TY - JOUR
T1 - Spatiotemporal data mining
T2 - A computational perspective
AU - Shekhar, Shashi
AU - Jiang, Zhe
AU - Ali, Reem Y.
AU - Eftelioglu, Emre
AU - Tang, Xun
AU - Gunturi, Venkata M.V.
AU - Zhou, Xun
N1 - Publisher Copyright:
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
PY - 2015/12
Y1 - 2015/12
N2 - 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.
AB - 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.
KW - Review
KW - Spatiotemporal data mining
KW - Spatiotemporal patterns
KW - Spatiotemporal statistics
KW - Survey
UR - http://www.scopus.com/inward/record.url?scp=84952765455&partnerID=8YFLogxK
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U2 - 10.3390/ijgi4042306
DO - 10.3390/ijgi4042306
M3 - Article
AN - SCOPUS:84952765455
SN - 2220-9964
VL - 4
SP - 2306
EP - 2338
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
IS - 4
ER -