Characteristics and Influencing Factors of Population Migration under Different Population Agglomeration Patterns—A Case Study of Urban Agglomeration in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Index System
2.4. Study Methods
2.4.1. Measuring of Population Agglomeration Patterns in UAs
- (1)
- Net Migration Rate
- (2)
- Population Agglomeration Degree
- (3)
- Population Primacy Degree
2.4.2. Measuring of Factors Influencing Population Agglomeration Degree in UAs
- (1)
- Linear regression model
- (2)
- Geographical detector model
3. Results
3.1. Typology of Population Agglomeration Patterns in UAs
3.1.1. Evolution Characteristics of Population Agglomeration in UAs
3.1.2. Classification Results of Population Agglomeration Patterns in Different UAs
3.2. Population Migration Characteristics of UAs with Different Population Agglomeration Types
3.2.1. Population Migration Characteristics of Weakly Polycentric UAs
3.2.2. Population Migration Characteristics of Weakly Monocentric UAs
3.2.3. Population Migration Characteristics of Strongly Monocentric UAs
3.2.4. Population Migration Characteristics of Strongly Polycentric UAs
3.3. Analysis on the Influencing Factors of Population Migration in UAs of Different Population Agglomeration Types
- (1)
- For the weakly polycentric UAs, only two influencing factors have weakened explanatory power, namely the growth index of enterprise structure above designated size (Gies) and the greening coverage rate of built-up area (Gcr). From 2000 to 2020, the explanatory power of total passenger volume (Tpv) on the spatial distribution of population agglomeration has always been the strongest, with the explanatory power of 2000, 2010, and 2020 being 0.223, 0.200, and 0.233, respectively, and passing the significance test at the 5% level. For cities in weak polycentric UAs, the inner cities tend to have relatively lower levels of economic development, weaker polarization effects of the central cities, and relatively limited attractiveness to the population, while high total passenger volume means a more convenient transportation network and stronger population mobility, which is conducive to enhancing the cities’ attractiveness to population agglomeration.
- (2)
- For the weakly monocentric UAs, although there are two influential factors with weakened explanatory power, namely average wage of on-the-job employees (Wag) and the standard rate of industrial wastewater treatment (Iwt), the explanatory power of industry location entropy index (Ilei), scale of fiscal expenditure (Exp), and total passenger volume in municipal districts (Tpvmd) on the spatial distribution of population agglomeration are always relatively strong from 2000 to 2020, among which the explanatory power of Ilei is 0.434, 0.501, and 0.554 in 2000, 2010, and 2020, respectively, and passes the significance test at the 5% level. The explanatory power of Tpvmd is 0.333, 0.479, and 0.596 in 2000, 2010, and 2020, respectively, and passes the significance test at the 5% level. Meanwhile, the explanatory power of Exp is 0.485 and 0.586 in 2010 and 2020, respectively, and passes the significance test at the 5% level. For cities in weakly monocentric UAs, their ability to provide more alternative employment opportunities and better-quality public services will attract more mobile people to work and live in the city and enjoy the various services it offers. At the same time, closer intra-city transportation links not only indicate greater population mobility but also contribute to lower commuting costs and higher levels of population agglomeration.
- (3)
- For the strongly monocentric UAs, although there are three influencing factors with weaker explanatory power, namely per capita GDP (PerGDP), teacher–student ratio in primary and secondary schools (Edu), and the number of beds in welfare institutions per ten thousand people (Wel), overall, the explanatory power of PerGDP and urbanization rate (Urb) on the spatial distribution of population agglomeration is still stronger from 2000 to 2020, with explanatory power evolving from 0.424 and 0.346 in 2000 and 0.494 and 0.420 in 2010 to 0.417 and 0.518 in 2020 and passing the significance test at the 1% level. For cities in strongly monocentric UAs, higher GDP per capita means higher level of economic development, higher demand for labor and population, and relatively higher level of labor wages that can be provided to meet the consumption needs of the population in the city, which can easily attract the population. Meanwhile, cities with higher urbanization rates also usually have higher levels of economic development, which is more attractive to the population and more conducive to generating scale agglomeration effects and increasing population agglomeration.
- (4)
- For the strongly polycentric UAs, there is a weakening of the explanatory power of the five influencing factors of Tpv, Gies, Wag, Edu, and Wel. Meanwhile, the explanatory power of Urb on the spatial distribution of population agglomeration is always the strongest from 2000 to 2020, with the explanatory power being 0.470, 0.547, and 0.593, respectively, and passing the significance test at the 1% level. This type of UAs tends to have multiple central cities with closer inter-city ties, making it easier for the population to disperse to the various levels of cities within the UAs, which also allows for greater mobility and thus higher population agglomeration.
4. Discussion
4.1. Comparative Study
4.2. Additional Analysis from Other Perspectives
4.3. Implications of the Study
4.4. Study Deficiencies and Prospects
5. Conclusions
- (1)
- UAs are the main areas with high population agglomeration in China. The more developed UAs are, the more attractive they are to the population, and the higher their population agglomeration degree and net migration rates would be. The attraction of UAs to population leads to an increase in the unevenness of population distribution in China, as well as the unevenness degree of population distribution within UAs with different levels of development in China.
