Using Classification Trees to Link Serial Crimes

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Mugford, Rebecca Lynn




In the investigative setting, police must often decide whether multiple crimes have been committed by a single offender. Using a variety of statistical techniques, studies have shown that it is possible to link serial crimes in a relatively accurate fashion using behavioural information (i.e., a process often referred to as behavioural linkage analysis; BLA). Despite this, practitioners often resist using these techniques, in a similar fashion to how clinical psychologists often resist actuarial techniques. In an attempt to develop an approach to BLA that may be better received by end users, this dissertation explored how classification trees (CTs) can be used to link serial crimes. Specifically, three variations of a CT approach were explored: a standard, single CT, an iterative CT (ICT), and the combination of multiple standard CTs and/or ICTs (i.e., a multiple model approach). Using separate samples of serial break and enters from Saint John, New Brunswick (N = 170) and serial sexual assaults from Quebec (N = 260), the ability of these approaches to link serial crimes were compared to one of the most commonly employed statistical approaches to BLA: main-effects logistic regression analysis. Generally, results revealed that all statistical approaches achieved high (and similar) levels of predictive accuracy; however, a number of potential advantages of a simple, standard CT approach were identified (e.g., transparency and ease-of-use). The findings reported in this dissertation have implications for BLA researchers (e.g., how behavioural domains are defined, how crime samples are selected, etc.) and police practitioners (e.g., the availability of a userfriendly statistical linking tool, the need for better data collection protocols, etc.). However, before a CT-based approach to BLA is implemented in practice, future research is required to address some of the limitations of the current research.


Criminology and Penology




Carleton University

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