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Spatio-temporal Data Mining

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Handbook of Regional Science

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Abstract

As the volume, variety, and veracity of spatio-temporal datasets increase, traditional statistical methods for dealing with such data are becoming overwhelmed. Nevertheless, spatio-temporal data are rich sources of information and knowledge, waiting to be discovered. The field of spatio-temporal data mining emerged out of a need to create effective and efficient techniques in order to turn big spatio-temporal data into meaningful information and knowledge. This chapter reviews the state of the art in spatio-temporal data mining research and applications, from conventional statistical methods to machine learning approaches in the big data era, with emphasis placed on three key areas: prediction, clustering/classification, and visualization.

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Author information

Authors and Affiliations

  1. SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College, London, UK

    Tao Cheng & James Haworth

  2. Department of Geomatics Engineering, Hacettepe University, Ankara, Turkey

    Berk Anbaroglu

  3. Department of Survey Engineering, Chulalongkorn University, Bangkok, Thailand

    Garavig Tanaksaranond

  4. National Institute for Cardiovascular Outcomes Research, Barts NHS Trust, London, UK

    Jiaqiu Wang

Authors
  1. Tao Cheng
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  2. James Haworth
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  3. Berk Anbaroglu
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  4. Garavig Tanaksaranond
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  5. Jiaqiu Wang
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Corresponding author

Correspondence to Tao Cheng .

Editor information

Editors and Affiliations

  1. Institute for Economic Geography and GIScience, Vienna University of Economics and Business, Vienna, Austria

    Manfred M. Fischer

  2. and Business Administration Department of Spatial Economics, Free University Faculty of Economics, Amsterdam, The Netherlands

    Peter Nijkamp

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Cheng, T., Haworth, J., Anbaroglu, B., Tanaksaranond, G., Wang, J. (2019). Spatio-temporal Data Mining. In: Fischer, M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36203-3_68-1

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  • DOI: https://doi.org/10.1007/978-3-642-36203-3_68-1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36203-3

  • Online ISBN: 978-3-642-36203-3

  • eBook Packages: Springer Reference Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

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