- (2)
- The population agglomeration patterns of Chinese UAs can be divided into four major categories, namely weakly polycentric, weakly monocentric, strongly monocentric, and strongly polycentric UAs, and will undergo the evolution pattern of weakly polycentric, weakly monocentric, strongly monocentric, and strongly polycentric UAs. From 2000 to 2020, China’s UAs are in a low-level stage dominated by weakly polycentric UAs. Additionally, it is also found that the types of UAs obtained by NTL data are generally consistent with the population agglomeration patterns of UAs derived from population data in this study.
- (3)
- From the perspective of factors influencing population agglomeration in UAs, the factors influencing population agglomeration patterns in different UAs are quite different. The explanatory power of total passenger volume to weakly polycentric UAs is always the strongest, the explanatory power of industrial location entropy index, scale of fiscal expenditure, and total passenger volume of municipal district is relatively strong for weakly monocentric UAs, while the explanatory power of per capita GDP and urbanization rate is relatively strong for strongly monocentric UAs, with the urbanization rate always being the strongest explanatory power for strongly polycentric UAs.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision; United Nations Department of Economic and Social Affairs: New York, NY, USA, 2018. [Google Scholar]
- Kundu, D.; Pandey, A.K. World urbanisation: Trends and patterns. In Developing National Urban Policies: Ways forward to Green and Smart Cities; Kundu, D., Sietchiping, R., Kinyanjui, M., Eds.; Springer: Singapore, 2020; pp. 13–49. [Google Scholar]
- Chen, Y.; Liu, Z.; Zhou, B. Population-environment dynamics across world’s top 100 urban agglomerations: With implications for transitioning toward global urban sustainability. J. Environ. Manag. 2022, 319, 115630. [Google Scholar] [CrossRef]
- Clark, B. Ebenezer Howard and The Marriage of Town and Country: An introduction to Howard’s Garden Cities of Tomorrow (selections). Organ. Environ. 2003, 16, 87–97. [Google Scholar] [CrossRef]
- Gottmann, J. Megalopolis or the urbanization of the northeastern seaboard. Econ. Geogr. 1957, 33, 189–200. [Google Scholar] [CrossRef]
- McGee, T.G. Managing the rural–urban transformation in East Asia in the 21st century. Sustain. Sci. 2008, 3, 155–167. [Google Scholar] [CrossRef]
- Scott, A.J. Globalization and the Rise of City-regions. Eur. Plan. Stud. 2001, 9, 813–826. [Google Scholar] [CrossRef]
- Lang, R.; Knox, P.K. The New Metropolis: Rethinking Megalopolis. Reg. Stud. 2009, 43, 789–802. [Google Scholar] [CrossRef]
- Chan, R.C.K.; Yao, S. Urbanization and sustainable metropolitan development in China: Patterns, problems and prospects. GeoJournal 1999, 49, 269–277. [Google Scholar] [CrossRef]
- Zhou, Y. Definition of urban place and statistical standards of urban population in China: Problem and solution. Asian Geogr. 1988, 7, 12–18. [Google Scholar]
- Xu, X.; Lin, X.; Zhou, C. A review of the research process of foreign metropolitan areas and its enlightenment. Urban Plan. Forum 2007, 168, 9–14. [Google Scholar]
- Fang, C.; Mao, Q.; Ni, P. Discussion on the scientific selection and development of China's urban agglomerations. Acta Geogr. Sin. 2015, 70, 515–527. [Google Scholar]
- Zhang, G.; Huang, W.; Zhou, C.; Cao, Y. Spatio-temporal characteristics of demographic distribution in China from the perspective of urban agglomeration. Acta Geogr. Sin. 2018, 73, 1513–1525. [Google Scholar]
- Ruyssen, I.; Rayp, G. Determinants of Intraregional Migration in Sub-Saharan Africa 1980–2000. J. Dev. Stud. 2014, 50, 426–443. [Google Scholar] [CrossRef]
- Liang, Z.; Li, Z.; Ma, Z. Changing Patterns of the Floating Population in China, 2000–2010. Popul. Dev. Rev. 2014, 40, 695–716. [Google Scholar] [CrossRef]
- Qi, W.; Abel, G.J.; Liu, S. Geographic transformation of China’s internal population migration from 1995 to 2015: Insights from the migration centerline. Appl. Geogr. 2021, 135, 102564. [Google Scholar] [CrossRef]
