Spatiotemporal clustering: a review

Mohd Yousuf Ansari, Amir Ahmad, Shehroz S. Khan, Gopal Bhushan, Mainuddin

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


An increase in the size of data repositories of spatiotemporal data has opened up new challenges in the fields of spatiotemporal data analysis and data mining. Foremost among them is “spatiotemporal clustering,” a subfield of data mining that is increasingly becoming popular because of its applications in wide-ranging areas such as engineering, surveillance, transportation, environmental and seismology studies, and mobile data analysis. This review paper presents a comprehensive review of spatiotemporal clustering approaches and their applications as well as a brief tutorial on the taxonomy of data types in the spatiotemporal domain and patterns. Additionally, the data pre-processing techniques, access methods, cluster validation, space–time scan statistics, software tools, and datasets used by various spatiotemporal clustering algorithms are highlighted.

Original languageEnglish
Pages (from-to)2381-2423
Number of pages43
JournalArtificial Intelligence Review
Issue number4
Publication statusPublished - Apr 1 2020


  • Cluster validation
  • Data mining
  • Patterns
  • Spatiotemporal clustering

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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