基于移动信号和机器学习的城市非正式供应商空间分区管理:以武汉为例

IF 12 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Heng Zhou , Kui Yang , Jingnan Huang , Jun Huang , Yixuan Gao , Jinting Zhang , Yong Chen , Mengya Yu
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引用次数: 0

摘要

供应商经济是城市非正规经济的重要组成部分,是增强社会包容性和刺激社会经济活动的宝贵“润滑剂”。然而,城市小贩空间也存在侵占公共空间、影响城市形象、造成交通拥堵、造成环境污染、引发安全问题等负面影响。研究城市供应商空间的分布特征及其形成机制,可以为制定与城市供应商空间相关的治理政策提供支持。现有的研究往往受到数据和方法的限制,主要依赖于案例研究,缺乏全面的城市调查。这种限制增加了空间治理政策失败的可能性。本文利用移动信令数据,构建了一种时空-人口筛选方法,在整个城市范围内识别供应商空间。利用XGBoost和SHAP分析供应商空间的影响机制,最终基于各因素的SHAP值进行聚类分析,识别供应商空间的敏感区域,从而提出供应商选址和治理建议。研究发现:(1)武汉市主城区非正规摊贩空间形成了2个一级集聚区和4个二级集聚区,主要与大型社区、学校、体育中心、商业区和医院相连。(2)影响厂商聚集的因素中,消费者需求相关因素的平均重要性最高,其次是设施密度相关因素,最后是交通环境相关因素。(3)非正规摊贩空间高吸引力区域的平均RFD(游憩设施密度)最高,而沿江沿湖区域一般不容易吸引摊贩。(4)我们发现了一些以前未被认识到的供应商分布,如红山区和江夏区的过渡带,这些区域代表了供应商空间管理的潜在发展区域。这些研究结果为大城市供应商空间治理提供了有价值的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Zoning management of urban informal vendor spaces using mobile signaling and machine learning: The case of Wuhan
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.
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来源期刊
Sustainable Cities and Society
Sustainable Cities and Society Social Sciences-Geography, Planning and Development
CiteScore
22.00
自引率
13.70%
发文量
810
审稿时长
27 days
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;
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