Bayesian journey‐to‐crime modelling of single and multiple crime‐type series in Baltimore County, MD

Abstract

AbstractThis study tested different Bayesian Journey‐to‐Crime (JTC) models on a sample group of 850 serial offenders apprehended in Baltimore County, MD from 1993 to 1997. In this research, Bayesian JTC models were being used to predict the home locations of the offenders. The sample group data included 133 assaults, 90 burglaries, 497 larcenies, 81 robberies, and 49 vehicle thefts. The main question this research aimed to answer was whether the addition of crimes of a different type to an existing crime series of a single type would result in more accurate and/or precise Bayesian JTC models. The standard practice by law enforcement has been to consider the same‐type crime series only when modelling the anchor point of the offender. Similarly, in research, geographic profiles have been constructed exclusively with the same‐type crime series. The results of this study clearly indicated that the inclusion of crimes of a different type into a single crime‐type series will result in significantly more accurate and more precise Bayesian JTC models. In contrast, crime series with predominantly assault and burglary showed results that were inconclusive or indicated no significant differences. These results should encourage law enforcement agencies to re‐evaluate their standard practice of constructing geographic profiles with only the same‐type crime series. Copyright © 2009 John Wiley & Sons, Ltd.