What does this research mean for the field?

In historic towns, modern spatial interference negatively impacts perceived historicity and attractiveness, whereas traditional architectural components, historical visibility, and blue-green spaces enhance perceived historicity, attractiveness, and comfort across both human and aerial scales. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Quantify human spatial perception across topological, visual interface, and historical semantic layers to guide the sustainable conservation and renewal of historic towns.

August 23, 2026SustainabilityOpen Access

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

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Authors

YXYusu Xu MWMao Wei AKAnqi Kang

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Overview

Spatial perception study demonstrates how urban evolution impacts historicity and comfort in an ancient town, highlighting pathways for human-centered heritage renewal.

Key Points

Quantify human spatial perception across topological, visual interface, and historical semantic layers to guide the sustainable conservation and renewal of historic towns.
Modeled street network topology across 1985, 2004, and 2024 using axial space syntax analysis.
Evaluated 140 images (55 human-view and 85 aerial-view) across perceived historicity, safety, attractiveness, and comfort via questionnaires completed by 264 valid respondents.
Applied deep-learning image recognition, rule-based coding, Spearman correlation, and SHAP-interpreted random forest regression models to quantify perceptual drivers.
Street network intelligibility fell from 0.13 in 2004 to 0.07 in 2024 as integration shifted away from the historic core toward modern traffic corridors.
At the human-view scale, modern interference negatively associated with historicity and attractiveness, whereas stairs, traditional architectural components, and micro-historical objects positively associated with historicity.
At the aerial-view scale, historical visibility, character integrity, and blue-green space metrics positively associated with historicity, attractiveness, and comfort, with random forest predictive performance strongest for aerial historicity.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a8aade47677a34114446642 https://doi.org/10.3390/su18168600

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August 23, 2026SustainabilityOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

YXYusu Xu MWMao Wei AKAnqi Kang

Discussion

Member takes

Overview

Spatial perception study demonstrates how urban evolution impacts historicity and comfort in an ancient town, highlighting pathways for human-centered heritage renewal.

Key Points

Quantify human spatial perception across topological, visual interface, and historical semantic layers to guide the sustainable conservation and renewal of historic towns.
Modeled street network topology across 1985, 2004, and 2024 using axial space syntax analysis.
Evaluated 140 images (55 human-view and 85 aerial-view) across perceived historicity, safety, attractiveness, and comfort via questionnaires completed by 264 valid respondents.
Applied deep-learning image recognition, rule-based coding, Spearman correlation, and SHAP-interpreted random forest regression models to quantify perceptual drivers.
Street network intelligibility fell from 0.13 in 2004 to 0.07 in 2024 as integration shifted away from the historic core toward modern traffic corridors.
At the human-view scale, modern interference negatively associated with historicity and attractiveness, whereas stairs, traditional architectural components, and micro-historical objects positively associated with historicity.
At the aerial-view scale, historical visibility, character integrity, and blue-green space metrics positively associated with historicity, attractiveness, and comfort, with random forest predictive performance strongest for aerial historicity.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a8aade47677a34114446642 https://doi.org/10.3390/su18168600

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