Spatiotemporal Data Mining: A Computational Perspective
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Department of Computer Science and Engineering, University of Minnesota, Twin Cities. 4-192, Keller Hall, 200 Union St. SE, Minneapolis, MN 55455, USA
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Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi. B-402 Academic Block, IIIT-Delhi Okhla, Phase III, New Delhi 110020, India
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Department of Management Sciences, University of Iowa. S210 John Pappajohn Business Building, Iowa City, IA 52242, USA
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Author to whom correspondence should be addressed.
Academic Editors: Emmanuel Stefanakis and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2306-2338; https://doi.org/10.3390/ijgi4042306
Received: 8 June 2015 / Revised: 20 September 2015 / Accepted: 12 October 2015 / Published: 28 October 2015
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
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. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.
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Keywords:
spatiotemporal data mining; survey; review; spatiotemporal statistics; spatiotemporal patterns
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MDPI and ACS Style
Shekhar, S.; Jiang, Z.; Ali, R.Y.; Eftelioglu, E.; Tang, X.; Gunturi, V.M.V.; Zhou, X. Spatiotemporal Data Mining: A Computational Perspective. ISPRS Int. J. Geo-Inf. 2015, 4, 2306-2338. https://doi.org/10.3390/ijgi4042306
AMA Style
Shekhar S, Jiang Z, Ali RY, Eftelioglu E, Tang X, Gunturi VMV, Zhou X. Spatiotemporal Data Mining: A Computational Perspective. ISPRS International Journal of Geo-Information. 2015; 4(4):2306-2338. https://doi.org/10.3390/ijgi4042306
Chicago/Turabian StyleShekhar, Shashi, Zhe Jiang, Reem Y. Ali, Emre Eftelioglu, Xun Tang, Venkata M.V. Gunturi, and Xun Zhou. 2015. "Spatiotemporal Data Mining: A Computational Perspective" ISPRS International Journal of Geo-Information 4, no. 4: 2306-2338. https://doi.org/10.3390/ijgi4042306
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