Tourist Expenditure Profiles in World Heritage Cities: A Conditional Inference Tree Approach

This study examines the relationship between sociodemographic factors and total tourist expenditure in the medium-sized UNESCO World Heritage Cities of Úbeda and Baeza, Spain. Using primary data from a structured survey of 1657 visitors, expenditure was analyzed across nine categories. The methodological approach combined descriptive statistics, non-parametric tests, and a Conditional Inference Tree (CTree) model to identify hierarchical segmentation patterns. The analysis revealed seven distinct visitor segments, with nationality as the strongest predictor, followed by employment status, income, and education. Foreign tourists, particularly retired individuals with university degrees, consistently showed higher expenditure levels, especially in accommodation and shopping. Domestic tourists displayed more heterogeneous spending profiles, influenced mainly by income and employment status. Accommodation, food, and shopping dominated expenditure structures across segments, while transport and recreation played a secondary role. By applying an interpretable CTree approach, the study uncovers interaction-based expenditure profiles that are often overlooked in conventional analyses, thereby offering a more nuanced basis for identifying high-spending tourists and informing destination management strategies.