In October 2014, New York University’s Center for Urban Science and Progress (CUSP) unveiled the Urban Observatory, as part of an urban informatics initiative for monitoring, recording, and modeling the actions and nonactions of New York City. Inspired by research methods in observational astronomy, the scientists at CUSP placed an 8 megapixel camera on top of a building in Downtown Brooklyn, which shoots one panoramic, long-distance image of Lower and Midtown Manhattan every 10 seconds. Using the Urban Observatory and a network of similar sensors, the scientists at CUSP are attempting to capture what they call “the pulse of the city,” formulating massive data sets that provide information regarding various domains of everyday life, ranging from energy efficiency to the detection of toxic releases. As urban informatics professionals, they imagine that the collected data will serve as “raw material” for policy making — once they have access to this raw material, the CUSP scientists will be able to model their predictions, and hope to ultimately (somehow) manufacture the steps required to reduce electricity consumption in office buildings, or to generate emergency responses to hazardous substances.
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The relationship between surveillance, big data and state power has been vociferously debated in both academic and popular press over the past several months (Boellerstoff 2013 and Crawford et al. 2014 among others). But what of instances where states leverage big data without an explicit surveillance focus? What kinds of questions should we be asking when big data appears in a project that doesn’t focus on, say, “security” (which we associate directly with surveillance) but on “welfare” or “development”? In this post, I explore this theme in the context of the ongoing Indian Unique Identification (UID) project (also known as “Aadhaar” or Foundation). The state-backed UID project wants to issue biometric-based identity numbers to all Indian residents, arguing that an ability to uniquely identity individuals is critical to the efficient administration of public welfare schemes. The biometric dataset that the UID is putting together towards its goal is already the largest of its kind in the world.
Speaking of Big Data« Read the rest of this entry »
The term “big data”  brings up the specter of a new positivism, as another one in the series of many ideological tropes that have sought to supplant the qualitative and descriptive sciences with numbers and statistics.
But what do scientists think of big data? Last year, in a widely circulated blog post titled “The Big Data Brain Drain: Why Science is in Trouble,” physicist Jake VanderPlas made the argument that the real reason big data is dangerous is because it moves scientists from the academy to corporations. « Read the rest of this entry »
by Dawn Nafus and Jamie Sherman
The Quantified Self (QS) is a global movement of people who numerically track their bodies. If you were to read popular press accounts like this, this and this, you could be forgiven for thinking that it was a self-absorbed technical elite who used arsenals of gadgets to enact a kind of self-imposed panopticon, generating data for data’s sake. Articles like this could easily make us believe that this group unquestioningly accepts the authority of numerical data in all circumstances (a myth nicely debunked here). Kanyi Maqubela sees a lack of diversity in “the quantified self.” On one hand, he is absolutely right to say that developing technologies to get upper middle class people who do yoga and shop at farmers markets to “control their behavior” is a spectacular misrecognition of the actual social problem at hand, and one that can be attributed directly to the design-for-me methodology so rampant in Silicon Valley. The charge works, however, only if we think about Quantified Self as if it were analogous to Kleenex: a brand name that can be used generically for the latest round of health and fitness gadgets technologies whose social significance (or lack thereof) is self-evident.
The Quantified Self that we have come to know is not a Kleenex. It is a particular social movement with specific social dynamics, people and practices. Even the most cursory ethnographic examination of actual practices of its members reveals a very different picture. We have been conducting this research for the past year and a half, alongside many other academics who have also been welcomed into the community. The Quantified Self that we know has very little to do with trying to control other people’s body size or fetishizing technology. Indeed, people who use pen and paper are community leaders alongside professional data analysts. As a social movement, QS maintains a big tent policy, such that the health care technology companies who do try to control other people’s body sizes also participate. But QS organizes its communities in ways that require people to participate as individuals with personal experiences, not as companies with a demo to sell. This relentless focus on the self we suspect does have cultural roots in neoliberalism and the practices of responsibilization Giddens identified so long ago, but it also does important cultural work in the context of big data.
