Co-Construction Mechanisms of Spatial Encoding and Communicability in Culture-Featured Districts—A Case Study of Harbin Central Street
Abstract
1. Introduction
2. Evaluation Indicators and Analysis Model Construction
2.1. Communication Evaluation Indicators and Empowerment Methods
2.1.1. Perception Evaluation Indicators
2.1.2. Infrastructure Evaluation Indicators
2.1.3. Behavioral Activity Evaluation Indicators
2.1.4. Communication Evaluation Index Weighting Method
2.2. Indicators of Factors Influencing the Spatial Information Encoding of Cultural Characteristic Blocks
Factors Influencing Spatial Information Encoding Indicators
3. Research Area
3.1. Selection of Research Subjects
3.2. Specific Regional Division
4. Data and Methods
4.1. Research Route
4.2. Research Methods
4.2.1. Semantic Segmentation of SVIs
4.2.2. ArcGIS Visual Spatial Data Processing Methods
4.2.3. Regression Analysis Method
4.3. Data Source
5. Characterization of the Spatial Communicative Distribution of Neighborhoods
5.1. Analysis of the Communicative Characteristics of Culturally Distinctive Blocks Based on Perceptual Evaluation
5.2. Characterization of the Communicative Character of Culturally Distinctive Neighborhoods Based on Infrastructure Evaluation
5.3. Analysis of Communication Characteristics of Culturally Distinct Blocks Based on Behavioral Activity Evaluation
5.4. Comprehensive Analysis of Communication in Culturally Distinct Neighborhoods
6. The Impact of Spatial Information Encoding Factors on the Communicability of Block Spaces
6.1. The Contribution of Spatial Information Encoding Factors to the Communicability of Urban Blocks
6.2. The Changing Characteristics of the Impact of Spatial Information Encoding Factors on the Communicability of Urban Blocks
6.3. Narrative of the Impact of Seven Spatial Configuration Factors on Spatial Communicability
6.4. Chapter Summary
7. Discussion and Conclusions
7.1. Discussion
7.2. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Evaluation Hierarchy | Evaluation Content | Evaluation Indicators |
---|---|---|
Infrastructure | Public building facilities | Security |
Transportation infrastructure | Diversity | |
Information infrastructure | Accessibility | |
Suitability, Readability | ||
Behavioral activities | Social entertainment activities | Freedom |
Public participation activities | Activity level, Participation | |
Perception evaluation | Experience | Satisfaction |
Attitude evaluation | Sense of value | |
Sense of identity | ||
Sense of place |
Primary Indicator (Weight) | Secondary Indicators | Weight |
---|---|---|
Perception evaluation index (0.25) | Activity satisfaction and sense of identity | 0.56 |
Text sentiment value | 0.44 | |
Infrastructure evaluation index (0.42) | Infrastructure security | 0.32 |
Diversity of infrastructure | 0.28 | |
Accessibility of infrastructure | 0.40 | |
Behavioral activity evaluation index (0.33) | Crowd duration | 0.53 |
Crowd dwell frequency | 0.47 |
Element Category | Indicator | Calculation Instructions |
---|---|---|
Street scene elements | Street greenness | The ratio of the pixels occupied by street greening to the total number of pixels in the image |
Walking accessibility | The ratio of pixels occupied by the sidewalk to the total number of pixels in the image | |
Architectural elements | Building density | Ratio of building footprint to space unit area in the region |
Diversity of architectural styles | The ratio of the number of buildings of a single style to the total number of spatial units | |
Road (accessibility) elements | Traffic facility density | The ratio of the number of transportation facilities to the area of the spatial unit |
Road density | The ratio of road length to the area of the spatial unit | |
Degree of mixing | POI mixing degree | The ratio of the number of various types of POIs to the area of spatial units |
POI density | The mixed status of different functional types of land within the region is measured using the entropy method |
Data Type | Data Content and Processing Process | Data Source |
---|---|---|
Dazhong Dianping data | Python3.10-based web scraping from Dianping in four store categories collects at least 30 reviews per category, targeting around 50,000 valid records | Dazhong Dianping official website |
Baidu Smart Eye Data | By programming in Python, scrape information such as user ID, mobile identification code, and recording time | Baidu Smart Eye Platform |
POI data | Crawl 23 major categories of POI data such as shopping services, science and education culture, and transportation facilities in the selected area through the POIKit1.3.0 software | Gaode Map API |
Street scene image data | Python is used to set up street-view sampling points every 30 m and crawl four images, yielding 9988 images in total. SegNet is utilized for semantic segmentation | Baidu Maps |
Architectural data | Python collects Gaode building data, including building number and floors. On-site street facade photos are taken to analyze architectural styles | Gaode Map |
Road network data | Mainly includes urban main roads, urban secondary roads, urban branch roads, and urban alleys within the research area | GOSM |
Public transportation subway station data | Crawling the bus and subway stations in the selected area using the POIKit software | Gaode Map API |
Element Category | Indicator | Contribution Level |
---|---|---|
Street scene elements | Street greenness | 0.228 |
Walking accessibility | 0.246 | |
Architectural elements | Building density | 0.164 |
Diversity of architectural styles | 0.101 | |
Road (accessibility) elements | Traffic facility density | 0.015 |
Road density | 0.081 | |
Degree of mixing | POI mixing degree | 0.085 |
POI density | 0.081 |
Dimension | Core Parameter | Optimal Range | Peak Value |
---|---|---|---|
Spatial Structure | Walking accessibility | 0.22–0.31 | Peak Value: 12.10 |
- | Road Network Density | 2.20–6.20 | Peak Value: 15.60 |
- | Building Density | 0.066–0.088 | Peak Value: 13.00 |
Environmental Quality | Block Greenery Level | 0.20–0.30 | Peak Value: 18.10 |
- | POI Mix Index | 11.11–14.10 | Peak Value: 15.20 |
Humanistic Vitality | POI Density | 1100–2900 | Peak Value: 20.10 |
- | Architectural Style Diversity | 1.10–6.60 | Peak Value: 26.20 |
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Zhu, H.; Pang, C. Co-Construction Mechanisms of Spatial Encoding and Communicability in Culture-Featured Districts—A Case Study of Harbin Central Street. Sustainability 2025, 17, 7059. https://doi.org/10.3390/su17157059
Zhu H, Pang C. Co-Construction Mechanisms of Spatial Encoding and Communicability in Culture-Featured Districts—A Case Study of Harbin Central Street. Sustainability. 2025; 17(15):7059. https://doi.org/10.3390/su17157059
Chicago/Turabian StyleZhu, Hehui, and Chunyu Pang. 2025. "Co-Construction Mechanisms of Spatial Encoding and Communicability in Culture-Featured Districts—A Case Study of Harbin Central Street" Sustainability 17, no. 15: 7059. https://doi.org/10.3390/su17157059
APA StyleZhu, H., & Pang, C. (2025). Co-Construction Mechanisms of Spatial Encoding and Communicability in Culture-Featured Districts—A Case Study of Harbin Central Street. Sustainability, 17(15), 7059. https://doi.org/10.3390/su17157059