Journal article
Spatiotemporal Data Mining: A Computational Perspective
ISPRS international journal of geo-information, Vol.4(4), pp.2306-2338
12/01/2015
DOI: 10.3390/ijgi4042306
Abstract
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.
Details
- Title: Subtitle
- Spatiotemporal Data Mining: A Computational Perspective
- Creators
- Shashi Shekhar - University of MinnesotaZhe Jiang - University of MinnesotaReem Y. Ali - University of MinnesotaEmre Eftelioglu - University of MinnesotaXun Tang - University of MinnesotaVenkata M. V. Gunturi - Indraprastha Institute of Information Technology DelhiXun Zhou - University of Iowa
- Resource Type
- Journal article
- Publication Details
- ISPRS international journal of geo-information, Vol.4(4), pp.2306-2338
- DOI
- 10.3390/ijgi4042306
- ISSN
- 2220-9964
- eISSN
- 2220-9964
- Publisher
- Mdpi
- Number of pages
- 33
- Grant note
- 1029711; IIS-1320580; 0940818; IIS-1218168 / National Science Foundation; National Science Foundation (NSF) HM1582-08-1-0017; HM0210-13-1-0005 / USDOD; United States Department of Defense University of Minnesota under OVPR U-Spatial
- Language
- English
- Date published
- 12/01/2015
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380385002771
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