Laboratoire de Mathématiques et de leurs Applications de Pau - MIRA, CNRS/Univ Pau & Pays Adour/E2S UPPA, UMR 5142, 64600, Anglet, France.
Ifremer - Laboratoire Environnement Ressources d'Arcachon, 1 Allée du Parc Montaury, 64600, Anglet, France.
Environ Monit Assess. 2019 Jul 30;191(8):524. doi: 10.1007/s10661-019-7666-y.
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.
一些环境研究使用非概率抽样设计从空间分布的总体中抽取样本。不幸的是，这些样本在统计上很难进行分析，并且会对总体特征产生有偏估计。空间平衡抽样设计是一种概率设计，它将抽样工作均匀地分布在资源上。这些设计对于环境抽样特别有用，因为它们在资源上产生了良好的样本覆盖，具有精确的基于设计的估计量，并且可以潜在地降低抽样成本。最受欢迎的空间平衡设计是广义随机划分分层（GRTS），它具有许多理想的特征，包括空间平衡样本、基于设计的估计量以及选择空间平衡过采样的能力。本文考虑了空间平衡抽样的流行程度，回顾了几种空间平衡抽样设计，并展示了如何在统计编程语言 R 中实现这些设计。我们希望提高空间平衡抽样的知名度，并鼓励环境科学家使用这些设计。
