Addressing location uncertainties in GPS-based activity monitoring: A methodological framework.
Addressing location uncertainties in GPS-based activity monitoring: A methodological framework.
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DOI：
10.1111/tgis.12231
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发表时间：
2017-08
期刊：
影响因子：
--
通讯作者：
Wilson GJ
中科院分区：
文献类型：
--
作者：
Wan N;Lin G;Wilson GJ
位置不确定性一直是从位置数据中进行信息挖掘的主要障碍。尽管电子和电信设备的发展增加了关于个人时空轨迹的数据量，并提高了数据的分辨率，但由于缺乏处理地点不确定性的方法，这种数据的潜力，特别是在环境健康研究方面的潜力尚未充分实现。本文描述了一种方法框架，用于从个人收集的全球定位系统(GPS)数据中获得关于人们持续活动的信息，这对各种环境健康研究至关重要。该框架由两个主要方法组成：(1)用于区分活动模式的模糊分类方法；(2)用于细化活动地点和室内外环境的尺度自适应方法。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，并能够生成关于个人生活轨迹的有用信息。
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.
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