论文标题
Covid-19对人类流动性的影响的时空分析：美国的情况
Spatial-temporal Analysis of COVID-19's Impact on Human Mobility: the Case of the United States
论文作者
论文摘要
Covid-19自2019年12月以来一直在影响包括人类流动性在内的社会生活的各个方面。从时间的角度来看，我们发现流动性模式的变化不一定与政府的政策和准则相关，而是与人们对大流行的认识有关，这反映在Google趋势中的搜索数据中。我们的结果表明，适应新情况的流动性模式平均需要14天。从空间的角度来看，我们使用来自多尺度动态人类移动流量数据集的移动性数据进行了州级网络分析和聚类。结果，我们发现1）同一集群中的状态的地理距离较短； 2）在出现最大数量的簇和冠状病毒相关搜索查询的峰值上，再次发现了14天的延迟； 3）从3月2日一周到4月6日（群集数量最多的一周），所有州的其他网络流量属性的大幅度降低，即程度，亲密和之间。
COVID-19 has been affecting every aspect of societal life including human mobility since December, 2019. In this paper, we study the impact of COVID-19 on human mobility patterns at the state level within the United States. From the temporal perspective, we find that the change of mobility patterns does not necessarily correlate with government policies and guidelines, but is more related to people's awareness of the pandemic, which is reflected by the search data from Google Trends. Our results show that it takes on average 14 days for the mobility patterns to adjust to the new situation. From the spatial perspective, we conduct a state-level network analysis and clustering using the mobility data from Multiscale Dynamic Human Mobility Flow Dataset. As a result, we find that 1) states in the same cluster have shorter geographical distances; 2) a 14-day delay again is found between the time when the largest number of clusters appears and the peak of Coronavirus-related search queries on Google Trends; and 3) a major reduction in other network flow properties, namely degree, closeness, and betweenness, of all states from the week of March 2 to the week of April 6 (the week of the largest number of clusters).
