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March 2020 Measuring human activity spaces from GPS data with density ranking and summary curves
Yen-Chi Chen, Adrian Dobra
Ann. Appl. Stat. 14(1): 409-432 (March 2020). DOI: 10.1214/19-AOAS1311

Abstract

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.

Citation

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Yen-Chi Chen. Adrian Dobra. "Measuring human activity spaces from GPS data with density ranking and summary curves." Ann. Appl. Stat. 14 (1) 409 - 432, March 2020. https://doi.org/10.1214/19-AOAS1311

Information

Received: 1 July 2019; Revised: 1 November 2019; Published: March 2020
First available in Project Euclid: 16 April 2020

zbMATH: 07200177
MathSciNet: MR4085099
Digital Object Identifier: 10.1214/19-AOAS1311

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 1 • March 2020
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