Journal of Geographical Systems

, Volume 18, Issue 4, pp 359–376 | Cite as

Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

Original Article

Abstract

Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people’s social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people’s interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships.

Keywords

Geographical impacts Space and place Spatially-embedded social networks Mobile phone data Individuals’ interaction 

JEL Classification

C18 D83 

References

  1. Agnew J (1987) Place and politics: the geographical mediation of state and society. Allen and Unwin, BostonGoogle Scholar
  2. Arentze T, Timmermans H (2008) Social networks, social interactions, and activity-travel behavior: a framework for microsimulation. Environ Plan 35:1012–1027CrossRefGoogle Scholar
  3. Barthélemy M (2011) Spatial networks. Phys Rep 499:1–101CrossRefGoogle Scholar
  4. Bollen KA (1989) Structural equations with latent variables. Wiley, New YorkCrossRefGoogle Scholar
  5. Cairncross F (2001) The death of distance: how the communications revolution will change our lives. Harvard Business Press, CambridgeGoogle Scholar
  6. Carrasco JA, Miller EJ (2006) Exploring the propensity to perform social activities: a social network approach. Transportation 33:463–480CrossRefGoogle Scholar
  7. Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th international conference on knowledge discovery and data mining, ACM, New York, pp 1082–1090Google Scholar
  8. Choudhury MD, Mason WA, Hofman JM, Watts DJ (2010) Inferring relevant social networks from interpersonal communication. In: Proceedings of the 19th international conference on world wide web, ACM, New York, pp 301–310Google Scholar
  9. Crandall DJ, Backstrom L, Cosley D, Suri S, Huttenlocher D, Kleinberg J (2010) Inferring social ties from geographic coincidences. Proc Natl Acad Sci USA 107:22436–22441CrossRefGoogle Scholar
  10. Eagle N, Pentland AS, Lazer D (2009) Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci USA 106:15274–15278CrossRefGoogle Scholar
  11. Erdős P, Rényi A (1976) On the evolution of random graphs. Sel Pap Alfréd Rényi 2:482–525Google Scholar
  12. Gao S, Liu Y, Wang Y, Ma X (2013) Discovering spatial interaction communities from mobile phone data. Trans GIS 17:463–481CrossRefGoogle Scholar
  13. González MC, Hidalgo CA, Barabási AL (2008) Understanding individual human mobility patterns. Nature 453:779–782CrossRefGoogle Scholar
  14. Goodchild MF (2015) Space, place and health. Annals of GIS 21:97–100CrossRefGoogle Scholar
  15. Granovetter MS (1973) The strength of weak ties. Am J Sociol 78:1360–1380CrossRefGoogle Scholar
  16. Hawelka B, Sitko I, Beinat E, Sobolevsky S, Kazakopoulos P, Ratti C (2014) Geo-located Twitter as proxy for global mobility patterns. Cartogr Geogr Inf Sci 41:260–271CrossRefGoogle Scholar
  17. Kang C, Ma X, Tong D, Liu Y (2012) Intra-urban human mobility patterns: an urban morphology perspective. Phys A 391:1702–1717CrossRefGoogle Scholar
  18. Kang C, Sobolevsky S, Liu Y, Ratti C (2013) Exploring human movements in Singapore: a comparative analysis based on mobile phone and taxicab usages. In: Proceedings of the 2nd international workshop on urban computing, no. 1. ACM, New YorkGoogle Scholar
  19. Kwan M-P (2007) Mobile communications, social networks, and urban travel: hypertext as a new metaphor for conceptualizing spatial interaction. Prof Geogr 59:434–446CrossRefGoogle Scholar
  20. Lewis RA, Reiley D (2008) Does retail advertising work? Measuring the effects of advertising on sales via a controlled experiment on Yahoo!. http://dx.doi.org/10.2139/ssrn.1865943
  21. Liben-Nowell D, Novak J, Kumar R, Raghavan P, Tomkins A (2005) Geographic routing in social networks. Proc Natl Acad Sci USA 102:11623–11628CrossRefGoogle Scholar
  22. Onnela J-P, Saramäki J, Hyvönen J, Szabó G, Lazer D, Kaski K, Kertész J, Barabási A-L (2007) Structure and tie strengths in mobile communication networks. Proc Natl Acad Sci USA 104:7332–7336CrossRefGoogle Scholar
  23. Onnela JP, Arbesman S, González MC, Barabási AL, Christakis NA (2011) Geographic constraints on social network groups. PLoS One 6:e16939CrossRefGoogle Scholar
  24. Phithakkitnukoon S, Smoreda Z, Olivier P (2012) Socio-geography of human mobility: a study using longitudinal mobile phone data. PLoS One 7:e39253CrossRefGoogle Scholar
  25. Pickles J (1995) Ground truth: the social implications of geographic information systems. Guilford Press, NewyorkGoogle Scholar
  26. Raeder T, Lizardo O, Hachen D, Chawla NV (2011) Predictors of short-term decay of cell phone contacts in a large scale communication network. Social Netw 33:245–257CrossRefGoogle Scholar
  27. Ratti C, Sobolevsky S, Calabrese F, Andris C, Reades J, Martino M, Claxton R, Strogatz SH (2010) Redrawing the map of Great Britain from a network of human interactions. PLoS One 5:e14248CrossRefGoogle Scholar
  28. Shi L, Chi G, Liu X, Liu Y (2015) Human mobility patterns in different communities: a mobile phone data-based social network approach. Ann GIS 21:15–26CrossRefGoogle Scholar
  29. Song C, Qu Z, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327:1018–1021CrossRefGoogle Scholar
  30. Sui D, Goodchild M (2011) The convergence of GIS and social media: challenges for GIScience. Int J Geogr Inf Sci 25:1737–1748CrossRefGoogle Scholar
  31. Sun L, Axhausen KW, Lee D-H, Huang X (2013) Understanding metropolitan patterns of daily encounters. Proc Natl Acad Sci USA 110:13774–13779CrossRefGoogle Scholar
  32. Takhteyev Y, Gruzd A, Wellman B (2012) Geography of Twitter networks. Social Netw 34(1):73–81CrossRefGoogle Scholar
  33. Tranos E, Nijkamp P (2015) Mobile phone usage in complex urban systems: a space–time, aggregated human activity study. J Geogr Syst 17:157–185CrossRefGoogle Scholar
  34. Tuan Y-F (1977) Space and place: the perspective of experience. University of Minnesota Press, MinneapolisGoogle Scholar
  35. Van den Berg P, Arentze T, Timmermans H (2009) Size and composition of ego-centered social networks and their effect on geographic distance and contact frequency. Transp Res Rec 2135:1–9CrossRefGoogle Scholar
  36. Walsh F, Pozdnoukhov A (2011) Spatial structure and dynamics of urban communities. In: Proceedings of the first workshop on pervasive urban applications (PURBA)Google Scholar
  37. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Institute of Remote Sensing and Geographical Information SystemsPeking UniversityBeijingChina

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