Geographically weighted methods and their use in network re-designs for environmental monitoring
Geographically weighted methods and their use in network re-designs for environmental monitoring
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DOI：
10.1007/s00477-014-0851-1
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发表时间：
2014-02
影响因子：
4.2
通讯作者：
P. Harris;Annemarie L. Clarke;S. Juggins;C. Brunsdon;M. Charlton
中科院分区：
文献类型：
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作者：
P. Harris;Annemarie L. Clarke;S. Juggins;C. Brunsdon;M. Charlton
给定初始空间采样活动，通常重要的是基于第一次的特性进行第二次更有针对性的活动。在这里，网络重新设计通过添加和/或删除站点来修改第一个，以便保留最大信息。通常，这种优化受到有限的抽样资金的限制，并寻求减少样本网络。在这个程度上，我们展示了使用地理加权的方法结合位置分配算法，作为一种手段，设计一个第二阶段的抽样活动，在单变量，双变量和多变量的背景下。作为一个案例研究，我们使用的淡水化学数据集，涵盖了大不列颠。应用两阶段的程序，使一个预先指定的网站数量的最佳识别，提供最大的空间和单变量/双变量/多变量的水化学信息的第二个运动。还进行了考虑到淡水地点对酸化的缓冲能力的网络重新设计。为了补充基本方法的使用，稳健的替代方案被用来减少异常观测对重新设计的影响。我们的非平稳重新设计框架是通用的，并提供了一个相对简单和可行的替代地统计重新设计程序，通常采用。特别是在多元的情况下，它代表了一个重要的方法进步。
Given an initial spatial sampling campaign, it is often of importance to conduct a second, more targeted campaign based on the properties of the first. Here a network re-design modifies the first one by adding and/or removing sites so that maximum information is preserved. Commonly, this optimisation is constrained by limited sampling funds and a reduced sample network is sought. To this extent, we demonstrate the use of geographically weighted methods combined with a location-allocation algorithm, as a means to design a second-phase sampling campaign in univariate, bivariate and multivariate contexts. As a case study, we use a freshwater chemistry data set covering much of Great Britain. Applying the two-stage procedure enables the optimal identification of a pre-specified number of sites, providing maximum spatial and univariate/bivariate/multivariate water chemistry information for the second campaign. Network re-designs that account for the buffering capacity of a freshwater site to acidification are also conducted. To complement the use of basic methods, robust alternatives are used to reduce the effect of anomalous observations on the re-designs. Our non-stationary re-design framework is general and provides a relatively simple and a viable alternative to geostatistical re-design procedures that are commonly adopted. Particularly in the multivariate case, it represents an important methodological advance.