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.


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    Title :

    Mobility Viewer: An Eulerian Approach for Studying Urban Crowd Flow


    Contributors:
    Ma, Yuxin (author) / Lin, Tao (author) / Cao, Zhendong (author) / Li, Chen (author) / Wang, Fei (author) / Chen, Wei (author)


    Publication date :

    2016-09-01


    Size :

    2028365 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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