The Spatiotemporal Pattern and Driving Mechanism of Urban Sprawl in China’s Counties

Xu Yang, Xuan Zou, Xueqi Liu (), Qixuan Li, Siqian Zou and Ming Li
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Xu Yang: School of Economics and Trade, Hunan University, Changsha 410079, China
Xuan Zou: School of Economics and Trade, Hunan University, Changsha 410079, China
Xueqi Liu: School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China
Qixuan Li: School of Public Administration, Hunan University, Changsha 410012, China
Siqian Zou: School of Business, University of Bristol, Bristol BS81TH, UK
Ming Li: School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China

Land, 2023, vol. 12, issue 3, 1-16

Abstract: Cities in China do not constitute a few global metropolises, but are characterized by heterogeneity. Studying counties can give us a comprehensive picture of urban sprawl in China. This study measured the sprawl index of 1880 counties in China from 2005 to 2020 for the first time and then revealed the evolution of their spatiotemporal characteristics and driving mechanisms. The results revealed the following. (1) China’s counties had a noticeable sprawling trend from 2005 to 2020, and their evolutionary process was characterized by spatiotemporal heterogeneity. (2) From 2005 to 2020, the counties’ sprawl gradually evolved into a spatial distribution pattern of high in the east and low in the west. The spatial distribution of sprawl in county and municipal districts had the characteristics of an interlocking distribution. (3) High–high cluster areas of CSI are mainly distributed in plains, and hilly, basin, and plateau areas tend to be low–low cluster areas. High–low outliers were distributed in a “point–line” pattern along the railroad lines and a cluster pattern near railroad intersections and central cities. Low–high outliers had the trend of encircling the high–high cluster areas. (4) The coefficient of the natural drivers was higher but tended to decrease, while the coefficient of economic and spatial drivers was lower but gradually increased. This study is the first to refine the study of urban sprawl to the county scale, which provides a reference for decision making to optimize the spatial structure of counties and thus promote high-quality development.

Keywords: county; urban sprawl; spatiotemporal evolution; driving mechanism; GeoDetector (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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