Heng Zhou , Kui Yang , Jingnan Huang , Jun Huang , Yixuan Gao , Jinting Zhang , Yong Chen , Mengya Yu
{"title":"Zoning management of urban informal vendor spaces using mobile signaling and machine learning: The case of Wuhan","authors":"Heng Zhou , Kui Yang , Jingnan Huang , Jun Huang , Yixuan Gao , Jinting Zhang , Yong Chen , Mengya Yu","doi":"10.1016/j.scs.2025.106858","DOIUrl":null,"url":null,"abstract":"<div><div>The vendor economy is an important component of the urban informal economy and serves as a valuable \"lubricant\" to enhance social inclusivity and stimulate socio-economic activity. However, urban vendor spaces have several negative effects, such as encroaching on public spaces, affecting the city's image, causing traffic congestion, contributing to environmental pollution, and triggering safety issues. Studying the distribution characteristics and formation mechanisms of urban vendor spaces can provide support for the formulation of governance policies related to these areas. Existing research is often limited by data and methods, predominantly relying on case studies, with a lack of comprehensive urban-level investigations. This limitation increases the likelihood of failure in spatial governance policies. This paper utilizes mobile signaling data and constructs a time-space-population screening method to identify vendor spaces across the entire urban area. It employs XGBoost and SHAP to analyze the influencing mechanisms of vendor spaces, and ultimately conducts a clustering analysis based on the SHAP values of various factors to identify sensitivity zones for vendor spaces, thereby proposing site selection and governance recommendations for vendors. The study finds that: (1) In the main urban area of Wuhan, informal street vendor spaces have formed two primary aggregation zones and four secondary aggregation zones, primarily linked to large communities, schools, sports centers, commercial districts, and hospitals. (2) Among the factors affecting vendor aggregation, consumer demand-related elements have the highest average importance, followed by facility density-related elements, and lastly traffic environment-related elements. (3) In high-attraction areas for informal street vendor spaces, the mean RFD (Recreational facilities density) is the highest, while areas along rivers and lakes generally do not attract vendors easily. (4) We have identified some previously unrecognized distributions of vendors, such as the transitional zones in Hongshan District and Jiangxia District, which represent potential development areas for vendor space management. These research findings provide valuable references for vendor space governance in large cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"133 ","pages":"Article 106858"},"PeriodicalIF":12.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670725007310","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The vendor economy is an important component of the urban informal economy and serves as a valuable "lubricant" to enhance social inclusivity and stimulate socio-economic activity. However, urban vendor spaces have several negative effects, such as encroaching on public spaces, affecting the city's image, causing traffic congestion, contributing to environmental pollution, and triggering safety issues. Studying the distribution characteristics and formation mechanisms of urban vendor spaces can provide support for the formulation of governance policies related to these areas. Existing research is often limited by data and methods, predominantly relying on case studies, with a lack of comprehensive urban-level investigations. This limitation increases the likelihood of failure in spatial governance policies. This paper utilizes mobile signaling data and constructs a time-space-population screening method to identify vendor spaces across the entire urban area. It employs XGBoost and SHAP to analyze the influencing mechanisms of vendor spaces, and ultimately conducts a clustering analysis based on the SHAP values of various factors to identify sensitivity zones for vendor spaces, thereby proposing site selection and governance recommendations for vendors. The study finds that: (1) In the main urban area of Wuhan, informal street vendor spaces have formed two primary aggregation zones and four secondary aggregation zones, primarily linked to large communities, schools, sports centers, commercial districts, and hospitals. (2) Among the factors affecting vendor aggregation, consumer demand-related elements have the highest average importance, followed by facility density-related elements, and lastly traffic environment-related elements. (3) In high-attraction areas for informal street vendor spaces, the mean RFD (Recreational facilities density) is the highest, while areas along rivers and lakes generally do not attract vendors easily. (4) We have identified some previously unrecognized distributions of vendors, such as the transitional zones in Hongshan District and Jiangxia District, which represent potential development areas for vendor space management. These research findings provide valuable references for vendor space governance in large cities.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;