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首页> 外文会议>International symposium on spatial accuracy assessment in natural resources and environmental science >Spatially-restricted Random Sampling Designs for Design-based and Model-based Estimation
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Spatially-restricted Random Sampling Designs for Design-based and Model-based Estimation

机译：用于基于设计和模型的估计的空间限制随机采样设计

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Studying natural resources and environmental problems in most situations requires that information be collected over space and time. Given the typical infeasibility of acquiring data continuously in space, a scheme for site selection is necessary. We focus on probability designs that incorporate randomization in site selection. Traditional designs include simple random sampling, spatially stratified random sampling, and systematic sampling. In some cases, survey designs have constructed a linear ordering of two-dimensional space and then used systematic sampling of the linear ordering to help spread the sample over space. Hierarchial randomization designs explicitly incorporate space in the randomization process. The resulting designs have better spatial properties in their ability to match the spatial pattern of the population being sampled. We provide a comparison of these properties with the traditional designs. We discuss how these designs spatially distribute sites, and how both design-based and model-based statistical inferences can be applied.
机译：在大多数情况下研究自然资源和环境问题要求在空间和时间内收集信息。鉴于在空间中连续获取数据的典型不可行度，需要一种用于站点选择的方案。我们专注于在网站选择中包含随机化的概率设计。传统设计包括简单的随机采样，空间分层随机采样和系统采样。在某些情况下，调查设计已经构建了二维空间的线性排序，然后使用线性排序的系统采样来帮助将样品扩散到空间上。分层随机化设计明确地合并随机化过程中的空间。由此产生的设计具有更好的空间特性，其能够匹配被采样的人口的空间模式。我们提供了与传统设计的这些性质的比较。我们讨论这些设计的空间分配网站，以及如何应用基于设计和基于模型的统计推论。

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