Autocart -- spatially-aware regression trees for ecological and spatial modeling
This provides a method for ecological and spatial modeling to handle high-order interactions while maintaining physical realism, though it appears incremental as an extension of existing spatial regression tree methods.
The authors tackled the problem of modeling complex ecological and spatial processes where linear models fail and existing regression trees produce unrealistic landscape characterizations, by developing the 'autocart' R package with a spatially aware splitting function and adaptive inverse distance weighting, demonstrating its efficacy on multiple datasets.
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.