Spatiotemporal Evolution Patterns of Urban Green Spaces and Influencing Factors in Shenyang

Abstract

Urban green spaces are an integral part of urban ecosystems and play a crucial role in improving the urban ecological environment and promoting sustainable urban development. This study aimed to quantify the spatiotemporal evolution of urban green spaces in central Shenyang and to assess how their relationships with selected urban development and socioeconomic drivers varied across space and time. Urban green space information for 2010, 2015, and 2020 was extracted from remote sensing imagery using ENVI and ArcGIS. Eight indicators—impervious surface intensity, population density, nighttime light intensity, economic density, industrial structure, road density, traffic accessibility, and land use intensity—were examined using spatial-pattern and contribution analyses, ordinary least squares regression (OLS), and geographically weighted regression (GWR). Urban green space areas were 330.56 km2 (23.55%), 344.55 km2 (24.55%), and 337.32 km2 (24.03%) in 2010, 2015, and 2020, respectively, representing a net increase of 6.76 km2 (2.05%) and an annual dynamic degree of 0.20% over the study period. Spatially, green spaces were concentrated in peripheral areas, fragmented in the central area, and prominently distributed along river corridors. At α = 0.05, coefficient-level OLS results showed negative associations with impervious surface intensity in 2010 (standardized β = −0.184, p < 0.001) and 2015 (β = −0.098, p = 0.005). In 2020, impervious surface intensity showed a small positive coefficient (β = 0.056, p = 0.043), whereas traffic accessibility showed a negative coefficient (β = −0.0556, p = 0.033). The OLS models had low explanatory power, with R2 values of 0.034, 0.008, and 0.007 and adjusted R2 values of 0.029, 0.003, and 0.002 for 2010, 2015, and 2020, respectively. The GWR local R2 values ranged from 0.000 to 0.310 and showed spatially shifting high-value areas, indicating marked spatial variation in model explanatory power. These findings provide case-specific evidence for Shenyang and a transferable analytical framework for spatially differentiated green space planning in comparable old industrial and rapidly restructuring cities.