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本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

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分类依据与纠错

AI 根据所提供的论文材料归类:摘要研究自然资源调查的空间抽样设计,不能仅因空间结构而归为网络或其他所列计量方向。

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代码、数据与补充材料

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关注这篇论文的作者
Don L Stevens
Anthony R Olsen

自然资源的空间均衡抽样

研究问题
自然资源调查如何选择在空间上均匀分散的概率样本,并兼顾不等纳入概率及环境抽样中常见的抽样框缺陷?
方法与思路
作者将二维空间映射为一维有序地址,经受限随机化后进行系统抽样,并扩展样本排序以保持连续子集的空间均衡。
材料陈述的贡献
摘要称,策略可容纳基于正辅助变量的不同纳入概率,适用于连续空间和有限总体,并有助于调整抽样框缺陷。
阅读关注点
应核对空间映射与随机化如何保证概率抽样、纳入概率的计算,以及连续编号子集的均衡性质与效率比较条件。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明。摘要未给出方差估计与效率比较结果,空间均衡不能据此被认定在所有场景更有效。

原文摘要

The spatial distribution of a natural resource is an important consideration in designing an efficient survey or monitoring program for the resource. Generally, sample sites that are spatially balanced, that is, more or less evenly dispersed over the extent of the resource, are more efficient than simple random sampling. We review a unified strategy for selecting spatially balanced probability samples of natural resources. The technique is based on creating a function that maps two-dimensional space into one-dimensional space, thereby defining an ordered spatial address. We use a restricted randomization to randomly order the addresses, so that systematic sampling along the randomly ordered linear structure results in a spatially well-balanced random sample. Variable inclusion probability, proportional to an arbitrary positive ancillary variable, is easily accommodated. The basic technique selects points in a two-dimensional continuum, but is also applicable to sampling finite populations or one-dimensional continua embedded in two-dimensional space. An extension of the basic technique gives a way to order the sample points so that any set of consecutively numbered points is in itself a spatially well-balanced sample. This latter property is extremely useful in adjusting the sample for the frame imperfections common in environmental sampling.

笔记与分组