Spatiotemporal Data Mining: A Computational Perspective
Shashi Shekhar,Zhe Jiang,Reem Y. Ali,Emre Eftelioglu,Xun Tang,Venkata M. V. Gunturi,Xun Zhou +6 more
TLDR
This survey reviews recent computational techniques and tools in spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families, focusing on several major pattern families.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. 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.read more
Citations
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Journal ArticleDOI
Spatio-Temporal Data Mining: A Survey of Problems and Methods
TL;DR: A broad survey of this relatively young field of spatio-temporal data mining is presented, and literature is classified into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining.
Journal ArticleDOI
Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95
Posted Content
Spatio-Temporal Data Mining: A Survey of Problems and Methods
TL;DR: A broad survey of this relatively young field of spatio-temporal data mining is presented and literature is classified into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining.
Journal ArticleDOI
Deep Learning for Spatio-Temporal Data Mining: A Survey
TL;DR: A comprehensive review of recent progress in applying deep learning techniques for spatio-temporal data mining can be found in this paper , where the authors categorize the spatiotemporal data into five different types, and then briefly introduce the deep learning models that are widely used in STDM.
Journal ArticleDOI
Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow
TL;DR: The results show that the proposed framework is able to detect the real distribution of flow outliers and outperforms the baseline algorithms for high-urban traffic flow.
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