- Jinghu Pan, J.L. Research on spatial pattern of population mobility among cities: A case study of “Tencent Migration” big data in “National Day–Mid-Autumn Festival” vacation. Geogr. Res. 2019, 38, 1678–1693. [Google Scholar]
- Zhu, Y.; Chen, W. The settlement intention of China’s floating population in the cities: Recent changes and multifaceted individual-level determinants. Popul. Space Place 2010, 16, 253–267. [Google Scholar] [CrossRef]
- Bosker, M.; Buringh, E. City seeds: Geography and the origins of the European city system. J. Urban Econ. 2017, 98, 139–157. [Google Scholar] [CrossRef]
- Gao, X.; Xu, Z.; Niu, F.; Long, Y. An evaluation of China’s urban agglomeration development from the spatial perspective. Spat. Stat. 2017, 21, 475–491. [Google Scholar] [CrossRef]
- Chen, L. Theoretical basis and empirical studies of agglomeration economy influencing urban economic growth: Literature review and prospect. Prog. Geogr. 2022, 41, 1325–1337. [Google Scholar] [CrossRef]
- Zhao, M.; Derudder, B.; Huang, J. Examining the transition processes in the Pearl River Delta polycentric mega-city region through the lens of corporate networks. Cities 2017, 60, 147–155. [Google Scholar] [CrossRef]
- Liu, X.; Yan, X.; Wang, W.; Titheridge, H.; Wang, R.; Liu, Y. Characterizing the polycentric spatial structure of Beijing Metropolitan Region using carpooling big data. Cities 2021, 109, 103040. [Google Scholar] [CrossRef]
- Deng, Y.; Liu, J.; Liu, Y.; Luo, A. Detecting Urban Polycentric Structure from POI Data. ISPRS Int. J. Geo-Inf. 2019, 8, 283. [Google Scholar] [CrossRef]
- Huang, Y.; Liao, R. Polycentric or monocentric, which kind of spatial structure is better for promoting the green economy? Evidence from Chinese urban agglomerations. Environ. Sci. Pollut. Res. 2021, 28, 57706–57722. [Google Scholar] [CrossRef]
- Wang, X.; Li, X.; Zhang, S. Has the polycentric spatial structure promoted high-quality urban development. China Popul. Resour. Environ. 2022, 32, 57–67. [Google Scholar]
- Zhu, Z.; Zheng, B.; He, Q. Study on Evolution of Spatial Structure of Pearl River Delta Urban Agglomeration and its Effects. Econ. Geogr. 2011, 31, 404–408. [Google Scholar]
- Wang, C.; Liu, X. The Influence of Polycentric Spatial Structure of Urban Agglomeration on Rural Revitalization: Based on 19 Urban Agglomerations in China. Econ. Geogr. 2023, 43, 55–63. [Google Scholar]
- Sun, T. Evolution of Agglomeration and Its Spatial Structure with Economic Growth in Three Major Metropolitan Regions of China. Econ. Geogr. 2016, 36, 63–70. [Google Scholar]
- Li, W.; Sun, B.; Zhang, T.; Zhang, Z. Polycentric spatial structure and its economic performance: Evidence from meta-analysis. Reg. Stud. 2022, 56, 1888–1902. [Google Scholar] [CrossRef]
- Li, Y.; Derudder, B. Dynamics in the polycentric development of Chinese cities, 2001–2016. Urban Geogr. 2020, 43, 272–292. [Google Scholar] [CrossRef]
- Liu, X.; Wang, M. How polycentric is urban China and why? A case study of 318 cities. Landsc. Urban Plan. 2016, 151, 10–20. [Google Scholar] [CrossRef]
- Yu, H.; Yang, J.; Li, T.; Jin, Y.; Sun, D. Morphological and functional polycentric structure assessment of megacity: An integrated approach with spatial distribution and interaction. Sustain. Cities Soc. 2022, 80, 103800. [Google Scholar] [CrossRef]
- Brezzi, M.; Veneri, P. Assessing Polycentric Urban Systems in the OECD: Country, Regional and Metropolitan Perspectives. Eur. Plan. Stud. 2014, 23, 1128–1145. [Google Scholar] [CrossRef]
- Phelps, N.A. Clusters, Dispersion and the Spaces in Between: For an Economic Geography of the Banal. Urban Stud. 2016, 41, 971–989. [Google Scholar] [CrossRef]
- Combes, P.-P.; Duranton, G.; Gobillon, L. Spatial wage disparities: Sorting matters! J. Urban Econ. 2008, 63, 723–742. [Google Scholar] [CrossRef]
- Connolly, C.; Keil, R.; Ali, S.H. Extended urbanisation and the spatialities of infectious disease: Demographic change, infrastructure and governance. Urban Stud. 2020, 58, 245–263. [Google Scholar] [CrossRef]
