Urban–rural income change: Influences of landscape pattern and administrative spatial spillover effect. (August 2018)

• Record Type: Journal Article
• Authors: Zeng, Chen; Song, Yan; He, Qingsong; Liu, Yu
• Is Part Of: Applied geography. Volume 97(2018)
• Page Start: 248
• Page End: 262
• Publication Date: 2018-08
• Subjects: Urban–rural income gap -- Spatial spillover effect -- Administrative restructuring -- Wuhan agglomeration; Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2018.06.003
• Languages: English
• ISSNs: 0143-6228

Abstract

Abstract: China is experiencing unprecedented urbanisation, and urban–rural livelihood is transforming in various ways, with urban–rural income being a representative aspect. In this context, we raised two issues that are related to our study on the changes in urban and rural income and the related influencing factors, that is, whether landscape pattern affects urban–rural income and whether different spatial adjacencies generate various impacts on urban–rural income. We incorporated landscape pattern indicators and the administrative spatial spillover effect into a spatial regression model to address these issues using the Wuhan agglomeration as an example. We used aggregation indexes (AI) and the proportion of other construction lands (CLP) to represent landscape patterns. Then, multiple strategies were used to accommodate different spatial adjacency situations at the county level by introducing the magnified spatial factor for strengthening specific scenarios of spatial interactions. Results revealed that AI and CLP showed a remarkable relationship with per capita urban disposable income (UDI), but CLP was more powerful than AI in affecting per capita rural net income (RNI). Spatiotemporal differences were observed in urban–rural incomes, and an administrative spatial spillover effect was observed in the period of 2005–2015. The most powerful spatial interaction emerged when urban districts were neighbours for UDI and when a county-level city, a suburban district and a county were neighbours for RNI in 2005 and 2015. Coupled with urbanisation and urban–rural integration, the administrative spillover effect weakened for UDI and functioned varyingly for RNI. These results reaffirmed the influence of urbanisation and economic development on urban–rural income, and regional disparity is expected be considered when corresponding policy implications are made in the future. Highlights: We incorporate landscape pattern and the administrative spatial spillover effect to explore the driving factors of income change. We devise multiple strategies to measure the administrative spatial spillover effect at the county level in the spatial model. The proportion of other construction land and the aggregation level of the landscape have the significant influences on urban–rural income. The most powerful spatial interaction emerged when urban districts were neighbors for urban income in 2005 and 2015. When a county-level city, a suburban district, or a county were neighbors, the spatial interaction is the most powerful for rural income.