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Title: Spatiotemporal data clustering : a survey of methods
Authors: Shi, Z 
Pun Cheng, LSC 
Keywords: Clustering
Spatiotemporal data
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: ISPRS international journal of geo-information, 2019, v. 8, no. 3, 112 How to cite?
Journal: ISPRS international journal of geo-information 
Abstract: 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.
EISSN: 2220-9964
DOI: 10.3390/ijgi8030112
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Shi Z, Pun-Cheng LS. Spatiotemporal Data Clustering: A Survey of Methods. ISPRS International Journal of Geo-Information. 2019; 8(3):112 is available at
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