Keeping Score: Predictive Analytics in Policing

被引:16
|
作者
Fitzpatrick, Dylan J. [1 ,2 ]
Gorr, Wilpen L. [2 ]
Neill, Daniel B. [3 ]
机构
[1] Carnegie Mellon Univ, Machine Learning Dept, Pittsburgh, PA 15213 USA
[2] Carnegie Mellon Univ, Heinz Coll, Pittsburgh, PA 15213 USA
[3] NYU, Ctr Urban Sci & Progress, Brooklyn, NY 11201 USA
来源
ANNUAL REVIEW OF CRIMINOLOGY, VOL 2 | 2019年 / 2卷
基金
美国安德鲁·梅隆基金会;
关键词
predictive policing; crime forecasting; crime hot spots; proactive policing; HOT-SPOTS; CRIME; VIOLENCE; CRIMINOLOGY; CONTAGION; PATTERNS; AREAS; SCAN;
D O I
10.1146/annurev-criminol-011518-024534
中图分类号
DF [法律]; D9 [法律];
学科分类号
0301 ;
摘要
Predictive analytics in policing is a data-driven approach to (a) characterizing crime patterns across time and space and (b) leveraging this knowledge for the prevention of crime and disorder. This article outlines the current state of the field, providing a review of forecasting tools that have been successfully applied by police to the task of crime prediction. Wethen discuss options for structured design and evaluation of a predictive policing program so that the benefits of proactive intervention efforts are maximized given fixed resource constraints. We highlight examples of predictive policing programs that have been implemented and evaluated by police agencies in the field. Finally, we discuss ethical issues related to predictive analytics in policing and suggest approaches for minimizing potential harm to vulnerable communities while providing an equitable distribution of the benefits of crime prevention across populations within police jurisdiction.
引用
收藏
页码:473 / 491
页数:19
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