城市建筑2026，Vol.23Issue(16)：10-13,35,5.DOI:10.19892/j.cnki.csjz.2026.16.04
基于可解释机器学习方法的居民街道公共空间满意度研究
Study on Residents'Satisfaction with Street Public Spaces Based on Explainable Machine Learning Methods
摘要
Abstract
This paper employs explainable machine learning methods to thoroughly investigate the residents'satisfaction with the quality of public spaces in Shinan and Shibei Districts of Qingdao.Using questionnaire surveys,the multi-dimensional evaluation data were collected,and the machine learning technique was used to analyze the factors influencing the public spaces of various streets.Through SHAP(SHapley Additive exPlanations)analysis,the key variables with significant influence on the satisfaction were identified,and the targeted optimization suggestions were proposed to improve the quality and residents'experience of public spaces.The research results not only provide empirical basis for the design and renovation of public spaces,but also lay a solid foundation for promoting sustainable urban development and enhancing residents'sense of happiness.关键词
公共空间质量/居民满意度/可解释机器学习/SHAP分析Key words
public space quality/resident satisfaction/explainable machine learning/SHAP analysis分类
建筑与水利引用本文复制引用
田野,臧晓琳,沙昂,陈天一..基于可解释机器学习方法的居民街道公共空间满意度研究[J].城市建筑,2026,23(16):10-13,35,5.基金项目
青岛市双百调研工程2024年度资助课题(2024-B-213) （2024-B-213）
