A permutation test to identify important attributes for linking crimes of serial offenders

Abstract

The modus operandi (MO) of a crime describes the unique characteristics that an offender imparts to it. Although in some instances, a serial offender's behavior is circumstantial, some MO behaviors may be consistent from one crime to the next. By investigating these behaviors, similar crimes can be linked to the same individual, but some attributes describing a crime may be more important for linking than others. Two strategies have historically been used to link crimes. One relies on expert criminal judgment, which may require manually sifting through thousands of crime records. In the second, similar attributes are grouped, and logistic regression is applied to coarse frequency summaries of those groupings. In this work, we introduce an intuitive statistical permutation test for assessing the importance of individual attributes in linking crimes, and we show how the results can be used to weight each one's importance to link crimes. By using the serial offenses of four sets of residential burglaries and six sets of robberies identified in Tempe, Arizona, the test is illustrated, and differences among attribute importance of these two types of crimes are highlighted. We demonstrate greater success in linking crimes when the test results are incorporated into a linking analysis.

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores

“…In the last type of approach, crime series clustering, all the clusters are found simultaneously. 8,[27][28][29][30][31][32][33][34][35] One of the earliest approaches we know of for clustering crimes is that of Dahbur and Muscarello, 29 who used a neural network approach. (This method had some serious flaws that required extensive heuristic post-processing after the clusters were created, but aimed at solving the more general problem of crime clustering.)…”