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

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.

KEYWORDS:

• Data analytics
• repeated arrests
• predictive policing

Acknowledgement

The authors express their sincere gratitude to the Policía de Investigaciones de Chile (PDI), and in particular the Centro Nacional de Análisis Criminal (CENACRIM), for their cooperation in this study, specifically in preparing the data set, support in the analysis of the information, and the interpretation of the results. The authors are grateful to Eduardo Valenzuela for his helpful comments and academic support, to Patricio Dominguez for his useful feedback, the editor and two anonymous referees.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported financially by a grant from the Centre for Public Policies, Pontificia Universidad Católica de Chile. Torres Irribarra acknowledges research support provided by FONDECYT grant 11180792. Asahi acknowledges research support provided by CEDEUS, ANID/FONDAP 15110020.