The R package SpatialML implements a spatial extension of the random forest algorithm including a geographically weighted random forest (currently for regression).

It is available here: https://CRAN.R-project.org/package=SpatialML

Changelog

Version: 0.1.6 (8 November 2023)

grf: a bug has been fixed.

Version: 0.1.5 (2 September 2022)

grf: The function now supports Geographically Weighted Random Forest regression.

grf.bw: Geographically Weighted Random Forest optimal bandwidth selection

grf.mtry.optim: This function calculates the optimal mtry for a given Random Forest (RF) model in a specified range of values. The optimal mtry value can then be used in the grf model.

Version: 0.1.3 (9 May 2019)

grf: This function refers to a geographical (local) version of the popular Random Forest algorithm

predict.grf: Predict Method for Geographical Random Forest

random.test.data: Random data generator

Datasets

Income: Mean household income at local authorities in Greece in 2011

Download SpatialML

SpatialML is available at the Comprehensive R Archive Network (CRAN): https://CRAN.R-project.org/package=SpatialML. A reference manual for SpatialML is available here…

References

Georganos S, Grippa T, Niang Gadiaga A, Linard C, Lennert M, Vanhuysse S, Mboga N, Wolff E, Kalogirou S. 2021. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto Int. 36(2):121–136. doi: 10.1080/10106049.2019.1595177.

Georganos S, Kalogirou S. 2019. Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests. ISPRS International Journal of Geo-Information. 11(9):471. https://doi.org/10.3390/ijgi11090471)