在面对天气危害的人类系统中剖析弹性曲线的原型和属性
1Urban Resilience.AI Lab, Zachry Department of Civil & Environmental Engineering, 3136 TAMU, College Station, TX, 77843-3136, USA. chawei0207@tamu.edu.
Scientific reports
|April 8, 2025
概括
这项研究使用数据驱动的弹性曲线确定了天气灾害后人类移动弹性的三个原型. 调查结果揭示了恢复的关键门,改善了灾害影响分析.
科学领域:
- 灾难科学 科学 灾难科学
- 城市研究 城市研究
- 人类地理的人类地理
背景情况:
- 弹性曲线是理解危险影响的概念工具,但缺乏经验基础.
- 更广泛的社区弹性是复杂的;有针对性的系统分析提供了有价值的见解.
- 经验性弹性曲线描述了系统如何吸收和从中断中恢复.
研究的目的:
- 在美国经历了天气相关的危险后,实证地描述人类移动性弹性模式.
- 用数据驱动的方法将弹性曲线分类为原型.
- 确定影响复苏动态的关键值.
主要方法:
- 从高分辨率的基于位置的移动数据分析了2000多个经验性弹性曲线.
- 应用k-means聚类来根据冲击和恢复特征对曲线进行分类.
- 确定恢复率和影响程度的关键值.
主要成果:
- 确定了三种人类移动性弹性原型:快速恢复 (I型),双式恢复 (II型) 和缓慢恢复 (III型).
- 在20%的冲击程度上确定了双方向恢复的断点;在60%以上的冲击程度上缓慢恢复.
- 当冲击超过20%时,双模性弹性需要每天2.5%的恢复率.
结论:
- 数据驱动的弹性曲线原型增强了对社区恢复模式的理解.
- 发现超越了概念性弹性曲线,转向经验性表征.
- 能够更好地预测和分析灾后社区的抗灾能力.
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