Exploratory techniques to analyse Ecuador's tourism industry

Abstract

The analysis of the operation of tourism companies will provide valid information for the design of policies to reactivate the tourism industry, which has been strongly affected during the pandemic generated by COVID-19. The objective of this paper is to use soft computing techniques to analyse tourism companies in Ecuador. First of all, dimensionality reduction methods are applied: principal component analysis, isometric feature mapping and locally linear embedding, on data of tourism enterprises in Ecuador for the year 2015. In addition, to verify the trend of operational variables, the data of tourism companies in Ecuador in 2019 and 2020 are analysed with dimensionality reduction methods that improve the interpretation by minimizing the loss of information. The data sets are analysed with k-means, k-medoids and Hierarchical Clustering, generating groups according to similar characteristics. The optimal number of clusters is determined with the following: Elbow Method, Silhouette Coefficient, Davies-Bouldin Index and Dunn Index. In addition, an analysis of the operation of tourism companies in the year 2020 concerning previous years is included. The study allows exploring Soft Computing techniques to identify important information for the definition of strategies that contribute to an effective reactivation of the tourist industry of Ecuador.