论文标题
使用蜂窝网络数据来表征共vid-19期间的人类迁移率模式
Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data
论文作者
论文摘要
在本文中,我们的目标是分析和比较锁骨前,锁定期间以及围绕COVID-19大流行的锁骨后阶段的细胞网络使用数据,以了解和模拟大流行期间的人类移动模式,并评估锁定对移动性的效果。为此,我们与巴西的主要蜂窝网络提供商之一合作,并收集和分析了从1400个天线的蜂窝网络连接,从2020年3月1日至2020年7月1日,里约热内卢及其郊区的所有用户及其郊区。我们的分析表明,在锁定阶段的总数降低到锁定阶段的总数增加到了85%,而lock lock ins lock又增加了55%,而在85%的锁定阶段则增加了55%的lock。我们观察到,随着越来越多的人远程工作,天线发生了转变,占总交通的前10%,与里约热内卢市中心的天线建立了连接数量,可大大降低和天线在其他位置取代他们的位置。我们还观察到,虽然近40-45%的用户在锁定阶段每天仅连接到1个天线,这表明没有移动性,但大约有4%的用户(即80k用户)连接到10个以上的天线,表明移动性很高。最后,我们设计了一种互动工具,可以展示不同粒度的移动性模式,可以帮助人们和政府官员了解个人的流动性以及特定社区中的共同案件的数量。我们的分析,推论和互动性展示基于大规模数据的移动性模式可以推断到世界其他城市,并有可能帮助设计更有效的大流行管理措施。
In this paper, our goal is to analyze and compare cellular network usage data from pre-lockdown, during lockdown, and post-lockdown phases surrounding the COVID-19 pandemic to understand and model human mobility patterns during the pandemic, and evaluate the effect of lockdowns on mobility. To this end, we collaborate with one of the main cellular network providers in Brazil, and collect and analyze cellular network connections from 1400 antennas for all users in the city of Rio de Janeiro and its suburbs from March 1, 2020 to July 1, 2020. Our analysis reveals that the total number of cellular connections decreases to 78% during the lockdown phase and then increases to 85% of the pre-COVID era as the lockdown eases. We observe that as more people work remotely, there is a shift in the antennas incurring top 10% of the total traffic, with the number of connections made to antennas in downtown Rio reducing drastically and antennas at other locations taking their place. We also observe that while nearly 40-45% users connected to only 1 antenna each day during the lockdown phase indicating no mobility, there are around 4% users (i.e., 80K users) who connected to more than 10 antennas, indicating very high mobility. Finally, we design an interactive tool that showcases mobility patterns in different granularities that can potentially help people and government officials understand the mobility of individuals and the number of COVID cases in a particular neighborhood. Our analysis, inferences, and interactive showcasing of mobility patterns based on large-scale data can be extrapolated to other cities of the world and has the potential to help in designing more effective pandemic management measures in the future.