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Sensing ‘socio-spatio’ interaction and accessibility from location-sharing services data

In: Accessibility and Spatial Interaction

Author

Listed:
  • Laurie A Schintler
  • Rajendra Kulkarni
  • Kingsley Haynes
  • Roger Stough

Abstract

Recently there have been significant efforts to mine location-sharing services data and other similar types of geo-social digital data to understand and analyse the complexity of human mobility patterns. Of these studies, very few studies have examined how mobility patterns vary across different regions of the United States. This chapter intends to fill this gap in the literature. Specifically, we use bipartite network modelling to derive a set of metrics for characterizing regional variations in the mobility patterns of individuals. Through this study, we also attempt to gain insight on the types of trips that location-sharing services data may represent. Lastly, we use a community detection to derive information on what we refer to as ‘mobility sheds’. For the purpose of this study, we use a sample of Brightkite location-sharing services data, collected by the Stanford Large Network Dataset collection, SNAP.

Suggested Citation

  • Laurie A Schintler & Rajendra Kulkarni & Kingsley Haynes & Roger Stough, 2014. "Sensing ‘socio-spatio’ interaction and accessibility from location-sharing services data," Chapters, in: Ana Condeço-Melhorado & Aura Reggiani & Javier Gutiérrez (ed.), Accessibility and Spatial Interaction, chapter 5, pages 92-110, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:15267_5
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    References listed on IDEAS

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    1. Schmutte, Ian M., 2014. "Free to Move? A Network Analytic Approach for Learning the Limits to Job Mobility," Labour Economics, Elsevier, vol. 29(C), pages 49-61.
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    Cited by:

    1. Zhenhua Chen & Laurie Schintler, 2015. "Sensitivity of location-sharing services data: evidence from American travel pattern," Transportation, Springer, vol. 42(4), pages 669-682, July.

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