Articles | Volume 17, issue 15
https://doi.org/10.5194/gmd-17-6007-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6007-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Random forests with spatial proxies for environmental modelling: opportunities and pitfalls
Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
Universitat Pompeu Fabra (UPF), Barcelona, Spain
Marvin Ludwig
Institute of Landscape Ecology, University of Münster, Münster, Germany
Edzer Pebesma
Institute for Geoinformatics, University of Münster, Münster, Germany
Cathryn Tonne
Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
Universitat Pompeu Fabra (UPF), Barcelona, Spain
CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
Hanna Meyer
Institute of Landscape Ecology, University of Münster, Münster, Germany
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Saved (final revised paper)
Latest update: 04 Sep 2026
Short summary
Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.
Spatial proxies, such as coordinates and distances, are often used as predictors in random...