Autocart -- spatially-aware regression trees for ecological and spatial modeling

TL;DR

Autocart introduces spatially-aware regression trees with novel splitting and weighting methods, effectively modeling complex ecological and spatial interactions while maintaining realistic landscape representations.

Contribution

It extends regression tree methods with spatial awareness and adaptive weighting, improving ecological and spatial modeling accuracy.

Findings

Effective modeling of complex spatial interactions

Produces physically realistic landscape representations

Demonstrated success on multiple datasets

Abstract

Many ecological and spatial processes are complex in nature and are not accurately modeled by linear models. Regression trees promise to handle the high-order interactions that are present in ecological and spatial datasets, but fail to produce physically realistic characterizations of the underlying landscape. The "autocart" (autocorrelated regression trees) R package extends the functionality of previously proposed spatial regression tree methods through a spatially aware splitting function and novel adaptive inverse distance weighting method in each terminal node. The efficacy of these autocart models, including an autocart extension of random forest, is demonstrated on multiple datasets. This highlights the ability of autocart to model complex interactions between spatial variables while still providing physically realistic representations of the landscape.