Geoforensics with Pollen Quantification
A Spatial Perspective
Bibliographic Data
| ID | 3140288 |
|---|---|
| Authors | Wangshu Mu (0000-0002-2171-8025, Beijing Normal University), Diane Tong (0000-0001-7005-5128, Arizona State University), T H Grubesic (0000-0003-4517-586X, University of California, Riverside), Hung-Chi Liu, Hung‐Chi Liu (0000-0002-0078-7363, Arizona State University), Edward Helderop (0000-0003-0590-5258, University of California, Riverside), Jennifer A Miller (0000-0003-0078-9155, The University of Texas at Austin), E J Bienenstock (0000-0001-8747-8872, Arizona State University) |
| Year | 2023 |
| Volume | 113 |
| Issue | 9 |
| Pages | 2031-2047 |
| Publication date | 2023-10-21 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the American Association of Geographers (JOURNAL) |
| Journal identifiers | ISSN: 2469-4452 • E-ISSN: 2469-4460 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/24694452.2023.2211155 |
| OpenAlex | W4383499077 |
| Language | EN |
| Citations received | 1 |
| References cited | 40 |
Geoforensic science investigates the location and time of criminal occurrences by integrating multiple fields, including geography, criminology, ecology, biology, and geology. The ubiquity, durability, and spatial-temporal predictability make pollen a frequently used biomarker in geoforensic investigations to help determine the provenance of hard-to-trace items, including computers, counterfeit products, digging equipment, clothing, and undetonated explosives. The recently developed Geoforensic Interdiction (GOFIND) model links the pollen combination collected from a sample object with the probability of locations traversed by the object. Although the GOFIND model improves over the traditional single-site joint probability approach and can be used to identify multiple locations simultaneously, substantial limitations remain. In particular, GOFIND requires specifying the number of locations traversed by an object in advance—a priori knowledge that is almost impossible to obtain in real-world applications. This article aims to introduce the GOFIND + model that leverages detected and undetected pollen to establish a probabilistic relation between pollen and the corresponding species distribution in the environment. Our simulation tests using the USDA CropScape data for the state of Texas show that the GOFIND + model outperforms the GOFIND model in predictive accuracy. Further, GOFIND + does not require that users specify the number of geographical stops and sites a priori. Key Words: geoforensics, GOFIND+, pollen, spatial optimization
A priori and a posteriori · Archaeology · Data mining · Data science · Geography · Interdiction · Pollen · Atmospheric and Environmental Gas Dynamics · Computer Science · Data-Driven Disease Surveillance · Wildlife-Road Interactions and Conservation · Artificial Intelligence · Ecology
Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph
Identifying the spatial footprint of pollen distributions using the Geoforensic Interdiction (Gofind) model
Supporting the Comparison of Choropleth Maps Using an Evolutionary Algorithm
A space–time parallel framework for fine-scale visualization of pollen levels across the Eastern United States
The maximal covering location problem
Spatial Optimization in Geography
Using Genetic Algorithms to Create Multicriteria Class Intervals for Choropleth Maps
Heuristics in Spatial Analysis
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |