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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    Titel :

    Mobility Viewer: An Eulerian Approach for Studying Urban Crowd Flow




    Erscheinungsdatum :

    2016




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik



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