Machine learning for policing: a case study on arrests in Chile

 

Title
Machine learning for policing: a case study on arrests in Chile
Author Elwin van ‘t Wout ,Christian Pieringer, David Torres Irribarra, Kenzo Asahi & Pilar Larroulet
Line(s) Access and Mobility
Year of Publication 2020
Journal Title Transportation Research Part C-Emerging Technologies
Keywords
 Data analytics, repeated arrests, predictive policing
Abstract
Police agencies expend considerable effort to anticipate future incidences of criminal behaviour. Since a large proportion of crimes are committed by a small group of individuals, preventive measures are often targeted on prolific offenders. There is a long-standing expectation that new technologies can improve the accurate identification of crime patterns. Here, we explore big data technology and design a machine learning algorithm for forecasting repeated arrests. The forecasts are based on administrative data provided by the national Chilean police agencies, including a history of arrests in Santiago de Chile and personal metadata such as gender and age. Excellent algorithmic performance was achieved with various supervised machine learning techniques. Still, there are many challenges regarding the design of the mathematical model, and its eventual incorporation into predictive policing will depend upon better insights into the effectiveness and ethics of preemptive strategies.
Doi https://doi.org/10.1080/10439463.2020.1779270
Corresponding Author Kenzo Asahi, kasahi@uc.cl