Detection of Urban Space Community Structures in Island-Oriented Cities Based on Smart Bus Card Data
Publication: Journal of Urban Planning and Development
Volume 150, Issue 4
Abstract
The urban space community is the basic unit used to demonstrate the urban spatial structure, and the space community structure of island-oriented cities is different from that of general inland cities because they are formed based on the island and the inland, respectively. Through the case study of Xiamen, based on smart card data, this paper divides the obtained origin–destination data into local and global levels with trip cutoff thresholds. The distribution of city centers is analyzed by detecting the overall urban space community structure and the two-level intraisland and cross-island urban space community structure with the PageRank and community detection algorithms. The results revealed that: (1) in the spatial structure of the island-oriented city, the cores are clustered on the island, while the centers are distributed in bands and dots outside the island; (2) the community units are distributed across the island; and (3) the space community boundary of the island-oriented city is quite different from the artificially defined administrative unit boundary. To summarize, by enhancing the delineation of urban spatial communities using the Gaussian kernel function and least-squares cross-validation, improving the model granularity, and integrating data from multiple public transport modes, this paper achieved a more precise division of urban spatial community structures. Consequently, it revealed the distinctive urban form and developmental pattern of the island-oriented city, holding significant implications for investigating urban spatial layout studies.
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Data Availability Statement
Some or all data, models, or codes generated or used during the study are proprietary or confidential and may only be provided with restrictions.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (No. 52078224) and the Overseas Study Program from China Scholarship Council.
References
Burger, M. J., B. van der Knaap, and R. S. Wall. 2014. “Polycentricity and the multiplexity of urban networks.” Eur. Plann. Stud. 22: 816–840. https://doi.org/10.1080/09654313.2013.771619.
Chen, Y., L. Wang, P. Yu, N. Nie, X. Yang, and Y. Chen. 2023. “Spatiotemporal linkages between administrative division adjustment and urban form: Political drivers of the urban polycentric structure.” Land 12 (9): 1674. https://doi.org/10.3390/land12091674.
Dong, Q.-F., D.-K. Chen, and T. Wang. 2019. “Urban community structure detection based on the OD of traffic analysis zones.” Mod. Phys. Lett. B 33 (13): 1950164. https://doi.org/10.1142/S0217984919501641.
Grydehøj, A. 2015. “Island city formation and urban island studies.” Area 47: 429–435. https://doi.org/10.1111/area.12207.
Haoming, Z., and L. Xiaoping. 2020. “Identifying changes in urban spatial structure using taxi trajectory data: A case study in Shenzhen.” [In Chinese.] Trop. Geogr. 40 (2): 217–228. http://www.rddl.com.cn/CN/10.13284/j.cnki.rddl.003224.
Hong, Y., and Y. Yao. 2019. “Hierarchical community detection and functional area identification with OSM roads and complex graph theory.” Int. J. Geogr. Inf. Sci. 33 (8): 1569–1587. https://doi.org/10.1080/13658816.2019.1584806.
Huai, Y., H. K. Lo, and K. F. Ng. 2021. “Monocentric versus polycentric urban structure: Case study in Hong Kong.” Transp. Res. Part A Policy Pract. 151: 99–118. https://doi.org/10.1016/j.tra.2021.05.004.
Li, Z.-T., W.-P. Nie, S.-M. Cai, Z.-D. Zhao, and T. Zhou. 2023. “Exploring the topological characteristics of urban trip networks based on taxi trajectory data.” Physica A 609: 128391. https://doi.org/10.1016/j.physa.2022.128391.
Liang, X., J. Zhao, L. Dong, and K. Xu. 2013. “Unravelling the origin of exponential law in intra-urban human mobility.” Sci. Rep. 3: 2983. https://doi.org/10.1038/srep02983.
Liu, X., L. Gong, Y. Gong, and Y. Liu. 2015. “Revealing travel patterns and city structure with taxi trip data.” J. Transp. Geogr. 43: 78–90. https://doi.org/10.1016/j.jtrangeo.2015.01.016.
Newman, M. E. 2004. “Fast algorithm for detecting community structure in networks.” Phys. Rev. E: Stat. Nonlinear Soft Matter Phys. 69 (6): 066133. https://doi.org/10.1103/PhysRevE.69.066133.
