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Human Movement Is Both Diffusive and Directed

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  • Mark Padgham

Abstract

Understanding the influence of the built environment on human movement requires quantifying spatial structure in a general sense. Because of the difficulty of this task, studies of movement dynamics often ignore spatial heterogeneity and treat movement through journey lengths or distances alone. This study analyses public bicycle data from central London to reveal that, although journey distances, directions, and frequencies of occurrence are spatially variable, their relative spatial patterns remain largely constant, suggesting the influence of a fixed spatial template. A method is presented to describe this underlying space in terms of the relative orientation of movements toward, away from, and around locations of geographical or cultural significance. This produces two fields: one of convergence and one of divergence, which are able to accurately reconstruct the observed spatial variations in movement. These two fields also reveal categorical distinctions between shorter journeys merely serving diffusion away from significant locations, and longer journeys intentionally serving transport between spatially distinct centres of collective importance. Collective patterns of human movement are thus revealed to arise from a combination of both diffusive and directed movement, with aggregate statistics such as mean travel distances primarily determined by relative numbers of these two kinds of journeys.

Suggested Citation

  • Mark Padgham, 2012. "Human Movement Is Both Diffusive and Directed," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-11, May.
  • Handle: RePEc:plo:pone00:0037754
    DOI: 10.1371/journal.pone.0037754
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    References listed on IDEAS

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    1. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    2. Handy, Susan & Cao, Xinyu & Mokhtarian, Patricia L., 2005. "Correlation or causality between the built environment and travel behavior? Evidence from Northern California," University of California Transportation Center, Working Papers qt5b76c5kg, University of California Transportation Center.
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    Cited by:

    1. Raturi, Varun & Hong, Jinhyun & McArthur, David Philip & Livingston, Mark, 2021. "The impact of privacy protection measures on the utility of crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 92(C).
    2. Hu, Yujie & Zhang, Yongping & Lamb, David & Zhang, Mingming & Jia, Peng, 2019. "Examining and optimizing the BCycle bike-sharing system – A pilot study in Colorado, US," Applied Energy, Elsevier, vol. 247(C), pages 1-12.
    3. Zhang, Yongping & Lin, Diao & Liu, Xiaoyue Cathy, 2019. "Biking islands in cities: An analysis combining bike trajectory and percolation theory," Journal of Transport Geography, Elsevier, vol. 80(C).
    4. O’Brien, Oliver & Cheshire, James & Batty, Michael, 2014. "Mining bicycle sharing data for generating insights into sustainable transport systems," Journal of Transport Geography, Elsevier, vol. 34(C), pages 262-273.
    5. Raja Jurdak, 2013. "The Impact of Cost and Network Topology on Urban Mobility: A Study of Public Bicycle Usage in 2 U.S. Cities," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-6, November.
    6. Saberi, Meead & Ghamami, Mehrnaz & Gu, Yi & Shojaei, Mohammad Hossein (Sam) & Fishman, Elliot, 2018. "Understanding the impacts of a public transit disruption on bicycle sharing mobility patterns: A case of Tube strike in London," Journal of Transport Geography, Elsevier, vol. 66(C), pages 154-166.
    7. Martin Zaltz Austwick & Oliver O’Brien & Emanuele Strano & Matheus Viana, 2013. "The Structure of Spatial Networks and Communities in Bicycle Sharing Systems," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-17, September.

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