Linking solved and unsolved crimes using offender behaviour

Offender behaviour is used to distinguish between crimes committed by the same person (linked crimes) and crimes committed by different people (unlinked crimes) through behavioural case linkage. There is growing evidence to support the use of behavioural case linkage by investigative organisations such as the police, but this research is typically limited to samples of solved crime that do not reflect how this procedure is used in real life. The current paper extends previous research by testing the potential for behavioural case linkage in a sample containing both solved and unsolved crimes. Discrimination accuracy is examined across crime categories (e.g. a crime pair containing a car theft and a residential burglary), across crime types (e.g. a crime pair containing a residential burglary and a commercial burglary), and within crime types (e.g. a crime pair containing two residential burglaries) using the number of kilometres (intercrime distance) and the number of days (temporal proximity) between offences to distinguish between linked and unlinked crimes. The intercrime distance and/or the temporal proximity were able to achieve statistically significant levels of discrimination accuracy across crime categories, across crime types, and within crime types as measured by Receiver Operating Characteristic (ROC) analysis. This suggests that behavioural case linkage can be used to assist the investigation, detection and prosecution of prolific and versatile serial offenders

A Comparison of Logistic Regression and Classification Tree Analysis for Behavioural Case Linkage

Much previous research on behavioural case linkage has used binary logistic regression to build predictive models that can discriminate between linked and unlinked offences. However, classification tree analysis has recently been proposed as a potential alternative owing to its ability to build user-friendly and transparent predictive models. Building on previous research, the current study compares the relative ability of logistic regression analysis and classification tree analysis to construct predictive models for the purposes of case linkage. Two samples are utilised in this study: a sample of 376 serial car thefts committed in the UK and a sample of 160 serial residential burglaries committed in Finland. In both datasets, logistic regression and classification tree models achieve comparable levels of discrimination accuracy, but the classification tree models demonstrate problems in terms of reliability or usability that the logistic regression models do not. These findings suggest that future research is needed before classification tree analysis can be considered a viable alternative to logistic regression in behavioural case linkage