Understanding COVID-19 Effects on Mobility: A Community-Engaged Approach
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Abstract (arXiv)
Given aggregated mobile device data, the goal is to understand the impact of COVID-19 policy interventions on mobility. This problem is vital due to important societal use cases, such as safely reopening the economy. Challenges include understanding and interpreting questions of interest to policymakers, cross-jurisdictional variability in choice and time of interventions, the large data volume, and unknown sampling bias. The related work has explored the COVID-19 impact on travel distance, time spent at home, and the number of visitors at different points of interest. However, many policymakers are interested in long-duration visits to high-risk business categories and understanding the spatial selection bias to interpret summary reports. We provide an Entity Relationship diagram, system architecture, and implementation to support queries on long-duration visits in addition to fine resolution device count maps to understand spatial bias. We closely collaborated with policymakers to derive the system requirements and evaluate the system components, the summary reports, and visualizations.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jan 2022.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 2.
4 papers.
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- 2025Towards Fine-Tuning-Based Site Calibration for Knowledge-Guided Machine Learning: A Summary of Results17 Dec 2025
- 2025Towards Physics-informed Diffusion for Anomaly Detection in Trajectories8 Jun 2025
- 2024Physics-Guided Abnormal Trajectory Gap Detection10 Mar 2024
- 2022Spatiotemporal Data Mining: A Survey26 Jun 2022