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Detecting spatial heterogeneity in the determinants of intercity migration in China
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dc.contributor.author | Li, Sen | |
dc.contributor.author | Gu, Hengyu | |
dc.contributor.author | Shen, Jianfa | |
dc.date.accessioned | 2025-02-25T16:30:13Z | |
dc.date.available | 2025-02-25T16:30:13Z | |
dc.date.issued | 2023-04-13 | |
dc.identifier | 309747977 | |
dc.identifier | fc0c5885-7b2d-476e-b9a8-5e492b12088f | |
dc.identifier | 85147503501 | |
dc.identifier.citation | Li , S , Gu , H & Shen , J 2023 , ' Detecting spatial heterogeneity in the determinants of intercity migration in China ' , Population, Space and Place , vol. 29 , no. 3 , e2649 . https://doi.org/10.1002/psp.2649 | en |
dc.identifier.issn | 1544-8444 | |
dc.identifier.other | ORCID: /0000-0003-2953-5757/work/178181778 | |
dc.identifier.uri | https://hdl.handle.net/10023/31502 | |
dc.description | Funding: This work has received support from CUHK Vice-Chancellor's PhD Scholarship and CUHK Research Committee Research Fellowship Scheme. | en |
dc.description.abstract | Spatial variations exist in migration patterns and the processes causing the observed patterns. Yet, far less attention has been given to the latter. Even if a few studies have engaged in spatial heterogeneity in the process of migration, the scale multiplicity is not emphasised. This research aims to address these gaps with evidence from Chinese cities. To facilitate international comparisons, we use data on intercity migration over a 5-year interval instead of the floating population. Following an elaboration of highly imbalanced migration patterns at the city scale, we detect spatial heterogeneity in the processes underlying these patterns based on 17 potential determinants in four domains (namely labour market conditions, site-specific amenities, agglomerative effects, and institutional dividends) by multiscale geographically weighted regression. The results indicate that cities' attractiveness to migrants depends on a broad spectrum of factors whose influences are location-dependent and exhibit specific patterns, including east–west, south–north, and southwest–northeast gradient patterns, as well as discrete clustered and concentric patterns. The diagnostic analysis has confirmed that the provenance of spatial-varying effects is irrelevant to the nonlinearity. These effects are also scale-sensitive, showing that the influencing scales of the labour market status and agglomeration factors tend to be larger (i.e., their effects are spatially stationary) than those relevant to amenities and policies, which vary considerably (i.e., the impacts of some factors change significantly over space). These findings regarding spatial heterogeneity deepen our understanding of migration in China and highlight the fact that there is no ‘one size fits all’ approach for government policies designed to attract migrants. | |
dc.format.extent | 16 | |
dc.format.extent | 2256973 | |
dc.language.iso | eng | |
dc.relation.ispartof | Population, Space and Place | en |
dc.rights | © 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. | en |
dc.subject | China | en |
dc.subject | Intercity migration | en |
dc.subject | Migration pattern | en |
dc.subject | Multiscale geographically weighted regression | en |
dc.subject | Scale multiplicity | en |
dc.subject | Spatial heterogeneity | en |
dc.subject | NS | en |
dc.subject | MCC | en |
dc.title | Detecting spatial heterogeneity in the determinants of intercity migration in China | en |
dc.type | Journal article | en |
dc.contributor.institution | University of St Andrews.School of Geography & Sustainable Development | en |
dc.contributor.institution | University of St Andrews.Population and Health Research | en |
dc.contributor.institution | University of St Andrews.Bell-Edwards Geographic Data Institute | en |
dc.identifier.doi | 10.1002/psp.2649 | |
dc.description.status | Peer reviewed | en |
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