Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/116988
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorLai, Z-
dc.creatorLi, Y-
dc.creatorHe, T-
dc.creatorLiu, X-
dc.date.accessioned2026-01-21T03:54:38Z-
dc.date.available2026-01-21T03:54:38Z-
dc.identifier.urihttp://hdl.handle.net/10397/116988-
dc.language.isoenen_US
dc.publisherSpringer Chamen_US
dc.rights© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rightsThe following publication Lai, Z., Li, Y., He, T. et al. Characterizing intra-urban population migration networks: a case study of Shenzhen, China. Comput.Urban Sci. 5, 46 (2025) is available at https://doi.org/10.1007/s43762-025-00207-8.en_US
dc.subjectComplex networks analysisen_US
dc.subjectDemographic factorsen_US
dc.subjectIntra-urban migrationen_US
dc.subjectRelocation patternsen_US
dc.titleCharacterizing intra-urban population migration networks : a case study of Shenzhen, Chinaen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume5-
dc.identifier.issue1-
dc.identifier.doi10.1007/s43762-025-00207-8-
dcterms.abstractIntra-urban migration plays a crucial role in shaping urban structure and socio-economic dynamics. Most existing studies rely on small-scale survey data or have a coarse spatial resolution, making it difficult to conduct detailed network analysis at the urban scale to fully understand the complexity and dynamics of migration patterns. To address the gap, this study conducted a subdistrict-level fine-grained network analysis, involving more than 800,000 relocation data with detailed demographic and housing information at subdistrict levels in Shenzhen in 2015, to explore the overall relocation patterns and the relocation differences among different groups. The findings reveal that short-distance relocations dominate, with major hubs serving as central points of population flow in the study area (e.g., Gongming and Shajing areas). The relocation patterns also indicate specific pathways guiding movement between city areas. Moreover, demographic factors such as marital status, education level, and age significantly influence relocation behaviour. For instance, elderly individuals move infrequently, but when they do, they often relocate over longer distances. Men tend to migrate to diverse areas, while women prefer similar ones. Highly educated individuals move longer distances, typically within economic core areas. Overall, our study provides new perspectives for understanding the complex mechanisms of intra-urban population migration.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationComputational urban science, Dec. 2025, v. 5, no. 1, 46-
dcterms.isPartOfComputational urban science-
dcterms.issued2025-12-
dc.identifier.scopus2-s2.0-105016722590-
dc.identifier.eissn2730-6852-
dc.identifier.artn46-
dc.description.validate202601 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe work is financially supported by a project (CDL1) from Research Institute for Land and Space (RILS), The Hong Kong Polytechnic University.en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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