mlsanityData to 7 Oct 2026

Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic

arXiv 2008.12238 ↗First posted 27 Aug 2020cs.SI physics.soc-phcited by 27

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Abstract (arXiv)

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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 1; 2023: 2; 2024: 8; 2025: 5; 2026: 4.

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  1. 2021Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the UScited by 16
  2. 2021TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labelscited by 11
  3. 2023Human mobility is well described by closed-form gravity-like models learned automatically from datacited by 11
  4. 2021From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniquescited by 7
  5. 2022Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactionscited by 5
  6. 2024When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challengescited by 4
  7. 2021Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Datacited by 3
  8. 2025Causal invariant geographic network representations with feature and structural distribution shiftscited by 3
  9. 2021Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagerycited by 2
  10. 2023STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19cited by 2
  11. 2024Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Modelscited by 2
  12. 2025A Survey of Physics-Informed AI for Complex Urban Systemscited by 2
15 more
  1. 2021Improving COVID-19 Forecasting using eXogenous Variablescited by 1
  2. 2024GeoAI-Enhanced Community Detection on Spatial Networks with Graph Deep Learningcited by 1
  3. 2025Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networkscited by 1
  4. 2025Combining digital data streams and epidemic networks for real time outbreak detectioncited by 1
  5. 2026Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearningcited by 1
  6. 2021Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)cited by 0
  7. 2024DemOpts: Fairness corrections in COVID-19 case prediction modelscited by 0
  8. 2024Network-Based Transfer Learning Helps Improve Short-Term Crime Prediction Accuracycited by 0
  9. 2024OpenUAS: Embeddings of Cities in Japan with Anchor Data for Cross-city Analysis of Area Usage Patternscited by 0
  10. 2024Optimizing Luxury Vehicle Dealership Networks: A Graph Neural Network Approach to Site Selectioncited by 0
  11. 2024Mobility-GCN: a human mobility-based graph convolutional network for tracking and analyzing the spatial dynamics of the synthetic opioid crisis in the USA, 2013-2020cited by 0
  12. 2025A Data-Driven Approach to Enhancing Gravity Models for Trip Demand Predictioncited by 0
  13. 2026Learning graph topology from metapopulation epidemic encoder-decodercited by 0
  14. 2026DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modelingcited by 0
  15. 2026EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulationcited by 0