An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering

Xiaojing Wu, Changxiu Cheng, R. Zurita-Milla, Changqing Song

Research output: Contribution to journalReview articleAcademicpeer-review

28 Citations (Scopus)
274 Downloads (Pure)


Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns.

Original languageEnglish
Pages (from-to)1822-1848
Number of pages27
JournalInternational journal of geographical information science
Issue number9
Early online date16 Feb 2020
Publication statusPublished - 1 Sept 2020


  • Spatio-temporal pattern
  • method selection
  • data mining
  • classification
  • clustering analysis
  • 22/2 OA procedure


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