The scaling of human interactions with city size

The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases.

[1]  A. Venables,et al.  The Spatial Economy: Cities, Regions, and International Trade , 2000 .

[2]  A. Azzalini,et al.  Statistical applications of the multivariate skew normal distribution , 2009, 0911.2093.

[3]  M E J Newman,et al.  Random graphs with clustering. , 2009, Physical review letters.

[4]  Jari Saramäki,et al.  Effects of time window size and placement on the structure of aggregated networks , 2012, ArXiv.

[5]  R. Arellano-Valle,et al.  The centred parametrization for the multivariate skew-normal distribution , 2008 .

[6]  Jari Saramäki,et al.  Persistence of social signatures in human communication , 2012, Proceedings of the National Academy of Sciences.

[7]  Christos Faloutsos,et al.  Mobile call graphs: beyond power-law and lognormal distributions , 2008, KDD.

[8]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[9]  R. Berk An introduction to sample selection bias in sociological data. , 1983 .

[10]  Zbigniew Smoreda,et al.  Are social networks technologically embedded?: How networks are changing today with changes in communication technology , 2005, Soc. Networks.

[11]  Jari Saramäki,et al.  Small But Slow World: How Network Topology and Burstiness Slow Down Spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Samuel Arbesman,et al.  Superlinear scaling for innovation in cities. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  R. Lewin The pace of life , 1976, Nature.

[14]  Hans Geser,et al.  Is the cell phone undermining the social order?: Understanding mobile technology from a sociological perspective , 2006 .

[15]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[16]  A. Barabasi,et al.  Analysis of a large-scale weighted network of one-to-one human communication , 2007, physics/0702158.

[17]  Lada A. Adamic,et al.  Computational Social Science , 2009, Science.

[18]  Jari Saramäki,et al.  Effects of time window size and placement on the structure of an aggregated communication network , 2012, EPJ Data Science.

[19]  G. Simmel The sociology of Georg Simmel , 1950 .

[20]  A. Pentland,et al.  Computational Social Science , 2009, Science.

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

[22]  S. Milgram The experience of living in cities. , 1970, Science.

[23]  B. Wellman Networks in the Global Village , 1998 .

[24]  Zbigniew Smoreda,et al.  Interplay between Telecommunications and Face-to-Face Interactions: A Study Using Mobile Phone Data , 2011, PloS one.

[25]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[26]  Markus Schläpfer,et al.  Measuring degree-degree association in networks. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Giorgio Topa,et al.  Social interactions, local spillovers and unemployment , 2001 .

[28]  C. Anthony Di Benedetto,et al.  Diffusion of Innovation , 2015 .

[29]  Manuel Cebrián,et al.  Limited communication capacity unveils strategies for human interaction , 2013, Scientific Reports.

[30]  Mark S. Granovetter The Impact of Social Structure on Economic Outcomes Social Networks and Economic Outcomes: Core Principles , 2022 .

[31]  Shilpa Chakravartula,et al.  Complex Networks: Structure and Dynamics , 2014 .

[32]  Hawoong Jeong,et al.  Statistical properties of sampled networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  C. Fischer "Urbanism as a Way of Life" , 1972 .

[34]  R. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991, Annals of Internal Medicine.

[35]  Michael Mitzenmacher,et al.  A Brief History of Generative Models for Power Law and Lognormal Distributions , 2004, Internet Math..

[36]  I. Kiss,et al.  The effect of network mixing patterns on epidemic dynamics and the efficacy of disease contact tracing , 2007, Journal of The Royal Society Interface.

[37]  Steven D. Levitt,et al.  Crime, Urban Flight, and the Consequences for Cities , 1996, Review of Economics and Statistics.

[38]  D. Helbing,et al.  Growth, innovation, scaling, and the pace of life in cities , 2007, Proceedings of the National Academy of Sciences.

[39]  Nitesh V. Chawla,et al.  Predictors of short-term decay of cell phone contacts in a large scale communication network , 2011, Soc. Networks.

[40]  Alex Pentland,et al.  Urban characteristics attributable to density-driven tie formation , 2012, Nature Communications.

[41]  Carsten Wiuf,et al.  Subnets of scale-free networks are not scale-free: sampling properties of networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[43]  J. Jacobs The Death and Life of Great American Cities , 1962 .

[44]  N. Eagle,et al.  Network Diversity and Economic Development , 2010, Science.

[45]  L. Bettencourt,et al.  Supplementary Materials for The Origins of Scaling in Cities , 2013 .

[46]  N. Milburn To Dwell Among Friends: Personal Networks in Town and City. , 1983 .

[47]  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.

[48]  Marián Boguñá,et al.  Tuning clustering in random networks with arbitrary degree distributions. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[49]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[50]  C. Fischer,et al.  To Dwell among Friends: Personal Networks in Town and City. , 1984 .

[51]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[52]  Deyuan Li,et al.  Estimation for the Generalized Pareto Distribution Using Maximum Likelihood and Goodness of Fit , 2011 .

[53]  A. C. Davison,et al.  Statistical models: Name Index , 2003 .

[54]  Sung Joong Kim Productivity of Cities , 1997 .

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

[56]  A. Azzalini The Skew‐normal Distribution and Related Multivariate Families * , 2005 .

[57]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.