灾难性事件响应中应用流量的异常时空模式检测
Detection of anomalous spatio-temporal patterns of app traffic in response to catastrophic events
AI总结：
本文利用 NetMob2023 法国城市手机应用流量数据，分析巴黎圣母院大火和里昂爆炸事件前后的信息传播时空模式，发现 Twitter 使用量从事件中心径向扩散的异常模式，并提出零模型与跟踪方法。
摘要：
在这项工作中，我们揭示了移动电话应用使用模式和信息传播在应对前所未有的突发事件引起的扰动时的规律。我们专注于对响应模式在空间和时间上进行分类，并跟踪其随时间的松弛过程。为此，我们使用 NetMob2023 Data Challenge 数据集，该数据集提供了法国多个城市的移动电话应用流量数据，空间分辨率为 100$m^2$，时间分辨率为 15 分钟，时间范围为 2019 年 3 月至 5 月。我们以不同移动应用的上传和下载数据量作为信息传递动态的代理指标，分析了 4 月 15 日灾难性的巴黎圣母院大火和 5 月 24 日发生在里昂市中心的爆炸事件之前、期间和之后的信息传播。我们识别了巴黎市内以及法国其他主要城市中响应巴黎圣母院大火的不同信息传递动态簇。我们发现了一个清晰的模式：在巴黎，应用 Twitter（现称为 X）的使用量显著高于基线水平，并从巴黎圣母院大教堂周边区域径向扩散到城市其他区域。我们在里昂市检测到了响应爆炸事件的类似模式。此外，我们提出了一个径向信息传播的零模型，并开发了随时间跟踪径向模式的方法。总体而言，我们展示了我们所设计的新型分析方法，说明这些方法如何为移动电话用户对非计划性灾难事件的响应提供新的视角，并深入揭示灾难期间信息在时间和空间上的传播方式。
英文摘要：
In this work, we uncover patterns of usage mobile phone applications and information spread in response to perturbations caused by unprecedented events. We focus on categorizing patterns of response in both space and time and tracking their relaxation over time. To this end, we use the NetMob2023 Data Challenge dataset, which provides mobile phone applications traffic volume data for several cities in France at a spatial resolution of 100$m^2$ and a time resolution of 15 minutes for a time period ranging from March to May 2019. We analyze the spread of information before, during, and after the catastrophic Notre-Dame fire on April 15th and a bombing that took place in the city centre of Lyon on May 24th using volume of data uploaded and downloaded to different mobile applications as a proxy of information transfer dynamics. We identify different clusters of information transfer dynamics in response to the Notre-Dame fire within the city of Paris as well as in other major French cities. We find a clear pattern of significantly above-baseline usage of the application Twitter (currently known as X) in Paris that radially spreads from the area surrounding the Notre-Dame cathedral to the rest of the city. We detect a similar pattern in the city of Lyon in response to the bombing. Further, we present a null model of radial information spread and develop methods of tracking radial patterns over time. Overall, we illustrate novel analytical methods we devise, showing how they enable a new perspective on mobile phone user response to unplanned catastrophic events, giving insight into how information spreads during a catastrophe in both time and space.
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