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SpatialML (version 1.8.2)

Spatial Machine Learning

Description

Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) ). Provides a Geographically Weighted Random Forest regression and a routine to find the optimal bandwidth (Georganos and Kalogirou (2022) ). A lightweight cross-validation helper for tuning the 'mtry' parameter of a random forest and a generator of synthetic spatial test data are also included. The package depends on 'ranger' as its single random-forest back-end.

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Version

Install

install.packages('SpatialML')

Monthly Downloads

254

Version

1.8.2

License

GPL (>= 2)

Maintainer

Stamatis Kalogirou

Last Published

July 6th, 2026

Functions in SpatialML (1.8.2)

predict.grf

Predict Method for a Geographically Weighted Random Forest
random.test.data

Random Data Generator
Income

Mean Household Income at the Local Authorities of Greece in 2011
grf.bw

Optimal Bandwidth Selection for a Geographically Weighted Random Forest
grf

Geographically Weighted Random Forest Model
rf.mtry.optim

Optimal mtry for a Random Forest via OOB or Cross-Validation
SpatialML-package

Spatial Machine Learning: Geographically Weighted Random Forest