Making Space in the Urban ‘Data Environment’: The Case of Predictive Policing
Topics: Political Geography
, Urban Geography
, Digital Geographies
Keywords: big data, urban space, political contestations, law enforcement, predictive algorithms
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 08:00 AM (Eastern Time (US & Canada)) - 2/26/2022 09:20 AM (Eastern Time (US & Canada))
Room: Virtual 28
Authors:
Liz Calhoun, University of Minnesota
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Abstract
Within the ever-expanding space of big data, algorithms enact processes of distillation that produce actionable outcomes, and this reduction thins possibilities for certain populations and spaces. This dynamic is encapsulated in the U.S. law enforcement strategy known as predictive policing, in which algorithms spatially locate criminal ‘hotspots’ based on police data, CCTV streams and physical sensors. Recently released documents detailing the NYPD’s test runs of three predictive software evince the complex relations that produce algorithmically conditioned spatial realities and reflect differing lived experiences often organized along racial lines. Evaluative measures tended to focus on size of prediction area, and the department opted for an algorithm that assigns areas using smaller, grid-fixed boxes. This has empirical effects –– boxes defining areas with vertical housing will encompass many more residents, and the history of urban planning strategies means higher concentrations will be in low-income areas – as well as political meaning: the boxes do not correspond to a juridical unit of space through which subjects can make claims on government, despite determining who will experience increased police presence. Rooted in critical investigation of these documents, this paper suggests that technical approaches to processing data translate the problem space of an algorithm into specific demarcations of material urban space. I examine what factors mattered to adjudicating the data space of criminological analytics and advance some initial thoughts as to how a spatially informed approach to data-driven policing may encourage a response that goes beyond recourse to individual’s data privacy rights.
Making Space in the Urban ‘Data Environment’: The Case of Predictive Policing
Category
Virtual Paper Abstract
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