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OA

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.