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. [ABSTRACT FROM AUTHOR]