Predictable Policing: Predictive Crime Mapping and Geographies of Policing and Race

This article draws on critical geographic engagements with policing and race and geographic information systems (GIS) to investigate the implications that predictive crime mapping has for racialized modes of urban policing. Focusing on the Chicago Police Department (CPD), it analyzes collaborations between geographic information scientists, crime experts, and police who have recently begun integrating temporal data into GIS-based maps to predict when and where future crimes will occur. The article builds the case that predictive crime mapping further entrenches and legitimizes racialized policing as it (1) rearticulates police data sets as scientifically valid and (2) correlates those data with other geocoded information to create new rationalizations for controlling racialized districts through differential policing practices. The article uses a mixed-methods approach that includes analysis of open-ended interviews with computer scientists involved with the CPD's Predictive Analytics Group and city technical documents to explain the recursive relation between GIS-based knowledge production and racialized policing. The article casts into relief the central role that the production of geographic information plays in current modes of racialized policing and how this contributes to the ongoing racial differentiation of urban geographies.