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Statistical Approach to Crime Linkage

NCJ Number
248976
Date Published
October 2014
Length
33 pages
Author(s)
Michael D. Porter
Agencies
NIJ-Sponsored
Publication Type
Research (Applied/Empirical), Report (Study/Research), Report (Grant Sponsored)
Grant Number(s)
2010-DE-BX-K255
Annotation
This is a report on the development of a statistical approach that identifies and groups crimes committed by the same offender, and it prioritizes suspects for further investigation.
Abstract
Bayes factors were used to determine the strength of evidence that two crimes have been committed by the same person. Using concepts from agglomerative hierarchical clustering, the Bayes factors for crime pairs were combined to provide similarity measures in comparing two crime series. This facilitates crime-series clustering and identification, as well as suspect prioritization. The models' ability to make correct linkages and predictions was demonstrated under real-world scenarios that involve a large number of solved and unsolved breaking-and-entering crimes. A naïve Bayes model for pairwise case linkage identified 82 percent of known linkages, with a 5-percent false positive rate. For crime-series identification, 77-89 percent of the additional crimes in a series were identified from a ranked list of 50 incidents. 6 tables, 6 figures, and 71 references
Date Created: July 20, 2021