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Addressing location uncertainties in GPS-based activity monitoring: A methodological framework

机译：解决基于GPS的活动监测的位置不确定性：一种方法论框架

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Location uncertainty has been a major barrier in information mining from location data. Although the development of electronic and telecommunication equipment has led to an increased amount and refined resolution of data about individuals' spatio-temporal trajectories, the potential of such data, especially in the context of environmental health studies, has not been fully realized due to the lack of methodology that addresses location uncertainties. This article describes a methodological framework for deriving information about people's continuous activities from individual-collected Global Positioning System (GPS) data, which is vital for a variety of environmental health studies. This framework is composed of two major methods that address critical issues at different stages of GPS data processing: (1) a fuzzy classification method for distinguishing activity patterns; and (2) a scale-adaptive method for refining activity locations and outdoor/indoor environments. Evaluation of this framework based on smartphone-collected GPS data indicates that it is robust to location errors and is able to generate useful information about individuals' life trajectories.
机译：位置不确定性一直是从位置数据中挖掘信息的主要障碍。虽然电子和电信设备的发展导致了有关个人时空轨迹的数据数量的增加和分辨率的提高，但由于缺乏解决位置不确定性的方法，这些数据的潜力，特别是在环境健康研究方面，尚未充分实现。本文描述了一个方法论框架，用于从个人收集的全球定位系统（GPS）数据中获取有关人类持续活动的信息，这对于各种环境健康研究至关重要。该框架由两种主要方法组成，用于解决GPS数据处理不同阶段的关键问题：（1）用于区分活动模式的模糊分类方法；（2）用于优化活动地点和室外/室内环境的规模自适应方法。基于智能手机收集的GPS数据对该框架进行的评估表明，该框架对定位误差具有鲁棒性，能够生成有关个人生活轨迹的有用信息。

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