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Sampling Soils in a Heterogeneous Research Plot
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spsurvey: Spatial Sampling Design and Analysis in R.

Michael Dumelle1, Tom Kincaid1, Anthony R Olsen1

  • 1United States Environmental Protection Agency.

Journal of Statistical Software
|February 17, 2023
PubMed
Summary
This summary is machine-generated.

The spsurvey R package uses the generalized random-tessellation stratified (GRTS) algorithm for spatially balanced sampling. This method provides more precise estimates than simple random sampling for spatial data analysis.

Keywords:
Horvitz-Thompsondesign-based inferencegeneralized random-tessellation stratified algorithminclusion probabilityspatial balancevariance estimation

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Area of Science:

  • Spatial statistics
  • Environmental sampling
  • Ecological survey design

Background:

  • Design-based statistical inference is crucial for analyzing spatial data.
  • Existing methods may not fully leverage spatial information for sampling.
  • The generalized random-tessellation stratified (GRTS) algorithm offers a framework for spatially balanced sampling.

Purpose of the Study:

  • Introduce the spsurvey R package for design-based statistical inference with spatial data.
  • Demonstrate the implementation of the GRTS algorithm for sample selection.
  • Showcase the various data analysis functions available within spsurvey.

Main Methods:

  • Utilized the generalized random-tessellation stratified (GRTS) algorithm via the grts() function in R.
  • Incorporated design features such as stratification, varying inclusion probabilities, and minimum distances.
  • Applied analysis functions including categorical and continuous variable analysis, and risk/trend analyses.

Main Results:

  • The GRTS algorithm enables the selection of spatially balanced samples.
  • Spatially balanced sampling using GRTS yields more precise parameter estimates compared to simple random sampling.
  • The spsurvey package provides a comprehensive suite of tools for spatial survey design and analysis.

Conclusions:

  • The spsurvey R package effectively implements the GRTS algorithm for spatial data.
  • GRTS-based sampling enhances the precision of statistical estimates in spatial studies.
  • The package offers valuable tools for researchers working with spatial survey data.