相关实验视频
Updated: Jul 14, 2025

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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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基于随机森林模型,探索社区城市绿地和武汉COVID-19风险的空间模式
Wenpei Li1, Fei Dai2,3, Jessica Ann Diehl1
1Department of Architecture, College of Design and Engineering, National University of Singapore, 117566, Singapore.
Heliyon
|October 9, 2023
概括
城市绿地 (UGS) 空间模式,而不仅仅是数量,在社区中显著影响COVID-19风险. 核心,边缘,循环和分支等特定的空间指标比整体UGS金额更有影响力.
科学领域:
- 城市规划和公共卫生
- 环境科学和流行病学.
背景情况:
- COVID-19突出了城市环境和疾病爆发之间的联系.
- 之前的研究指出,城市绿地 (UGS) 量与COVID-19风险之间存在相关性,但忽视了空间模式.
- 在人口密集的地区,UGS空间配置对于管理疾病风险至关重要.
研究的目的:
- 调查UGS数量与空间模式在预测社区层面的COVID-19风险方面的相对重要性.
- 确定影响城市环境疾病风险的关键UGS空间指标.
主要方法:
- 使用一个随机森林 (RF) 回归模型.
- 分析了武汉44个社区的数据.
- 评估了城市绿色空间的8个指标,重点关注数量和空间模式指标.
主要成果:
- 八个UGS指标共同解释了COVID-19风险变化的35%.
- 空间模式指标 (核心,边缘,循环,分支) 是比UGS量更重要的预测指标.
- 发现UGS数量是最不起作用的因素.
结论:
- 在社区中,UGS空间模式对于减轻COVID-19风险至关重要.
- 研究结果表明,优化UGS配置,而不仅仅是增加面积,对公共卫生有好处.
- 这项研究为可持续的城市设计和流行病准备提供了新的视角.
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