Identification of some factors influencing soil transfer on shoes

In criminal activities, soil can be transferred from a crime scene to items linked with a perpetrator; for example, shoes, cars or tools. Several parameters will influence the quantity of soil transferred in a given scenario. The knowledge of the most influential factors can help the expert to assess the evidence using a logical approach at the activity level or to predict the amount of soil that can be expected in a given scenario. The influence of five chosen parameters, namely the shoe profile, shoe size, walker's weight, soil type and soil humidity were assessed using Design of Experiment (DOE) in order to understand their influence on soil quantity transferred on shoes.The Faced Central Composite Design (FCCD) using a quadratic model was found to be highly significant, thus they could be adequately used to model and to interpret the amount of soil recovered from one shoe.These designs demonstrate that the characteristics of the donor (soil type and soil humidity), as well as a combination of these two factors have a very significant impact on the soil transfer. The characteristics of the receptor (shoe profile, shoe size and walker's weight) also have an impact on the transfer but to a lesser extent.Globally, this research provides valuable information for the forensic scientist both in investigative mode: evaluation of the soil quantity possibly transferred on shoes, and in the evaluative steps: is the quantity of soil found on the suspect shoes in accordance to the proposition/scenario given by the prosecution and the defence?     

Predicting Initiator and Near Repeat Events in Spatiotemporal Crime Patterns: An Analysis of Residential Burglary and Motor Vehicle Theft

Near repeat analysis has been increasingly used to measure the spatiotemporal clustering of crime in contemporary criminology. Despite its predictive capacity, the typically short time frame of near repeat crime patterns can negatively affect the crime prevention utility of near repeat analysis. Thus, recent research has argued for a greater understanding of the types of places that are most likely to generate near repeat crime patterns. The current study contributes to the literature through a spatiotemporal analysis of residential burglary and motor vehicle theft in Indianapolis, IN. Near Repeat analyses were followed by multinomial logistic regression models to identify covariates related to the occurrence of initiator (the first event in a near repeat chain) and near repeat (the subsequent event in a near repeat chain) events. The overall findings provide additional support for the argument that neighborhood context can influence the formation and context of spatiotemporal crime patterns.