Spatiotemporal Data Mining
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.
KeywordsCellular Automaton Geographically Weighted Regression Spatial Object Sequential Pattern Mining Thematic Attribute
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).
- Box G, Jenkins G (1970) Time series analysis: forecasting and control. Holden-Day, San FranciscoGoogle Scholar
- Cheng T, Haworth J, Wang J (2011a) Spatio-temporal autocorrelation of road network data. J Geograph Syst. http://www.springerlink.com/content/4l84v7072737621p/ Accessed 12 Oct 2011
- Fischer MM (2006) Spatial analysis and geocomputation. Springer, Berlin/ HeidelbergGoogle Scholar
- Gilbert N (2007) Agent-based models. Sage, LondonGoogle Scholar
- Hägerstrand T (1970) What about people in regional science? Papers Reg Sci 24(1):1–12Google Scholar
- Kraak MJ, Klomp A (1995) A classification of cartographic animations: towards a tool for the design of dynamic maps in a gis environment. In: Proceedings of the seminar on teching animated cartography. Madrid, Spain, pp 29–35Google Scholar
- LeSage JP, Pace RK (2011) Pitfalls in higher order model extensions of basic spatial regression methodology. http://www.be.wvu.edu/econ_seminar/documents/11-12/lesage.pdf. Accessed on 15 Nov 2011
- Manley E, Cheng T, Emmonds A (2011) Understanding route choice by using agent-based simulation. In: Proceedings of 11th international conference of geocomputation, London, 20–22 July 2011, pp 54–58Google Scholar
- Miller HJ, Han J (2009) Geographic data mining and knowledge discovery: an overview. In: Miller H, Han J (eds) Geographic data mining and knowledge discovery, 2nd edn. Taylor and Francis, Boca RatonGoogle Scholar
- Thomas JJ, Cook KA (2005) Illuminating the path: the research and development agenda for visual analytics. IEEE, Los AlamitosGoogle Scholar
- Vapnik V (1999) The nature of statistical learning theory, 2nd edn. Springer, LondonGoogle Scholar