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MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering
Technical University of Darmstadt, Germany.
Technical University of Darmstadt, Germany.
Technical University of Darmstadt, Germany.
Fraunhofer IAIS, Germany ; City University, UK.
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2016 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 22, no 1, p. 11-20Article in journal (Refereed) Published
Abstract [en]

Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people in certain time intervals) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods.

We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called MobilityGraphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that MobilityGraphs support the identification of regular daily and weekly movement patterns of resident population.

Place, publisher, year, edition, pages
IEEE, 2016. Vol. 22, no 1, p. 11-20
Keywords [en]
visual analytics, movement data, networks, graphs, temporal aggregation, spatial aggregation, flows, clustering
National Category
Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-45578DOI: 10.1109/TVCG.2015.2468111ISI: 000364043400006Scopus ID: 2-s2.0-84947094033OAI: oai:DiVA.org:lnu-45578DiVA, id: diva2:843884
Conference
IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2015), 25-30 October 2015, Chicago, IL, USA
Available from: 2015-07-31 Created: 2015-07-31 Last updated: 2018-01-11Bibliographically approved

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Kerren, Andreas

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