Daily_flow state level

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the 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 analysing millions of anonymous mobile phone users’ visit trajectories 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 behaviour 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.在不同地理尺度下理解动态的人类移动变化与空间交互模式，对于监测、评估大流行期间非药物干预措施（Non-Pharmaceutical Interventions，如居家令）的影响至关重要。在本数据简介中，我们介绍了一套定期更新的全美多尺度动态人类移动流数据集，其数据起始时间为2020年3月1日。通过分析SafeGraph提供的数百万匿名手机用户前往各类场所的到访轨迹，我们计算、聚合并推导得到了三种地理尺度下的每日及每周动态起讫点（Origin-Destination，O-D）人口流动数据，三种尺度分别为普查街区（Census Tract）、县（County）与州（State）。本移动流数据集与公开可用数据源之间具有较高相关性，这验证了所生成数据的可靠性。这套具备高时空分辨率、可按不同地理尺度随时间更新的人类移动流数据集，可用于监测疫情传播动态、为公共卫生政策制定提供决策参考，并加深我们对这场史无前例的公共卫生危机下人类行为变化的理解。这套最新的起讫点流动开放数据集，可支撑诸多其他社会感知与交通应用场景。