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.
翻译:这项工作为在区域范围内收集的诸如移动定位数据来源目的地矩阵等高频和高维数据引入了实时异常探测系统。考虑到不同来源的数据在时间和空间上的不同颗粒度,该系统的设计简单,但对于数据的多样性来说是稳健的,目的是发现向特定区域流动的突然增加和流动的突然下降。该方法旨在帮助决策者或从业人员,并使人们能够对异常现象进行可视化,以及估计COVID-19相关封闭或提升措施对人流动的影响,以及发现与大型聚集有关的新爆发的可能性。