- Cervero, R. Efficient Urbanisation: Economic Performance and the Shape of the Metropolis. Urban Stud. 2016, 38, 1651–1671. [Google Scholar] [CrossRef]
- Zhu, D.; Wang, Y.; Peng, S.; Zhang, F. Influence Mechanism of Polycentric Spatial Structure on Urban Land Use Efficiency: A Moderated Mediation Model. Int. J. Environ. Res. Public Health 2022, 19, 16478. [Google Scholar] [CrossRef]
- Han, S.; Sun, B.; Zhang, T. Mono- and polycentric urban spatial structure and PM2.5 concentrations: Regarding the dependence on population density. Habitat Int. 2020, 104, 102257. [Google Scholar] [CrossRef]
- Han, S.; Miao, C. Does a Polycentric Spatial Structure Help to Reduce Industry Emissions? Int. J. Environ. Res. Public Health 2022, 19, 8167. [Google Scholar] [CrossRef]
- Yang, J.; French, S.; Holt, J.; Zhang, X. Measuring the Structure of U.S. Metropolitan Areas, 1970–2000. J. Am. Plan. Assoc. 2012, 78, 197–209. [Google Scholar] [CrossRef]
- Chen, H.; Luo, H.; Song, J. Population distribution and industrial evolution of the Tokyo Metropolitan Area. Prog. Geogr. 2020, 39, 1498–1511. [Google Scholar] [CrossRef]
- Yi, D.; Shi, Y. Population Distribution, Growth Pole and Incubation of World-class Megalopolis: A Comparison between Northeastern Megalopolis in the United States and Beijing-Tianjin-Hebei Megalopolis in China. Popul. Res. 2016, 40, 87–98. [Google Scholar]
- Hajrasouliha, A.H.; Hamidi, S. The typology of the American metropolis: Monocentricity, polycentricity, or generalized dispersion? Urban Geogr. 2016, 38, 420–444. [Google Scholar] [CrossRef]
- Bailey, N.; Turok, I. Central Scotland as a Polycentric Urban Region: Useful Planning Concept or Chimera? Urban Stud. 2016, 38, 697–715. [Google Scholar] [CrossRef]
- Yan, D.; Sun, W.; Wang, Y.; Xu, S. Change in distribution and growth shifts of population in the Yangtze River Delta and influencing factors. Prog. Geogr. 2020, 39, 2068–2082. [Google Scholar] [CrossRef]
- Xue, F.; Li, M.; Dang, A. Centrality and Symmetry of People Flow Network Structure of the Yangtze River Delta Urban Agglomeration at Multi-Spatial Scales. Econ. Geogr. 2020, 40, 49–58. [Google Scholar]
- Zheng, B.; Zhong, Y. Study on Spatial Structure of Population Migration Network of Urban Agglomeration in the Middle Yangtze River Based on Complex Network. Econ. Geogr. 2020, 40, 118–128. [Google Scholar]
- He, Y.; Zhou, G.; Tang, C.; Fan, S.; Guo, X. The spatial organization pattern of urban-rural integration in urban agglomerations in China: An agglomeration-diffusion analysis of the population and firms. Habitat Int. 2019, 87, 54–65. [Google Scholar] [CrossRef]
- Wang, Z.; Yang, S.; Gong, F.; Liu, S. Identification of Urban Agglomerations Deformation Structure Based on Urban-flow Space: A Case Study of the Yangtze River Delta Urban Agglomeration. Sci. Geogr. Sin. 2017, 37, 1337–1344. [Google Scholar]
- Shi, Y.; Zhu, Y.; Feng, D.; Wang, F.; Xiong, W. Polycentric Network Development Patterns of Zhongyuan Urban Agglomeration. Sci. Geogr. Sin. 2012, 32, 1431–1438. [Google Scholar]
- Ye, Q.; Zhang, L.; Peng, P.; Huang, J. The Network Characteristics of Urban Agglomerations in the Middle Reaches of the Yangtze River Based on Baidu Migration Data. Econ. Geogr. 2017, 37, 53–59. [Google Scholar]
- Song, J.; Fang, C.; Song, D. Spatial Structure Stability of Urban Agglomerations in China. Acta Geogr. Sin. 2006, 61, 1311–1325. [Google Scholar]
- Sun, B.; Hua, J.; Li, W.; Zhang, T. Spatial structure change and influencing factors of city clusters in China: From monocentric to polycentric based on population distribution. Prog. Geogr. 2017, 36, 1294–1303. [Google Scholar]
- Li, J.; Zhang, W.; Sun, T.; Zhang, A. Characteristics of clustering and economic performance of urban agglomerations in China. Acta Geogr. Sin. 2014, 69, 474–484. [Google Scholar]
- Liu, X.; Derudder, B.; Wu, K. Measuring polycentric urban development in China: An intercity transportation network perspective. Reg. Stud. 2016, 50, 1302–1315. [Google Scholar] [CrossRef]
