Predictive Policing Theory

Predictive policing is changing law enforcement. New place-based predictive analytic technologies allow police to predict where and when a crime might occur. Data-driven insights have been operationalized into concrete decisions about police priorities and resource allocation. In the last few years, place-based predictive policing has spread quickly across the nation, offering police administrators the ability to identify higher crime locations, to restructure patrol routes, and to develop crime suppression strategies based on the new data.This chapter suggests that the debate about technology is better thought about as a choice of policing theory. In other words, when purchasing a particular predictive technology, police should be doing more than simply choosing the most sophisticated predictive model; instead they must first make a decision about the type of policing response that makes sense in their community. Foundational questions about whether we want police officers to be agents of social control, civic problem-solvers, or community partners lie at the heart of any choice of which predictive technology might work best for any given jurisdiction.This chapter then examines predictive policing technology as a choice about policing theory and how the purchase of a particular predictive tool becomes – intentionally or unintentionally – a statement about police role. Interestingly, these strategic choices map on to existing policing theories. Three of the traditional policing philosophies – hot spot policing , problem-oriented policing, and community-based policing have loose parallels with new place-based predictive policing technologies like PredPol, Risk Terrain Modeling (RTM), and HunchLab. This chapter discusses these leading predictive policing technologies as illustrative examples of how police can choose between prioritizing additional police presence, targeting environmental vulnerabilities, and/or establishing a community problem-solving approach as a different means of achieving crime reduction.https://digitalcommons.wcl.american.edu/facsch_bk_contributions/1185/thumbnail.jp