Studying human movement citywide is important for understanding mobility and transportation patterns. Rather than investigating the trajectories of individuals, we employ an Eulerian approach to analyze the crowd flows among a geographical network and a social network, which are extracted from mobile phone data. We design a suite of visualization techniques to illustrate the dynamic evolutions of the flow over the networks. We contribute the design and implementation of a visual analytics system, which is called Mobility Viewer, that supports situation-aware understanding and visual reasoning of human mobility. We exemplify our approach with a real citywide data set of seven million users in two months.
Mobility Viewer: An Eulerian Approach for Studying Urban Crowd Flow
IEEE Transactions on Intelligent Transportation Systems ; 17 , 9 ; 2627-2636
2016-09-01
2028365 byte
Article (Journal)
Electronic Resource
English
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