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.

明晰不同地理尺度下的动态人类流动变化与空间交互模式，对于监测与量化疫情期间非药物干预措施（如居家令）的影响至关重要。本数据简报介绍了一套定期更新的美国全域多尺度动态人类流动数据集，数据起始时间为2020年3月1日。研究团队依托SafeGraph提供的数百万匿名手机用户到访各类场所的轨迹数据，计算、聚合并推导出三种地理尺度下的每日与每周动态起点-终点（origin-to-destination, O-D）人口流动量，三种尺度分别为人口普查街区、郡与州。本流动数据集与公开可用数据源间具有高度相关性，验证了所生成数据的可靠性。这套具备高时空分辨率、覆盖不同地理尺度且随时间更新的人类流动数据集，可用于监测疫情传播动态、为公共卫生政策制定提供参考，并助力我们深入理解此次史无前例的公共卫生危机下人类行为的变化。这套最新的起点-终点流动开放数据集，还可支撑诸多社会感知与交通应用场景。