Spatial-social network visualization for exploratory data analysis
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Visual comparison for information visualization
Information Visualization - Special issue on State of the Field and New Research Directions
Flowstrates: an approach for visual exploration of temporal origin-destination data
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visualizing interchange patterns in massive movement data
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many variables are associated with each flow, such as the number of migrants for different age groups, income levels, and occupations. It is a challenging task to visualize such data and discover network structures, multivariate relations, and their geographic patterns simultaneously. This paper addresses these challenges by developing an integrated interactive visualization framework that consists three coupled components: (1) a spatially constrained graph partitioning method that can construct a hierarchy of geographical regions (communities), where there are more flows or connections within regions than across regions; (2) a multivariate clustering and visualization method to detect and present multivariate patterns in the aggregated region-to-region flows; and (3) a highly interactive flow mapping component to map both flow and multivariate patterns in the geographic space, at different hierarchical levels. The proposed approach can process relatively large data sets and effectively discover and visualize major flow structures and multivariate relations at the same time. User interactions are supported to facilitate the understanding of both an overview and detailed patterns.