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Detecting and Reducing Biases in Cellular-Based Mobility Data Sets

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)es
dc.contributor.authorRodríguez Carrión, Alicia
dc.contributor.authorGarcía Rubio, Carlos
dc.contributor.authorCampo Vázquez, María Celeste
dc.date.accessioned2019-02-07T08:27:28Z
dc.date.available2019-02-07T08:27:28Z
dc.date.issued2018-09-25
dc.description.abstractCorrectly estimating the features characterizing human mobility from mobile phone traces is a key factor to improve the performance of mobile networks, as well as for mobility model design and urban planning. Most related works found their conclusions on location data based on the cells where each user sends or receives calls or messages, data known as Call Detail Records (CDRs). In this work, we test if such data sets provide enough detail on users' movements so as to accurately estimate some of the most studied mobility features. We perform the analysis using two different data sets, comparing CDRs with respect to an alternative data collection approach. Furthermore, we propose three filtering techniques to reduce the biases detected in the fraction of visits per cell, entropy and entropy rate distributions, and predictability. The analysis highlights the need for contextualizing mobility results with respect to the data used, since the conclusions are biased by the mobile phone traces collection approach.en
dc.description.sponsorshipThis research was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness through TEC2017-84197-C4-1-R (Inteligencia de fuentes abiertas para redes electricas inteligentes seguras), TEC2014-54335-C4-2-R (INRISCO: INcident monitoRing In Smart COmmunities), and IPT-2011-1272-430000 (MONOLOC) projects.en
dc.format.extent17
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationRodríguez-Carrión,A., García-Rubio,C., Campo,C. (2018). Detecting and Reducing Biases in Cellular-Based Mobility Data Sets. Entropy, 20 (10), 736.en
dc.identifier.doihttps://doi.org/10.3390/e20100736
dc.identifier.issn1099-4300
dc.identifier.publicationissue10
dc.identifier.publicationtitleEntropyen
dc.identifier.publicationvolume20
dc.identifier.urihttps://hdl.handle.net/10016/28004
dc.identifier.uxxiAR/0000022426
dc.language.isoengen
dc.publisherMDPIen
dc.relation.projectIDGobierno de España. TEC2017-84197-C4-1-Res
dc.relation.projectIDGobierno de España. TEC2014-54335-C4-2-Res
dc.relation.projectIDGobierno de España. IPT-2011-1272-430000es
dc.rights© 2018 by the authors; licensee MDPI, Basel, Switzerland.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherHuman mobilityen
dc.subject.otherCell-based locationen
dc.subject.otherPing-pong effecten
dc.subject.otherMobility data sets entropyen
dc.subject.otherMobility data sets predictabilityen
dc.titleDetecting and Reducing Biases in Cellular-Based Mobility Data Setsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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