Spatially balanced sampling designs for environmental surveys

Type Article
Date 2019-08
Language English
Author(s) Kermorvant Claire1, D’amico Frank1, Bru Noëlle1, Caill-Milly NathalieORCID2, Robertson Blair3
Affiliation(s) 1 : Laboratoire de Mathématiques et de leurs Applications de Pau – MIRACNRS/Univ Pau & Pays Adour/E2S UPPA, Anglet, France
2 : Ifremer, Laboratoire Environnement Ressources d’Arcachon,Anglet, France
3 : School of Mathematics and StatisticsUniversity of CanterburyChristchurch, New Zealand
Source Environmental Monitoring And Assessment (0167-6369) (Springer Science and Business Media LLC), 2019-08 , Vol. 191 , N. 8 , P. 524 (7p.)
DOI 10.1007/s10661-019-7666-y
WOS© Times Cited 18
Keyword(s) BAS, GRTS, LPM, Probabilistic sampling, Spatially balanced

Some environmental studies use non-probabilistic sampling designs to draw samples from spatially distributed populations. Unfortunately, these samples can be difficult to analyse statistically and can give biased estimates of population characteristics. Spatially balanced sampling designs are probabilistic designs that spread the sampling effort evenly over the resource. These designs are particularly useful for environmental sampling because they produce good-sample coverage over the resource, they have precise design-based estimators and they can potentially reduce the sampling cost. The most popular spatially balanced design is Generalized Random Tessellation Stratified (GRTS), which has many desirable features including a spatially balanced sample, design-based estimators and the ability to select spatially balanced oversamples. This article considers the popularity of spatially balanced sampling, reviews several spatially balanced sampling designs and shows how these designs can be implemented in the statistical programming language R. We hope to increase the visibility of spatially balanced sampling and encourage environmental scientists to use these designs.

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