| Registro completo |
| Provedor de dados: |
ArchiMer
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| País: |
France
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| Título: |
Spatially balanced sampling designs for environmental surveys
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| Autores: |
Kermorvant, Claire
D’amico, Frank
Bru, Noëlle
Caill-milly, Nathalie
Robertson, Blair
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| Data: |
2019-08
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| Ano: |
2019
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| Palavras-chave: |
BAS
GRTS
LPM
Probabilistic sampling
Spatially balanced
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| Resumo: |
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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| Tipo: |
Text
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| Idioma: |
Inglês
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| Identificador: |
https://archimer.ifremer.fr/doc/00509/62063/66268.pdf
DOI:10.1007/s10661-019-7666-y
https://archimer.ifremer.fr/doc/00509/62063/
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| Editor: |
Springer Science and Business Media LLC
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| Formato: |
application/pdf
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| Fonte: |
Environmental Monitoring And Assessment (0167-6369) (Springer Science and Business Media LLC), 2019-08 , Vol. 191 , N. 8 , P. 524 (7p.)
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| Direitos: |
info:eu-repo/semantics/openAccess
restricted use
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