Data from: Prediction limits of mobile phone activity modeling

Thanks to their widespread usage, mobile devices have become one of the main sensors of human behavior and digital traces left behind can be used as a proxy to study urban environments. Exploring the nature of the spatio-temporal patterns of mobile phone activity could thus be a crucial step towards understanding the full spectrum of human activities. Using ten months of mobile phone records from Greater London resolved in both space and time, we investigate the regularity of human telecommunication activity on urban scales. We evaluate several options for decomposing activity timelines into typical and residual patterns, accounting for the strong periodic and seasonal components. We carry out our analysis on various spatial scales, showing that regularity increases as we look at aggregated activity in larger spatial units with more activity in them. We examine the statistical properties of the residuals and show that it can be explained by noise and specific outliers. Also, we look at sources of deviations from the general trends, which we find to be explainable based on knowledge of the city structure and places of attractions.We show examples how some of the outliers can be related to external factors such as specific social events.

得益于移动设备的广泛普及，其已成为捕捉人类行为的核心感知传感器之一，设备遗留的数字痕迹可作为代理变量（proxy），用于开展城市环境相关研究。因此，探究手机通信活动的时空模式本质，是全面理解人类活动全貌的关键一环。本研究使用大伦敦地区为期十个月、具备时空分辨率的手机通信记录，探究城市尺度下人类通信活动的规律性。针对活动时序中显著的周期性与季节成分，我们评估了多种将活动时序分解为典型模式与残差模式的方法。我们在多空间尺度下开展分析，结果表明：当以聚集度更高、活动量更大的更大空间单元统计聚合活动时，通信活动的规律性会随之提升。我们对残差的统计特性展开分析，证实其可通过噪声与特定异常值加以解释。此外，我们还探究了偏离总体趋势的诱因，发现这些偏差可依托城市结构与知名景点的相关信息得到合理解释。我们还通过实例展示，部分异常值可与特定社会事件等外部因素建立关联。