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.
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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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Cheng, T., Haworth, J., Anbaroglu, B., Tanaksaranond, G., Wang, J. (2014). Spatiotemporal Data Mining. In: Fischer, M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23430-9_68
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DOI: https://doi.org/10.1007/978-3-642-23430-9_68
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