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Analyzing urban mobility paths based on users' activity in social networks

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Analyzing urban mobility paths based on users' activity in social networks

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dc.contributor.author Rodríguez, L. es_ES
dc.contributor.author Palanca Cámara, Javier es_ES
dc.contributor.author Del Val Noguera, Elena es_ES
dc.contributor.author Rebollo Pedruelo, Miguel es_ES
dc.date.accessioned 2020-06-05T03:32:11Z
dc.date.available 2020-06-05T03:32:11Z
dc.date.issued 2020-01 es_ES
dc.identifier.issn 0167-739X es_ES
dc.identifier.uri http://hdl.handle.net/10251/145396
dc.description.abstract [EN] This work presents an approach to model how the activity in social media of the citizens reflects the activity in the city. The proposal includes a gravitational model that deforms the surface of the city based on the intensity of the activity in different zones. The information is extracted from geolocated tweets (n = 1.48 x 10(6)). Furthermore, this activity affects how people move in a city. The path a user follows is calculated using the geolocation of the tweets that he or she publishes along the day. Several models are evaluated and compared using the Hausdorfs distance (d(H)). The combination of gravitational potential with attraction to the destination points provides the best results, with d(H) = 1176 against the Manhattan (d(H) = 1203) or the geodesic (d(H) = 1417) alternatives. Finally, the analysis is repeated with the data segmented by gender (n=2,826 paths, men=1,910, women=916). The results validate (p=0.000334) the studies that affirm that men travel longer distances (d(M) = 4.73 km, alpha(m) = 26.1 degrees) with rectilinear trajectories, whereas women have shorter and more angled paths (d(w) = 4.5 km, alpha(w) = 32.2 degrees), obtaining p values in path lengths and p=0.006 in the angles. (C) 2019 Elsevier B.V. All rights reserved. es_ES
dc.description.sponsorship This work is partially supported by Spanish Government Project TIN2015-65515-C4-1-R and the Post-doc grant Ref. SP20170057. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Future Generation Computer Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Complex network es_ES
dc.subject Social media es_ES
dc.subject Mobility es_ES
dc.subject Gender es_ES
dc.subject Smart cities es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.subject.classification BIBLIOTECONOMIA Y DOCUMENTACION es_ES
dc.title Analyzing urban mobility paths based on users' activity in social networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.future.2019.07.072 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2015-65515-C4-1-R/ES/ARQUITECTURA PERSUASIVA PARA EL USO SOSTENIBLE E INTELIGENTE DE VEHICULOS EN FLOTAS URBANAS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Rodríguez, L.; Palanca Cámara, J.; Del Val Noguera, E.; Rebollo Pedruelo, M. (2020). Analyzing urban mobility paths based on users' activity in social networks. Future Generation Computer Systems. 102:333-346. https://doi.org/10.1016/j.future.2019.07.072 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.future.2019.07.072 es_ES
dc.description.upvformatpinicio 333 es_ES
dc.description.upvformatpfin 346 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 102 es_ES
dc.relation.pasarela S\392601 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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