- Lan, F.; Da, H.; Wen, H.; Wang, Y. Spatial Structure Evolution of Urban Agglomerations and Its Driving Factors in Mainland China: From the Monocentric to the Polycentric Dimension. Sustainability 2019, 11, 610. [Google Scholar] [CrossRef]
- Borderon, M.; Sakdapolrak, P.; Muttarak, R.; Kebede, E.; Pagogna, R.; Sporer, E. Migration influenced by environmental change in Africa: A systematic review of empirical evidence. Demogr. Res. 2019, 41, 491–544. [Google Scholar] [CrossRef]
- Reuveny, R.; Moore, W.H. Does Environmental Degradation Influence Migration_Emigration to Developed Countries in the Late 1980s and 1990s. Soc. Sci. Q. 2009, 90, 461–479. [Google Scholar] [CrossRef]
- Hoffmann, R.; Dimitrova, A.; Muttarak, R.; Crespo Cuaresma, J.; Peisker, J. A meta-analysis of country-level studies on environmental change and migration. Nat. Clim. Chang. 2020, 10, 904–912. [Google Scholar] [CrossRef]
- Alperovich, G. Economic development and population concentration. Econ. Dev. Cult. Chang. 1992, 41, 63–74. [Google Scholar] [CrossRef]
- Liu, Y.; Shen, J. Spatial patterns and determinants of skilled internal migration in China, 2000–2005. Pap. Reg. Sci. 2014, 93, 749–771. [Google Scholar] [CrossRef]
- Ye, C.; Zhu, J.; Li, S.; Yang, S.; Chen, M. Assessment and analysis of regional economic collaborative development within an urban agglomeration: Yangtze River Delta as a case study. Habitat Int. 2019, 83, 20–29. [Google Scholar] [CrossRef]
- Zhang, P.; Zhao, Y.; Zhu, X.; Cai, Z.; Xu, J.; Shi, S. Spatial structure of urban agglomeration under the impact of high-speed railway construction: Based on the social network analysis. Sustain. Cities Soc. 2020, 62, 102404. [Google Scholar] [CrossRef]
- Zhou, C.; Li, M.; Zhang, G.; Chen, J.; Zhang, R.; Cao, Y. Spatiotemporal characteristics and determinants of internal migrant population distribution in China from the perspective of urban agglomerations. PLoS ONE 2021, 16, e0246960. [Google Scholar] [CrossRef]
- Wang, J.; Liu, B.; Li, Y. Spatial-temporal characteristics and influencing factors of population distribution and floating changes in Beijing-Tianjin-Hebei region. Geogr. Res. 2018, 37, 1802–1817. [Google Scholar]
- Chen, M.; Guo, S.; Lu, D. Characteristics and spatial patterns of floating population in the Beijing-Tianjin-Hebei urban agglomeration under the background of new urbanization. Prog. Geogr. 2018, 37, 363–372. [Google Scholar]
- Sun, T.; Han, Z.; Wang, L.; Li, G. Suburbanization and subcentering of population in Beijing metropolitan area: A nonparametric analysis. Chin. Geogr. Sci. 2012, 22, 472–482. [Google Scholar] [CrossRef]
- He, X.; Cao, Y.; Zhou, C. Evaluation of Polycentric Spatial Structure in the Urban Agglomeration of the Pearl River Delta (PRD) Based on Multi-Source Big Data Fusion. Remote Sens. 2021, 13, 3639. [Google Scholar] [CrossRef]
- Zeng, C.; Song, Y.; Cai, D.; Hu, P.; Cui, H.; Yang, J.; Zhang, H. Exploration on the spatial spillover effect of infrastructure network on urbanization: A case study in Wuhan urban agglomeration. Sustain. Cities Soc. 2019, 47, 101476. [Google Scholar] [CrossRef]
- Fang, C.; Bao, C.; Ma, H. China Urban Agglomeration Development Report in 2016; Science Press: Beijing, China, 2017. [Google Scholar]
- National Bureau of Statistics. China City Statistical Yearbook 2021; China Statistics Press: Beijing, China, 2020. [Google Scholar]
- Office of the Leading Group of the State Council for the Fifth National Population Census. Tabulation on 2000 China Population Census by County; China Statistics Press: Beijing, China, 2000. [Google Scholar]
- Office of the Leading Group of the State Council for the Seventh National Population Census. Tabulation on 2020 China Population Census by County; China Statistics Press: Beijing, China, 2020. [Google Scholar]
- He, C.; Chen, T.; Mao, X.; Zhou, Y. Economic transition, urbanization and population redistribution in China. Habitat Int. 2016, 51, 39–47. [Google Scholar] [CrossRef]
- Hunt, G.L. Equilibrium and disequilibrium in migration modelling. Reg. Stud. 1993, 27, 341–349. [Google Scholar] [CrossRef]
