Autor según el artículo: Ayebakuro Orama, Jonathan; Huertas, Assumpcio; Borras, Joan; Moreno, Antonio; Anton Clave, Salvador
Departamento: Geografia
Autor/es de la URV: Anton Clavé, Salvador / Borràs Nogués, Joan / Huertas Roig, Maria Asuncion / Moreno Ribas, Antonio / Orama, Ayebakuro Jonathan
Palabras clave: Tourist flows Tourist clusters Tourism destinations Social media data Services Search Networks Mobility patterns Location Information-technology Hospitality Experience Big-data analytics Behavior Artificial intelligence
Resumen: In order to enhance tourists' experiences, Destination Management Organizations need to know who their tourists are, their travel preferences, and their flows around the destination. The study develops a methodology that, through the application of Artificial Intelligence techniques to social media data, creates clusters of tourists according to their mobility and visiting preferences at the destination. The applied method improves the knowledge about the different mobility patterns of tourists (the most visited points and the main flows between them within a destination) depending on who they are and what their preferences are. Clustering tourists by their travel mobility permits uncovering much more information about them and their preferences than previous studies. This knowledge will allow DMOs and tourism service providers to offer personalized services and information, to attract specific types of tourists to certain points of interest, to create new routes, or to enhance public transport services.
Grupo de investigación: Grup de Recerca d'Anàlisi Territorial i Estudis Turístics (GRATET)
Áreas temáticas: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: ayebakurojonathan.orama@estudiants.urv.cat antonio.moreno@urv.cat sunsi.huertas@urv.cat salvador.anton@urv.cat salvador.anton@urv.cat
Identificador del autor: 0000-0002-2622-3224 0000-0003-3945-2314 0000-0001-6684-4220 0000-0001-9818-2778 0000-0001-9818-2778
Fecha de alta del registro: 2024-10-12
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/2076-3417/12/12/5834
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Applied Sciences-Basel. 12 (12): 5834-
Referencia de l'ítem segons les normes APA: Ayebakuro Orama, Jonathan; Huertas, Assumpcio; Borras, Joan; Moreno, Antonio; Anton Clave, Salvador (2022). Identification of Mobility Patterns of Clusters of City Visitors: An Application of Artificial Intelligence Techniques to Social Media Data. Applied Sciences-Basel, 12(12), 5834-. DOI: 10.3390/app12125834
DOI del artículo: 10.3390/app12125834
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications
Repositori URV