A Multi-Scale Framework for Quantifying Spatial Perception in Sustainable Historic-Town Conservation and Renewal: Evidence from Yun’an Ancient Salt Town, China

Abstract

Sustainable historic-town conservation requires preserving historical patterns and cultural memory while understanding how spatial environments shape everyday perception. Using Yun’an Ancient Salt Town in Chongqing, China, this study develops a three-layer framework of spatial topology, scene interfaces, and historical semantic elements. Space syntax, deep-learning image recognition, rule-based coding, image-based questionnaires, Spearman correlation, random forest regression, and SHAP were used to examine perceived historicity, safety, attractiveness, and comfort. The analysis combined axial models for 1985, 2004, and 2024 with 140 images—55 human view and 85 aerial view—each evaluated by 264 valid respondents. Yun’an’s street network shifted from a salt-production and transport structure toward modern traffic corridors; integration declined in the historic core, and intelligibility fell from 0.13 in 2004 to 0.07 in 2024. At the human-view scale, modern interference was negatively associated with historicity and attractiveness, whereas stairs, traditional components, and micro-historical objects were positively associated with historicity. At the aerial-view scale, historical visibility, character integrity, and blue-green-space indicators were associated with historicity, attractiveness, and comfort. Cross-validated random forest performance was strongest for aerial-view historicity and limited for other outcomes. The findings support a human-centered, culturally sustainable renewal pathway linking historical-street continuity, townscape integration, blue-green quality, and heritage-node activation.