- Cao, Z.; Zheng, X.; Liu, Y.; Li, Y.; Chen, Y. Exploring the changing patterns of China’s migration and its determinants using census data of 2000 and 2010. Habitat Int. 2018, 82, 72–82. [Google Scholar] [CrossRef]
- Shen, S.; Shen, G. Analysis on the Spatial Structure of Inter-provincial Migrant in China. Popul. J. 2020, 42, 103–112. [Google Scholar]
- Fu, Y.; Gabriel, S.A. Labor migration, human capital agglomeration and regional development in China. Reg. Sci. Urban Econ. 2012, 42, 473–484. [Google Scholar] [CrossRef]
- Wang, Z.; Xu, J.; Zhu, C.; Qi, Y.; Xu, L. The County Accessibility Divisions in China and Its Correlation with Population Distribution. Acta Geogr. Sin. 2010, 65, 416–426. [Google Scholar]
- Chen, Y.; Mei, L. Quantitative Analysis of Population Distribution and Influencing Factors of Resource-based Cities in Northeast China. Sci. Geogr. Sin. 2018, 38, 402–409. [Google Scholar]
- Bereitschaft, B.; Cammack, R. Neighborhood diversity and the creative class in Chicago. Appl. Geogr. 2015, 63, 166–183. [Google Scholar] [CrossRef]
- Cui, C.; Wang, Z.; He, P.; Yuan, S.; Niu, B.; Kang, P.; Kang, C. Escaping from pollution: The effect of air quality on inter-city population mobility in China. Environ. Res. Lett. 2019, 14, 124025. [Google Scholar] [CrossRef]
- Cao, G.; Liu, J.; Liu, T. Examining the role of air quality in shaping the landscape of China’s internal migration: Phase characteristics, push and pull effects. Geogr. Res. 2021, 40, 199–212. [Google Scholar]
- Buch, T.; Hamann, S.; Niebuhr, A.; Rossen, A. What Makes Cities Attractive? The Determinants of Urban Labour Migration in Germany. Urban Stud. 2014, 51, 1960–1978. [Google Scholar] [CrossRef]
- Liu, S.; Deng, Y.; Hu, Z. Research on Classification Methods and Spatial Patterns of the Regional Types of China’s Floating Population. Acta Geogr. Sin. 2010, 65, 1187–1197. [Google Scholar]
- Liu, R.; Feng, Z.; You, Z. Research on the Spatial Pattern and Formation Mechanisms of Population Agglomeration and Shrinking in China. China Popul. Resour. Environ. 2010, 20, 89–94. [Google Scholar]
- Jefferson, M. The Law of the Primate City. Geogr. Rev. 1939, 29, 226–232. [Google Scholar] [CrossRef]
- Meijers, E.J.; Burger, M.J. Spatial Structure and Productivity in US Metropolitan Areas. Environ. Plan. A Econ. Space 2010, 42, 1383–1402. [Google Scholar] [CrossRef]
- Wang, J.; Xu, C. Geodetector: Principle and prospective. Acta Geogr. Sin. 2017, 72, 116–134. [Google Scholar]
- Li, H.; Zhang, M.; Wang, R. The effects of regional geographical factors on children's respiratory diseases in Jingyuan, Ningxia. Geogr. Res. 2019, 38, 2889–2898. [Google Scholar]
- Cao, G.; Chen, S.; Liu, T. Changing spatial patterns of internal migration to five major urban agglomerations in China. Acta Geogr. Sin. 2021, 76, 1334–1349. [Google Scholar]
- Guan, X.; Wei, H.; Lu, S.; Su, H. Mismatch distribution of population and industry in China: Pattern, problems and driving factors. Appl. Geogr. 2018, 97, 61–74. [Google Scholar] [CrossRef]
Type | Data Sources | Accessed Date | Acquisition Websites |
---|---|---|---|
Demographic Data | Tabulation on the Population Census of the People’s Republic of China by County(2000, 2010, 2020) | 15 September 2021 | http://www.stats.gov.cn/ (accessed on 25 September 2021) [74,75] |
Economic Data | China City Statistical Yearbook (2001, 2011, 2021) | 1 September 2021 | https://data.cnki.net/yearBook/ (accessed on 25 September 2021) [73] |
Vector Layer | Resource and Environmental Data Center | 25 September 2021 | http://www.resdc.cn/ (accessed on 25 September 2021) |
NTL Data | The National Center for Environmental Information (NCEI) of National Oceanic and Atmospheric Administration (NOAA) (2000, 2010, 2020) | 5 September 2021 | https://eogdata.mines.edu/products/vnl/ (accessed on 25 September 2021) |
Factors | Name of Indicator (Abbreviation) | Representational Meaning | References |
---|---|---|---|
economic development factors | per capita GDP (PerGDP) | reflecting the regional economic development degree | [62,85] |
Proportion of tertiary industry in GDP (Ind) | reflecting the urban industrial modernization level | [67,76] | |
