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规模依赖的环境影响城市绿色空间情绪:整合多式联络社交媒体分析和可解释的空间模型

Jiaqi Zhang¹, Jin Rui², Chenfan Cai³

¹Edinburgh School of Architecture and Landscape Architecture, Edinburgh College of Art, University of Edinburgh, 74 Lauriston Place, Scotland, EH3 9DF, Edinburgh, United Kingdom.

Journal of environmental management

|December 12, 2025

概括

周围环境的特征显著影响公众对城市绿地 (UGS) 的情绪,比绿地本身更重要. 这项研究使用了社交媒体数据来分析这些复杂的,规模依赖的关系,以改善城市规划.

科学领域:

环境科学环境科学
城市规划城市规划
公共卫生公共卫生

背景情况:

城市绿地 (UGS) 提供了受空间及其周围环境 (SE) 影响的公共卫生效益.
使用多式联网社交媒体数据评估SE对UGS情绪的影响存在挑战.
了解这些关联对于有效的城市规划和UGS管理至关重要.

研究的目的:

综合评估环境特征与公众对UGS的情绪之间的关联.
揭示UGS不同尺度的非线性关联,协同作用和空间异质性.
开发一种更准确,更有效的方法来获取公众对UGS的见解.

主要方法:

整合夏普利添加式扩展 (SHAP) 和地理加权回归 (GWR).
分析了来自北京的280个UGS的59,880个社交媒体文本条目和49,501个图像.
结合照片和基于文本的情绪分析.

主要成果:

在确定UGS情绪方面,周围环境 (SE) 特性比UGS属性更有影响力.
建筑覆盖率,GDP和人口密度是SE的主要贡献者.
环境变量对UGS情绪产生了非线性,交互性和地理异质的影响.
可访问性根据UGS类型 (社区的积极,非社区的负面) 显示出不同的影响.
地面积比率对社区和非社区UGS的情绪产生了积极的影响.

结论:

欧元区的特点显著地塑造了公众对UGS的情绪,具有复杂的,依赖规模的效应.
开发的方法提供了一个强大的方法来分析公众对UGS的看法.
通过考虑SE特征和空间变化,研究结果支持更精确的UGS规划决策.

关键词:

可解释的人工智能面部表情识别面部表情识别地理空间情绪分析多式联络社交媒体多式联络社交媒体基于文本的情绪分析.

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