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Modeling and Understanding Intrinsic Characteristics of Human Mobility

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Handbook of Mobile Data Privacy

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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. Modeling these processes has however been hindered so far 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, Cambridge, MA, USA

    Jameson L. Toole

  2. Imperial College London, Dept. of Computing, London, UK

    Yves-Alexandre de Montjoye

  3. Imperial College London, Data Science Institute, London, UK

    Yves-Alexandre de Montjoye

  4. Media Lab, MIT, Cambridge, MA, USA

    Alex (Sandy) Pentland

  5. Department of Civil and Environmental Engineering, MIT, Cambridge, MA, 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 Yves-Alexandre de Montjoye .

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Editors and Affiliations

  1. IBM Watson Health Headquarters, Cambridge, MA, USA

    Aris Gkoulalas-Divanis

  2. Dipartimento di Informatica, University of Milan, Milan, Italy

    Claudio Bettini

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Toole, J.L., Montjoye, YA.d., González, M.C., Pentland, A.(. (2018). Modeling and Understanding Intrinsic Characteristics of Human Mobility. In: Gkoulalas-Divanis, A., Bettini, C. (eds) Handbook of Mobile Data Privacy . Springer, Cham. https://doi.org/10.1007/978-3-319-98161-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-98161-1_2

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