Feature Integration for Geospatial Information: A Review and Outlook
The global trend of ever increasing volumes of digital data is also evident in spatial and spatiotemporal datasets, as it is estimated that approximately 80 percent of all databases have a spatial component. Furthermore the spatial community is witnessing a diversification in data availability, with complementary datasets available from diverse providers. Thus, government agencies using spatial information are now faced with the challenge of using heterogeneous spatial and spatiotemporal datasets. Consequently, there is an ever-increasing need for reliable and highly automated spatial data integration. The integration may be spatial, whereby two or more datasets depicting complementary information for the same area are brought together to extend their coverage or to aggregate their content; or temporal, whereby two or more similar datasets conveying the same thematic information at different time instances are brought together to analyze the evolution of their content. In this chapter we present some of the dominant approaches for these two types of information integration for geospatial applications, both at the spatial and the spatiotemporal domain. We also present some of the recently developed approaches to spatial and spatiotemporal queries, and discuss their role in spatiotemporal pattern detection and analysis.
KeywordsData Integration Spatiotemporal Pattern Spatiotemporal Data Very Large Data Base Spatiotemporal Domain
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