LAUSR

The focus of the current study is to examine the collection and analysis of traces that are related to crime scene behaviors in sexual homicide cases as well as the factors influencing the solving of these crimes. Using 230 sexual homicide cases from the SHielD database, we computed two neural network models based on the multi-layer perceptron algorithm. First, we determined whether certain crime scene characteristics predicted the collection and analysis of traces (dependent variable for Model 1). Not surprisingly, the results indicate that trace collection and analysis were more likely to occur in sexual homicide cases with crime scene behaviors exhibiting the highest risk for trace transfer (e.g. close interactions with the victim) as well as the best conditions for trace persistence (e.g. body is found indoors). Situational and physical aspects of the crime scene are thus taken into account when deciding on the collection and analysis of traces. Second, we examined the situations in which the collection and analysis of traces contributes to crime solving (dependent variable for Model 2). The results suggest that the collection and analysis of traces does not necessarily predict the resolution of the case. Specifically, the analyses show that the collection and analysis of traces is useful for crime solving when: (1) the offenders' behaviors increase the opportunities for leaving traces at the crime scene, and (2) when the environmental and temporal aspects are favorable to the collection of traces. The impact of trace collection and analysis on case resolution is thus depending on the context of the case. Furthermore, the subsequent steps, such as the result of the trace analysis, the introduction into a database, the obtention of a result from this comparison, etc. might also affect case resolution, and thus interfere in the link between trace collection and analysis and case resolution.