Date of Award
Master of Arts
Dawnie Steadman, Michael Kenyhercz
Homicide victims are often discovered by accident or located through witness testimony, both of which are unreliable methods. Moving a victim’s body from the scene of the crime to a secondary disposal site may further complicate their discovery, delaying recovery, identification, and evidence collection. Homicides are exponentially more difficult to investigate, solve, and prosecute without a body. In the medicolegal context, a body disposal site prediction model is an alternative to relying on luck or witness testimony. Predictive models were created using body disposal data collected from the Office of the Chief Medical Examiner (OCME), Connecticut, to explore the feasibility of predicting body disposal sites. Three models were created: one inductive model using non-sites mimicking complete spatial randomness (CSR), one inductive model using non-sites mimicking population density (PD), and one deductive model using nonsites mimicking complete spatial randomness (CSR). Spatial statistical analyses confirm that body disposal locations are inhomogeneously distributed across Connecticut. Both the inductive and deductive CSR models generated the most optimal predictive models. Results indicate predictive models of body disposal locations are 49 – 59% more likely to predict body disposal sites in Connecticut than random chance. At present, the models are most successful at predicting body disposal sites in urban areas. The results indicate that predictive models of body disposal location have a real possibility of narrowing search areas and maximizing resources for law enforcement when searching for missing victims. Future modeling efforts should address predicting rural body disposal site location.
Gundel, Annemarie Catherine, "Geographic Information Systems (GIS) and Predictive Modeling of Body Disposal Sites. " Master's Thesis, University of Tennessee, 2017.