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New York City's Open Data Bill

Kristin Scott

In March 2012, the New York City council signed into legislation the Open Data Bill1, which requires all city agencies to make the data they collect publicly available. NYC Open Data 2 is the city’s public website that publishes the data in its raw form and enables powerful possibilities for the city’s data to be “transformed into usable intelligence.”Thinking about NYC’s open data initiatives primarily within the framework of urban planning, we can see how power is systematically and spatially organized through effort that shape not only the city’s digital, but also physical, environment.

Although Jane Jacob’s4 book, The Death and Life of Great American Cities, was published more than fifty years ago, the concerns she raised about the possible deleterious effects of urban planning on the vitality of neighborhoods remain relevant and are often problematized within recent discourses of gentrification, urban sprawl, or urban renewal projects.5 While Jacobs spoke specifically about changes in the physical urban landscape, the incorporation of digital technologies into the urban built environment signals the latest development in contemporary urban planning. Precisely because of urban digitality’s inherent invisibility, and considering the ways in which spaces are socially—and increasingly digitally—constructed, it becomes all the more crucial to consider the various discursive strategies of inclusion and exclusion that can result.

Through “aggressive data mining and analysis” New York City seeks to reveal the “complete ‘digital fingerprint’ of just about any complex urban problem,” producing information that assists city officials in determining how to address various urban problems.However, while such data may assist city officials in identifying high need or high-risk areas for increased city services and funding, the aggregation of such data can also lead to a form of neighborhood and building profiling that is not much different in nature or results than racial, ethnic, or economic profiling. NYC, for example, has had major problems with illegal conversions of rental properties, in which apartments were being altered to illegally and unsafely cram tenants into smaller spaces, a practice that often resulted in a large number of fires and loss of life and property. Facing great difficulty monitoring or predicting these occurrences, the city conducted an analysis of aggregated data collected from various city agencies—information such as that collected about fires in the city, property owner’s financial records, history of building complaints, construction dates, and neighborhood demographics—and began to identify a pattern of neighborhoods at high risk for illegal conversions.7 The strategic identification of these areas then helped officials target certain buildings for more immediate and increased inspections. As a result of the city’s ability to analyze and interpret such data— aggregated from individual data sets that, independently, were not particularly revealing of future problems—the rate for vacating illegal conversions rose from 13% to 80% in one year.8 

Thus, while the use of this kind of aggregated data, on the one hand, is credited with preventing fires and saving lives, it is also an example of how socio-economically disadvantaged persons can become further marginalized by exclusionary data-driven measures. In this instance, those living in illegal conversions, most likely due to a lack of affordable housing, become a part of the larger state apparatus that functions, through the interpretation and use of aggregated data, to regulate its population. Unlike racial or ethnic profiling, however, this kind of neighborhood profiling is somewhat masked; “the focus of and basis for the profiling,” in the case of spatial or community profiling, argues Stephen Rice and Michael White, “is officially lifted from the demographic variables of race and placed on related but less controversial variables,” such as tax records and building inspection reports.9 Furthermore, when considering the open data sets that are used to predict future population trends, such as those that track birth rates, ethnicity, property values, and so forth, the aggregate of information that becomes available reflects the efficiency of the government to then manage and control the population and exercise its disciplinary power; the body and its activities, therefore, are also under constant surveillance and examination.

Foucault’s analyses of how power functions in tandem with space have often been interpreted by urban planning theorists as “negative institutionalized oppression.”10 While Foucault focused on various apparatuses that functioned to discipline and order the environment, part of Foucault’s understanding of urban planning was the use of a spatial dispositive to ‘reform the moral.’11 As can be seen in NYC’s use of open data to identify urban problems such as illegal conversions, bodies become subject to power mechanisms through a multilayered system of epistemological fields, including the ways in which ‘moral’ behavior and determinations of good elements of society are produced and visualized through quantitative (data) and qualitative (analyses) divisions of space. 

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