How Self-Quantifiers are Creating a Gold Mine of Big Data for Cities

I’m a compulsive self-quantifier. At one time or another over my adult life, I have tracked nearly every aspect of my day-to-day experience.

I’ve counted my hours slept, calories consumed and burned, money spent, steps taken, hours worked, word written, songs played and thoughts thunk. I’m a compulsive user of RunKeeper to track my exercise, and was recently delighted to become an early adopter of Shine, a Kickstarter-funded activity monitor.

I’m not alone in my obsession. Last year, Nike released its latest activity monitor, the Nike+ FuelBand bracelet, to much ballyhoo. As of spring, 2013, there were more than 11 million users in the Nike+ community, all busily tracking their own steps, sprints and swims. Combine that with Nike’s competitors like Jawbone and FitBit, as well as all the other metrics we commonly track—calorie consumption, spending and so forth—and we see that a significant slice of North Americans are participating in the quantified self movement.

Ordinary people are (often unwittingly) contributing to a massive and rapidly-growing horde of data about their moment-to-moment experiences. Corporations similarly stockpile data about their customers, and use that data to optimize their business. This is the (often creepy) promise of Big Data.

How, then, could citizens voluntarily participate to help municipalities learn about and respond to their needs?

What is the collective exercise data of thousands of Vancouverites but an articulation of where, how and when they like to sweat? Do you want to know which section of the Baden Powell trail is most popular with runners? Just ask Strava. Do you want to discover the average speed that cyclists ride around Stanley Park? Check RunKeeper.

Likewise, we might be able to discover the health benefits of a new farmer’s market by querying local consumption data logged in diet journaling apps like FitDay or MyPlate. On the other hand, we might also observe the greasy impact of a new pizza joint or McDonalds in a neighbourhood.

It seems like each week I learn that a new aspect of our lives has become trackable. Each one has implications for the digital city with a curious city hall. When do our citizens actually sleep, and for how long? Which of our dog parks are most popular, and with what kinds of dogs? What temperature do people like to keep their homes at on a rainy November day? How is our citizens’ overall health (we’re currently inventing the future on this front)?

This approach doesn’t offer a perfect statistical model. We’ll suffer some selection bias because, for example, not all exercisers have smartphones, and not all smartphone users track their activity. This model isn’t a replacement for statisticians, surveyors and those rubber hoses laid across the road.

However, these are inexpensive and readily-available data sets. It’s easy to imagine city workers consulting a rich real-time dashboard of information culled from dozens of these services.

We measure to know ourselves better. And, as Dr. Mark Gerstein, a professor of bioinformatics at Yale University observes, “if you watch something, you tend to get better at improving on it.” If this approach works for individuals, why not for whole cities?

Photo: Lululemon