Multilevel Change of Urban Green Space and Spatiotemporal Heterogeneity Analysis of Driving Factors

Huimin Wang (), Canrui Lin, Sihua Ou, Qianying Feng, Kui Guo, Xiaojian Wei and Jiazhou Xie
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Huimin Wang: College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Canrui Lin: College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Sihua Ou: College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Qianying Feng: College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Kui Guo: College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Xiaojian Wei: School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China
Jiazhou Xie: College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China

Sustainability, 2024, vol. 16, issue 11, 1-20

Abstract: Analyzing the change trend of urban green space (UGS) and exploring related driving forces can provide scientific reference for sustainable development in rapidly urbanizing areas. However, the spatial and temporal driving mechanisms of the drivers on UGS patterns at different scales are still not deeply understood. Based on the GlobeLand30 land cover data, nighttime lighting data and spatial statistics from 2000 to 2020, this study analyzed the size, shape and diversity of UGS in Guangzhou at the urban level, gradient level and township level with multiple landscape indices. Diversity means the richness of UGS patch types. The selected indices include percent of landscape (PLAND), largest path index (LPI), landscape shape index (LSI), aggregation index (AI) and Shannon’s diversity index (SHDI). The spatiotemporal heterogeneity of the drivers was then explored using the spatiotemporal weighted regression (GTWR) method. Results showed the following: (1) During 2000−2020, the total amount of UGS in Guangzhou increased slightly and then decreased gradually. UGS was mainly transferred into artificial surfaces (lands modified by human activities). (2) The UGS landscape showed a non-linear trend along the urban–rural gradient and fluctuated more in the interval of 20–60% urbanization level. PLAND, LPI and AI decreased significantly in areas with higher levels of urbanization. LSI increased and SHDI decreased significantly in areas with lower levels of urbanization. At township level, the landscape indices showed significant spatial autocorrelation. They transformed from discrete changes at the edge and at the junction of the administrative district to large-scale aggregated change, especially in northern areas. (3) The size of UGSs was mainly influenced by natural factors and population density, but their shape and diversity were mainly influenced by socio-economic factors. More regular shapes of green patches were expected in higher urbanization areas. Population agglomeration positively influenced green space patterns in the northeastern and southern regions (Zengcheng, Conghua and Nansha). Meanwhile the negative influence of urban expansion on the green space pattern in the central and southern regions decreased over time. This study contributes to an in-depth understanding of how the key factors affect the different changes of UGS with time and space and provides methodological support for the long-term zoning planning and management of UGS.

Keywords: urban green space; landscape pattern; spatial and temporal geographically weighted regression; urbanization; driving factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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