Zonal Differentiation and Feature-Contribution Patterns of Visual Perception in Historic Districts: An Explainable Machine Learning Approach Using Street View Imagery—A Case Study of Jimei School Village, Xiamen

Abstract

From a Historic Urban Landscape (HUL) perspective, varying conservation intensities and statutory boundaries may create “zonal differentiation” within historic districts. However, conventional homogenized renewal strategies frequently overlook this heterogeneity, affecting physical townscapes and human perception. This study analyzes 1714 panoramic street-view images from the Core Protection Zone and Construction Control Zone of Jimei School Village. Eleven objective visual features and six model-predicted perception dimensions were examined, with locally experienced participants providing contextual validation. Integrating K-Means clustering, Random Forest modeling, and SHapley Additive exPlanations (SHAP) feature attribution, the study investigates nonlinear, model-based contribution patterns across the two zones. Results reveal significant but non-binary differences in objective features and predicted perceptions. Street-view typologies show zonal tendencies while also indicating within-zone diversity and cross-zone overlap. Feature attribution shows that color composition, greenery, building interfaces, and vehicle presence are more prominent in the Core Protection Zone, whereas greenery, spatial openness, and road-space organization play stronger roles in the Construction Control Zone. This study establishes an interpretable street-view-based framework for historic-district assessment, providing empirical support for differentiated and human-oriented zonal renewal.