  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.
