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城市公园空间格局对公众满意度的影响:来自中国上海的证据

Effects of the spatial patterns of urban parks on public satisfaction: evidence from Shanghai, China.

作者信息

Li Jie, Fu Jing, Gao Jun, Zhou Rui, Wang Keyue, Zhou Kaiyue

机构信息

School of Ecological Technology and Engineering, Shanghai Institute of Technology, Shanghai, 201418 China.

School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai, 200234 China.

出版信息

Landsc Ecol. 2023;38(5):1265-1277. doi: 10.1007/s10980-023-01615-z. Epub 2023 Mar 1.

DOI:10.1007/s10980-023-01615-z
PMID:37051135
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9975882/
Abstract

CONTEXT

Urban parks need to meet the growing demand for activities vital to residents' well-being and urban development. A holistic understanding of public satisfaction with urban parks is a prerequisite for improving management.

OBJECTIVE

The spatial patterns and composition of urban parks vary greatly, and the objective of this study is to comprehensively investigate public satisfaction with urban parks and the impact of their structure.

METHODS

With the metropolis of Shanghai, China, as an example, we employed 111,814 social media data sets for 50 urban park sites to quantify public satisfaction via the long short-term memory model. We analyzed the internal, boundary and external dimensions of spatial patterns and described the internal landscape patterns from the perspectives of size, heterogeneity, aggregation, shape, diversity and landscape elements. Moreover, we used all-subset regression and hierarchical partitioning to quantify the relationship and mechanism of action between spatial patterns and public satisfaction.

RESULTS

The results indicate that the mean value of public satisfaction with urban parks was 0.716 (ranging from 0 to 1), which is generally positive or neutral. Satisfaction was influenced by the internal, boundary, and external spatial patterns of urban parks. The independent contribution rates of external transportation facility density (51.49%) and internal edge density (48.51%) to satisfaction.

CONCLUSIONS

We highlight the roles of spatial patterns, especially the degree of external transportation convenience and the edge density of urban parks at the landscape level. The findings provide guidance and recommendations for the planning and design of public satisfaction-oriented urban parks.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10980-023-01615-z.

摘要

背景

城市公园需要满足对居民福祉和城市发展至关重要的活动日益增长的需求。全面了解公众对城市公园的满意度是改善管理的先决条件。

目的

城市公园的空间格局和构成差异很大,本研究的目的是全面调查公众对城市公园的满意度及其结构的影响。

方法

以中国上海大都市为例,我们采用了50个城市公园站点的111814个社交媒体数据集,通过长短期记忆模型量化公众满意度。我们分析了空间格局的内部、边界和外部维度,并从面积、异质性、聚集度、形状、多样性和景观要素等角度描述了内部景观格局。此外,我们使用全子集回归和层次划分来量化空间格局与公众满意度之间的关系及作用机制。

结果

结果表明,公众对城市公园的满意度平均值为0.716(范围为0至1),总体呈积极或中性。满意度受到城市公园内部、边界和外部空间格局的影响。外部交通设施密度(51.49%)和内部边缘密度(48.51%)对满意度的独立贡献率。

结论

我们强调了空间格局的作用,特别是在景观层面上外部交通便利性和城市公园边缘密度的程度。研究结果为以公众满意度为导向的城市公园规划和设计提供了指导和建议。

补充信息

在线版本包含可在10.1007/s10980-023-01615-z获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba9c/9975882/be4f6007f91b/10980_2023_1615_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba9c/9975882/cf5bfb7666d0/10980_2023_1615_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba9c/9975882/b6dbb908833d/10980_2023_1615_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba9c/9975882/fb411482b89a/10980_2023_1615_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba9c/9975882/be4f6007f91b/10980_2023_1615_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba9c/9975882/cf5bfb7666d0/10980_2023_1615_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba9c/9975882/b6dbb908833d/10980_2023_1615_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba9c/9975882/fb411482b89a/10980_2023_1615_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba9c/9975882/be4f6007f91b/10980_2023_1615_Fig4_HTML.jpg

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An Impact Asymmetry Analysis of Small Urban Green Space Attributes to Enhance Visitor Satisfaction.小尺度城市绿地属性对游客满意度影响的非对称分析。
Int J Environ Res Public Health. 2022 Mar 2;19(5):2922. doi: 10.3390/ijerph19052922.
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Fine-grained assessment of greenspace satisfaction at regional scale using content analysis of social media and machine learning.
利用社交媒体内容分析和机器学习对区域尺度的绿地满意度进行细粒度评估。
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