Spbsampling:一个空间平衡采样的R包

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Francesco Pantalone, R. Benedetti, Federica Pierismoni
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引用次数: 1

摘要

支撑空间平衡抽样理论的基本思想是,距离较近的单位比距离较远的单位提供的关于推断目标的信息较少。因此,最好是选择一个分布在总体上的样本,或者一个空间平衡的样本。这种情况在环境、地质、生物和农业调查中很容易理解,在这些调查中,人口的主要特征通常是地理参考。由于传统的抽样设计通常不利用空间特征,并且由于考虑到有关空间依赖性的信息是可取的,为了实现这一目标,已经开发了几种抽样设计。在本文中,我们介绍了R包Spbsampling,它提供了执行三种特定采样设计的功能,以实现上述目的。特别是,这些采样设计使用距离矩阵的汇总索引来实现空间平衡样本。从这个意义上说,包的适用性要广泛得多,因为距离矩阵可以根据不同于地理坐标的变量来定义单位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spbsampling: An R Package for Spatially Balanced Sampling
The basic idea underpinning the theory of spatially balanced sampling is that units closer to each other provide less information about a target of inference than units farther apart. Therefore, it should be desirable to select a sample well spread over the population of interest, or a spatially balanced sample . This situation is easily understood in, among many others, environmental, geological, biological, and agricultural surveys, where usually the main feature of the population is to be geo-referenced. Since traditional sampling designs generally do not exploit the spatial features and since it is desirable to take into account the information regarding spatial dependence, several sampling designs have been developed in order to achieve this objective. In this paper, we present the R package Spbsampling , which provides functions in order to perform three specific sampling designs that pursue the aforementioned purpose. In particular, these sampling designs achieve spatially balanced samples using a summary index of the distance matrix. In this sense, the applicability of the package is much wider, as a distance matrix can be defined for units according to variables different than geographical coordinates.
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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