Bayesian journey-to-crime modelling of juvenile and adult offenders by gender in Manchester

This study tested the Bayesian journey-to-crime (JTC) model by gender and age for serial offenders from Manchester, England. The data were 4056 crimes committed by 171 serial offenders between 2003 and 2006. The data were subdivided by gender and age group to examine whether accuracy and precision varied by these subgroups. In general, the centre of minimum distance was found to be the most accurate measure, but the Bayesian risk and product measures were found to be the most precise measurements. The traditional ‘distance decay’ type of JTC function did not produce estimates that are as accurate nor as precise as the Bayesian approach. Tests were conducted on whether specific gender and age group JTC functions and origin–destination matrices improved predictability. With the exception of juvenile male offenders, the general functions were more accurate and precise. In terms of building an accurate and precise geographical profiling methodology, the results point to the need to include information on the likely predisposition of neighbourhoods to produce offenders as well as information on the crime opportunities available to offenders. Copyright © 2009 John Wiley & Sons, Ltd.     

Finding a serial burglar's home using distance decay and conditional origin–destination patterns: a test of empirical Bayes journey-to-crime estimation in the Hague

Can we tell where an offender lives from where he or she commits crimes? Journey-to-crime estimation is a tool that uses crime locations to tell us where to search for a serial offender's home. In this paper, we test a new method: empirical Bayes journey-to-crime estimation. It differs from previous methods because it utilises an ‘origin–destination’ rule in addition to the ‘distance decay’ rule that prior methods have used. In the new method, the profiler not only asks ‘what distances did previous offenders travel between their home and the crime scenes?’ but also ‘where did previous offenders live who offended at the locations included in the crime series I investigate right now?’. The new method could not only improve predictive accuracy, it could also reduce the traditional distinction between marauding and commuting offenders. Utilising the CrimeStat software, we apply the new method to 62 serial burglars in The Hague, The Netherlands, and show that the new method has higher predictive accuracy than methods that only exploit a distance decay rule. The new method not only improves the accuracy of predicting the homes of commuters—offenders who live outside their offending area—it also improves the search for marauders—offenders who live inside their offending area. After presenting an example of the application of the technique for prediction of a specific burglar, we discuss the limitations of the method and offer some suggestions for its future development. Copyright © 2009 John Wiley & Sons, Ltd.     

Introduction to the special issue on Bayesian journey-to-crime modelling

In this special issue of the Journal of Investigative Psychology and Offender Profiling, we explore a Bayesian approach to journey-to-crime (JTC) estimation with an emphasis on the statistical models used. The approach conceptualises the probability that an offender lives at one location as the product of the probability distribution from a JTC estimate along with the probability distribution of other offenders who committed crimes in the same locations. The Bayesian approach is appropriate as the second part is conditional on the first part. The introduction gives the background behind the methodology and suggests how future improvements can be made by integrating new information. Finally, the papers in the special issue are introduced. Copyright © 2009 John Wiley & Sons, Ltd.