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Spatiotemporal clustering: a review

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Abstract

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.

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Acknowledgements

This work is supported by Ministry of Electronics and Information Technology, Government of India under the Visvesvaraya Ph.D. scheme.

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Authors and Affiliations

  1. Defence Scientific Information and Documentation Centre (DESIDOC), Defence R&D Organisation (DRDO), Metcalfe House, New Delhi, Delhi, 110054, India

    Mohd Yousuf Ansari & Gopal Bhushan

  2. College of Information Technology, United Arab Emirates University, Al Ain, UAE

    Amir Ahmad

  3. Toronto Rehabilitation Institute, University Health Network, Toronto, Canada

    Shehroz S. Khan

  4. Department of Electronics and Communication, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, 110025, India

    Mainuddin

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  1. Mohd Yousuf Ansari
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  2. Amir Ahmad
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  3. Shehroz S. Khan
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  4. Gopal Bhushan
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  5. Mainuddin
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Correspondence to Mainuddin.

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Ansari, M.Y., Ahmad, A., Khan, S.S. et al. Spatiotemporal clustering: a review. Artif Intell Rev 53, 2381–2423 (2020). https://doi.org/10.1007/s10462-019-09736-1

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