A novel dual-domain clustering algorithm for inhomogeneous spatial point event
Data Technologies and Applications
ISSN: 2514-9288
Article publication date: 26 October 2020
Issue publication date: 2 November 2020
Abstract
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.
Keywords
Acknowledgements
The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions, which greatly helped to improve the quality of the manuscript.Funding: This research was funded by the Talent research start-up fund project of Nanjing Forestry University, grant number GXL2018049.
Citation
Zhu, J., Yang, J., Di, S., Zheng, J. and Zhang, L. (2020), "A novel dual-domain clustering algorithm for inhomogeneous spatial point event", Data Technologies and Applications, Vol. 54 No. 5, pp. 603-623. https://doi.org/10.1108/DTA-08-2019-0142
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited