Multi-method analysis of urban green space accessibility: Influences of land use, greenery types, and individual characteristics factors
K. Zhang, Mingze Chen
Abstract
Urban green spaces (UGS) are an important foundation for supporting sustainable urban development and benefiting the well-being of residents. However, access to green spaces is a complex and dynamic process. Existing studies have mainly used a single method to assess UGS accessibility, and research on influencing factors has less focused on a multi-variable perspective. In this study, we innovatively integrated four methods—Container, Distance, Gravity, and 2SFCA—to assess UGS accessibility at the LSOA level in Inner London. We examined the impact of urban land use patterns, green space types, and individual characteristics on UGS accessibility. Then, Spearman's correlation analysis and the Ordinary Least Squares (OLS) regression model were used to check the relationship between multiple variables with UGS accessibility. The main findings are as follows: (1) The UGS accessibility results based on the multi-method reflect the variation in the distribution of UGS in Inner London, with more than 80% of LSOAs having below-average UGS accessibility; (2) UGS accessibility is significantly influenced by multiple factors, particularly race, income, education, crime, office, residential, and non-park (multiple green space types beyond parks). This study highlights inequalities in UGS accessibility and suggests strategies for policymakers to improve the integration of UGS in urban planning.