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A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data

Analyzing human mobility with geo-location data collected from smartphones has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns using the GPS data. In particular, we view this problem from a probabilistic perspective. A non-parametric Bayesian mo...

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Detalles Bibliográficos
Autores principales: Qian, Weizhu, Lauri, Fabrice, Gechter, Franck
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274334/
http://dx.doi.org/10.1007/978-3-030-50146-4_34
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author Qian, Weizhu
Lauri, Fabrice
Gechter, Franck
author_facet Qian, Weizhu
Lauri, Fabrice
Gechter, Franck
author_sort Qian, Weizhu
collection PubMed
description Analyzing human mobility with geo-location data collected from smartphones has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns using the GPS data. In particular, we view this problem from a probabilistic perspective. A non-parametric Bayesian modeling method, the Infinite Gaussian Mixture Model (IGMM) is used to estimate the probability density of the daily mobility. We also utilize the Kullback-Leibler (KL) divergence as the metrics to measure the similarity of different probability distributions. Combining the IGMM and the KL divergence, we propose an automatic clustering algorithm to discover mobility patterns for each individual user. Finally, the effectiveness of our method is validated on the real user data collected from different real users.
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spelling pubmed-72743342020-06-05 A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data Qian, Weizhu Lauri, Fabrice Gechter, Franck Information Processing and Management of Uncertainty in Knowledge-Based Systems Article Analyzing human mobility with geo-location data collected from smartphones has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns using the GPS data. In particular, we view this problem from a probabilistic perspective. A non-parametric Bayesian modeling method, the Infinite Gaussian Mixture Model (IGMM) is used to estimate the probability density of the daily mobility. We also utilize the Kullback-Leibler (KL) divergence as the metrics to measure the similarity of different probability distributions. Combining the IGMM and the KL divergence, we propose an automatic clustering algorithm to discover mobility patterns for each individual user. Finally, the effectiveness of our method is validated on the real user data collected from different real users. 2020-05-18 /pmc/articles/PMC7274334/ http://dx.doi.org/10.1007/978-3-030-50146-4_34 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Qian, Weizhu
Lauri, Fabrice
Gechter, Franck
A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data
title A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data
title_full A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data
title_fullStr A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data
title_full_unstemmed A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data
title_short A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data
title_sort probabilistic approach for discovering daily human mobility patterns with mobile data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274334/
http://dx.doi.org/10.1007/978-3-030-50146-4_34
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