Patterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different modes of reasoning about crime, and the way in which they feed back into policing cultures, opaque. Ultimately, this invites a stronger reflection about the political nature of patterns.
For several years now, crime prediction software operating on the basis of data analysis and algorithmic pattern detection has been employed by police departments throughout the world. As these technologies aim at forestalling criminal events, they may aptly be understood as elements of preventive strategies. Do they also initiate a logic of preemptive policing, as several authors have suggested? Using the example of crime prediction software that is used in German-speaking countries, the article shows how current forms of predictive policing echo classical modes of risk calculation: usually employed in connection with domestic burglary, they help police to identify potential high-risk areas by extrapolating past crime patterns into the future. However, preemptive elements also emerge, to the extent that the software fosters 'possibilistic' thinking in police operations. Moreover, current advances in crime prediction technologies give us a quite different picture of a probable future of preemptive policing. Following a general trend of data-driven government that draws on self-learning algorithms and heterogeneous data sources, crime prediction software will likely be integrated into assemblages of predictive technologies where criminal events are indeed foreclosed before they can unfold and emerge, implying preemptive police action.
Although the revolutionary potential of predictive policing has often been exaggerated, this novel policing strategy nonetheless implies something substantially new: the underlying methods of (crime) data analysis. Moreover, these police prediction tools matter not only because of their capacity to generate near-term crime predictions but also because they have the potential to generally enhance police-related data crunching, ultimately giving rise to the comprehensive datafication of police work, creating an ongoing drive for extensive data collection and, hence, surveillance. This paper argues that because of its enablement of crime data analysis in general, predictive policing software will be an important incubator for datafied police work, especially when executed via data mining platforms, because it has made police authorities aware that the massive amounts of crime data they possess are quite valuable and can now be easily analyzed. These data are perceived to be even more useful when combined with external data sets and when processed on the largest possible scale. Ultimately, significant transformative effects are to be expected for policing, especially in relation to data collection practices and surveillance imperatives.
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