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Activity Number: 322 - Analyses in Ecology, Epidemiology, and Environmental Policy
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #318068
Title: Autocart: Spatially Aware Regression Trees for Ecological and Spatial Modeling
Author(s): Ethan Ancell* and Brennan Bean
Companies: Utah State University and Utah State University
Keywords: Machine learning; ecology; spatial statistics; regression trees; autocorrelation
Abstract:

Many ecological and spatial processes are complex in nature and are not accurately characterized by linear models. Machine learning methods promise to handle the high-order interactions that are present in ecological and spatial datasets but often fail to produce physically realistic characterizations of the underlying landscape. The "autocart" (autocorrelated regression trees) R package extends the functionality of existing spatial regression tree methods through a spatially aware splitting function and adaptive inverse distance weighting interpolation in each terminal node. The efficacy of autocart is demonstrated on several spatial datasets. In each case, the autocart approach produces a physically realistic representation of the response variable across geographical space without sacrificing model accuracy. Most importantly, the applications demonstrate the computational feasibility and accessibility of spatially aware regression trees for general use in R.


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