License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.77
URN: urn:nbn:de:0030-drops-189723
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18972/
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Wang, Haoyu ; Miller, Jennifer A.

How to Improve Joint Suitability Mapping for Search Space Reduction? (Short Paper)

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LIPIcs-GIScience-2023-77.pdf (1 MB)

Abstract

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.

BibTeX - Entry

@InProceedings{wang_et_al:LIPIcs.GIScience.2023.77,
  author =	{Wang, Haoyu and Miller, Jennifer A.},
  title =	{{How to Improve Joint Suitability Mapping for Search Space Reduction?}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{77:1--77:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18972},
  URN =		{urn:nbn:de:0030-drops-189723},
  doi =		{10.4230/LIPIcs.GIScience.2023.77},
  annote =	{Keywords: forensic geolocation, species distribution modeling, conditional probability, search space reduction}
}

Keywords: forensic geolocation, species distribution modeling, conditional probability, search space reduction
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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