Using mobile phone data registration to determine urban mobility patterns: A comparative perspective from Iran and China
Urban forms are central to the operation of cities. However, traditional approaches rarely capture human mobility. In this study, we used mobile phone data from Iran (Tehran/Balad.ir) and China (Shanghai/Gaode Maps) to introduce a demand-driven “urban pattern of need.” Using density-based spatial clustering of applications with Noise (radius of neighborhood, ε = 1 km, minimum number of points, min_samples = 50) and kernel density estimation, we analyzed anonymized global positioning system traces (with 15-min windows in Tehran) and multimodal mobility data (5-min frequency in Shanghai). Key findings include a 40% commute asymmetry in Tehran (p<0.05) and 68% of Shanghai’s bike-share trips under 2 km, reflecting differences in urban morphologies shaped by governance. The results validate adaptive urbanism informed by real-time mobility analysis, synthesizing theory with data-driven planning.
Anjomshoaa, A., Duarte, F., Rennings, D., Matarazzo, T. J., Desouza, P., & Ratti, C. (2018). City scanner: Building and scheduling a mobile sensing platform for smart city services. IEEE Internet of things Journal, 5(6):4567-4579. https://doi.org/10.1109/jiot.2018.2839058
Batty, M. (2013). The New Science of Cities. United States: MIT Press.
Bengtsson, L., Lu, X., Thorson, A., Garfield, R., & Von Schreeb, J. (2011). Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geospatial study in Haiti. PLoS Medicine, 8(8):e1001083. https://doi.org/10.1371/journal.pmed.1001083
Dorostkar, E. (2025). Discovering the urban pattern through human mobility and virtual space. Journal of Urban Management, 14:607-614. https://doi.org/10.1016/j.jum.2025.01.004
Eisenman, S. B., Lane, N. D., Miluzzo, E., Peterson, R. A., Ahn, G., & Campbell, A. T. (2006). Metrosense Project: People-centric Sensing at Scale. In: Proceedings of the Workshop on World-Sensor-Web (WSW 2006). Boulder.
Fenichel, E. P., Castillo-Chavez, C., Ceddia, M. G., Chowell, G., Parra, P. A. G., Hickling, G. J., et al. (2011). Adaptive human behavior in epidemiological models. Proceedings of the National Academy of Sciences, 108(15):6306-6311. https://doi.org/10.1073/pnas.1011250108
Funk, S., Gilad, E., Watkins, C., & Jansen, V. A. (2009). The spread of awareness and its impact on epidemic outbreaks. Proceedings of the National Academy of Sciences, 106(16):6872-6877. https://doi.org/10.1073/pnas.0810762106
Gao, S., Rao, J., Kang, Y., Liang, Y., Kruse, J., Doepfer, D., et al. (2020). Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic. arXiv preprint arXiv:2004.11430.
Harrington, L. C., Scott, T. W., Lerdthusnee, K., Coleman, R. C., Costero, A., Clark, G. G., et al. (2005). Dispersal of the dengue vector Aedes aegypti within and between rural communities. The American Journal of Tropical Medicine and Hygiene, 72(2), 209-220.
Harvey, D. (1989). The Urban Experience. United States: Johns Hopkins University Press.
Honicky, R., Brewer, E. A., Paulos, E., & White, R. (2008). N-smarts: Networked Suite of Mobile Atmospheric Real-time Sensors. In: Proceedings of the second ACM SIGCOMM Workshop on Networked Systems for Developing Regions, p. 25-30. https://doi.org/10.1145/1397705.1397713
Jacobs, J. (1961). The Death and Life of Great American Cities. Vol. 21. United States: Modern Library, p. 13-25.
Liang, Y., Gao, S., Cai, Y., Foutz, N. Z., & Wu, L. (2020). Calibrating the dynamic Huff model for business analysis using location big data. Transactions in GIS, 24(3):681-703. https://doi.org/10.1111/tgis.12624
Liebman, K. A., Stoddard, S. T., Morrison, A. C., Rocha, C., Minnick, S., Sihuincha, M., et al. (2012). Spatial dimensions of dengue virus transmission across interepidemic and epidemic periods in Iquitos, Peru (1999-2003). PLoS Neglected Tropical Diseases, 6(2):e1472. https://doi.org/10.1371/journal.pntd.0001472
Lu, X., Wetter, E., Bharti, N., Tatem, A. J., & Bengtsson, L. (2013). Approaching the limit of predictability in human mobility. Scientific Reports, 3(1):2923. https://doi.org/10.1038/srep02923
Lynch, K. (1960). The Image of the City. United States: MIT Press.
Muir, L. E., & Kay, B. H. (1998). Aedes aegypti survival and dispersal estimated by mark-release-recapture in northern Australia. The American Journal of Tropical Medicine and Hygiene, 58(3):277-282. https://doi.org/10.4269/ajtmh.1998.58.277
Najarsadeghi, M., & Dorostkar, E. (2022). How do measure the triangle of human mobility in urban nightlife? Cities, 130:103944. https://doi.org/10.1016/j.cities.2022.103944
Perkins, T. A., Scott, T. W., Le Menach, A., & Smith, D. L. (2013). Heterogeneity, mixing, and the spatial scales of mosquito-borne pathogen transmission. PLoS Computational Biology, 9(12):e1003327. https://doi.org/10.1371/journal.pcbi.1003327
Prestby, T., App, J., Kang, Y., & Gao, S. (2020). Understanding neighborhood isolation through spatial interaction network analysis using location big data. Environment and Planning A: Economy and Space, 52(6):1027-1031. https://doi.org/10.10.1177/0308518X19891911
Prosper, O., Ruktanonchai, N., & Martcheva, M. (2012). Assessing the role of spatial heterogeneity and human movement in malaria dynamics and control. Journal of Theoretical Biology, 303:1-14. https://doi.org/10.1016/j.jtbi.2012.02.010
Stoddard, S. T., Forshey, B. M., Morrison, A. C., Paz-Soldan, V. A., Vazquez-Prokopec, G. M., Astete, H., et al. (2013). House-to-house human movement drives dengue virus transmission. Proceedings of the National Academy of Sciences, 110(3):994-999. https://doi.org/10.1073/pnas.1213349110
Tonekaboni, N. H., Ramaswamy, L., Mishra, D., Grundstein, A., Kulkarni, S., & Yin, Y. (2018). Scouts: A Smart Community Centric Urban Heat Monitoring Framework. In: Proceedings of the 1st ACM SIGSPATIAL Workshop on Advances on Resilient and Intelligent Cities. Seattle, Washingtonp, p. 27-30.
Volz, E. M., Miller, J. C., Galvani, A., & Ancel Meyers, L. (2011). Effects of heterogeneous and clustered contact patterns on infectious disease dynamics. PLoS Computational Biology, 7(6):e1002042. https://doi.org/10.1371/journal.pcbi.1002042
Wesolowski, A., Eagle, N., Tatem, A. J., Smith, D. L., Noor, A. M., Snow, R. W., & Buckee, C. O. (2012). Quantifying the impact of human mobility on malaria. Science, 338(6104):267-270. https://doi.org/10.1126/science.1223467
Yin, Y., Tonekaboni, N. H., Grundstein, A., Mishra, D. R., Ramaswamy, L., & Dowd, J. (2020). Urban ambient air temperature estimation using hyperlocal data from smart vehicle-borne sensors. Computers, Environment and Urban Systems, 84:101538. https://doi.org/10.1016/j.compenvurbsys.2020.101538
Zhang, L., Li, X., & Gao, S. (2020). Smart urban mobility: A comparative study of Chinese governance models. Journal of Urban Technology, 27(3):45-67.