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Nat Commun. 2015 Jan 21;6:6007. doi: 10.1038/ncomms7007.

Uncovering the spatial structure of mobility networks.

Author information

1
1] Institut de Physique Théorique, CEA-CNRS (URA 2306), Orme-des-Merisiers Batiment 774, F-91191 Paris, France [2] Géographie-Cités, CNRS-Paris 1-Paris 7 (UMR 8504), 13 rue du four, FR-75006 Paris, France.
2
IFISC, Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus Universitat de les Illes Balears, Palma de Mallorca E-07122, Spain.
3
Nommon Solutions and Technologies, calle Cañas 8, Madrid E-28043, Spain.
4
Telefonica Research, Madrid E-28050, Spain.
5
1] Institut de Physique Théorique, CEA-CNRS (URA 2306), Orme-des-Merisiers Batiment 774, F-91191 Paris, France [2] Centre d'Analyse et de Mathématique Sociales, EHESS-CNRS (UMR 8557), 190-198 avenue de France, FR-75013 Paris, France.

Abstract

The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has relevance for many applications. An important example is seen in origin-destination matrices, which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method, which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in 31 Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally, the method allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure.

PMID:
25607690
DOI:
10.1038/ncomms7007

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