Authors: Leah Horgan*, UC Irvine, Sarah Hamid, Carceral Tech Resistance Network
Topics: Spatial Analysis & Modeling, History of Geography
Keywords: spatial analysis, crime analysis, spatial statistics, platforms, data visualization, mapping, spatial data science, critical data studies
Session Type: Paper
Presentation File: No File Uploaded
When Clifford Shaw and Henry McKay mapped juvenile delinquency in Chicago, they didn't seek to understand crime so much as they wanted to map urbanism. Almost sixty years later, however, their method has overwhelmed the practice of policing in America. At first, crime investigation—historically invested in bodies, identities, and categories of being—does not appear to be a natural fit for spatial analysis. But with the rapid proliferation of computational policing, crime governance is now largely conceptualized as a spatial problem. This mode of understanding, part of the larger economy of mapping tools for governance, contends that people, relationships, and events are spatially connected, that that “near things” are more related than “distant things”, and that ‘spatializing problems’ offers a new way of thinking through aggregates and individuals. In practice, spatial statistics collapse diverse processes of data collection and analysis, contriving a terrain of tangible space/time where a given event is no longer located in a person, but comes about through statistically justified assumptions about spatial dependence. In this paper, we track three vectors: the history of spatial statistics in policing, the burgeoning industry (tech platforms and academic ventures) of spatial analysis that brings ever more disparate actors into the fold of spatial analysis for problem solving, and the consequences of spatializing crime and how crime-like logics are then applied to isomorphic social/administrative problems. We ask: what objects are a true or natural fit for spatial analysis? And how does an object—like crime—get transformed so as to fit better.
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