Investment scale of fixed assets (Fai) | reflecting the urban economic development vitality | [78] | |
Total passenger volume (Tpv) | reflecting the convenience of personal mobility ability of other cities outside the city | [65,82] | |
Total freight volume (Tfv) | reflecting the convenience of goods mobility ability of other cities outside the city | [82] | |
Total passenger volume in municipal districts (Tpvmd) | reflecting the convenience of personal mobility ability within the city | [81] | |
Industry location entropy index (Ilei) | reflecting the urban employment structure | [13] | |
Growth index of enterprise structure above Designated Size (Gies) | reflecting the economic development vitality of urban industrial subjects | [13] | |
social conditions factors | Scale of fiscal expenditure (Exp) | reflecting the ability to provide urban public services and infrastructure | [82] |
Average wage of on-the-job employees (Wag) | reflecting the urban average wage level | [63,66] | |
Teacher–student ratio in primary and secondary schools (Edu) | reflecting the ability to provide education resources | [80,85] | |
Number of beds in welfare institutions per ten thousand people (Wel) | reflecting the ability to provide medical service | [67,82] | |
Standard rate of industrial wastewater treatment (Iwt) | reflecting the urban production environment conditions | [82] | |
Greening coverage rate of built-up area (Gcr) | reflecting the urban living environment conditions | [84,85] | |
Urbanization rate (Urb) | reflecting the population migration degree to city | [76,86] |
Type | Name | ||
---|---|---|---|
2000 | 2010 | 2020 | |
Weakly Polycentric UAs | CYN, BBG, MYZ, CGZ, HBCC, GZH, NYI, HBEY, CSX, MSLN, NTM, LZXN (n = 12) | CYN, BBG, MYZ, CGZ, HBCC, HBEY, CSX, MSLN, LZXN (n = 9) | CYN, BBG, MYZ, CGZ, HBCC, HBEY, MSLN (n = 7) |
Weakly Moncentric UAs | NTM, GZH, NYI (n = 3) | LZXN, NTM, GZH, NYI, CSX (n = 5) | |
Strongly Moncentric UAs | CDCQ, YRD (n = 2) | CDCQ, YRD, BTH (n = 3) | CDCQ, YRD, BTH (n = 3) |
Strongly Polycentric UAs | WCFS, SDP, CPL, PRD, BTH (n = 5) | WCFS, SDP, CPL, PRD (n = 4) | WCFS, SDP, CPL, PRD (n = 4) |
Characteristics | Weakly Polycentric UAs | Weakly Moncentric UAs | Strongly Moncentric UAs | Strongly Polycentric UAs |
---|---|---|---|---|
Population agglomeration degree | low | lower | higher | high |
Total population | small | larger | large | large |
Population primacy degree | low | higher | high | lower |
Population increment | The population increment of central cities is larger while the polarization effect is weaker | The primary city is experiencing more population growth and the polarization effect is increasing | The population growth of the central cities is large, and the polarization effect is more prominent | Large incremental growth in central cities, narrowing the gap with the primary city and increasing the trickle-down effect |
Total in-migrant population | low | lower | higher | higher |
Population attraction degree | weaker | weaker, but improving | stronger | strong |
Population outflow | The inflow of population is large in the municipal districts of central cities, while the outflow of population is large in the surrounding counties. | The inflow of population in the municipal district of the central city is large, the inflow of population in the municipal district of the general city is small, and the majority of the county area shows a large outflow of population. | Both the municipal districts of the central cities and the surrounding counties show a large inflow of population; the municipal districts of the general cities show a large inflow of population, while the surrounding counties mostly show a large outflow of population | The central cities have high population inflows in their municipal districts and surrounding counties have lower population outflows |
Natural population growth rate | high | higher | lower | lower |
Indicators | Coefficient (t Statistic) | VIF | ||||
---|---|---|---|---|---|---|
2000 | 2010 | 2020 | 2000 | 2010 | 2020 | |
