The detection of spatial clusters and outliers is critical to a number of spatial data analysis techniques. Many techniques embed spatial clustering components with the aim of exploring spatial variability and patterns in a data set, caused by the spatial association that generally affects most spatial data. A frontier challenge in spatial data analysis is to extend techniques—originally designed for univariate analysis—to a multivariate context, in order to be able to cope with the increasing complexity and variety of modern spatial data. This article proposes an exploratory procedure to detect and classify clusters and outliers in a multivariate spatial data set. Cluster and outlier detection relies on recently introduced multivariate extensions of the well‐established local indicators of spatial association statistics. Two new indicators are proposed enabling the classification of multivariate clusters and outliers, not directly achievable with any already established technique. The procedure is fully implemented using free and open source geospatial software and libraries. The raw source code is made available for future reviews and replications. Empirical results from early applications on both synthetic and real spatial data are discussed. Advantages and limitations of the introduced procedure are outlined according to the empirical results.

中文翻译：

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局部多元聚类和空间关联离群的分类技术

空间聚类和离群值的检测对于许多空间数据分析技术至关重要。许多技术都嵌入了空间聚类组件，其目的是探索数据集中的空间变异性和模式，这是由通常影响大多数空间数据的空间关联引起的。空间数据分析的一项前沿挑战是将最初为单变量分析设计的技术扩展到多变量环境，以便能够应对不断增长的复杂性和现代空间数据的多样性。本文提出了一种探索性程序，用于检测和分类多元空间数据集中的聚类和离群值。聚类和离群值检测依赖于最近引入的对空间关联统计数据的公认局部指标的多元扩展。提出了两个新的指标，可以对多元聚类和离群值进行分类，而这是任何现有技术都无法直接实现的。使用免费和开源的地理空间软件和库可以完全实现该过程。原始源代码可用于将来的检查和复制。讨论了早期应用在合成和实际空间数据上的经验结果。根据经验结果概述了引入方法的优点和局限性。讨论了早期应用在合成和实际空间数据上的经验结果。根据经验结果概述了引入方法的优点和局限性。讨论了早期应用在合成和实际空间数据上的经验结果。根据经验结果概述了引入方法的优点和局限性。