{"url":"/dataset/mobility-flow","name":"Mobility Flow","full_name":null,"description_markdown":"This is a multiscale dynamic human mobility flow dataset across the United States, with data starting from January 1st, 2019. By analyzing 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.\r\n\r\nSuch 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.","description_withheld":null,"homepage":"","introduced_date":"2020-08-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/multiscale-dynamic-human-mobility-flow","title":"Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic","first_author":null,"url":null},"license":{"name":"MIT License","url":"https://github.com/GeoDS/COVID19USFlows/blob/master/LICENSE.txt"},"modalities":[],"tasks":[],"languages":[],"variants":["Mobility Flow"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}