Comparison of social structures within cities of very different sizes

People make a city, making each city as unique as the combination of its inhabitants. However, some cities are similar and some cities are inimitable. We examine the social structure of 10 different cities using Twitter data. Each city is decomposed to its communities. We show that in many cases one city can be thought of as an amalgamation of communities from another city. For example, we find the social network of Manchester is very similar to the social network of a virtual city of the same size, where the virtual city is composed of communities from the Bristol network. However, we cannot create Bristol from Manchester since Bristol contains communities with a social structure that are not present in Manchester. Some cities, such as Leeds, are outliers. That is, Leeds contains a particularly wide range of communities, meaning we cannot build a similar city from communities outside of Leeds. Comparing communities from different cities, and building virtual cities that are comparable to real cities, is a novel approach to understand social networks. This has implications when using social media to inform or advise residents of a city.

[1]  M. Batty The Size, Scale, and Shape of Cities , 2008, Science.

[2]  Jukka-Pekka Onnela,et al.  Taxonomies of networks from community structure. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  George Kingsley Zipf,et al.  Human behavior and the principle of least effort , 1949 .

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

[5]  Mark Newman,et al.  Networks: An Introduction , 2010 .

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

[7]  Frank Southworth,et al.  The geography of metropolitan carbon footprints , 2009 .

[8]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[9]  L. Bettencourt,et al.  Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities , 2010, PloS one.

[10]  Michael Batty,et al.  Cities and Complexity: Understanding Cities Through Cellular Automata, Agent-Based Models and Fractals , 2005 .

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

[12]  D. Dodman Blaming cities for climate change? An analysis of urban greenhouse gas emissions inventories , 2009 .

[13]  J. Coleman Introduction to Mathematical Sociology , 1965 .

[14]  L. Bettencourt,et al.  A unified theory of urban living , 2010, Nature.

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

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

[17]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[18]  Kenneth E. Boulding,et al.  Introduction to Mathematical Sociology. , 1966 .

[19]  Alessandra Conversi,et al.  Comparative Analysis , 2009, Encyclopedia of Database Systems.

[20]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.

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