PerGDP | 0.213 (1.301) | −0.465 *** (−2.623) | 0.106 (0.555) | 7.92 | 7.71 | 5.02 |
Ind | 0.394 (1.505) | 0.083 (0.354) | 1.734 *** (4.710) | 2.38 | 2.44 | 2.33 |
Fai | −0.281 *** (−2.807) | −0.012 (−0.091) | −0.269 ** (−2.441) | 5.16 | 7.11 | 5.49 |
Tpv | 0.098 (1.276) | 0.255 *** (3.390) | 0.056 (0.937) | 2.48 | 3.19 | 2.11 |
Tfv | −0.055 (−0.617) | −0.053 (−0.655) | 0.006 (0.085) | 3.42 | 2.83 | 2.38 |
Tpvmd | 0.001 (0.025) | 0.003 (0.073) | 0.043 (0.645) | 3.96 | 3.31 | 4.69 |
Ilei | 0.232 ** (2.600) | 0.183 *** (2.828) | 0.406 *** (5.391) | 5.24 | 3.54 | 4.47 |
Gies | 0.192 * (1.937) | 0.456 *** (4.396) | 0.409 *** (4.167) | 8.68 | 6.92 | 5.36 |
Exp | 0.190 ** (2.156) | −0.437 *** (−2.702) | −0.344 ** (−2.248) | 5.56 | 9.16 | 8.27 |
Wag | 0.124 (0.478) | 0.415 (1.385) | −0.108 (−0.310) | 3.39 | 3.39 | 3.1 |
Edu | −0.548 ** (−2.513) | −0.828 *** (−3.230) | −0.802 ** (−2.580) | 2.76 | 1.47 | 1.44 |
Wel | −0.589 *** (−3.150) | −0.482 ** (−2.562) | −0.602 *** (−2.745) | 3.16 | 1.86 | 1.89 |
Iwt | 0.146 * (1.738) | 0.078 (0.591) | −0.056 (−0.103) | 1.23 | 1.31 | 1.12 |
Gcr | 0.035 (0.360) | −0.066 (−0.619) | 0.246 (0.536) | 1.5 | 1.28 | 1.5 |
Urb | −0.001 (−0.005) | 1.077 *** (3.710) | 0.398 (1.022) | 5.24 | 5.54 | 4.05 |
Indicators | Weakly Polycentric UAs | Weakly Moncentric UAs | Strongly Moncentric UAs | Strongly Polycentric UAs | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
2000 | 2010 | 2020 | 2000 | 2010 | 2020 | 2000 | 2010 | 2020 | 2000 | 2010 | 2020 | |
PerGDP | 0.051 | 0.006 | 0.136 * | 0.050 | 0.200 | 0.299 | 0.424 *** | 0.494 *** | 0.417 *** | 0.167 | 0.182 | 0.179 |
Ind | 0.022 | 0.019 | 0.054 | 0.239 | 0.351 | 0.302 | 0.048 | 0.229 ** | 0.133 | 0.208 * | 0.267 ** | 0.293 ** |
Fai | 0.010 | 0.018 | 0.016 | 0.050 | 0.578 *** | 0.539 ** | 0.178 * | 0.308 *** | 0.336 *** | 0.068 | 0.069 | 0.091 |
Tpv | 0.223 *** | 0.200 ** | 0.233 *** | 0.230 | 0.270 | 0.301 | 0.129 | 0.305 *** | 0.236 ** | 0.176 | 0.249 ** | 0.167 |
Tfv | 0.045 | 0.029 | 0.060 | 0.306 | 0.242 | 0.476 ** | 0.138 | 0.287 *** | 0.289 *** | 0.026 | 0.068 | 0.215 * |
Tpvmd | 0.076 | 0.129 * | 0.119 | 0.333 * | 0.479 ** | 0.596 *** | 0.249 ** | 0.219 ** | 0.269 ** | 0.228 ** | 0.195 * | 0.470 *** |
Ilei | 0.102 | 0.199 ** | 0.213 *** | 0.434 | 0.501 ** | 0.554 *** | 0.199 * | 0.313 *** | 0.367 *** | 0.105 | 0.099 | 0.352 *** |
Gies | 0.141 * | 0.054 | 0.030 | 0.167 | 0.464 ** | 0.419 * | 0.238 ** | 0.323 *** | 0.345 *** | 0.177 | 0.187 * | 0.060 |
Exp | 0.085 | 0.013 | 0.099 | 0.284 | 0.485 ** | 0.586 *** | 0.316 *** | 0.234 ** | 0.378 *** | 0.247 ** | 0.171 | 0.290 ** |
Wag | 0.012 | 0.011 | 0.083 | 0.313 | 0.010 | 0.178 | 0.308 *** | 0.475 *** | 0.446 *** | 0.297 ** | 0.186 * | 0.151 |
Edu | 0.058 | 0.122 * | 0.160 ** | 0.082 | 0.060 | 0.124 | 0.182 * | 0.148 | 0.108 | 0.113 | 0.177 | 0.108 |
Wel | 0.048 | 0.078 | 0.049 | 0.134 | 0.159 | 0.210 | 0.225 ** | 0.047 | 0.046 | 0.194 * | 0.033 | 0.117 |
Iwt | 0.062 | 0.052 | 0.094 | 0.140 | 0.255 | 0.123 | 0.087 | 0.152 | 0.127 | 0.012 | 0.069 | 0.066 |
Gcr | 0.199 ** | 0.100 | 0.166 ** | 0.272 | 0.277 | 0.287 | 0.038 | 0.162 | 0.193 * | 0.025 | 0.134 | 0.026 |
Urb | 0.075 | 0.037 | 0.079 | 0.199 | 0.174 | 0.461 *** | 0.346 *** | 0.420 *** | 0.518 *** | 0.470 *** | 0.547 *** | 0.593 *** |
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Cao, Y.; He, X.; Zhou, C. Characteristics and Influencing Factors of Population Migration under Different Population Agglomeration Patterns—A Case Study of Urban Agglomeration in China. Sustainability 2023, 15, 6909. https://doi.org/10.3390/su15086909
Cao Y, He X, Zhou C. Characteristics and Influencing Factors of Population Migration under Different Population Agglomeration Patterns—A Case Study of Urban Agglomeration in China. Sustainability. 2023; 15(8):6909. https://doi.org/10.3390/su15086909
Chicago/Turabian StyleCao, Yongwang, Xiong He, and Chunshan Zhou. 2023. "Characteristics and Influencing Factors of Population Migration under Different Population Agglomeration Patterns—A Case Study of Urban Agglomeration in China" Sustainability 15, no. 8: 6909. https://doi.org/10.3390/su15086909