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基于影响不对称分析的口袋公园使用 满意度关键影响因子识别研究
王颜,胡宏*
0
作者简介:
摘要:
:已有大量文献聚焦城市绿地使用行为与人群感知评 价,但针对口袋公园这类小微绿地的使用满意度形成机制尚缺 乏深入解析。基于非线性影响不对称分析方法,将口袋公园分 为道路围合型、社区附属型和自然本底型,选取5个维度23个 环境因子构建指标体系,收集606份调查问卷,应用奖惩对比 分析模型和梯度提升决策树算法,识别口袋公园使用满意度关 键影响因子。研究表明:不同类型口袋公园使用人群关注的维 度不同;口袋公园环境因子与总体满意度之间呈现非线性不对 称关系;管理养护类因子位于所有公园的最优先提升等级,而 环境质量类因子多位于品质提升等级。研究结果可为相关部门 因地制宜制定不同类型口袋公园营建策略、合理优化建设时序 提供参考。
关键词:  风景园林  影响不对称分析  口袋公园  满意度  梯度提升决策树
DOI:
基金项目:
Identifying Key Impact Factors of User Satisfactionof Pocket Parks Using Impact Asymmetry Analysis
WANG Yan,HU Hong
Abstract:
Previous studies have concluded that there is a linear relationship between environmental design and usage satisfaction in pocket park, and this study applied impact-asymmetry analysis to compare the non-linear relationship between environmental factors and overall usage satisfaction of pocket park under different spatial distribution characteristics in Nanjing. The study found that the relative importance of environmental factors in pocket parks differed between construction environments, with the aesthetic design of the greenery, the pruning of the greenery and the proportion of green paving being the most important environmental factors affecting the overall satisfaction to road enclosed, community attached and natural background pocket park respectively. Most of the important environmental factors have a non-linear asymmetric influence on overall satisfaction. Based on the degree of asymmetry, the importance level of environmental factors in pocket park can be classified, to prioritize the improvement of environmental quality in pocket parks. In the future, it is necessary to pay attention to the level of management and maintenance of pocket park, the quality of green vegetation design and arrangement, the planning and layout of public service facilities and the communication channels for users' suggestions and demands, based on the adoption of differentiated optimization strategies.
Key words:  landscape architecture  impact asymmetry analysis  pocket park  satisfaction  gradient boosting decision tree

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