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城市景观栅格网络多因素相关性分析

Multi-factor correlation analysis of urban landscape grid network

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【作者】 石坚韧赵秀敏王竹

【Author】 SHI Jian-ren1,2,ZHAO Xiu-min3,WANG Zhu1 (1.College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,China;2.Department of Urban Planning,Zhejiang Gongshang University,Hangzhou 310018,China;3.Department of Environmental Design,Zhejiang Gongshang University,Hangzhou 310018,China)

【机构】 浙江大学建筑工程学院浙江工商大学城乡规划系浙江工商大学环境设计系

【摘要】 针对我国现阶段城市景观趋同化的现状,"城市景观网络化"方法结合了典型的景观都市杭州的城市建设,对其景观栅格网络进行实证研究.采用栅格分析方法,提出"自然山水网络"、"历史发展网络"、"道路交通网络"、"用地性质网络"4个子网络,采用单因素方差分析、相关分析、重回归分析,逐一分析各层网络之间的相互关系,根据主观满意度求得景观网络的适宜性回归方程,并针对杭州景观规划的实践成果和质化要素进行了最优尺度分析.结果发现:各景观网络具有复合层次上的相关性,并影响着人的主观判断;不同的景观切片在节奏密度与空间尺度2个维度上有明显差异.

【Abstract】 Based on the current developing situation of urban landscope convergence in cities a concept of urban landscape network was practiced with the case study of Hangzhou City construction.A grid analysis of the landscape grid network was conducted,and it formed four sub-networks:natural scenery,historical development,road transportation,and land use.Variance analysis,correlation analysis,multiple regression analysis were used to examine the relationship between the four networks,and achieved a regression equation of the landscape network in accordance with the subjective satisfaction evaluation concerning the landscape.Finally,the optimal scale analysis was conducted based on the practice of Hangzhou landscape planning and the qualitative landscape elements of the city.The results show that these networks have correlation with each other at several aspects,and they influence human evaluation.In addition,different landscape spots are distinguished at the level of rhythm density index and spatial scale index.

【关键词】 城市景观网络化景观栅格
【Key words】 urban landscapenetwork configurationlandscape grid
【基金】 浙江省自然科学基金资助项目(Y1090989);中国博士后科学基金资助项目(20080431315);浙江省教育厅科技资助项目(Y200906289)
  • 【文献出处】 浙江大学学报(工学版) ,Journal of Zhejiang University(Engineering Science) , 编辑部邮箱 ,2010年09期
  • 【分类号】TU986
  • 【被引频次】4
  • 【下载频次】364
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