Integrating SolVES and social media analytics to quantify social value spatial patterns and driving mechanisms in mega-scale green spaces

Abstract

Assessing the social values (SVs) of mega-scale green spaces (MSGs) in regenerated brownfields remains challenging due to spatial heterogeneity and perceptual dynamics. This study developed a dual-modal framework integrating the Social Values for Ecosystem Services (SolVES) model and social media analytics (1,086 reviews) to assess SVs in Luogang Park (1,270 ha), Hefei. Results revealed: (1) spatial polarization of SVs, with aesthetic and recreational hot spots clustering in cultural landmarks (mean value index, M-VI = 10), while biodiversity cold spots (M-VI = 6) were dispersed in ecological zones; (2) key drivers included proximity to roads (20–50 m buffer, contribution = 32.7%) and density of service facilities (p < 0.01); and (3) ecological values (e.g., life-sustaining functions) received 58% less public attention than aesthetic values, as identified through semantic analysis. Based on these findings, spatial optimization strategies-such as cultural-recreational corridors and nature education zones-along with a diversified management mechanism were proposed. The framework advances ecosystem services (ES) assessment by reconciling spatial quantification (AUC > 0.8) and semantic perception, offering a transferable tool for equitable and multifunctional green space planning.