使用机器学习和街景图像评估绿色空间暴露对心理压力感知的非线性影响
Tianlin Zhang1, Lei Wang1, Yazhuo Zhang2
1School of Architecture, Tianjin University, Tianjin, China.
Frontiers in public health
|October 3, 2024
概括
城市绿地 (GS) 暴露可以减少心理压力,但它的有效性有一个值. 在城市规划中优化GS水平是幸福的关键.
科学领域:
- 城市规划和环境心理学
- 地理空间分析和机器学习.
背景情况:
- 城市绿色空间 (GS) 是面对城市挑战的以自然为基础的解决方案.
- 需要进一步探索GS暴露的减压效应和机制.
- 机器学习和街景图像为城市环境的大规模分析提供了新的方法.
研究的目的:
- 调查GS暴露对上海心理压力感知的复杂影响.
- 开发和应用一个框架,用机器学习和街景数据来测量城市压力感知.
- 揭示GS暴露与居民心理压力之间的非线性关系.
主要方法:
- 构建了一个多维的心理压力感知尺度.
- 集成的机器学习算法 (SVM,XGBoost) 与街景图像.
- 用志愿者提供的压力评分作为标记数据用于模型训练和预测.
主要成果:
- 在上海市中心的GS暴露通常很低,空间差异很大.
- GS暴露对心理压力减轻有积极影响.
- 观察到一个值效应:超过0.35的GS暴露显示了减轻压力减轻的回报.
结论:
- 建议在有针对性的城市规划中将压力感知值与GS暴露结合起来.
- 强调在城市设计中考虑GS的"剂量效应"的重要性.
- 开发的框架可以适应在各种城市环境中的应用.
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