Money Circulation, Trackable Items, and the Emergence of Universal Human Mobility Patterns

In this article, we report on the discovery of statistical regularities, mathematical laws, and universal characteristics underlying multiscale human mobility. Our study is based on the generation of proxy networks for global human travel behavior from pervasive user data collected at the world's largest bill- tracking Web site and trajectories of trackable items (known as travel bugs) recorded at a geocaching Web. From this pervasive data, we extract multiscale human traffic networks for the US and European countries that cover distances of a few to a few thousand kilometers. Proxy networks permit reliable estimates of statistical features such as degree, flux, and traffic weight distributions. The authors show that despite cultural and national differences, universal properties exist in a diverse set of traffic networks along with important insight into traffic-related phenomena such as the geographic spread of emergent infectious diseases.

[1]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[2]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Jerry P. Gollub,et al.  Advanced Physics in the High Schools , 2002 .

[4]  H. Stanley,et al.  Optimizing the success of random searches , 1999, Nature.

[5]  Alessandro Vespignani,et al.  The effects of spatial constraints on the evolution of weighted complex networks , 2005, physics/0504029.

[6]  D. Cummings,et al.  Strategies for containing an emerging influenza pandemic in Southeast Asia , 2005, Nature.

[7]  C. Gardiner Handbook of Stochastic Methods , 1983 .

[8]  D. Cummings,et al.  Strategies for mitigating an influenza pandemic , 2006, Nature.

[9]  H. Kowarzyk Structure and Function. , 1910, Nature.

[10]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[11]  Alessandro Vespignani,et al.  Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions , 2007, PLoS medicine.

[12]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[14]  P. A. Prince,et al.  Lévy flight search patterns of wandering albatrosses , 1996, Nature.

[15]  Nicolas E. Humphries,et al.  Scaling laws of marine predator search behaviour , 2008, Nature.

[16]  Alessandro Vespignani,et al.  Vulnerability of weighted networks , 2006, physics/0603163.

[17]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[18]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.