Exploring Heterogeneous and Non-Linear Effects of the Built Environment on Street Quality: A Computational Approach Towards Precise Regeneration

As a key strategy for broader sustainability, effective street regeneration requires a precise understanding of the built environment’s influence mechanisms. However, existing approaches often overlook the functional heterogeneity of streets and the non-linearity of their influence mechanisms. Addressing this gap, we developed an approach to analyze these mechanisms of the built environment, differentiated by street function. Integrating multi-source urban data, street quality was measured across three dimensions (visual quality, vibrancy, and functionality), and specialized weights for streets were determined according to their dominant functions. Applying this approach in Shanghai, we explained the non-linear effects of the built environment for each street function type through separate GBDT models and SHAP analysis. The results reveal that the influence mechanisms of built environment factors vary significantly across dominant street functions. Specifically, the heterogeneity of critical activation thresholds and saturation points provides direct evidence for more targeted regeneration strategies. Key findings highlight that a strong sense of enclosure is a priority for the quality of residential street, as measured by a low Sky View Factor. In contrast, vertical development intensity is a priority for commercial streets, as Floor Area Ratio requires a high activation threshold to exert a positive influence. In short, this research provides a computational approach that enables precise and data-driven interventions, which contribute to sustainable urban development.