Random Spatial Forests
Abstract
We introduce random spatial forests, a method of bagging regression trees allowing for spatial correlation. Our main contribution is the development of a computationally efficient tree building algorithm which selects each split of the tree adjusting for spatial correlation. We evaluate two different approaches for estimation of random spatial forests, a pseudo-likelihood approach combining random forests with kriging and a non-parametric version for a general class of spatial smoothers. We show improved prediction accuracy of our method compared to existing two-step approaches combining random forests and kriging across a range of numerical simulations and demonstrate its performance on elemental carbon, organic carbon, silicon, and sulfur measurements across the continental United States from 2009-2010.
Cite
@article{arxiv.2006.00150,
title = {Random Spatial Forests},
author = {Travis Hee Wai and Michael T. Young and Adam A. Szpiro},
journal= {arXiv preprint arXiv:2006.00150},
year = {2020}
}