ITR: Advanced Algorithms for Spatial-Temporal Interactions in Distributed GIS

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This research focuses on the analysis of spatial interactions in distributed GIS environments. Data related to systems of spatial interactions includes spatial flows and locational and attribute data pertaining to origins and destinations. Given that these datasets are distributed across multiple nodes of a computer network, this research aims to: 1) study mechanisms of data partitioning and develop a metadata structure that describes the partition; 2) develop decomposable algorithms for gravity modeling that minimize communication cost; and 3) develop efficient algorithms for learning and classifying flow patterns using distributed data sources. The approach is to let the databases reside at their native sites. The algorithms dynamically decompose themselves into partial computations that are executed at individual database sites, and local results are composed to obtain the same global results that would have been obtained if the databases had been moved to a common site. The algorithms can find the decompositions to match the distribution of data across the network. This research will have significant impact on many problems that need to process distributed data. For example, it can enable GIS systems to analyze spatial-temporal datasets distributed over the Internet without having to move the databases to a common site.