Zhang Lei, Darzi Aref, Ghader Sepehr, Pack Michael L, Xiong Chenfeng, Yang Mofeng, Sun Qianqian, Kabiri Aliakbar, Hu Songhua
Maryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD.
Center for Advanced Transportation Technology Laboratory, University of Maryland, College Park, MD.
Transp Res Rec. 2023 Apr;2677(4):168-180. doi: 10.1177/03611981211043813. Epub 2021 Sep 18.
The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.
该研究团队利用受隐私保护的移动设备位置数据,并将其与新冠肺炎病例数据和人口普查数据相结合,打造了一个新冠肺炎影响分析平台,该平台能够让用户了解新冠肺炎传播以及政府指令对出行和社交距离的影响。该平台每日更新,通过一个交互式分析工具,持续向决策者通报新冠肺炎对其所在社区的影响。该研究团队对匿名的移动设备位置数据进行了处理,以识别出行情况,并生成了一系列变量,包括社交距离指数、居家人数百分比、前往工作场所和非工作场所的出行情况、出城旅行以及出行距离。为保护隐私,结果汇总到县和州层面,并按每个县和州的总人口进行了比例换算。该研究团队将他们每日更新且可追溯至2020年1月1日用于基准测试的数据及研究结果向公众公开,以帮助政府官员做出明智决策。本文对该平台进行了概述,并描述了用于处理数据和生成平台指标的方法。