The scaling of human interactions with city size

By Markus Schläpfer, Luís M. A. Bettencourt, Mathias Raschke, Rob Claxton, Zbigniew Smoreda, Geoffrey B. West, Carlo Ratti. Journal of the Royal Society Interface, 2012

356 citations recorded by the source corpus.

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.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/8b4752185186853c9c2089f06a6392ff13599e86

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Citations in the corpus

Dynamic population mapping using mobile phone data (2014, background): “In the 1990s, a growing interest in the global mapping of human populations emerged (8, 9), leading to the advanced development of methodologies that undertake the spatial downscaling of human population count data from censuses summarized over large and irregular administrative units to grid square”
Complex systems: physics beyond physics (2016, background): “in innovations found in a city [88]), of mobile phone records [89, 92, 93] or taxi movements [94] as proxies for spatio-temporal distributions of people, etc.”
A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015, background): “This dataset [Data citations 6,7] contains data about the interaction between single squares between the Milan/Trentino Grid and the other Italian provinces.”
The statistical physics of cities (2019, background): “For example, empirical measurements [76] of the total number of mobile phone contacts and the total communication activity give an exponent around 1.”