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多层次的移动模式和人类运动的限制
Dominik J Schindler1, Jonathan Clarke1, Mauricio Barahona1
1Department of Mathematics, Imperial College London, London SW7 2BX, UK.
Royal Society open science
|October 13, 2023
概括
COVID-19 流行病重塑了人类的流动性,揭示了内在的尺度. 脸书运动数据显示,在封锁期间,移动性最初恢复到微小规模,然后随着限制的放松,扩大到更粗的规模.
科学领域:
- 计算社会科学 计算社会科学
- 网络科学 网络科学
- 流行病学 流行病学
背景情况:
- COVID-19 流行病对全球人类流动模式产生了重大影响.
- 了解多个规模的人类流动性对于公共卫生和政策至关重要.
- 现有的行政划分 (例如,NUTS地区) 可能不准确地反映实际的人类流动.
研究的目的:
- 在英国首次COVID-19封锁之前和期间使用Facebook运动数据分析人类流动的多层次性质.
- 确定内在的流动性尺度,比行政区域更好地代表人类的流动.
- 模拟封锁限制对移动模式和恢复的影响.
主要方法:
- 利用Facebook Movement绘制了英国第一次封锁之前和期间的数据.
- 在英国移动图表上使用无监督的多层次社区检测,以提取各种粗度级别的流动社区.
- 应用了线性衰变冲击模型来量化锁定效应和恢复时间.
主要成果:
- 确定了人类流动的强有力的分区,在多个规模的流动社区中,超过了传统的行政区域.
- 细尺寸的隔壁与英国的上班地带紧密相匹配,并捕捉到更广泛的流动模式.
- 观察到的移动性在封锁期间恢复到细尺度,并在限制解除后扩展到更粗的尺度,冲击强度和恢复的量化区域变化.
结论:
- 人类流动性表现出内在的多层次结构,这些数据驱动的社区检测比行政边界更好地捕捉到.
- 由于COVID-19的封锁,移动模式暂时发生了变化,导致在逐渐扩张之前恢复到更细微的尺度.
- 该研究提供了一个定量框架,用于理解和建模大规模中断期间的移动动态.
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