Spatially balanced sampling designs for environmental surveys.
Environ Monit Assess
; 191(8): 524, 2019 Jul 30.
Article
em En
| MEDLINE
| ID: mdl-31363924
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Monitoramento Ambiental
/
Modelos Estatísticos
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Environ Monit Assess
Assunto da revista:
SAUDE AMBIENTAL
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
França