Exploring Regional Determinants of Tourism Success in the Eurozone: An Unsupervised Machine Learning Approach †

Abstract

This paper presents an initial analysis of the factors influencing tourism success at the NUTS 2 regional level across the Eurozone from 2010 to 2019. Utilizing an extensive dataset that includes economic, demographic, and tourism-specific indicators, we employ unsupervised machine learning techniques, primarily K-means clustering and Principal Component Analysis (PCA), to unearth underlying patterns and relationships. Our study reveals distinct clusters of regions characterized by varying degrees of economic prosperity, infrastructure development, and tourism activity. Through K-means clustering, we identified optimal groupings of regions that share similar characteristics in terms of GDP per capita, unemployment rates, tourist arrivals, and overnight stays, among other metrics. Subsequent PCA provided deeper insights into the most influential factors driving these clusters, offering a reduced-dimensional perspective that highlights the primary axes of variation. The findings underscore significant disparities in tourism success across the Eurozone, with economic robustness and strategic infrastructural investments emerging as key drivers. Regions with higher GDP per capita and lower unemployment rates tend to exhibit higher tourism metrics, suggesting that economic health is a substantial contributor to regional tourism appeal and capacity. This paper contributes to the literature by demonstrating how machine learning can be applied to regional tourism data to better understand and strategize for tourism development. The insights garnered from this study are poised to assist policy-makers and tourism planners in crafting targeted interventions aimed at enhancing tourism competitiveness in underperforming regions.