Liu Xiaoyan, Yang Saini, Huang Xiao, An Rui, Xiong Qiangqiang, Ye Tao
State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China.
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China.
Cities. 2023 Jan;132:104104. doi: 10.1016/j.cities.2022.104104. Epub 2022 Nov 14.
The COVID-19 pandemic has brought huge challenges to sustainable urban and community development. Although some recovery signals and patterns have been uncovered, the intra-city recovery process remains underexploited. This study proposes a comprehensive approach to quantify COVID-19 recovery leveraging fine-grained human mobility records. Taking Wuhan, a typical COVID-19 affected megacity in China, as the study area, we identify accurate recovery phases and select appropriate recovery functions in a data-driven manner. We observe that recovery characteristics regarding duration, amplitude, and velocity exhibit notable differences among urban blocks. We also notice that the recovery process under a one-wave outbreak lasts at least 84 days and has an S-shaped form best fitted with four-parameter Logistic functions. More than half of the recovery variance can be well explained and estimated by common variables from auxiliary data, including population, economic level, and built environments. Our study serves as a valuable reference that supports data-driven recovery quantification for COVID-19 and other crises.
新冠疫情给城市和社区的可持续发展带来了巨大挑战。尽管已发现一些复苏信号和模式，但城市内部的复苏进程仍未得到充分利用。本研究提出了一种利用细粒度人类移动记录来量化新冠疫情复苏情况的综合方法。以中国受新冠疫情影响的典型特大城市武汉为研究区域，我们以数据驱动的方式确定了准确的复苏阶段并选择了合适的复苏函数。我们观察到，城市街区在复苏持续时间、幅度和速度方面的特征存在显著差异。我们还注意到，单波疫情下的复苏过程至少持续84天，呈S形，最适合用四参数逻辑函数来拟合。超过一半的复苏差异可以通过辅助数据中的常见变量（包括人口、经济水平和建成环境）得到很好的解释和估计。我们的研究为新冠疫情及其他危机的数据驱动复苏量化提供了有价值的参考。
