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Modeling and Understanding Intrinsic Characteristics of Human Mobility

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Social Phenomena

Part of the book series: Computational Social Sciences ((CSS))

Abstract

Humans are intrinsically social creatures and our mobility is central to understanding how our societies grow and function. Movement allows us to congregate with our peers, access things we need, and exchange information. Human mobility has huge impacts on topics like urban and transportation planning, social and biologic spreading, and economic outcomes. So far, modeling these processes has been hindered by a lack of data. This is radically changing with the rise of ubiquitous devices. In this chapter, we discuss recent progress deriving insights from the massive, high resolution data sets collected from mobile phone and other devices. We begin with individual mobility, where empirical evidence and statistical models have shown important intrinsic and universal characteristics about our movement: we, as human, are fundamentally slow to explore new places, relatively predictable, and mostly unique. We then explore methods of modeling aggregate movement of people from place to place and discuss how these estimates can be used to understand and optimize transportation infrastructure. Finally, we highlight applications of these findings to the dynamics of disease spread, social networks, and economic outcomes.

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Notes

  1. 1.

    United Nations Department of Economic and Social Affairs—World Urbanization Prospects—2014 Update. http://esa.un.org/unpd/wup/Highlights/WUP2014-Highlights.pdf.

  2. 2.

    http://www.worldpop.org.uk/ebola/.

  3. 3.

    GSMA European Mobile Industry Observatory 2011 http://www.gsma.com/publicpolicy/wp-content/uploads/2012/04/emofullwebfinal.pdf.

  4. 4.

    ITU. (2013) ICT Facts and Figures http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2013-e.pdf.

  5. 5.

    Lookout (2010) Introducing the App Genome Project https://blog.lookout.com/blog/2010/07/27/introducing-the-app-genome-project/.

  6. 6.

    Hubway Data Visualization Challenge (2012) http://hubwaydatachallenge.org/.

  7. 7.

    New York taxi details can be extracted from anonymized data, researchers say (2014) http://www.theguardian.com/technology/2014/jun/27/new-york-taxi-details-anonymised-data-researchers-warn.

  8. 8.

    Flowing data—Where People Run in Major Cities http://flowingdata.com/2014/02/05/where-people-run/.

  9. 9.

    Cell-Phone Data Might Help Predict Ebola’s Spread (2014) http://www.technologyreview.com/news/530296/cell-phone-data-might-help-predict-ebolas-spread/

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Author information

Authors and Affiliations

  1. Engineering Systems Division, MIT, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA

    Jameson L. Toole

  2. Media Lab, MIT, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA

    Yves-Alexandre de Montjoye & Alex (Sandy) Pentland

  3. Department of Civil and Environmental Engineering, MIT, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA

    Marta C. González

Authors
  1. Jameson L. Toole
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  2. Yves-Alexandre de Montjoye
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  3. Marta C. González
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  4. Alex (Sandy) Pentland
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Correspondence to Jameson L. Toole .

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Editors and Affiliations

  1. Centre de Physique Théorique, Aix-Marseille Université Campus de Luminy, Case 907, Marseille, France

    Bruno Gonçalves

  2. MoBS Lab, Northeastern University, Boston, Massachusetts, USA

    Nicola Perra

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Toole, J.L., de Montjoye, YA., González, M.C., Pentland, A.(. (2015). Modeling and Understanding Intrinsic Characteristics of Human Mobility. In: Gonçalves, B., Perra, N. (eds) Social Phenomena. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-14011-7_2

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