Evolution Method of Built Environment Spatial Quality in Historic Districts Based on Spatiotemporal Street View: A Case Study of Tianjin Wudadao
Abstract
1. Introduction
2. Related Work
2.1. Definition and Dynamic Evaluation of Built Environmental Framework Quality for Historic Districts
2.2. The Potential and Limitations of Street View Images in Built Environment Spatial Quality
2.3. The Integration Progress of Deep Learning and Emotional Perception Models for Evaluation the Built Environment Spatial Quality
2.4. The Built Environment Spatial Quality Research in Historic Districts
3. Experimental Area
3.1. Experimental Area Overview
3.2. Data Collection Method
4. Evaluation Model for the Built Environment Spatial Quality in Historic Districts
4.1. Principle of the Method
4.2. Emotion Perception Model for Evaluation the Built Environment Spatial Quality
4.3. “Emotion–Scene” Coupling Analysis Framework
5. Experimental Analysis
5.1. Results of Emotion Perception Model for the Study Area
5.1.1. Beautiful Emotion Perception
Spatiotemporal Evolution of Beautiful Sentiment
Driving Mechanisms
5.1.2. Boring Emotion Perception
Spatiotemporal Evolution of Boring Sentiment
Driving Mechanisms
5.1.3. Depressing Emotion Perception
Spatiotemporal Evolution of Depressing Sentiment
Driving Mechanisms
5.1.4. Lively Emotion Perception
Spatiotemporal Evolution of Lively Sentiment
Driving Mechanisms
5.1.5. Safety Emotion Perception
Spatiotemporal Evolution of Safety Sentiment
Driving Mechanisms
5.1.6. Wealthy Emotion Perception
Spatiotemporal Evolution of Wealthy Sentiment
Driving Mechanisms
5.1.7. Summary of Policy Documents
5.1.8. Summary of Six Types of Sentiment
5.2. Results of the “Emotion–Scene” Coupling Analysis Framwork
5.2.1. Beautiful Driving Factors
Analysis of the Driving Scenes for the Emotion of Beauty
Planning Insights
5.2.2. Boring Driving Factors
Analysis of the Driving Scenes for the Emotion of Boring
Planning Insights
5.2.3. Depressing Driving Factors
Analysis of the Driving Scenes for the Emotion of Depressing
Planning Insights
5.2.4. Lively Driving Factors
Analysis of the Driving Scenes for Lively Emotion
Planning Insights
5.2.5. Safety Driving Factors
Analysis of Driving Scenes for Safety Perception
Planning Insights
5.2.6. Wealthy Driving Factors
Analysis of Driving Scenes for Wealthy Perception
Planning Insights
6. Summary and Outlook
6.1. Main Findings of the Study
6.2. Limitations of the Study
6.3. Future Research Directions
6.4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Emotion | Corresponding Optimization Policy |
---|---|
Beautiful Emotion | Balance heritage preservation with equitable development; increase greenery; maintain pedestrian-friendly environments; minimize visual clutter; invest equitably in peripheral areas |
Boring Emotion | Enhance functional diversity; activate peripheral micro-public spaces; regularly introduce diverse cultural activities and mixed-use developments |
Depressing Emotion | Implement architectural controls on building density and height; manage construction dynamically (shorter barrier periods, transparent fencing); create open spaces and architectural setbacks; introduce small-scale spatial interventions (e.g., pocket parks, art installations) |
Lively Emotion | Encourage community-driven initiatives; implement flexible zoning policies; support cultural markets and mixed-use spaces |
Safety Emotion | Apply time-specific management strategies (e.g., pedestrian-only streets at night); enhance smart security and street lighting; introduce localized policing solutions (micro-police stations) |
Wealthy Emotion | Promote inclusive development (retain affordable commercial options); establish equitable resource distribution and scenic compensation mechanisms for peripheral areas |
Classification | Scenario Factors |
---|---|
Transportation facilities | Bus station/indoor, Subway station/platform, Railway station/platform, Airport terminal, Highway, Runway, Race track, Parking lot, Parking lot/outdoor, Parking garage/outdoor, Garage/outdoor, Lane |
Recreation | Park, Playground, Skating rink/outdoor, Skating rink/outdoor, Ski resort, Beer garden, Picnic area, Promenade, Terrace, Zen garden, Market/outdoor, Arcade |
Residential area | Residential area, Apartment building/outdoor, Luxury house, Ordinary house, Courtyard, Roof garden, Balcony/outdoor, House, Inn/outdoor |
Public facilities | Atrium/public, Hospital, Library/outdoor, Campus, Fire station, Fire exit, Prison, Prison cell, Embassy |
Commercial area | Repair shop, Gas station, Fast food restaurant eatery/outdoor, Department store/outdoor, Storefront, Ticket booth, Restaurant/Outdoor restaurant terrace, Telephone booth |
Industry and Infrastructure | Industrial area, Construction site, Building façade, Car factory, Car showroom, Motel, Dam, Canal/city, Server room, Lock, Lock chamber, Loading dock, Barn door, Drying room, Stable, Slum, Tree house, Hunting lodge/outdoors |
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Hu, L.; Liu, Y.; Yu, B. Evolution Method of Built Environment Spatial Quality in Historic Districts Based on Spatiotemporal Street View: A Case Study of Tianjin Wudadao. Buildings 2025, 15, 1953. https://doi.org/10.3390/buildings15111953
Hu L, Liu Y, Yu B. Evolution Method of Built Environment Spatial Quality in Historic Districts Based on Spatiotemporal Street View: A Case Study of Tianjin Wudadao. Buildings. 2025; 15(11):1953. https://doi.org/10.3390/buildings15111953
Chicago/Turabian StyleHu, Lujin, Yu Liu, and Bing Yu. 2025. "Evolution Method of Built Environment Spatial Quality in Historic Districts Based on Spatiotemporal Street View: A Case Study of Tianjin Wudadao" Buildings 15, no. 11: 1953. https://doi.org/10.3390/buildings15111953
APA StyleHu, L., Liu, Y., & Yu, B. (2025). Evolution Method of Built Environment Spatial Quality in Historic Districts Based on Spatiotemporal Street View: A Case Study of Tianjin Wudadao. Buildings, 15(11), 1953. https://doi.org/10.3390/buildings15111953