Modeling Moving Objects over Multiple Granularities
Annals of Mathematics and Artificial Intelligence
IEEE Computer Graphics and Applications
The haloed line effect for hidden line elimination.
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IEEE Transactions on Visualization and Computer Graphics
Visually driven analysis of movement data by progressive clustering
Information Visualization
Towards a taxonomy of movement patterns
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IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
Spatial Generalization and Aggregation of Massive Movement Data
IEEE Transactions on Visualization and Computer Graphics
Interactive visualization of multivariate trajectory data with density maps
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
IEEE Transactions on Visualization and Computer Graphics
Composite Density Maps for Multivariate Trajectories
IEEE Transactions on Visualization and Computer Graphics
Transforming GIS Data into Functional Road Models for Large-Scale Traffic Simulation
IEEE Transactions on Visualization and Computer Graphics
Visualization of vessel movements
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
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Massive amount of movement data, such as daily trips made by millions of passengers in a city, are widely available nowadays. They are a highly valuable means not only for unveiling human mobility patterns, but also for assisting transportation planning, in particular for metropolises around the world. In this paper, we focus on a novel aspect of visualizing and analyzing massive movement data, i.e., the interchange pattern, aiming at revealing passenger redistribution in a traffic network. We first formulate a new model of circos figure, namely the interchange circos diagram, to present interchange patterns at a junction node in a bundled fashion, and optimize the color assignments to respect the connections within and between junction nodes. Based on this, we develop a family of visual analysis techniques to help users interactively study interchange patterns in a spatiotemporal manner: 1) multi-spatial scales: from network junctions such as train stations to people flow across and between larger spatial areas; and 2) temporal changes of patterns from different times of the day. Our techniques have been applied to real movement data consisting of hundred thousands of trips, and we present also two case studies on how transportation experts worked with our interface.