Probability Modelling for Optimization of Evidence Searches

Evidence searches (seeking human remains or objects related to criminal cases) have characteristics that differ from searches for lost persons. Often, evidence searches are focused on relatively small areas and seek hard-to-detect objects that may have been initially concealed by criminal behavior and subsequently scattered by animal activity or environmental changes. Because of these characteristics, the success rate for evidence searches can be quite low. For that reason, it is imperative to focus search efforts in areas that have the highest likelihood of containing sought-after evidence. Traditional application of search theory involves mapping planning regions, which are assigned Probability of Area (POA) and Probability Density (Pden) values via a consensus process. Search segments mapped within planning regions inherit their POA and Pden from their parent planning regions. We describe an application of search theory concepts aimed at optimizing the success of evidence searches. The approach consists of: Mapping search segments of uniform size; identifying Evidence Probability Factors (EPFs) based on terrain analysis, historical criminal behavior, and animal behavior; assigning relative values to EPFs via a proportional consensus process, and then representing EPFs on a map to develop a probability mosaic which provides POA and Pden for each search segment. KEY WORDS: Search theory, search planning, probability modelling, human remains, evidence search.