MEASURING HUMAN ACTIVITY SPACES FROM GPS DATA WITH DENSITY RANKING AND SUMMARY CURVES
MEASURING HUMAN ACTIVITY SPACES FROM GPS DATA WITH DENSITY RANKING AND SUMMARY CURVES
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DOI：
10.1214/19-aoas1311
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发表时间：
2020-03-01
影响因子：
1.8
通讯作者：
Dobra, Adrian
中科院分区：
文献类型：
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作者：
Chen, Yen-Chi;Dobra, Adrian
活动空间是评估个人动态暴露于与日常生活活动期间访问的多个空间背景相关的社会和环境风险因素的基础。在本文中，我们调查现有的方法来测量活动空间的几何形状，大小和结构，基于GPS数据，并解释其局限性。我们建议解决这些缺点，通过一个非参数的方法称为密度排名，也通过三个总结曲线：质量-体积曲线，贝蒂数曲线和持久性曲线。我们引入了一种新的人类活动空间混合模型并研究了它的渐进性质。我们证明了核密度估计，这在目前的时间，是最普遍的方法之一，用于测量活动空间，是不是一个稳定的估计，其结构。我们说明了我们的方法的实用价值与模拟研究，并与最近收集的GPS数据集，其中包括10个人在六个月内访问的位置。
Activity spaces are fundamental to the assessment of individuals' dynamic exposure to social and environmental risk factors associated with multiple spatial contexts that are visited during activities of daily living. In this paper we survey existing approaches for measuring the geometry, size and structure of activity spaces, based on GPS data, and explain their limitations. We propose addressing these shortcomings through a nonparametric approach called density ranking and also through three summary curves: the mass-volume curve, the Betti number curve and the persistence curve. We introduce a novel mixture model for human activity spaces and study its asymptotic properties. We prove that the kernel density estimator, which at the present time, is one of the most widespread methods for measuring activity spaces, is not a stable estimator of their structure. We illustrate the practical value of our methods with a simulation study and with a recently collected GPS dataset that comprises the locations visited by 10 individuals over a six months period.