The assessment of water quality across space and time is of considerable interest for both agricultural and public health reasons. The standard method to assess the water quality of a sub-catchment, or a group of sub-catchments, usually involves collecting point measurements of water quality and other additional information such as the date and time of measurements, rainfall amounts, the land use and soil type of the catchment and the elevation. Some of this auxiliary information is point-referenced data, measured at the exact location, whereas other such as land use is areal data often recorded in a compositional format at the catchment or sub-catchment level. The spatial change of support inherited by this data collection process breaks the natural link between the response variable and the predictors. In this paper, we present an approach to reconstruct this link by using a categorical latent variable that identifies the land use that most likely influences water quality in each sub-catchment. This constitutes the spatial clustering layer of the model. Each cluster is associated with an estimated temporal variability common to water quality measurements. The strength of this approach lies in the temporal variation identifying each cluster, allowing decision makers to make inform decision regarding land use and its influence over water quality. We demonstrate the potential of this approach with data from a water quality research study in the Mount Lofty range, in South Australia.

中文翻译：

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成分数据的混合模型方法：推断土地利用对点参考水质测量的影响

出于农业和公共卫生的原因，跨空间和时间的水质评估具有相当大的意义。评估一个子集水区或一组子集水区水质的标准方法通常涉及收集水质的点测量和其他附加信息，例如测量的日期和时间、降雨量、土地利用和流域的土壤类型和海拔。其中一些辅助信息是在精确位置测量的点参考数据，而其他如土地利用是区域数据，通常以流域或子流域级别的组合格式记录。此数据收集过程继承的支持空间变化打破了响应变量和预测变量之间的自然联系。在本文中，我们提出了一种通过使用分类潜在变量来重建这种联系的方法，该变量识别最有可能影响每个子集水区水质的土地利用。这构成了模型的空间聚类层。每个集群都与水质测量常见的估计时间可变性相关联。这种方法的优势在于识别每个集群的时间变化，使决策者能够就土地利用及其对水质的影响做出明智的决策。我们使用来自南澳大利亚洛夫蒂山 (Mount Lofty) 山脉的水质研究数据证明了这种方法的潜力。这构成了模型的空间聚类层。每个集群都与水质测量常见的估计时间可变性相关联。这种方法的优势在于识别每个集群的时间变化，使决策者能够就土地利用及其对水质的影响做出明智的决策。我们使用来自南澳大利亚洛夫蒂山 (Mount Lofty) 山脉的水质研究数据证明了这种方法的潜力。这构成了模型的空间聚类层。每个集群都与水质测量常见的估计时间可变性相关联。这种方法的优势在于识别每个集群的时间变化，使决策者能够就土地利用及其对水质的影响做出明智的决策。我们使用来自南澳大利亚洛夫蒂山 (Mount Lofty) 山脉的水质研究数据证明了这种方法的潜力。