Inferring mobility measures from GPS traces with missing data
Inferring mobility measures from GPS traces with missing data
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DOI：
10.1093/biostatistics/kxy059
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发表时间：
2020-04-01
期刊：
影响因子：
2.1
通讯作者：
Onnela, Jukka-Pekka
中科院分区：
文献类型：
--
作者：
Barnett, Ian;Onnela, Jukka-Pekka
随着具有全球定位系统 (GPS) 功能的智能手机的普及，将个人水平的移动模式与从情绪障碍到手术康复等各种以患者为中心的结果相关联的大规模研究正在成为现实。过去类似的研究规模较小，并为受试者提供了可穿戴 GPS 设备。这些设备通常连续收集移动轨迹，数据中没有明显的间隙，因此数据丢失的问题已被安全地忽略。利用受试者自己的智能手机可以扩大规模并延长此类研究的持续时间，但同时也带来了巨大的挑战：为了节省智能手机的电池，GPS 只能在一小部分时间内处于活动状态，通常不到 10%，从而导致巨大的数据丢失问题。我们引入了一种基于观测数据的加权重采样的原则性统计方法来估算缺失的移动轨迹，然后我们使用不同的移动度量进行总结。我们比较了线性插值（LI）方法的优势，线性插值是一种处理缺失数据的流行方法，无论是分析还是通过模拟经验数据的缺失。我们的结论是，我们的插补方法在理论上和 Geolife 数据集中 182 个人的 GPS 移动轨迹样本上都更好地反映了人类的流动性，其中相对于 LI，插补导致所有流动性特征的平均误差减少了 10 倍。
With increasing availability of smartphones with Global Positioning System (GPS) capabilities, large-scale studies relating individual-level mobility patterns to a wide variety of patient-centered outcomes, from mood disorders to surgical recovery, are becoming a reality. Similar past studies have been small in scale and have provided wearable GPS devices to subjects. These devices typically collect mobility traces continuously without significant gaps in the data, and consequently the problem of data missingness has been safely ignored. Leveraging subjects' own smartphones makes it possible to scale up and extend the duration of these types of studies, but at the same time introduces a substantial challenge: to preserve a smartphone's battery, GPS can be active only for a small portion of the time, frequently less than 10%, leading to a tremendous missing data problem. We introduce a principled statistical approach, based on weighted resampling of the observed data, to impute the missing mobility traces, which we then summarize using different mobility measures. We compare the strengths of our approach to linear interpolation (LI), a popular approach for dealing with missing data, both analytically and through simulation of missingness for empirical data. We conclude that our imputation approach better mirrors human mobility both theoretically and over a sample of GPS mobility traces from 182 individuals in the Geolife data set, where, relative to LI, imputation resulted in a 10-fold reduction in the error averaged across all mobility features.