  Understanding dynamic human mobility changes and spatial interaction patterns
at different geographic scales is crucial for assessing the impacts of
non-pharmaceutical interventions (such as stay-at-home orders) during the
COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated
multiscale dynamic human mobility flow dataset across the United States, with
data starting from March 1st, 2020. By analyzing millions of anonymous mobile
phone users' visits to various places provided by SafeGraph, the daily and
weekly dynamic origin-to-destination (O-D) population flows are computed,
aggregated, and inferred at three geographic scales: census tract, county, and
state. There is high correlation between our mobility flow dataset and openly
available data sources, which shows the reliability of the produced data. Such
a high spatiotemporal resolution human mobility flow dataset at different
geographic scales over time may help monitor epidemic spreading dynamics,
inform public health policy, and deepen our understanding of human behavior
changes under the unprecedented public health crisis. This up-to-date O-D flow
open data can support many other social sensing and transportation
applications.

Social and Information Networks;Physics and Society