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Spatiotemporal Data Mining

  • Tao Cheng
  • James Haworth
  • Berk Anbaroglu
  • Garavig Tanaksaranond
  • Jiaqiu Wang
Reference work entry

Abstract

As the number, volume and resolution of spatio-temporal datasets increases, traditional statistical methods for dealing with such data are becoming overwhelmed. Nevertheless, the spatio-temporal data are rich sources of information and knowledge, waiting to be discovered. The field of spatio-temporal data mining (STDM) emerged out of a need to create effective and efficient techniques in order to turn the massive data into meaningful information and knowledge. This chapter reviews the state of the art in STDM research and applications, with emphasis placed on three key areas, including spatio-temporal prediction and forecasting, spatio-temporal clustering and spatio-temporal visualization. The future direction and research challenges of STDM are discussed at the end of this chapter.

Keywords

Cellular Automaton Geographically Weighted Regression Spatial Object Sequential Pattern Mining Thematic Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is part of the STANDARD project – Spatio-Temporal Analysis of Network Data and Road Developments (standard.cege.ucl.ac.uk), supported by the UK Engineering and Physical Sciences Research Council (EP/G023212/1) and Transport for London (TfL).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Tao Cheng
    • 1
  • James Haworth
    • 1
  • Berk Anbaroglu
    • 1
  • Garavig Tanaksaranond
    • 1
  • Jiaqiu Wang
    • 1
  1. 1.SpaceTimeLab, Department of Civil, Environmental and Geomatic EngineeringUniversity College LondonLondonUK

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