Visual–Morphological Drivers of Restorative Perception in Dog-Friendly Urban Green Spaces

This study examines how visual features and green space morphology jointly shape restorative perception in dog-friendly urban green spaces using a data-driven analytical framework. A self-constructed dataset integrating street-view imagery, landscape element composition, and morphological metrics was developed to quantify visual entropy, visual richness, and spatial structure. Ten dimensions of visual perception were modeled using an XGBoost framework optimized with a genetic algorithm, achieving high predictive performance (R2 = 0.827–0.989). Streetscape analysis revealed relatively stable visual entropy but pronounced heterogeneity in visual richness, reflecting variability in color, form, and spatial layering. Element-level decomposition showed the visual dominance of natural components, particularly trees, sky, and grass. Piecewise linear regression further identified threshold-dependent and dimension-specific effects of green space proportion, fragmentation, patch size, connectivity, aggregation, and shape complexity. Moderate fragmentation and aggregation enhanced perceived complexity and stimulation, whereas excessive shape complexity reduced most restorative responses.