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A Statistical Approach to Crime Linkage

NCJ Number
255192
Date Published
Unknown
Annotation
This article reports the development of a statistical approach to criminal linkage analysis that discovers and groups crime events that share a common offender and prioritizes suspects for further investigation.
Abstract
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 the proposed 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 percent of actual linkages with a 5-percent false positive rate. For crime series identification, 74 percent-89 percent of the additional crimes in a crime series can be identified from a ranked list of 50 incidents. (publisher abstract modified)
Date Created: January 28, 2021