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Purpose. The study extends research by Santtila et al. (2008) by investigating the effectiveness of linking cases of serial homicide using behavioural patterns of offenders, analysed through Bayesian reasoning. The study also investigates the informative value of individual behavioural variables in the linking process. Methods.Offender behaviour was coded from official documents relating to 116 solved homicide cases belonging to 19 separate series. The basis of the linkage analyses was 92 behaviours coded as present or absent in the case based on investigator observations on the crime scene. We developed a Bayesian method for linking crime cases and judged its accuracy using cross-validation. We explored the information added by individual behavioural variables, first, by testing if the variable represented purely noise with respect to classification, and second, by excluding variables from the original model, one by one, by choosing the behaviour that had the smallest effect on classification accuracy. Results.The model achieved a classification accuracy of 83.6% whereas chance expectancy was 5.3%. In simulated scenarios of only one and two known cases in a series, the accuracy was 59.0 and 69.2%, respectively. No behavioural variable represented pure noise but the same level of accuracy was achieved by analysing a set of 15, as analysing all 92 variables. Conclusion.The study illustrates the utility of analysing individual behavioural variables through Bayesian reasoning for crime linking. Feasible applied use of the approach is illustrated by the effectiveness of analysing a small set of carefully chosen variables.Analyses of the behavioural aspects of a crime have increasingly become a part of crime investigation. One domain of this is crime linking; that is, drawing the conclusion that

Purpose – The purpose of this paper is to explore the differences (if any) between serial and hard-to-solve one-off homicides, and to determine if it is possible to distinguish the two types of homicides based on offence behaviours and victim characteristics. Design/methodology/approach – A sample of 116 Italian serial homicides was compared to 45 hard-to-solve one-off homicides. Hard-to-solve one-off homicides were defined as having at least 72 hours pass between when the offence came to the knowledge of the police and when the offender was caught. Logistic regression was used to predict whether a killing was part of a series or a one-off offence. Findings – The serial killers targeted more strangers and prostitutes, displayed a higher level of forensic awareness both before and after the killing, and had more often an apparent sexual element in their offence. Conversely, the one-off homicides were found to include more traits indicative of impulsive and expressive behaviour. The model demonstrated a good ability (AUC=0.88) to predict whether a homicide belonged to the serial or one-off category. Research limitations/implications – The findings should be replicated using local homicide data to maximise the validity of the model in countries outside of Italy. Practical implications – Being able to distinguish between serial and one-off homicides based on information available at a new crime scene could be practically useful for homicide investigators managing finite resources. Originality/value – Studies comparing serial homicides to one-off homicides are scarce, and there are no studies explicitly trying to predict whether a homicide is an isolated case or part of a series.

Purpose. We aimed (1) to describe distances from home to offence locations (journey‐to‐crime) of offenders in difficult‐to‐solve homicides and rapes as well as robberies against businesses; (2) to see whether the distances in these offences differ from each other; and (3) to test whether selected features related to the offence would be associated with the distances. Methods. Lists of difficult‐to‐solve (DTS) homicides (N = 99) and rapes (N = 56) as well as robberies against businesses (N = 275) from the city of Milan (Italy) were acquired. The collected data consisted of home and offence location coordinates with information on the behaviour of the offenders (for rapes and homicides). The journey‐to‐crime functions were calibrated using the journey‐to‐crime module of CrimeStatIII©. Results. Most distances were short. In homicides, distances were below 1 km, in rapes below 2 km whereas in robberies against businesses almost 6 km. Some crime features were correlated with the distances in rape and homicide cases. Combining the behavioural information to a spatial behaviour measure allowed for better prediction of travelled distances compared to using single variables. Conclusions. The results have practical implications for crime investigations as the crime features explored were, as a rule, such that they would be known by the police prior to the offender being identified. A general theoretical framework for binding together journey‐to‐crime distances and offender crime scene behaviour and other important crime features is needed.

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