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.

翻译：考虑到综合移动设备数据,目标是了解COVID-19政策干预对流动的影响,这个问题由于重要的社会使用案例,例如安全重新开放经济,因而至关重要,挑战包括理解和解释决策者感兴趣的问题、干预的选择和时间的跨辖区差异、数据量大和未知抽样偏差。相关工作探讨了COVID-19对旅行距离、在家时间和不同利益点的访客人数的影响。然而,许多决策者有兴趣长期访问高风险商业类别,了解空间选择偏差以解释摘要报告。我们提供了实体关系图、系统架构和执行,以支持关于长期访问的查询,此外还提供了精细的分辨率装置计数地图,以了解空间偏差。我们与决策者密切合作,以了解系统要求并评价系统组成部分、摘要报告和可视化。