Research on the influence of spontaneous commercial space on the commercial vitality of historical and cultural districts.

Spontaneous commercial spaces play a crucial role in shaping the vitality of historic districts, yet their spatial characteristics and impact on commercial activity remain understudied. This study employs Mask R-CNN deep learning, random forest regression analysis, and SHAP (Shapley Additive Explanations) to systematically identify and quantify the influence of spontaneous commercial spaces on commercial vitality. Based on a dataset comprising 4217 annotated images collected from Wuhan's Tanhualin Historic District, the study classifies spontaneous commercial spaces into five spatial types and examines their correlation with commercial vitality distribution. The results reveal that convex and scatter-occupying spontaneous spaces have the most significant positive impact, increasing commercial vitality by an average of 22.4% and 16.8%, respectively. SHAP analysis further highlights nonlinear interactions between crowd density and spatial typology, demonstrating that high-density areas amplify the contribution of convex spaces to vitality. Additionally, the spatial density of spontaneous commercial spaces shows a strong correlation with pedestrian flow intensity (R = 0.9062, p < 0.01), indicating their critical role in local economic dynamics. Compared to traditional manual research and spatial analysis methods, the computer vision and interpretable machine learning approaches employed in this study enhance analytical efficiency and causal clarity, providing urban planners with a robust framework for monitoring and evaluating spontaneous commercial spaces. Furthermore, we propose a predictive framework to evaluate the potential of existing commercial streets for future development. The model suggests that areas with 12-18 spontaneous commercial points per 100 square meters exhibit the highest commercial vitality, offering a reference for urban renewal strategies.