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Documents authored by Wang, Haoyu

Document
DOI: 10.4230/LIPIcs.GIScience.2025.19
Search Space Reduction Using Species Distribution Modeling with Simulated Pollen Signatures

Authors: Haoyu Wang, Jennifer A. Miller, and Shalene Jha

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Microscopic trace materials, such as pollen, are an important category of forensic evidence recovered during investigations. As an environmentally ubiquitous substance that can attach to various surfaces, pollen enables the linking of objects and people in space and time. In this study, we assessed the extent to which the search space could be reduced using simulated pollen signatures. These signatures were compiled by randomly selecting pairs of geographic coordinates on the Earth’s terrestrial land and querying the Global Biodiversity Information Facility (GBIF) database to identify plant taxa within 50 meters of the coordinates. These taxa were then treated as the parent taxa of the pollen, simulating the hypothetical attachment of pollen signatures to objects or individuals. For each identified pollen taxon, we modeled habitat suitability for the parent plant taxa and combined the spatial distributions to refine the geolocation search area. Since the actual coordinates for these locations of interest were known, we were able to evaluate the global performance of the search space reduction under the assumption of an extreme constraint that no other contextual information was available.
Cite as

Haoyu Wang, Jennifer A. Miller, and Shalene Jha. Search Space Reduction Using Species Distribution Modeling with Simulated Pollen Signatures. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 19:1-19:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{wang_et_al:LIPIcs.GIScience.2025.19,
  author =	{Wang, Haoyu and Miller, Jennifer A. and Jha, Shalene},
  title =	{{Search Space Reduction Using Species Distribution Modeling with Simulated Pollen Signatures}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{19:1--19:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.19},
  URN =		{urn:nbn:de:0030-drops-238485},
  doi =		{10.4230/LIPIcs.GIScience.2025.19},
  annote =	{Keywords: geoforensics, species distribution modeling, search space reduction}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.77
How to Improve Joint Suitability Mapping for Search Space Reduction? (Short Paper)

Authors: Haoyu Wang and Jennifer A. Miller

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

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.
Cite as

Haoyu Wang and Jennifer A. Miller. How to Improve Joint Suitability Mapping for Search Space Reduction? (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 77:1-77:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@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/entities/document/10.4230/LIPIcs.GIScience.2023.77},
  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}
}
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