Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>) including a geographically weighted random forest (Georganos S, Kalogirou S. A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests. ISPRS International Journal of Geo-Information. 2022; 11(9):471.


Version: 0.1.5 (2 September 2022)

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


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

Download SpatialML

SpatialML is available at the Comprehensive R Archive Network (CRAN): A reference manual for SpatialML is available here…


Stefanos Georganos, Tais Grippa, Assane Niang Gadiaga, Catherine Linard, Moritz Lennert, Sabine Vanhuysse, Nicholus Mboga, Eléonore Wolff & Stamatis Kalogirou (2019) Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling, Geocarto International, DOI: 10.1080/10106049.2019.1595177