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arXiv · 2006.00150

Random Spatial Forests

Travis Hee Wai·Michael T. Young·Adam A. Szpiro
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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.

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Travis Hee Wai, Michael T. Young, Adam A. Szpiro. 2020-05-30. Random Spatial Forests. https://arxiv.org/abs/2006.00150

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