Purpose

The spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.

Design/methodology/approach

In this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.

Findings

The effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.

Originality/value

Traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.

中文翻译：

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非均匀空间点事件的新型双域聚类算法

目的

空间属性和非空间属性是空间点的两个重要特征，它们在许多地理信息系统应用程序中属于两个不同的属性域。双重聚类算法同时考虑了空间和非空间属性，其中一个聚类不仅在空间域具有很高的邻近度，而且在非空间域具有很高的相似性。在地理数据集中，传统的双重空间聚类算法会发现存在集群间不均匀性的同质空间相邻集群，这些空间点在非空间域中描述。为了克服此限制，提出了一种新颖的双域聚类算法（DDCA），该算法考虑了空间接近性和属性相似性以及不均匀性的存在。

设计/方法/方法

在该算法中，首先采用具有边长约束的Delaunay三角剖分来构造对象之间的空间邻近关系。然后，设计了一种基于统计变化检测的聚类策略来获得具有相似属性的聚类。

发现

通过对模拟数据集和真实空间事件的实验说明了该算法的有效性和实用性。研究发现，在考虑非均匀性的前提下，该算法可以自适应，准确地检测具有空间邻近性和相似的非空间属性的聚类。

创意/价值

传统的双重空间聚类算法发现遭受簇间不均匀性影响的同质空间相邻簇，其中这些空间点在非空间域中描述。此处的研究为开发双重空间聚类方法做出了贡献，该方法同时考虑了空间接近性和属性相似性以及不均匀性的存在。对这些聚类的检测有助于了解地理现象的局部模式，例如土地用途分类，空间模式研究和大地理数据分析。