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Bidirectional imputation of spatial GPS trajectories with missingness using sparse online Gaussian Process
Journal of the American Medical Informatics Association ( IF 6.4 ) Pub Date : 2021-06-08 , DOI: 10.1093/jamia/ocab069
Gang Liu 1 , Jukka-Pekka Onnela 1
Affiliation  

Abstract
Objective
We propose a bidirectional GPS imputation method that can recover real-world mobility trajectories even when a substantial proportion of the data are missing. The time complexity of our online method is linear in the sample size, and it provides accurate estimates on daily or hourly summary statistics such as time spent at home and distance traveled.
Materials and Methods
To preserve a smartphone’s battery, GPS may be sampled only for a small portion of time, frequently <10%, which leads to a substantial missing data problem. We developed an algorithm that simulates an individual’s trajectory based on observed GPS location traces using sparse online Gaussian Process to addresses the high computational complexity of the existing method. The method also retains the spherical geometry of the problem, and imputes the missing trajectory in a bidirectional fashion with multiple condition checks to improve accuracy.
Results
We demonstrated that (1) the imputed trajectories mimic the real-world trajectories, (2) the confidence intervals of summary statistics cover the ground truth in most cases, and (3) our algorithm is much faster than existing methods if we have more than 3 months of observations; (4) we also provide guidelines on optimal sampling strategies.
Conclusions
Our approach outperformed existing methods and was significantly faster. It can be used in settings in which data need to be analyzed and acted on continuously, for example, to detect behavioral anomalies that might affect treatment adherence, or to learn about colocations of individuals during an epidemic.


中文翻译:

使用稀疏在线高斯过程对具有缺失的空间 GPS 轨迹进行双向插补

摘要
客观的
我们提出了一种双向 GPS 插补方法,即使在大量数据丢失的情况下,它也可以恢复真实世界的移动轨迹。我们的在线方法的时间复杂度与样本量呈线性关系,它提供了对每日或每小时汇总统计数据的准确估计,例如在家花费的时间和旅行的距离。
材料和方法
为了保护智能手机的电池,GPS 可能仅在一小部分时间内进行采样,通常小于 10%,这会导致严重的数据丢失问题。我们开发了一种算法,该算法基于观察到的 GPS 位置轨迹,使用稀疏在线高斯过程来模拟个人的轨迹,以解决现有方法的高计算复杂度。该方法还保留了问题的球面几何形状,并通过多个条件检查以双向方式估算丢失的轨迹以提高准确性。
结果
我们证明了(1)估算的轨迹模拟了真实世界的轨迹,(2)摘要统计的置信区间在大多数情况下涵盖了基本事实,以及(3)如果我们有超过3个月的观察;(4) 我们还提供了关于最佳抽样策略的指南。
结论
我们的方法优于现有方法,并且速度明显更快。它可用于需要对数据进行连续分析和采取行动的环境中,例如,检测可能影响治疗依从性的行为异常,或了解流行病期间个人的托管情况。
更新日期:2021-08-07
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