移动数据异常检测及其在COVID-19态势感知中的应用
Anomaly Detection of Mobility Data with Applications to COVID-19 Situational Awareness
摘要：
本文介绍了一个实时异常检测系统，用于在区域尺度上收集的高频和高维数据，如移动定位数据的源-目的地矩阵。考虑到来自不同来源的数据在时间和空间上的不同粒度，该系统被设计成简单的，但对数据多样性具有鲁棒性，目的是检测朝向特定区域的移动性的突然增加以及移动的突然下降。该方法旨在帮助政策制定者或从业人员，并使其能够可视化异常情况，并估计COVID-19相关遏制或解除措施对人员流动的影响，以及发现与大型集会相关的潜在新疫情。
英文摘要：
This work introduces a live anomaly detection system for high frequency and high-dimensional data collected at regional scale such as Origin Destination Matrices of mobile positioning data. To take into account different granularity in time and space of the data coming from different sources, the system is designed to be simple, yet robust to the data diversity, with the aim of detecting abrupt increase of mobility towards specific regions as well as sudden drops of movements. The methodology is designed to help policymakers or practitioners, and makes it possible to visualise anomalies as well as estimate the effect of COVID-19 related containment or lifting measures in terms of their impact on human mobility as well as spot potential new outbreaks related to large gatherings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
