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.

摘要

警察机构花费大量精力来预测未来的犯罪行为。由于大部分犯罪是由一小群人实施的，因此预防措施往往针对多产的罪犯。长期以来，人们一直期望新技术可以提高对犯罪模式的准确识别。在这里，我们探索大数据技术并设计机器学习算法来预测重复逮捕。这些预测基于智利国家警察机构提供的行政数据，包括智利圣地亚哥的逮捕历史以及性别和年龄等个人元数据。通过各种监督机器学习技术实现了出色的算法性能。尽管如此，在数学模型的设计方面仍然存在许多挑战，