Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?
Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?
复制标题
DOI：
10.3390/ijgi9110675
复制
发表时间：
2020-11-01
影响因子：
3.4
通讯作者：
Chen, Baixu
中科院分区：
文献类型：
--
作者：
Huang, Xiao;Li, Zhenlong;Chen, Baixu
在这项研究中，我们以数据驱动的方式调查了导致家庭居住时间时间序列差异的潜在驱动因素，旨在为未来流行病的更好缓解策略提供有利于政策制定的基础知识。以亚特兰大地铁为研究案例，我们进行趋势驱动的分析进行Kmeans时间序列聚类细粒度的家庭居住时间记录从SafeGraph。此外，我们应用ANOVA（方差分析）结合事后Tukey检验来评估识别的时间序列集群中16个重新编码的人口/社会经济变量（来自ACS 2014-2018估计）的统计差异。我们发现，人口统计学/社会经济变量可以解释对居家命令做出反应的居家时间差异，这可能导致不同的COVID-19风险暴露。结果进一步表明，社会弱势群体不太可能遵守留在家里的命令，指出社会弱势群体和其他人之间存在的社交距离措施的有效性存在巨大差距。我们的研究表明，美国长期存在的不平等问题阻碍了社交距离措施的有效实施。
In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey's test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014-2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures.