Spatiotemporal Data Mining: A Computational Perspective

ISPRS International Journal of Geo-Information

Shashi Shekhar¹  Zhe Jiang¹  Reem Y. Ali¹  Emre Eftelioglu¹  Xun Tang¹  Venkata M. V. Gunturi⁴  Xun Zhou³  Emmanuel Stefanakis² 

[1] Department of Computer Science and Engineering, University of Minnesota, Twin Cities. 4-192, Keller Hall, 200 Union St. SE, Minneapolis, MN 55455, USA; E-Mails:Department of Computer Science and Engineering, University of Minnesota, Twin Cities. 4-192, Keller Hall, 200 Union St. SE, Minneapolis, MN 55455, USA;;Department of Management Sciences, University of Iowa. S210 John Pappajohn Business Building, Iowa City, IA 52242, USA; E-Mail:;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; E-Mail:

关键词: spatiotemporal data mining;    survey;    review;    spatiotemporal statistics;    spatiotemporal patterns;   

DOI  :  10.3390/ijgi4042306

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.