Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/117315
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorNa, Y-
dc.creatorLiu, X-
dc.date.accessioned2026-02-11T02:55:13Z-
dc.date.available2026-02-11T02:55:13Z-
dc.identifier.issn1361-1682-
dc.identifier.urihttp://hdl.handle.net/10397/117315-
dc.language.isoenen_US
dc.publisherWiley-Blackwellen_US
dc.subjectAcademic talent mobilityen_US
dc.subjectComplex networken_US
dc.subjectORCIDen_US
dc.subjectSpatial heterogeneityen_US
dc.subjectUrban agglomerationen_US
dc.titleSpatio-temporal dynamics of China's researcher mobility network, 1968–2020en_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume29-
dc.identifier.issue7-
dc.identifier.doi10.1111/tgis.70133-
dcterms.abstractThe mobility of scientific researchers is a key driver of knowledge flow and regional innovation; however, its spatial organization and structuring in China remains incompletely understood. Using the Web of Science data spanning more than five decades (1968–2020), this study reconstructs the evolution of China's researcher mobility network at both the urban agglomeration and city levels. Through network analysis and additional structural indicators, we identify a four-stage progression from a sparse, coastal-centered system to a nationally integrated yet increasingly divided network. The Yangtze River Delta, Beijing–Tianjin–Hebei, and the Greater Bay Area have historically been the dominant coastal regions. At the same time, the Middle Reaches of the Yangtze River became a secondary hub, and most inland areas mainly acted as sources of researchers. Inflows grew more concentrated over time, with the Gini coefficient rising from 0.12 (1968–2000) to 0.53 (2016–2020). At the city level, analyzing betweenness centrality alongside the Talent Mobility Balance Index reveals a structural–functional mismatch: some hubs, such as Beijing and Wuhan, serve as key connectors but experience persistent net outflows, while cities like Shenzhen and Zhengzhou attract large inflows despite having limited structural importance. This typology—dual-advantage, hub-loss, magnet non-hub, and peripheral-vulnerable—illustrates a “bridge–magnet” division of labor within China's scientific mobility system. The findings expand theories of scientific mobility by linking multi-scalar structures with their functional impacts, and they offer insights for tailored governance strategies aimed at balancing efficiency and fairness in the distribution of research talent.-
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationTransactions in GIS, Nov. 2025, v. 29, no. 7, e70133-
dcterms.isPartOfTransactions in GIS-
dcterms.issued2025-11-
dc.identifier.scopus2-s2.0-105020381977-
dc.identifier.eissn1467-9671-
dc.identifier.artne70133-
dc.description.validate202602 bcjz-
dc.description.oaNot applicableen_US
dc.identifier.SubFormIDG000961/2026-01en_US
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported by the General Research Fund of Hong Kong (15204121), National Natural Science Foundation of China (42171455).en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2027-11-30en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2027-11-30
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