Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

Example mobility graph of London City
Original map image (background) is taken from

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 at 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.

Annex: Dataset:
London dataset (based on georeferenced Twitter data)
Video: Software: Contributors:
Tatiana von Landesberger, Felix Brodkorb, Philipp Roskosch are with Technical University of Darmstadt, Germany.
Natalia Andrienko, Gennady Andrienko are with Fraunhofer IAIS, Bonn, Germany and with City University, London, UK.
Andreas Kerren is with Linnæus University, Växjö, Sweden.

Related Publication:
von Landesberger, T., Brodkorb, F., Roskosch, P., Andrienko, N., Andrienko, G., Kerren, A.
MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering
bib, Preprint PDF, VAST, 2015

Example images (click to enlarge):


Technische Universität Darmstadt

Interactive Graphics Systems Group

Fraunhoferstr. 5
64283 Darmstadt

Tel. +49 6151 155 679

icon email office@gris.tu-

A A A | Print Print | Legal note Legal Notes | Contact Contact
to topto top