The linking of burglary crimes using offender behaviour: Testing research cross-nationally and exploring methodology

Purpose. The current study tests whether existing behavioural case linkage findings from the United Kingdom (UK) will replicate abroad with a sample of residential burglaries committed in Finland. In addition, a previously discussed methodological issue is empirically explored. Methods. Seven measures of behavioural similarity, geographical proximity, and temporal proximity are calculated for pairs of burglary crimes committed by 117 serial burglars in Finland. The ability of these seven measures to distinguish between pairs of crimes committed by the same offender (linked pairs) and different offenders (unlinked pairs) is tested using logistic regression and receiver operating characteristic (ROC) analysis. Two methodologies for forming the unlinked pairs are compared; one representing the ‘traditional’ approach used by research and, the other, a new approach that represents a potentially more realistic and statistically sound approach to testing case linkage. Results. A wider range of offender behaviours were able to distinguish between linked and unlinked crime pairs in the current Finnish sample than in previous UK-based research. The most successful features were the kilometre-distance between crimes (the intercrime distance), the number of days separating offences (temporal proximity), and a combination of target, entry, internal, and property behaviours (the combined domain). There were no statistically significant differences between the two methodological approaches. Conclusions. The current findings demonstrate that a wider range of offender behaviours can be used to discriminate between linked and unlinked residential burglary crimes committed in Finland than in the UK. The use of a more realistic and statistically sound methodology does not lead to substantial changes in case linkage findings

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