标题： 面向移动电话跟踪数据的数据处理流程 显示英文标题

标题： Toward a Data Processing Pipeline for Mobile-Phone Tracking Data

摘要： 随着手机变得无处不在，高频的智能手机定位数据正被越来越多的研究人员用于研究个人在日常生活中移动模式及其对健康、行为和其他结果的影响。 一个复杂的数据显示管道支撑着利用手机追踪数据的实证研究。 该管道的一个关键组成部分是将原始的时间戳位置转换为可分析的数据对象，通常是时空“轨迹”。 在本文中，我们分解了支撑青少年健康与发展的背景（AHDC）研究的数据分析管道的关键部分，这是一项针对居住在俄亥俄州哥伦布市大都会区的青少年的大规模纵向研究。 认识到AHDC研究人员使用的定制“分箱算法”类似于时间序列过滤算法，我们提出了一种统计框架——一种受分箱算法启发的形式化概率模型和计算推理方法，用于将噪声时间戳地理定位观测转换为捕捉旅行和稳定时期的移动轨迹。 我们的框架不同于分箱算法，它可以通过粒子吉布斯算法进行正式平滑，与原始分箱算法相比，提高了轨迹估计的准确性。 我们认为，我们的框架可以作为未来手机追踪研究的默认数据处理工具。 显示英文摘要

摘要： As mobile phones become ubiquitous, high-frequency smartphone positioning data are increasingly being used by researchers studying the mobility patterns of individuals as they go about their daily routines and the consequences of these patterns for health, behavioral, and other outcomes. A complex data pipeline underlies empirical research leveraging mobile phone tracking data. A key component of this pipeline is transforming raw, time-stamped positions into analysis-ready data objects, typically space-time "trajectories." In this paper, we break down a key portion of the data analysis pipeline underlying the Adolescent Health and Development in Context (AHDC) Study, a large-scale, longitudinal study of youth residing in the Columbus, OH metropolitan area. Recognizing that the bespoke "binning algorithm" used by AHDC researchers resembles a time-series filtering algorithm, we propose a statistical framework - a formal probability model and computational approach to inference - inspired by the binning algorithm for transforming noisy, time-stamped geographic positioning observations into mobility trajectories that capture periods of travel and stability. Our framework, unlike the binning algorithm, allows for formal smoothing via a particle Gibbs algorithm, improving estimation of trajectories as compared to the original binning algorithm. We argue that our framework can be used as a default data processing tool for future mobile-phone tracking studies.