Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>). Future updates include more local machine learning methods as well as a geographically weighted random forest.

Changelog

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

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