Randomized Controlled Field Trials of Predictive Policing

The concentration of police resources in stable crime hotspots has proven effective in reducing crime, but the extent to which police can disrupt dynamically changing crime hotspots is unknown. Police must be able to anticipate the future location of dynamic hotspots to disrupt them. Here we report...

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Vydáno v:Journal of the American Statistical Association Ročník 110; číslo 512; s. 1399 - 1411
Hlavní autoři: Mohler, G. O., Short, M. B., Malinowski, Sean, Johnson, Mark, Tita, G. E., Bertozzi, Andrea L., Brantingham, P. J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Alexandria Taylor & Francis 01.12.2015
Taylor & Francis Group, LLC
Taylor & Francis Ltd
Témata:
algorithms
Analysts
Applications and Case Studies
Best practice
Clinical trials
Community policing
Crime
Crime prevention
experimental design
Experimental methods
Experiments
field experimentation
Field trials
Forecasting
Intelligence
Law enforcement
Machine learning
Mapping
Medical research
Patrols
Point processes
Police
Police departments
Policing
Policing dosage
prediction
Predictions
randomized clinical trials
Research design
risk
Statistics
United Kingdom
United Kingdom
United Kingdom > UK
ISSN:1537-274X, 0162-1459, 1537-274X
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Shrnutí:The concentration of police resources in stable crime hotspots has proven effective in reducing crime, but the extent to which police can disrupt dynamically changing crime hotspots is unknown. Police must be able to anticipate the future location of dynamic hotspots to disrupt them. Here we report results of two randomized controlled trials of near real-time epidemic-type aftershock sequence (ETAS) crime forecasting, one trial within three divisions of the Los Angeles Police Department and the other trial within two divisions of the Kent Police Department (United Kingdom). We investigate the extent to which (i) ETAS models of short-term crime risk outperform existing best practice of hotspot maps produced by dedicated crime analysts, (ii) police officers in the field can dynamically patrol predicted hotspots given limited resources, and (iii) crime can be reduced by predictive policing algorithms under realistic law enforcement resource constraints. While previous hotspot policing experiments fix treatment and control hotspots throughout the experimental period, we use a novel experimental design to allow treatment and control hotspots to change dynamically over the course of the experiment. Our results show that ETAS models predict 1.4–2.2 times as much crime compared to a dedicated crime analyst using existing criminal intelligence and hotspot mapping practice. Police patrols using ETAS forecasts led to an average 7.4% reduction in crime volume as a function of patrol time, whereas patrols based upon analyst predictions showed no significant effect. Dynamic police patrol in response to ETAS crime forecasts can disrupt opportunities for crime and lead to real crime reductions.
Bibliografie:http://dx.doi.org/10.1080/01621459.2015.1077710
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ISSN:1537-274X
0162-1459
1537-274X
DOI:10.1080/01621459.2015.1077710