An example from our ethnography can illustrate this. At a recent Quantified Self meeting on the West Coast, discussion turned to “habit formation.” Sean, one of the organizers of the group, was talking about his frustration with tracking apps organized around “streaks.” He felt great to have kept his new “habit” seventy times in a row, but “when your mother gets ill and you miss a week, poof! It’s gone.” He was looking for something that would offer a metric for what he called the “strength” of a habit. He felt that would be much more encouraging for him: after all, the habit does not just go away because the data does. Other participants mentioned various kinds of moving averages that would be nice, and the conversation wandered into a debate over whether “habits” was a negative framework to use, and whether “practices” were more constructive.
Later in the evening, two men, David and Tom, were talking about Tom’s recent purchase of a Jawbone Up—one of many devices that will track movements and infer various things from them, like sleep or exercise. Tom showed us the visualization of his sleep data that appeared to show that he falls asleep quite quickly most nights. That information was encouraging as he had been concerned about his sleep. While he was not entirely certain how the bracelet-style device measured sleep cycles, he conjectured that it must have to do with motion. In any case, he felt like he was more rested just knowing that “in fact” he was sleeping well. The group laughed, and then continued to wonder collectively about just how the thing “decided” what sleep cycle you were in. Discussion turned to other devices that incorporated other indicators like skin temperature, perspiration, heart rate and brainwaves. A certain watch had all the sensors David wanted. He could use it for more than just sleep tracking, but it had limits. He knew the watch could track his heart rate, but he wanted to see the variability of his heart rate because he had been curious about the physical expression of moods. The watch only gave a pulse, as if there were no other interpretation of the underlying signals from the heart.
The relationship between “habit formation” and the limitations of devices is significant. On one hand, the habits/practices that most participants sought to instill in themselves generally (though not always) adhered to normative guidelines around health and good citizenship: exercise more, work more effectively, keep moods elevated, etc. On the other hand, these clearly are not passive consumers swallowing blindly the parameters of “what’s good for them.” In many ways they see their activities as a response to big data and big science dictums that make claims about the healthy body from on high. In the face of generalized, anonymous one-size-fits-all prescriptions derived from population studies, they seek to understand what is right for me. What is the optimal bedtime for me? Under what diet regime do I feel my best? What activities (sleep, caffeine, wheat, dairy, and other usual suspects) are particularly correlated with mood or energy in my life?
If people in this movement appear narcissistic on the surface, it is because of their focus on the self. The insistence on the agency of each person to track, understand, and decide for themselves what is right “for them” does draw on cultural threads of individualism, but they do it in ways that refrain from making assumptions about what is right for others. While the self is the site of internalization of dominant big data visions that do control people in Foucauldian, biopolitical ways, here it is also, at the same time, a means of resistance. QSers self-track in an effort to re-assert dominion over their bodies by taking control of the data that many of us produce simply by being part of a digitally interconnected world. When participants cycle through multiple devices, it is often not because they fetishize the technology, but because they have a more expansive, emergent notion of the self that does not settle easily into the assumptions built into any single measurement. They do this using the technical tools available, but critically rather than blindly. It is not radical to be sure, but a soft resistance, one that draws on and participates in the cultural resources available.
The eagerness with which pundits seize on the Quantified Self as a generic brand, a Kleenex style term to toss around, speaks to the ways that QS practices cohere with current ideologies and practices of self in the mainstream. Yet to stop there, to overlook the particulars of what actual QSers do, how they do it and why, is to miss the social significance of the Quantified Self as a movement. It is not the nerdy devices they enthuse over, nor the sometimes mundane self-transformations they seek to achieve, but the explicitness with which they confront the question of what the cultural dominance of data means for me. Answering this question requires a critical and questioning point of view. Within Quantified Self, like snowflakes, no two tissues are alike: now, how do we count that?
 Greenhalgh, S. 2012. “Weighty subjects: The biopolitics of the U.S. war on fat.” American Ethnologist, 39:3, pp. 471-487
 Oudshoorn, N., Rommes, E., & Stienstra, M. 2004. Configuring the user as everybody: Gender and design cultures in information and communication technologies. Science, Technology & Human Values, 29(1), 30-63
 Ken anderson pointed out the Kleenex comparison to us.
 Cheney-Lippold, J. 2011. A new algorithmic identity : Soft biopolitics and the modulation of control. Theory, Culture & Society, 28, 164-181.