A Statistical Approach to Crime Linkage

TL;DR

This paper introduces a statistical method using Bayes factors and hierarchical clustering to link crimes, identify series, and prioritize suspects, demonstrating high accuracy in real-world crime data.

Contribution

It develops a novel Bayesian clustering framework for crime linkage analysis, improving accuracy in linking crimes and identifying series over previous methods.

Findings

82% of true linkages identified with 5% false positives

77%-89% of series crimes correctly identified from top 50

Effective in real-world crime datasets

Abstract

The object of this paper is to develop a statistical approach to criminal linkage analysis that discovers and groups crime events that share a common offender and prioritizes suspects for further investigation. Bayes factors are used to describe the strength of evidence that two crimes are linked. Using concepts from agglomerative hierarchical clustering, the Bayes factors for crime pairs are combined to provide similarity measures for comparing two crime series. This facilitates crime series clustering, crime series identification, and suspect prioritization. The ability of our models to make correct linkages and predictions is demonstrated under a variety of real-world scenarios with a large number of solved and unsolved breaking and entering crimes. For example, a na\"ive Bayes model for pairwise case linkage can identify 82\% of actual linkages with a 5\% false positive rate. For crime series identification, 77\%-89\% of the additional crimes in a crime series can be identified from a ranked list of 50 incidents.