Open AccessPredictive Policing and the Platformization of Police Work
Author(s) -
Simon Egbert
Publication year - 2019
Publication title -
surveillance and society
Language(s) - English
Resource type - Journals
ISSN - 1477-7487
DOI - 10.24908/ss.v17i1/2.12920
Subject(s) - work (physics) , transformative learning , crime analysis , data collection , big data , computer science , relation (database) , data science , computer security , scale (ratio) , criminology , sociology , data mining , engineering , mechanical engineering , pedagogy , social science , physics , quantum mechanics
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.