Spatiotemporal data mining: A computational perspective

Shashi Shekhar, Zhe Jiang, Reem Y. Ali, Emre Eftelioglu, Xun Tang, Venkata M.V. Gunturi, Xun Zhou

Research output: Contribution to journalArticlepeer-review

108 Scopus citations


Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

Original languageEnglish (US)
Pages (from-to)2306-2338
Number of pages33
JournalISPRS International Journal of Geo-Information
Issue number4
StatePublished - Dec 2015

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1029711, IIS-1320580, 0940818 and IIS-1218168, the USDOD under Grant No. HM1582-08-1-0017 and HM0210-13-1-0005, and the University of Minnesota under OVPR U-Spatial. We would like to thank Kim Koffolt for the helpful comments in improving the readability of the paper. We would also like to thank Pradeep Mohan for helpful advice.

Publisher Copyright:
© 2015 by the authors; licensee MDPI, Basel, Switzerland.


  • Review
  • Spatiotemporal data mining
  • Spatiotemporal patterns
  • Spatiotemporal statistics
  • Survey


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