Pons, P., and M. Latapy. 2005. “Computing communities in large networks using random walks.” In Proc., Computer and Information Sciences, Int. Symp. on Computer and Information Sciences 2005, Lecture Notes in Computer Science, edited by P. Yolum, T. Güngör, F. Gürgen, and C. Özturan, 284–293. Berlin: Springer.
Qiao, M., Y. Wang, S. Wu, A. Luo, S. Ruan, and Y. Gu. 2019. “Fine-grained subjective partitioning of urban space using human interactions from social media data.” IEEE Access 7: 52085–52094. https://doi.org/10.1109/ACCESS.2019.2911664.
Reichardt, J., and S. Bornholdt. 2006. “Statistical mechanics of community detection.” Phys. Rev. E: Stat. Nonlinear Soft Matter Phys. 74 (1): 016110. https://doi.org/10.1103/PhysRevE.74.016110.
Rodrigue, J. P., C. Comtois, and B. Slack. 2017. The geography of transport systems, 4th ed. Abingdon, Oxon: Routledge.
Rosvall, M., and C. T. Bergstrom. 2008. “Maps of random walks on complex networks reveal community structure.” Proc. Natl. Acad. Sci. U S A 105 (4): 1118–1123. https://doi.org/10.1073/pnas.0706851105.
Shi, R.-Z., Y. Gao, and C. Yang. 2022. “Structural characteristics of multiscale jobs-housing space based on SCD: A case study of Xiamen.” Urban Plann. Forum 4: 87–94. https://doi.org/10.16361/j.upf.202204011.
Streeter, C. L., and D. F. Gillespie. 1992. “Social network analysis.” J. Social Serv. Res. 16: 201–222. https://doi.org/10.1300/J079v16n01_10.
Wu, C., D. Smith, and M. Wang. 2021. “Simulating the urban spatial structure with spatial interaction: A case study of urban polycentricity under different scenarios.” Comput. Environ. Urban Syst. 89: 101677. https://doi.org/10.1016/j.compenvurbsys.2021.101677.
Wu, J. 1990. Urban form in China: Structure, characteristics and evolution. Nanjing: Jiangsu Science and Technology Press.
Wu, Y., L. Wang, L. Fan, M. Yang, Y. Zhang, and Y. Feng. 2020. “Comparison of the spatiotemporal mobility patterns among typical subgroups of the actual population with mobile phone data: A case study of Beijing.” Cities 100: 102670. https://doi.org/10.1016/j.cities.2020.102670.
Yan, J., Z. Huang, T. Zhao, Y. Zhang, and F. Chang. 2023. “Transit travel community detection and evolutionary analysis: A case study of Shenzhen.” Sustainability 15 (7): 5900. https://doi.org/10.3390/su15075900.
Zhang, Y., S. Marshall, M. Cao, E. Manley, and H. Chen. 2021. “Discovering the evolution of urban structure using smart card data: The case of London.” Cities 112: 103157. https://doi.org/10.1016/j.cities.2021.103157.
Zhong, C., S. M. Arisona, X. Huang, M. Batty, and G. Schmitt. 2014. “Detecting the dynamics of urban structure through spatial network analysis.” Int. J. Geogr. Inf. Sci. 28: 2178–2199. https://doi.org/10.1080/13658816.2014.914521.
Zhou, M., Y. Yue, Q. Li, and D. Wang. 2016. “Portraying temporal dynamics of urban spatial divisions with mobile phone positioning data: A complex network approach.” ISPRS Int. J. Geo-Inf. 5: 240. https://doi.org/10.3390/ijgi5120240.
Ziwei, C., W. Cheng, C. Delai, and L. Lei. 2020. “Alighting stop determination of transit passengers based on expanded history trip records.” [In Chinese.] J. Nanjing Univ. (Nat. Sci.) 2, 227–235. https://doi.org/10.13232/j.cnki.jnju.2020.02.008.
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Published In
Journal of Urban Planning and Development
Volume 150 • Issue 4 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jun 7, 2023
Accepted: Jun 27, 2024
Published online: Aug 28, 2024
Published in print: Dec 1, 2024
Discussion open until: Jan 28, 2025
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