Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Renormalization of human mobility based on a revised electric circuit model and a new gravity relation

Zhihua Zhong, Hideki Takayasu, and Misako Takayasu*

  • *Contact author: takayasu@comp.isct.ac.jp

Phys. Rev. Research 7, 013235 – Published 3 March, 2025

DOI: https://doi.org/10.1103/PhysRevResearch.7.013235

Abstract

Human movement in an urban environment consists of a complex system where the results and observations of an analysis may be dependent on the choice of resolutions. In this study, a two-dimensional lattice weighted undirected graph embedded in Euclidean space is constructed to represent human flow in an urban environment. Specifically, each node represents an area whereas a link consists of a road connecting two adjacent areas. People moving in a grid under a specific resolution are aggregated, and the sum of their velocity is assigned as the link weight. Based on the analysis of real-world GPS data, we first find that the human potential in the revised electric circuit model (RECM) can characterize the spatial-temporal human movement trends in an urban environment independent of different observation resolutions. Second, we propose several renormalization methods for three analogical concepts in the RECM, namely, human current, human resistance, and human potential. Each of these can be used to describe spatial-temporal flow patterns, infrastructure level, and movement trends, respectively, to reveal their relation under different spatial resolutions. Third, we find a nontrivial scaling relation, which may be regarded as a new type of gravity relation, to characterize the heterogeneous spatial distribution of human conductivity. Although the power exponent of distance γ in the above relation slightly changes at different spatial scales, a nontrivial relation is derived to explain and estimate the exponent automatically. Overall, these discoveries may lead to new approaches for establishing a cross-scale human mobility description framework.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (37)

