Building and analyzing the US airport network based on en-route location information

From a complex network perspective, this study sets out two aims around the US airport network (USAN) which is built from en-route location information of domestic flights in the US. First, we analyze the structural properties of the USAN with respect to its binary and weighted graphs, and second we explore the airport patterns, which have wide-ranging implications. Results from the two graphs indicate the following. (1) The USAN exhibits scale-free, small-world and disassortative mixing properties, which are consistent with the mainstream perspectives. Besides, we find (2) a remarkable power relationship between the structural measurements in the binary graph and the traffic measurements in the weighted counterpart, namely degree versus capacity and attraction versus volume. On the other hand, investigation of the airport patterns suggests (3) that all the airports can be classified into four categories based on multiple network metrics, which shows a complete typology of the airports. And it further indicates (4) that there is a subtle relationship between the airport traffic and the geographical constraints as well as the regional socioeconomic indicators.

[1]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[2]  M. Guida,et al.  Topology of the Italian airport network: A scale-free small-world network with a fractal structure? , 2007 .

[3]  Hernán D. Rozenfeld,et al.  Laws of population growth , 2008, Proceedings of the National Academy of Sciences.

[4]  Daoli Zhu,et al.  Empirical analysis of the worldwide maritime transportation network , 2008, 0806.0472.

[5]  Michael T. Gastner,et al.  The complex network of global cargo ship movements , 2010, Journal of The Royal Society Interface.

[6]  Reik V. Donner,et al.  Urban road networks — spatial networks with universal geometric features? , 2011, ArXiv.

[7]  Roger Guimerà,et al.  Modeling the world-wide airport network , 2004 .

[8]  Paul Upham,et al.  Environmental Capacity of Aviation: Theoretical Issues and Basic Research Directions , 2001 .

[9]  Jiang-Hai Qian,et al.  Network topology and correlation features affiliated with European airline companies , 2009 .

[10]  Roger Guimerà,et al.  Cartography of complex networks: modules and universal roles , 2005, Journal of statistical mechanics.

[11]  Bin Jiang,et al.  Scaling of geographic space from the perspective of city and field blocks and using volunteered geographic information , 2010, Int. J. Geogr. Inf. Sci..

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

[13]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[14]  H. Miller.  Tobler's First Law and Spatial Analysis , 2004 .

[15]  Alessandro Vespignani,et al.  Epidemic dynamics in finite size scale-free networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Pan Di,et al.  Weighted complex network analysis of travel routes on the Singapore public transportation system , 2010 .

[17]  Luis E C Rocha,et al.  Structural evolution of the Brazilian airport network , 2008, 0804.3081.

[18]  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.

[19]  Duncan J. Watts,et al.  Six Degrees: The Science of a Connected Age , 2003 .

[20]  Stig Nordbeck,et al.  URBAN ALLOMETRIC GROWTH , 1971 .

[21]  Alexander Rives,et al.  Modular organization of cellular networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[22]  W. Li,et al.  Statistical analysis of airport network of China. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  P. Upham,et al.  Environmental capacity and airport operations: current issues and future prospects , 2003 .

[24]  Q. Vuong.  Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[25]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[26]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[27]  César A. Hidalgo,et al.  Scale-free networks , 2008, Scholarpedia.

[28]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[29]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[30]  Alessandro Vespignani,et al.  Detecting rich-club ordering in complex networks , 2006, physics/0602134.

[31]  Ganesh Bagler,et al.  Analysis of the airport network of India as a complex weighted network , 2004, cond-mat/0409773.

[32]  R Pastor-Satorras,et al.  Dynamical and correlation properties of the internet. , 2001, Physical review letters.

[33]  Alessandro Vespignani,et al.  Characterization and modeling of weighted networks , 2005 .

[34]  Andrzej Grabowski,et al.  MIXING PATTERNS IN A LARGE SOCIAL NETWORK , 2008 .

[35]  Fahui Wang,et al.  Exploring the network structure and nodal centrality of China , 2011 .

[36]  Calyampudi Radhakrishna Rao,et al.  Stochastic Processes: Theory and Methods , 2001 .

[37]  Bin Jiang,et al.  Exploring Human Mobility Patterns Based on Location Information of US Flights , 2011, ArXiv.