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Description
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'' (autocorrelative 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.
Publisher
Utah State University
Publication Date
12-10-2020
Disciplines
Mathematics | Physical Sciences and Mathematics
Recommended Citation
Ancell, Ethan, "Autocart: Spatially-Aware Regression Trees for Ecological and Spatial Modeling" (2020). Fall Student Research Symposium 2020. 1.
https://digitalcommons.usu.edu/fsrs2020/1