Robertson B L, Brown J A, McDonald T, Jaksons P
Department of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
Biometrics. 2013 Sep;69(3):776-84. doi: 10.1111/biom.12059. Epub 2013 Jul 11.
To design an efficient survey or monitoring program for a natural resource it is important to consider the spatial distribution of the resource. Generally, sample designs that are spatially balanced are more efficient than designs which are not. A spatially balanced design selects a sample that is evenly distributed over the extent of the resource. In this article we present a new spatially balanced design that can be used to select a sample from discrete and continuous populations in multi-dimensional space. The design, which we call balanced acceptance sampling, utilizes the Halton sequence to assure spatial diversity of selected locations. Targeted inclusion probabilities are achieved by acceptance sampling. The BAS design is conceptually simpler than competing spatially balanced designs, executes faster, and achieves better spatial balance as measured by a number of quantities. The algorithm has been programed in an R package freely available for download.
为自然资源设计一个高效的调查或监测项目时，考虑资源的空间分布非常重要。一般来说，空间平衡的抽样设计比非空间平衡的设计效率更高。空间平衡设计会选择一个在资源范围内均匀分布的样本。在本文中，我们提出了一种新的空间平衡设计，可用于从多维空间中的离散和连续总体中选择样本。我们将这种设计称为平衡验收抽样，它利用哈尔顿序列来确保所选位置的空间多样性。通过验收抽样实现目标包含概率。与其他空间平衡设计相比，平衡验收抽样设计在概念上更简单，执行速度更快，并且从多个数量指标衡量能实现更好的空间平衡。该算法已被编写成一个可免费下载的R包程序。
