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.
Highlights
Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge
: (1) we provide a taxonomy of spatiotemporal data types; (2) we provide a taxonomy of spatial and spatiotemporal statistics organized by different data types; (3) we survey common computational techniques for all major spatiotemporal pattern families, including spatiotemporal outliers, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots and change patterns
Moving objects on a spatiotemporal network need to be studied from a traveler’s perspective, i.e., the Lagrangian frame of reference [190,191,192] instead of a snapshot view. This is because a traveler moving along a chosen path in a spatiotemporal network would experience a road-segment for the time at which he/she arrives at that segment, which may be distinct from the original departure-time at the start of the journey
Summary
Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Domain interpretation input spatiotemporal data preprocessing, exploratory spacetime analysis spatiotemporal data mining algorithms output patterns post-processing computational spatiotemporal statistical foundation techniques. Spatial and spatiotemporal data science survey without statistical foundation Koperski 1996, Ester 1997, Roddick 1999, Miller 2009, with statistical foundation comprehensive coverage on spatiotemporal pattern families We hope this survey contributes to spatiotemporal data mining research in filling these two gaps. Organization of the paper: This survey starts with the characteristics of the data inputs of spatiotemporal data mining (Section 2) and an overview of its statistical foundation (Section 3) It describes in detail six main output patterns of spatiotemporal data mining related to outliers, association and tele-coupling, prediction, partitioning and summarization, hotspot, and change patterns (Section 4).
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