The compactness of spatial structure in Chinese cities: measurement, clustering patterns and influencing factors. Issue 1 (16th December 2020)

• Record Type: Journal Article
• Authors: Zhao, Fangqi; Tang, Lina; Qiu, Quanyi; Wu, Gang
• Is Part Of: Ecosystem health and sustainability. Volume 6:Issue 1(2020)
• Publication Date: 2020-12-16
• Subjects: Compact city -- urban spatial structure -- point of interest -- geographically weighted regression; Ecosystem health -- Periodicals; Environmental health -- Periodicals; Environmental policy -- Periodicals; Environmental engineering -- Periodicals; Environmental ethics -- Periodicals; Ecosystem management -- Periodicals; Ecosystem management; Environmental engineering; Environmental ethics; Environmental health; Environmental policy; Periodicals; 577
• DOI: 10.1080/20964129.2020.1743763
• Languages: English
• ISSNs: 2096-4129

Abstract

ABSTRACT: Rapid urbanization in China has led to an excessive urban expansion of built-up areas, which makes quantitative research on compact city important. We adopted density and the degree of mixed land use to measure the compactness of 160 Chinese cities. Spatial autocorrelation analysis was performed to identify spatial clustering patterns, and the relationships between compactness and five variables were explored through regression models. The result shows that in nearly half of the cases, the calculated values of two indices are less than the average. The high or low values of density and the degree of mixed land use tend to be spatially clustered. The hot spot regions of density and the degree of mixed land use lie mainly in the south of China, while the north present as cold spots or the insignificant regions. Urban compactness can be affected by multifaceted factors and the relationships between compactness and five variables are not consistent throughout the areas of analysis. The GWR model can identify this phenomenon and provides a better fit than the OLS model. This study proposed a new approach to measure the compactness, and the results of GWR analysis can conducive to appropriate policy-making based on different local conditions.