Localizing people across space and over time is a relevant and challenging problem in many modern applications. Smartphone ubiquity gives the opportunity to collect useful individual data as never before. In this work, the focus is on location data collected by smartphone applications. We propose a kernel-based density estimation approach that exploits cyclical spatio-temporal patterns of people to estimate the individual location density at any time, uncertainty included. Model parameters are estimated by maximum likelihood cross-validation. Unlike classic tracking methods designed for high spatio-temporal resolution data, the approach is suitable when location data are sparse in time and are affected by non-negligible errors. The approach is applied to location data collected by the Earthquake Network citizen science project which carries out a worldwide earthquake early warning system based on smartphones. The approach is parsimonious and is suitable to model location data gathered by any location-aware smartphone application.

在许多现代应用中，随着时间的推移跨空间对人员进行本地化是一个相关且具有挑战性的问题。智能手机无处不在为收集有用的个人数据提供了前所未有的机会。在这项工作中，重点是由智能手机应用程序收集的位置数据。我们提出了一种基于核的密度估计方法，该方法利用人的周期性时空模式来随时估计个人位置密度（包括不确定性）。通过最大似然交叉验证估计模型参数。与专为高时空分辨率数据设计的经典跟踪方法不同，该方法适用于位置数据时间稀疏且受到不可忽略的误差影响的情况。该方法适用于地震网络公民科学项目收集的位置数据，该项目实施了基于智能手机的全球地震预警系统。该方法是简约的，适合于建模任何位置感知智能手机应用程序收集的位置数据。