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Reusing Geospatial Data of Invasive Alien Insect Species From the Literature: Significance, Challenges, and Potential
Shuhao Tan1,2, Yiqi Xu1,3, Qiaoling Lin1,3
1State Key Laboratory of Animal Biodiversity Conservation and Integrated Pest Management, Institute of Zoology, Chinese Academy of Sciences, Beijing, P. R. China.
Integrative Zoology
|August 4, 2026
Summary
Geospatial data in invasive alien insect species (IAIS) research are often locked in maps. Extracting this data from publications can improve global IAIS monitoring and governance.
Area of Science:
- Invasion Biology
- Geospatial Science
- Data Science
Background:
- Geospatial data are crucial for understanding invasion dynamics, especially for invasive alien insect species (IAIS).
- Published literature from 2016-2026 contains a substantial repository of geospatial data, primarily in thematic maps.
- Assessing the role and reuse potential of this published geospatial data is vital for advancing invasion biology.
Purpose of the Study:
- To conduct a bibliometric analysis of published geospatial data related to IAIS dispersal.
- To evaluate the characteristics of geospatial data in IAIS publications, including visual representation, spatial scale, data reuse, and accessibility.
- To propose methods for overcoming data accessibility limitations and enhancing the reuse of geospatial data for improved IAIS monitoring.
Main Methods:
- Bibliometric analysis of IAIS-related publications from 2016 to 2026.
- Analysis of publications based on four dimensions: visual representation, spatial scale, data reuse, and data accessibility.
- Exploration of dataset integration across regions, time periods, and species to evaluate data reuse value.
- Development of proposed computational techniques for extracting quantitative data from thematic map figures.
Main Results:
- 59.0% of IAIS publications presented geospatial data, predominantly as point-based data (80.1%) at a regional scale (54.9%).
- High adoption of geospatial data reuse (74.7%) was observed, but 51.0% of publications lacked downloadable source data.
- The absence of raw data significantly hinders geospatial data reuse, limiting integrated analyses.
Conclusions:
- Published geospatial data in IAIS research is substantial but often inaccessible, hindering comprehensive analysis and reuse.
- Developing computational techniques to extract quantitative data from thematic maps is essential for unlocking this data.
- Transforming static map images into computable knowledge will enhance global IAIS monitoring, governance, and data sharing efforts.
