Geographical data are generally autocorrelated. In this case, it is preferable to select spread units. In this paper, we propose a new method for selecting well-spread samples from a finite spatial population with equal or unequal inclusion probabilities. The proposed method is based on the definition of a spatial structure by using a stratification matrix. Our method exactly satisfies given inclusion probabilities and provides samples that are very well spread. A set of simulations shows that our method outperforms other existing methods such as the generalized random tessellation stratified or the local pivotal method. Analysis of the variance on a real dataset shows that our method is more accurate than these two. Furthermore, a variance estimator is proposed.

中文翻译：

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使用弱关联向量的空间扩展采样

地理数据通常是自相关的。在这种情况下，最好选择扩展单元。在本文中，我们提出了一种新方法，用于从具有相等或不等包含概率的有限空间总体中选择分布良好的样本。所提出的方法基于通过使用分层矩阵来定义空间结构。我们的方法完全满足给定的包含概率，并提供分布非常好的样本。一组模拟表明，我们的方法优于其他现有方法，例如广义随机细分分层或局部关键方法。对真实数据集的方差分析表明，我们的方法比这两种方法更准确。此外，还提出了方差估计器。