Application of multivariate statistical techniques and predictive modeling for the comparative analysis of the behavior of domestic and foreign tourists in the Chimborazo Fauna Production Reserve (2015–2025)
DOI:
https://doi.org/10.70577/sp2s0g33Keywords:
Multivariate Analysis, Predictive Modeling, Tourism Demand, Chimborazo Fauna Production Reserve, Tourists.Abstract
This study comparatively analyzed the behavior of domestic and international tourists visiting the Chimborazo Fauna Production Reserve during the 2015–2025 period through the application of multivariate statistical techniques and predictive models. A quantitative, non-experimental, and longitudinal research design was employed using monthly visitor records and survey data collected from tourists. The analytical approach included descriptive statistics, correlation analysis, Principal Component Analysis (PCA), cluster analysis, Multivariate Analysis of Variance (MANOVA), logistic regression, decision trees, and ARIMA models. [Articulo p...ntación-02 | Word]
The results revealed a strong association between domestic, international, and total visitor flows. PCA condensed approximately 90% of the total variability into three principal components. Cluster analysis identified three tourism segments: low flow (13.9%), medium flow (50.9%), and high flow (35.2%). MANOVA confirmed significant multivariate differences associated with seasonality (Pillai = 0.266; p < 0.001), while logistic regression achieved excellent predictive performance (AUC = 0.963). Furthermore, the ARIMA model adequately captured the temporal and seasonal dynamics of tourism demand. [Articulo p...ntación-02 | Word]
The findings indicate that tourism demand in the Chimborazo Fauna Production Reserve is primarily influenced by seasonality and visitor flow intensity. The integration of multivariate statistical methods and predictive modeling enabled the identification of behavioral patterns, the segmentation of tourism periods, and the development of useful tools for the sustainable planning and management of protected natural areas.
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