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ISPRS Int. J. Geo-Inf. 2012, 1(3), 256-271; doi:10.3390/ijgi1030256

A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic

1
Doctoral College Geographic Information Science, University of Salzburg, Schillerstraße 30, A-5020 Salzburg, Austria
2
Department of Geoinformatics (Z_GIS), University of Salzburg, Hellbrunnerstraße 34, A-5020 Salzburg, Austria
*
Author to whom correspondence should be addressed.
Received: 28 August 2012 / Revised: 29 September 2012 / Accepted: 16 October 2012 / Published: 2 November 2012
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Abstract

In this paper we present a visual analytics approach for deriving spatio-temporal patterns of collective human mobility from a vast mobile network traffic data set. More than 88 million movements between pairs of radio cells—so-called handovers—served as a proxy for more than two months of mobility within four urban test areas in Northern Italy. In contrast to previous work, our approach relies entirely on visualization and mapping techniques, implemented in several software applications. We purposefully avoid statistical or probabilistic modeling and, nonetheless, reveal characteristic and exceptional mobility patterns. The results show, for example, surprising similarities and symmetries amongst the total mobility and people flows between the test areas. Moreover, the exceptional patterns detected can be associated to real-world events such as soccer matches. We conclude that the visual analytics approach presented can shed new light on large-scale collective urban mobility behavior and thus helps to better understand the “pulse” of dynamic urban systems. View Full-Text
Keywords: visual analytics; urban dynamics; urban mobility; social sensing; big data; collective human behavior; spatio-temporal patterns; geographic information science visual analytics; urban dynamics; urban mobility; social sensing; big data; collective human behavior; spatio-temporal patterns; geographic information science
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Sagl, G.; Loidl, M.; Beinat, E. A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic. ISPRS Int. J. Geo-Inf. 2012, 1, 256-271.

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