlete is concerned about personal performance and metrics. Many people are athletes to be a part of a team, while simultaneously getting exercise. Throughout my interviews and surveys, almost every person identified their primary reason for participating as the social aspect of playing with others and less around their performance in the sport itself. People want to be a part of a team, or a community, and sports are a great opportunity for this. Pick-up games, group fitness classes and yoga, and organized co-ed sports, are all outlets that people find to be a part of a community of people around a shared interest.
I believe that there’s 3 types of communities on the internet. The first is those of Facebook and Twitter, which exist entirely in the digital world. I can sit in front of a computer 24 hours a day and participate in the Twitter community. The second type of community are those that digitally connect people around their individual behaviors in the real world, such as Foodspotting and foursquare. Nike+ is a great example of this as well. By going out for a run and uploading my data, I can share it with my friends and see their runs, even challenging them to the fastest mile, wherever they might be. But nowhere on the site can I invite someone to join me for a run in person. This third type community goes beyond digitally linking those with shared interests, it actively brings them together in the real world. Skillshare brings people together for an hour to learn around a shared topic, but nowhere on the site can I take a class. Similarily, MeetUp invites me to join an interest group, but minimizes the amount of communication that takes place online, they prefer that I save it for real world interactions.
Its nothing short of remarkable that technology like Nike+ has given us the ability to digitally re-create the experience of having coaches, teammates, and the motivation in the absence of having them in the real world. But its imperative that these features augment the experience of being active with others, rather than replace it.
Last night, I finished a 4.5 mile run from school to home with an 8:30 pace. I know this because my Nike+ watch told me. It also attempted to give me an “attaboy” about my fastest 5k, but I wasn’t paying attention. I was too busy talking to a someone in my building who was arriving home at the same time, disheartened that he hadn’t gone for a run today. We chatted briefly about my route and his last run and then parted ways, but not before agreeing to find a time to go for a run together.
Eric Fischer, of “See Something or Say Something” and “Locals vs Tourists” fame (not to mention an artist in MoMA’s “Talk to Me” exhibit), has taken inspiration from my Nike+ project and created his own using San Francisco MapMyRun data. He went to great lengths to acquire a data set that could help tell this story, and also turned my attention to a blog post that brings up a great criticism of my project. I love seeing the similar patterns emerge between New York, London, and San Francisco :: people gravitating towards water, parks, and bridges. And again, no map of San Francisco is needed beneath it, the runners have created their own map of SF, from the landmass all the way down to individual streets.
After seeing Cooper Smith’s visualizations of data from runners in New York City, I wanted to see what similar data sets would look like for other cities. Nike+ doesn’t have public GPS logs, but MapMyRundoes, if you are willing to spend several hours clicking through search results to hit the “Download” buttons, so that’s what I did to get the tracks for these 771 runs (from June 13 through August 9) in San Francisco.
As Open Source Planning has pointed out, uploaded runs come from a fairly small, self-selected group of people, the most obvious result of which is the total absence of the southeastern corner of the city from this map. It is also a very self-conscious process, so it is biased toward intentional, and often intentionally difficult, trips made for their own sake, and away from the repetitive patterns of everyday life.
Unfortunately the MapMyRun tracklogs do not have date and time stamps, so it is not possible to do the time of day, pace, and interruption analyses that Cooper Smith did. I should have done direction of travel, though.
Last year, in Nicholas Felton’s information visualization class, we were given 1,000 runs of Nike+ data from the NYC area and worked to visualize it in a way that told a compelling story. You can view that project here.
Over the summer, Wired UK contacted me and asked if I’d be interested in doing a followup to the project, this time with 10,000 runners (some 6 million+ lines of data) in London. It was a great opportunity to further explore this running data, this time with a city I’m far less familiar with, which led to some really fun discoveries in the data. In the end, we decided on this heat map direction.
I also worked with them to concept around what the iPad version should show. We decided to render 24 separate heat maps, one for each hour of the day, allowing the user the ability to scrub back and forth through the day. Lots of interesting insights here, but definitely the most interesting is how people stop running through parks at 7 pm. My hypothesis is that runners no longer feel safe in these parks once it becomes dark.
I hope to do a more thorough writeup on my portfolio site this week. In the meantime, UK friends keep an eye out for the November issue. My Fellow Americans, worry not, you can still grab the iPad version here
…and another look at runners’ directions. The Brooklyn Bridge is being used almost exclusively to funnel runners into Brooklyn, while the Manhattan Bridge is used to cross from Brooklyn to Manhattan. It appears these bridges were appropriately named. The Williamsburg Bridge is a perfect mix of both directions, with the majority of runner’s taking the side of the bridge going with traffic.
Last weekend I was running the bridges loop in downtown Portland, and I realized that I had crossed paths with several people twice in my run. It got me thinking about how I had picked to run the route counter-clockwise, while others ran it clockwise. Personally, I deal with it this question a lot - the first day at the Nike berm I was convinced I was disobeying some unwritten rule to run clockwise. Other times, I pick loops based on where I can start uphill and end downhill.
But what would this data look like collectively? Are all people running one direction, or are they randomly picking a direction?
If a runner’s position changes towards the south, his path is drawn yellow. If he’s running north, blue. Here, using the loops of Central Park, we can see that most runners choose to run the loops clockwise, regardless of the direction.
A new timelapse video for my Nike plus audit
Source: bit.ly
