The Hidden Image of the City: Sensing Community Well-Being from Urban Mobility

  • Neal Lathia
  • Daniele Quercia
  • Jon Crowcroft
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7319)

Abstract

A key facet of urban design, planning, and monitoring is measuring communities’ well-being. Historically, researchers have established a link between well-being and visibility of city neighbourhoods and have measured visibility via quantitative studies with willing participants, a process that is invariably manual and cumbersome. However, the influx of the world’s population into urban centres now calls for methods that can easily be implemented, scaled, and analysed. We propose that one such method is offered by pervasive technology: we test whether urban mobility—as measured by public transport fare collection sensors—is a viable proxy for the visibility of a city’s communities. We validate this hypothesis by examining the correlation between London urban flow of public transport and census-based indices of the well-being of London’s census areas. We find that not only are the two correlated, but a number of insights into the flow between areas of varying social standing can be uncovered with readily available transport data. For example, we find that deprived areas tend to preferentially attract people living in other deprived areas, suggesting a segregation effect.

Keywords

Mobility Urban Analysis Sensors Well-Being 

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References

  1. 1.
    Lynch, K.: The Image of the City. MIT Press, Cambridge (1960)Google Scholar
  2. 2.
    Milgram, S.: The Individual in a Social World, 3rd edn. Pinter and Martin, London (2010)Google Scholar
  3. 3.
    Lynn, P.: Maintaining Cross-Sectional Representativeness in a Longitudinal General Population Survey. Understanding Society Working Paper (June 2011)Google Scholar
  4. 4.
    Froehlich, J., Neumann, J., Oliver, N.: Sensing and Predicting the Pulse of the City through Shared Bicycling. In: 21st IJCAI, Pasadena, California (2009)Google Scholar
  5. 5.
    Rachuri, K., et al.: EmotionSense: A Mobile Phones based Adaptive Platform for Experimental Social Psychology Research. In: ACM UbiComp (2010)Google Scholar
  6. 6.
    Eagle, N., Pentland, S.: Reality Mining: Sensing Complex Social Systems. Pers. Ubiquitous Computing 10, 255–268 (2006)CrossRefGoogle Scholar
  7. 7.
    Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban Computing with Taxicabs. In: ACM UbiComp (2011)Google Scholar
  8. 8.
    Soto, V., Frias-Martinez, V., Virseda, J., Frias-Martinez, E.: Prediction of Socioeconomic Levels Using Cell Phone Records. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 377–388. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Lathia, N., Capra, L.: How Smart is Your Smartcard? Measuring Travel Behaviours, Perceptions, and Incentives. In: ACM UbiComp (2011)Google Scholar
  10. 10.
    Bawa-Cavia, A.: Sensing the Urban: Using Location-Based Social Network Data in Urban Analysis. In: Pervasive PURBA Workshop (2011)Google Scholar
  11. 11.
    Girardin, F., et al.: Digital Footprinting: Uncovering Tourists with User-Generated Content. IEEE Pervasive Computing 7 (2008)Google Scholar
  12. 12.
    Eagle, N., Macy, M., Claxton, R.: Network Diversity and Economic Development. Science 328 (2010)Google Scholar
  13. 13.
    Noble, M., et al.: The English Indices of Deprivation. The Department of Communities and Local Government (March 2008)Google Scholar
  14. 14.
    Weinstein, L.S.: Tfl’s contactless ticketing: Oyster and beyond. In: Transport for London, London, UK (September 2009)Google Scholar
  15. 15.
    Lathia, N., Froehlich, J., Capra, L.: Mining Public Transport Usage for Personalised Intelligent Transport Systems. In: IEEE ICDM (2010)Google Scholar
  16. 16.
    Gonzalez, M., Hidalgo, C., Barabasi, A.L.: Understanding Individual Human Mobility Patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  17. 17.
    Quercia, D., Ellis, J., Capra, L., Crowcroft, J.: Tracking “Gross Community Happiness” from Tweets. In: ACM CSCW (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Neal Lathia
    • 1
  • Daniele Quercia
    • 1
  • Jon Crowcroft
    • 1
  1. 1.The Computer LaboratoryUniversity of CambridgeUK

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