Spatiotemporal Data Mining: A Computational Perspective

• Published in: ISPRS international journal of geo-information Vol. 4; no. 4; pp. 2306 - 2338
• Main Authors: Shekhar, Shashi, Jiang, Zhe, Ali, Reem, Eftelioglu, Emre, Tang, Xun, Gunturi, Venkata, Zhou, Xun
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-12-2015
• Subjects: review; spatiotemporal data mining; spatiotemporal patterns; spatiotemporal statistics; survey
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi4042306

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.