  1. Q. Zhang, C. Huang, L. Xia, Z. Wang, S. M. Yiu, and R. Han, Spatial-temporal graph learning with adversarial contrastive adaptation, in International Conference on Machine Learning (PMLR, 2023), Vol. 202, pp. 41151–41163.
  2. M. Shimosaka, K. Maeda, T. Tsukiji, and K. Tsubouchi, Forecasting Urban Dynamics with Mobility Logs by Bilinear Poisson Regression (ACM Press, New York, 2015), pp. 535–546.
  3. Y. Zheng, F. Liu, and H.-P. Hsieh, U-air: When urban air quality inference meets big data, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM Press, New York, 2013).
  4. M. González-Méndez, C. Olaya, I. Fasolino, M. Grimaldi, and N. Obregón, Agent-based modeling for urban development planning based on human needs. Conceptual basis and model formulation, Land Use Policy 101, 105110 (2021).
  5. V. Belik, T. Geisel, and D. Brockmann, Natural human mobility patterns and spatial spread of infectious diseases, Phys. Rev. X 1, 011001 (2011).
  6. J. Yuan, Y. Zheng, and X. Xie, Discovering Regions of Different Functions in a City using Human Mobility and POIs (ACM Press, New York, 2012), pp. 186–194.
  7. G. K. Zipf, The P1 P2/D hypothesis: On the intercity movement of persons, Am. Sociol. Rev. 11, 677 (1946).
  8. H. Barbosa, M. Barthelemy, G. Ghoshal, C. R. James, M. Lenormand, T. Louail, R. Menezes, J. J. Ramasco, F. Simini, and M. Tomasini, Human mobility: Models and applications, Phys. Rep. 734, 1 (2018).
  9. M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, Understanding individual human mobility patterns, Nature (London) 453, 779 (2008).
  10. B. Jiang, J. Yin, and S. Zhao, Characterizing the human mobility pattern in a large street network, Phys. Rev. E 80, 021136 (2009).
  11. C. Song, T. Koren, P. Wang, and A.-L. Barabási, Modelling the scaling properties of human mobility, Nat. Phys. 6, 818 (2010).
  12. R. B. Matthews, N. G. Gilbert, A. Roach, J. G. Polhill, and N. M. Gotts, Agent-based land-use models: A review of applications, Landscape Ecol. 22, 1447 (2007).
  13. M. Luca, G. Barlacchi, B. Lepri, and L. Pappalardo, A survey on deep learning for human mobility, ACM Comput. Surv. 55, 1 (2023).
  14. F. Simini, G. Barlacchi, M. Luca, and L. Pappalardo, A deep gravity model for mobility flows generation, Nat. Commun. 12, 6576 (2021).
  15. W. Dang, H. Wang, S. Pan, P. Zhang, C. Zhou, X. Chen, and J. Wang, Predicting Human Mobility via Graph Convolutional Dual-Attentive Networks (ACM Press, New York, 2022), pp. 192–200.
  16. C. E. Gehlke and K. Biehl, Certain effects of grouping upon the size of the correlation coefficient in census tract material, J. Am. Stat. Assoc. 29, 169 (1934).
  17. S. Openshaw, The Modifiable Areal Unit Problem, Concepts and Techniques in Modern Geography (Geobooks, 1984).
  18. C. Song, Z. Qu, N. Blumm, and A.-L. Barabási, Limits of predictability in human mobility, Science 327, 1018 (2010).
  19. A. Cuttone, S. Lehmann, and M. C. González, Understanding predictability and exploration in human mobility, EPJ Data Sci. 7, 2 (2018).
  20. K. Smolak, K. Siła-Nowicka, J.-C. Delvenne, M. Wierzbiński, and W. Rohm, The impact of human mobility data scales and processing on movement predictability, Sci. Rep. 11, 15177 (2021).
  21. L. Alessandretti, U. Aslak, and S. Lehmann, The scales of human mobility, Nature (London) 587, 402 (2020).
  22. M. Mazzoli, A. Molas, A. Bassolas, M. Lenormand, P. Colet, and J. J. Ramasco, Field theory for recurrent mobility, Nat. Commun. 10, 3895 (2019).
  23. Y. Shida, J. Ozaki, H. Takayasu, and M. Takayasu, Potential fields and fluctuation-dissipation relations derived from human flow in urban areas modeled by a network of electric circuits, Sci. Rep. 12, 9918 (2022).
  24. Z. Zhong, H. Takayasu, and M. Takayasu, Human mobility description by physical analogy of electric circuit network based on GPS data, Sci. Rep. 14, 13380 (2024).
  25. R. Wilcox, Trimming and Winsorization, Encycl. Biostat. 8, 5531 (2005).
  26. P. Feldmann and R. A. Rohrer, Proof of the number of independent Kirchhoff equations in an electrical circuit, IEEE Trans. Circuit Syst. 38, 681 (1991).
  27. E. López, S. V. Buldyrev, S. Havlin, and H. E. Stanley, Anomalous transport in scale-free networks, Phys. Rev. Lett. 94, 248701 (2005).
  28. G. Korniss, M. B. Hastings, K. E. Bassler, M. J. Berryman, B. Kozma, and D. Abbott, Scaling in small-world resistor networks, Phys. Lett. A 350, 324 (2006).
  29. B. Martín, E. Ortega, R. Cuevas-Wizner, A. Ledda, and A. De Montis, Assessing road network resilience: An accessibility comparative analysis, Transp. Res. Part D: Transport Environ. 95, 102851 (2021).
  30. K. Lynch, Reconsidering the Image of the City (Springer, Berlin, 1984).
  31. R. Z. Farahani, E. Miandoabchi, W. Y. Szeto, and H. Rashidi, A review of urban transportation network design problems, Eur. J. Oper. Res. 229, 281 (2013).
  32. Q. Pan and S. Sharifi-Asl, Transportation Land Use Modeling and Policy (Mavs Open Press, 2024).
  33. C. Wan, Z. Yang, D. Zhang, X. Yan, and S. Fan, Resilience in transportation systems: A systematic review and future directions, Transport Rev. 38, 479 (2018).
  34. J. F. McDonald and P. J. Prather, Suburban employment centres: The case of chicago, Urban Studies 31, 201 (1994).
  35. S. Siedentop, S. Fina, and A. Krehl, Greenbelts in Germany's regional plansan effective growth management policy? Landscape Urban Plann. 145, 71 (2016).
  36. C. Zhong, M. Schläpfer, S. Müller Arisona, M. Batty, C. Ratti, and G. Schmitt, Revealing centrality in the spatial structure of cities from human activity patterns, Urban Stud. 54, 437 (2017).
  37. Y. Fukuda, Land prices and agglomeration: Theory and evidence from the Tokyo metropolitan area, J. Jpn. Int. Econ. 58, 101094 (2020).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation