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ISPRS Int. J. Geo-Inf. 2019, 8(3), 112;

Spatiotemporal Data Clustering: A Survey of Methods

Department of Land Survey and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
Author to whom correspondence should be addressed.
Received: 28 November 2018 / Revised: 13 February 2019 / Accepted: 24 February 2019 / Published: 28 February 2019
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Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as transportation, social media analysis, crime analysis, and human mobility analysis. The development of ST data analysis methods can uncover potentially interesting and useful information. Due to the complexity of ST data and the diversity of objectives, a number of ST analysis methods exist, including but not limited to clustering, prediction, and change detection. As one of the most important methods, clustering has been widely used in many applications. It is a process of grouping data with similar spatial attributes, temporal attributes, or both, from which many significant events and regular phenomena can be discovered. In this paper, some representative ST clustering methods are reviewed, most of which are extended from spatial clustering. These methods are broadly divided into hypothesis testing-based methods and partitional clustering methods that have been applied differently in previous research. Research trends and the challenges of ST clustering are also discussed. View Full-Text
Keywords: clustering; spatiotemporal data; survey clustering; spatiotemporal data; survey

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Shi, Z.; Pun-Cheng, L.S. Spatiotemporal Data Clustering: A Survey of Methods. ISPRS Int. J. Geo-Inf. 2019, 8, 112.

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