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

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.