Social factors of urban greening: Demographics, zoning, and social capital

IF 3.9 Q2 ENVIRONMENTAL SCIENCES
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引用次数: 0

Abstract

This study explored the association between greening and social capital in neighborhoods, considering demographics and zoning by urban planning. The target area encompassed the urban areas of Kyoto City, Japan, which has a long tradition of landscape policy and neighborhood associations. Greening was evaluated using two automated methods: 1) horizontal green coverage of the land was calculated via the Normalized Difference Vegetation Index in satellite images, and 2) green visibility in streetscape from a human perspective was estimated by combining Google Street View images and a machine learning model. Public government data were used for demographics and zoning, and social capital was evaluated using survey data from the local government. After performing the elastic net models, variables that had explanatory power for each greening index were selected. Similar reasonable associations were found for each of the indices with the zoning categories. However, for both zoning and demographics, different variables were selected. Importantly, the social capital variable was selected only for the green visibility in streetscape, showing a negative correlation between them, unlike in previous studies. These results suggest that the association between urban greening and social relationships can change depending on the context of the target regions and measurements of greening.

城市绿化的社会因素:人口、分区和社会资本
本研究在考虑人口统计和城市规划分区的基础上,探讨了邻里绿化与社会资本之间的关系。目标区域包括日本京都市的城区,该市拥有悠久的景观政策和邻里协会传统。绿化评估采用了两种自动化方法:1)通过卫星图像中的归一化差异植被指数计算土地的水平绿化覆盖率;2)结合谷歌街景图像和机器学习模型,从人的角度估算街景的绿化能见度。人口统计和分区使用了公共政府数据,社会资本则使用当地政府的调查数据进行评估。在执行弹性网模型后,选择了对每个绿化指数具有解释力的变量。结果发现,每个指数与分区类别之间都存在类似的合理关联。不过,对于分区和人口统计,选择的变量有所不同。重要的是,社会资本变量仅针对街景绿化能见度进行了选择,显示出两者之间的负相关,这与以往的研究不同。这些结果表明,城市绿化与社会关系之间的关联会因目标区域的环境和绿化的测量而发生变化。
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来源期刊
City and Environment Interactions
City and Environment Interactions Social Sciences-Urban Studies
CiteScore
6.00
自引率
3.00%
发文量
15
审稿时长
27 days
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