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

arXiv: https://arxiv.org/abs/2008.12238 · HTML: https://mlsanity.com/paper/2008.12238/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 28 of 28)

- [Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US](https://mlsanity.com/paper/2106.01315/) (2021)
- [TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels](https://mlsanity.com/paper/2110.03664/) (2021)
- [Human mobility is well described by closed-form gravity-like models learned automatically from data](https://mlsanity.com/paper/2312.11281/) (2023)
- [From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques](https://mlsanity.com/paper/2107.00948/) (2021)
- [Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions](https://mlsanity.com/paper/2210.08041/) (2022)
- [When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges](https://mlsanity.com/paper/2402.03349/) (2024)
- [Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Data](https://mlsanity.com/paper/2110.11584/) (2021)
- [Causal invariant geographic network representations with feature and structural distribution shifts](https://mlsanity.com/paper/2503.19382/) (2025)
- [Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery](https://mlsanity.com/paper/2103.01717/) (2021)
- [STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19](https://mlsanity.com/paper/2301.08648/) (2023)
- [Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Models](https://mlsanity.com/paper/2406.04382/) (2024)
- [A Survey of Physics-Informed AI for Complex Urban Systems](https://mlsanity.com/paper/2506.13777/) (2025)
- [Improving COVID-19 Forecasting using eXogenous Variables](https://mlsanity.com/paper/2107.10397/) (2021)
- [GeoAI-Enhanced Community Detection on Spatial Networks with Graph Deep Learning](https://mlsanity.com/paper/2411.15428/) (2024)
- [Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks](https://mlsanity.com/paper/2502.01680/) (2025)
- [Combining digital data streams and epidemic networks for real time outbreak detection](https://mlsanity.com/paper/2511.07163/) (2025)
- [Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning](https://mlsanity.com/paper/2608.29360/) (2026)
- [Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)](https://mlsanity.com/paper/2108.03400/) (2021)
- [DemOpts: Fairness corrections in COVID-19 case prediction models](https://mlsanity.com/paper/2405.09483/) (2024)
- [Network-Based Transfer Learning Helps Improve Short-Term Crime Prediction Accuracy](https://mlsanity.com/paper/2406.06645/) (2024)
- [OpenUAS: Embeddings of Cities in Japan with Anchor Data for Cross-city Analysis of Area Usage Patterns](https://mlsanity.com/paper/2407.19872/) (2024)
- [Optimizing Luxury Vehicle Dealership Networks: A Graph Neural Network Approach to Site Selection](https://mlsanity.com/paper/2408.13961/) (2024)
- [Mobility-GCN: a human mobility-based graph convolutional network for tracking and analyzing the spatial dynamics of the synthetic opioid crisis in the USA, 2013-2020](https://mlsanity.com/paper/2409.09945/) (2024)
- [A Data-Driven Approach to Enhancing Gravity Models for Trip Demand Prediction](https://mlsanity.com/paper/2506.01964/) (2025)
- [Learning graph topology from metapopulation epidemic encoder-decoder](https://mlsanity.com/paper/2603.02349/) (2026)
- [DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling](https://mlsanity.com/paper/2606.09086/) (2026)
- [EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulation](https://mlsanity.com/paper/2607.22655/) (2026)
- [EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models](https://mlsanity.com/paper/2610.02744/) (2026)

## 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.