spsurvey: Spatial Sampling Design and Analysis in R

spsurvey: Spatial Sampling Design and Analysis in R

design-based inference, generalized random-tessellation stratified algorithm, HorvitzThompson, inclusion probability, spatial balance, variance estimation.

spsurvey is an R package for design-based statistical inference, with a focus on spatial data. spsurvey provides the generalized random-tessellation stratified (GRTS) algorithm to select spatially balanced samples via the grts() function. The grts() function
flexibly accommodates several sampling design features, including stratification, varying inclusion probabilities, legacy (or historical) sites, minimum distances between sites,
and two options for replacement sites. spsurvey also provides a suite of data analysis
options, including categorical variable analysis (cat_analysis()), continuous variable
analysis (cont_analysis()), relative risk analysis (relrisk_analysis()), attributable
risk analysis (attrisk_analysis()), difference in risk analysis (diffrisk_analysis()),
change analysis (change_analysis()), and trend analysis (trend_analysis()). In this
manuscript, we first provide background for the GRTS algorithm and the analysis approaches and then show how to implement them in spsurvey. We find that the spatially
balanced GRTS algorithm yields more precise parameter estimates than simple random
sampling, which ignores spatial information.

Michael Dumelle

http://jstatsoft.org/article/view/v105i03

United States
Environmental
Protection Agency

January 2022

Fajar bagus W

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