  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).
