remap: Regionalized Models with Spatially Smooth Predictions

Traditional spatial modeling approaches assume that data are second-order stationary, which is rarely true over large geographical areas. A simple way to model nonstationary data is to partition the space and build models for each region in the partition. This has the side effect of creating discontinuities in the prediction surface at region borders. The regional border smoothing approach ensures continuous predictions by using a weighted average of predictions from regional models. The R package remap is an implementation of regional border smoothing that builds a collection of spatial models. Special consideration is given to distance calculations that make remap package scalable to large problems. Using the remap package, as opposed to global spatial models, results in improved prediction accuracy on test data. These accuracy improvements, coupled with their computational feasibility, illustrate the efficacy of the remap approach to modeling nonstationary data.

Jadon Wagstaff (Huntsman Cancer Institute, University of Utah) , Brennan Bean (Utah State University)

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For attribution, please cite this work as

Wagstaff & Bean, "remap: Regionalized Models with Spatially Smooth Predictions", The R Journal, 2023

BibTeX citation

  author = {Wagstaff, Jadon and Bean, Brennan},
  title = {remap: Regionalized Models with Spatially Smooth Predictions},
  journal = {The R Journal},
  year = {2023},
  note = {},
  doi = {10.32614/RJ-2023-004},
  volume = {14},
  issue = {4},
  issn = {2073-4859},
  pages = {160-178}