基于主成分分析的城市开放空间满意度宏观因子研究——以南京主城区为分析对象
发布时间：2019-01-05 15:00
【摘要】：通过城市开放空间满意度评价的调查,运用统计学的主成分分析法将中观层次的评价因子降维成开放空间规划所需的若干宏观因子,设计出了城市开放空间满意度的宏观因子提取与分析方法。以南京主城区为研究对象,选取18个不同类型的开放空间为研究样本,说明该方法的运用过程。在统计学意义上得出了南京主城区开放空间规划应着重考虑的3个宏观层面的因子:感知度、活力度和需求度,并以宏观因子得分为依据对研究样本进行了归类,找出了样本间潜在的联系。实验过程证明了研究方法的可靠性。
[Abstract]:Through the investigation of the satisfaction evaluation of urban open space, the evaluation factors at the middle level are reduced into some macro factors for the open space planning by using the principal component analysis method of statistics. The macro factor extraction and analysis method of urban open space satisfaction is designed. Taking the main urban area of Nanjing as the research object, 18 different types of open space are selected as the research samples to illustrate the application process of the method. On the basis of statistics, three macro factors should be considered in Nanjing urban open space planning: perception degree, vitality degree and demand degree, and the study samples are classified on the basis of macro factor scores. Potential connections between the samples were identified. The experimental results show the reliability of the method.
【作者单位】： 南京林业大学风景园林学院;
【基金】：国家自然科学基金项目(51278113) 江苏省教育厅高校自然科学研究面上项目(13KJB220004) 南京林业大学高学历人才基金项目(GXL201321)
【分类号】：F299.27
本文编号：2401923
[Abstract]:Through the investigation of the satisfaction evaluation of urban open space, the evaluation factors at the middle level are reduced into some macro factors for the open space planning by using the principal component analysis method of statistics. The macro factor extraction and analysis method of urban open space satisfaction is designed. Taking the main urban area of Nanjing as the research object, 18 different types of open space are selected as the research samples to illustrate the application process of the method. On the basis of statistics, three macro factors should be considered in Nanjing urban open space planning: perception degree, vitality degree and demand degree, and the study samples are classified on the basis of macro factor scores. Potential connections between the samples were identified. The experimental results show the reliability of the method.
【作者单位】： 南京林业大学风景园林学院;
【基金】：国家自然科学基金项目(51278113) 江苏省教育厅高校自然科学研究面上项目(13KJB220004) 南京林业大学高学历人才基金项目(GXL201321)
【分类号】：F299.27
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，本文编号：2401923
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