GeoThinneR: An R Package for Efficient Spatial Thinning of Species Occurrences and Point Data
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909608487944192 |
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| author | Mestre-Tomás, J. |
| author_facet | Mestre-Tomás, J. |
| contents | In this paper we present GeoThinneR, an R package for efficient and flexible spatial thinning of species occurrence data. Spatial thinning is a widely used preprocessing step in species distribution modeling (SDM) that can help reduce sampling bias, but existing R implementations rely on brute-force algorithms that scale poorly with large datasets. GeoThinneR implements multiple thinning approaches, including ensuring a minimum distance between points, subsampling points on a grid, and filtering based on decimal precision. To handle large datasets, it introduces two optimized algorithms based on local kd-trees and adaptive neighbor estimation, which greatly reduce memory usage and execution time. Additional functionalities such as group-wise thinning and point prioritization are included to facilitate its use in SDM workflows. We here provide performance benchmarks using both simulated and real-world data to demonstrate substantial performance improvements over existing tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07867 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GeoThinneR: An R Package for Efficient Spatial Thinning of Species Occurrences and Point Data Mestre-Tomás, J. Populations and Evolution Applications In this paper we present GeoThinneR, an R package for efficient and flexible spatial thinning of species occurrence data. Spatial thinning is a widely used preprocessing step in species distribution modeling (SDM) that can help reduce sampling bias, but existing R implementations rely on brute-force algorithms that scale poorly with large datasets. GeoThinneR implements multiple thinning approaches, including ensuring a minimum distance between points, subsampling points on a grid, and filtering based on decimal precision. To handle large datasets, it introduces two optimized algorithms based on local kd-trees and adaptive neighbor estimation, which greatly reduce memory usage and execution time. Additional functionalities such as group-wise thinning and point prioritization are included to facilitate its use in SDM workflows. We here provide performance benchmarks using both simulated and real-world data to demonstrate substantial performance improvements over existing tools. |
| title | GeoThinneR: An R Package for Efficient Spatial Thinning of Species Occurrences and Point Data |
| topic | Populations and Evolution Applications |
| url | https://arxiv.org/abs/2505.07867 |