Regionalized Models with Spatially Continuous Predictions at the Borders

Wagstaff, Jadon

doi:10.26076/f25f-3104

Search citation statements

Order By: Relevance

Paper Sections

Select...

Computer Code Availability and Competing Interests1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. This article is based on the first author's master's thesis (Wagstaff, 2021). 2 The regional border smoothing approach Regional border smoothing is the process of using regional models to make predictions that are globally continuous.…”

Section: Computer Code Availability and Competing Interestsmentioning

confidence: 99%

remap: Regionalized Models with Spatially Smooth Predictions

Wagstaff

Bean

2023

The R Journal

View full text Add to dashboard Cite

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.

show abstract

Section: Computer Code Availability and Competing Interestsmentioning

confidence: 99%