Identification of multi-attribute functional urban areas under a perspective of community detection: A case study

Identifying functional urban areas is a significant research of considerable interest in many important fields such as city planning and facility location problem. Traditionally, we identify the function of urban areas from the macro-level perspective. With the availability of human digital footprints, investigation of functional urban areas from a micro-level perspective becomes possible. In this paper, we identified the functional urban areas of a metropolitan city in China by some metrics of community detection based on the social network of mobile phone users. The result shows that there are close relations between urban area and individual communication network, which can help us identify the function of areas more conveniently.

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

[2]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[3]  Harry Eugene Stanley,et al.  Calling patterns in human communication dynamics , 2013, Proceedings of the National Academy of Sciences.

[4]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[5]  Mikko Alava,et al.  Patterns, Entropy, and Predictability of Human Mobility and Life , 2012, PloS one.

[6]  Francisco Saldanha-da-Gama,et al.  Facility location and supply chain management - A review , 2009, Eur. J. Oper. Res..

[7]  Rosario N. Mantegna,et al.  A comparative analysis of the statistical properties of large mobile phone calling networks , 2014, Scientific Reports.

[8]  Weidong Xiao,et al.  Core-Based Dynamic Community Detection in Mobile Social Networks , 2013, Entropy.

[9]  Carlo Ratti,et al.  Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome , 2011, IEEE Transactions on Intelligent Transportation Systems.

[10]  Daniel Gatica-Perez,et al.  Contextual conditional models for smartphone-based human mobility prediction , 2012, UbiComp.

[11]  Dreaming the Rational City: The Myth of American City Planning. , 1985 .

[12]  Sha Tao Mobile phone-based vehicle positioning and tracking and its application in urban traffic state estimation , 2012 .

[13]  Rosario N. Mantegna,et al.  Statistically validated mobile communication networks: the evolution of motifs in European and Chinese data , 2014, ArXiv.

[14]  Da Ruan,et al.  Fuzzy group decision-making for facility location selection , 2003, Inf. Sci..

[15]  Dino Pedreschi,et al.  A classification for community discovery methods in complex networks , 2011, Stat. Anal. Data Min..

[16]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[17]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

[18]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[19]  Caroline O. Buckee,et al.  The impact of biases in mobile phone ownership on estimates of human mobility , 2013, Journal of The Royal Society Interface.

[20]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[21]  Zhi-Qiang Jiang,et al.  Communication cliques in mobile phone calling networks , 2015, ArXiv.