Optimizing Wilderness Search and Rescue: A Bayesian GIS Analysis

D. Kim Rossmo PhD School of Criminal Justice, Texas State University Lorie Velarde MSc Irvine Police Department Thomas

Mahood MSc Formerly with Riverside Mountain Rescue Unit USA

Email krossmo@txstate.edu



Wilderness search and rescue operations function under critical time pressures and resource constraints. For optimal deployment, personnel must be assigned to prioritized search areas following some form of probability map. Incident commanders often have to generate such maps from different sources of information, some of which may be incomplete or imperfect. Here, we use a case study of the search for a lost person in Joshua Tree National Park in Southern California to illustrate how various types of evidence – previous search tracks and a cell phone tower ping – can be integrated, using Bayes’ theorem, into an optimal probability search map.

KEY WORDS: Wilderness Search and Rescue, Lost Persons, Bayesian Analysis, Resource Optimization

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