Machine learning techniques as a tool for predicting overtourism: The case of Spain

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Title: Machine learning techniques as a tool for predicting overtourism: The case of Spain
Authors: Perles Ribes, José Francisco | Ramón-Rodríguez, Ana B. | Moreno-Izquierdo, Luis | Such Devesa, María Jesús
Research Group/s: Economía del Turismo, Recursos Naturales y Nuevas Tecnologías (INNATUR) | Internacionalización de la Empresa y Comercio Exterior
Center, Department or Service: Universidad de Alicante. Departamento de Análisis Económico Aplicado
Keywords: Early warning system | Hypothesis testing | Machine learning | Overtourism | Prediction
Knowledge Area: Economía Aplicada
Issue Date: Nov-2020
Publisher: John Wiley & Sons
Citation: International Journal of Tourism Research. 2020, 22(6): 825-838.
Abstract: One of the most challenging tasks for tourism scientists is the prediction of potential overtourism situations in the tourist destinations. Until now, some efforts have been proposed for the purpose of establishing early warning systems. However, none of the attempts has tried to make use of a powerful prediction tool that is currently available: machine learning techniques. This article seeks to fill this gap in the existing literature by proposing the use of machine learning techniques in order to predict overtourism issues on a sample of Spanish tourist cities specialized in both, urban and sun and beach tourism products.
ISSN: 1099-2340 (Print) | 1522-1970 (Online)
DOI: 10.1002/jtr.2383
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2020 John Wiley & Sons Ltd
Peer Review: si
Publisher version:
Appears in Collections:INV - INNATUR - Artículos de Revistas
INV - ECO-IA - Artículos de Revistas
INV - Internacionalización de la Empresa y Comercio Exterior - Artículos de Revistas

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