Machine learning techniques as a tool for predicting overtourism : The case of Spain
| Autor: | Ana Belén Ramón-Rodríguez, José Francisco Perles-Ribes, María Jesús Such-Devesa, Luis Moreno-Izquierdo |
|---|---|
| Přispěvatelé: | Universidad de Alicante. Departamento de Análisis Económico Aplicado, Economía del Turismo, Recursos Naturales y Nuevas Tecnologías (INNATUR), Internacionalización de la Empresa y Comercio Exterior |
| Rok vydání: | 2020 |
| Předmět: |
Overtourism
business.industry Geography Planning and Development Economía Aplicada Transportation Machine learning computer.software_genre Hypothesis testing Early warning system Tourism Leisure and Hospitality Management Political science Artificial intelligence Prediction business computer Nature and Landscape Conservation Statistical hypothesis testing |
| Zdroj: | International Journal of Tourism Research. 22:825-838 |
| ISSN: | 1522-1970 1099-2340 |
| DOI: | 10.1002/jtr.2383 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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