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Ranking places in attributed temporal urban mobility networks

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  • Mirco Nanni
  • Leandro Tortosa
  • José F Vicent
  • Gevorg Yeghikyan

Abstract

Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of “hotspots” of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of “hotspots” and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.

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  • Mirco Nanni & Leandro Tortosa & José F Vicent & Gevorg Yeghikyan, 2020. "Ranking places in attributed temporal urban mobility networks," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-25, October.
    • Handle: RePEc:plo:pone00:0239319
    DOI: 10.1371/journal.pone.0239319
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    References listed on IDEAS

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    1. Alec Kirkley & Hugo Barbosa & Marc Barthelemy & Gourab Ghoshal, 2018. "From the betweenness centrality in street networks to structural invariants in random planar graphs," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    2. Ke Nie & Zhensheng Wang & Qingyun Du & Fu Ren & Qin Tian, 2015. "A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China," Sustainability, MDPI, vol. 7(3), pages 1-16, March.
    3. Jeff Alstott & Ed Bullmore & Dietmar Plenz, 2014. "powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    4. Sergio Rey & Richard Smith, 2013. "A spatial decomposition of the Gini coefficient," Letters in Spatial and Resource Sciences, Springer, vol. 6(2), pages 55-70, July.
    5. Thomas Louail & Maxime Lenormand & Miguel Picornell & Oliva García Cantú & Ricardo Herranz & Enrique Frias-Martinez & José J. Ramasco & Marc Barthelemy, 2015. "Uncovering the spatial structure of mobility networks," Nature Communications, Nature, vol. 6(1), pages 1-8, May.
    6. Federico Botta & Charo I del Genio, 2017. "Analysis of the communities of an urban mobile phone network," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
    7. Agryzkov, Taras & Tortosa, Leandro & Vicent, Jose F., 2016. "New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 14-29.
    8. Andrea Fontanari & Nassim Nicholas Taleb & Pasquale Cirillo, 2017. "Gini estimation under infinite variance," Papers 1707.01370, arXiv.org, revised Dec 2017.
    9. Luis Bettencourt & Geoffrey West, 2010. "A unified theory of urban living," Nature, Nature, vol. 467(7318), pages 912-913, October.
    10. Meead Saberi & Hani S. Mahmassani & Dirk Brockmann & Amir Hosseini, 2017. "A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks," Transportation, Springer, vol. 44(6), pages 1383-1402, November.
    11. Porta, Sergio & Crucitti, Paolo & Latora, Vito, 2006. "The network analysis of urban streets: A dual approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 853-866.
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    Cited by:

    1. He, Yifan & Zhao, Chen & Zeng, An, 2022. "Ranking locations in a city via the collective home-work relations in human mobility data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    2. Li, Zhitao & Tang, Jinjun & Zhao, Chuyun & Gao, Fan, 2023. "Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    3. Zhang, Yifan & Ng, S. Thomas, 2021. "Unveiling the rich-club phenomenon in urban mobility networks through the spatiotemporal characteristics of passenger flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).

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