• Media type: Electronic Conference Proceeding; E-Article; Text
  • Title: How to Improve Joint Suitability Mapping for Search Space Reduction? (Short Paper)
  • Contributor: Wang, Haoyu [Author]; Miller, Jennifer A. [Author]
  • imprint: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2023
  • Language: English
  • DOI: https://doi.org/10.4230/LIPIcs.GIScience.2023.77
  • Keywords: search space reduction ; conditional probability ; species distribution modeling ; forensic geolocation
  • Origination:
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  • Description: Geoforensic analyses are used to identify the location history of objects or people of interest. An effective method for location history identification is to use joint probability or suitability of trace materials. Species distribution models have been used to derive joint suitability distributions using suitable biotic trace evidence such as pollen. One of the key objectives for such analyses is to effectively reduce potential search space and search effort for investigators. This research presents a novel framework for modeling the habitat suitability of pollen identified at the plant species-level to generate joint suitability maps. We provide major limitations and challenges faced by current geolocation analyses based on species distribution models, including opportunities to improve the joint suitability analyses for search space reduction. A conditional probability approach for geolocation identification is also demonstrated for possible future applications in real-world forensic cases.
  • Access State: Open Access