Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China
The urban–rural income gap is a principal indicator for evaluating the sustainable development of a region, and even the comprehensive strength of a country. The study of the urban–rural income gap and its changing spatial patterns and influence factors is an important basis for the formulation of i...
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MDPI AG
2021-09-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/10/10/1002 |
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| author | Ping Zhang Weiwei Li Kaixu Zhao Sidong Zhao |
| author_facet | Ping Zhang Weiwei Li Kaixu Zhao Sidong Zhao |
| author_sort | Ping Zhang |
| collection | DOAJ |
| description | The urban–rural income gap is a principal indicator for evaluating the sustainable development of a region, and even the comprehensive strength of a country. The study of the urban–rural income gap and its changing spatial patterns and influence factors is an important basis for the formulation of integrated urban–rural development planning. In this paper, we conduct an empirical study on 84 county-level cities in Gansu Province by using various analysis tools, such as GIS, GeoDetector and Boston Consulting Group Matrix. The findings show that: (1) The urban–rural income gap in Gansu province is at a high level in spatial correlation and agglomeration, leading to the formation of a stepped and solidified spatial pattern. (2) Different factors vary greatly in influence, for example, per capita Gross Domestic Product, alleviating poverty policy and urbanization rate are the most prominent, followed by those such as floating population, added value of secondary industry and number of Internet users. (3) The driving mechanism becomes increasingly complex, with the factor interaction effect of residents’ income dominated by bifactor enhancement, and that of the urban–rural income gap dominated by non-linear enhancement. (4) The 84 county-level cities in Gansu Province are classified into four types of early warning zones, and differentiated policy suggestions are made in this paper. |
| first_indexed | 2024-03-10T06:27:04Z |
| format | Article |
| id | doaj.art-8f7f0aa6b92a4be090b4a64aa9e9d8f9 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-10T06:27:04Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-8f7f0aa6b92a4be090b4a64aa9e9d8f92023-11-22T18:50:05ZengMDPI AGLand2073-445X2021-09-011010100210.3390/land10101002Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of ChinaPing Zhang0Weiwei Li1Kaixu Zhao2Sidong Zhao3College of Civil Engineering and Architecture, Jiaxing University, Jiaxing 314001, ChinaCollege of Landscape and Architectural Engineering, Guangxi Agricultural Vocational University, Nanning 530007, ChinaCollege of Urban and Environmental Science, Northwest University, Xi’an 710127, ChinaSchool of Architecture, Southeast University, Nanjing 210096, ChinaThe urban–rural income gap is a principal indicator for evaluating the sustainable development of a region, and even the comprehensive strength of a country. The study of the urban–rural income gap and its changing spatial patterns and influence factors is an important basis for the formulation of integrated urban–rural development planning. In this paper, we conduct an empirical study on 84 county-level cities in Gansu Province by using various analysis tools, such as GIS, GeoDetector and Boston Consulting Group Matrix. The findings show that: (1) The urban–rural income gap in Gansu province is at a high level in spatial correlation and agglomeration, leading to the formation of a stepped and solidified spatial pattern. (2) Different factors vary greatly in influence, for example, per capita Gross Domestic Product, alleviating poverty policy and urbanization rate are the most prominent, followed by those such as floating population, added value of secondary industry and number of Internet users. (3) The driving mechanism becomes increasingly complex, with the factor interaction effect of residents’ income dominated by bifactor enhancement, and that of the urban–rural income gap dominated by non-linear enhancement. (4) The 84 county-level cities in Gansu Province are classified into four types of early warning zones, and differentiated policy suggestions are made in this paper.https://www.mdpi.com/2073-445X/10/10/1002urban–rural income gapspatial patterndriving mechanismChina |
| spellingShingle | Ping Zhang Weiwei Li Kaixu Zhao Sidong Zhao Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China Land urban–rural income gap spatial pattern driving mechanism China |
| title | Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China |
| title_full | Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China |
| title_fullStr | Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China |
| title_full_unstemmed | Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China |
| title_short | Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China |
| title_sort | spatial pattern and driving mechanism of urban rural income gap in gansu province of china |
| topic | urban–rural income gap spatial pattern driving mechanism China |
| url | https://www.mdpi.com/2073-445X/10/10/1002 |
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