A study on the landscape evaluation of sculpture vignettes in residential areas based on SBE

被引:0
|
作者
Liu S. [1 ]
Sun Y. [1 ]
Ouyang H. [1 ]
Ma X. [2 ]
机构
[1] School of Life Science and Technology, Lingnan Normal University, Guangdong, Zhanjiang
[2] Partner Design Studio (SZ) Co. Ltd, Guangdong, Shenzhen
关键词
aesthetic variability; AGV; gray clustering method; regression analysis; SBE;
D O I
10.2478/amns.2023.2.00128
中图分类号
学科分类号
摘要
Sculpture landscape in urban residential areas plays an important role in creating an urban landscape and is significant in highlighting the urban taste and strengthening urban temperament. This study is based on SBE and the gray clustering method to evaluate the sculptured landscape in residential areas and mainly selects the aesthetic consistency and aesthetic difference data of the surveyed groups for quantitative analysis. In this paper, the sculptures in the residential area of Chengdu city center were used as the main objects for the current situation research and beauty evaluation, and regression analysis was conducted. The study results show that the surveyed groups rated the randomly selected sculpture landscapes on a scale of-1 to 2. The ratings of the selected six dimensions were mainly positive, indicating that the ratings of the beauty of the sculpture landscapes in residential areas were high. The SBE values were all positive, between 0 and 0.5, indicating that the surveyed groups rated these samples more positively and the beauty ratings were better. However, there are still some shortcomings in the residential sculpture landscape, including insufficient post-management, insufficient culture, artistry, diversity, and science popularization, and insufficient cultural symbolism of residential sculpture. Based on the SBE evaluation results, it is of great significance to focus on the post-management of residential sculpture landscape and the construction of urban cultural symbols to construct spiritual civilization and improve environmental quality in cities. © 2023 Shuang Liu et al., published by Sciendo.
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