Primitives for the manipulation of general subdivisions and the computation of Voronoi
ACM Transactions on Graphics (TOG)
Effect ordering for data displays
Computational Statistics & Data Analysis - Data visualization
A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Constructing and reconstructing the reorderable matrix
Information Visualization
Semiology of graphics
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
IEEE Computer Graphics and Applications
Generating Graphs for Visual Analytics through Interactive Sketching
IEEE Transactions on Visualization and Computer Graphics
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
Spatial ordering and encoding for geographic data mining and visualization
Journal of Intelligent Information Systems
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Visual analytics apporach to user-controlled evacuation scheduling
Information Visualization - Special issue on visual analytics science and technology
A pandemic influenza modeling and visualization tool
Journal of Visual Languages and Computing
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Inferring human mobility patterns from anonymized mobile communication usage
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
Visual Mobility Analysis using T-Warehouse
International Journal of Data Warehousing and Mining
International Journal of Multimedia Data Engineering & Management
| Hi-index | 0.00 |
Population mobility, i.e. the movement and contact of individuals across geographic space, is one of the essential factors that determine the course of a pandemic disease spread. This research views both individual-based daily activities and a pandemic spread as spatial interaction problems, where locations interact with each other via the visitors that they share or the virus that is transmitted from one place to another. The research proposes a general visual analytic approach to synthesize very large spatial interaction data and discover interesting (and unknown) patterns. The proposed approach involves a suite of visual and computational techniques, including (1) a new graph partitioning method to segment a very large interaction graph into a moderate number of spatially contiguous subgraphs (regions); (2) a reorderable matrix, with regions 'optimally' ordered on the diagonal, to effectively present a holistic view of major spatial interaction patterns; and (3) a modified flow map, interactively linked to the reorderable matrix, to enable pattern interpretation in a geographical context. The implemented system is able to visualize both people's daily movements and a disease spread over space in a similar way. The discovered spatial interaction patterns provide valuable insight for designing effective pandemic mitigation strategies and supporting decision-making in time-critical situations.