A Statistical Approach to Crime Linkage

• Activity Number: 370
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Defense and National Security
• Author(s): Michael Porter*
• Companies: The University of Alabama
• Keywords: crime ; linkage ; criminal ; clustering ; classification ; security

Abstract

The object of criminal linkage analysis is to discover and group crime events that share a common offender and prioritize 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 naive 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.