Clustering Thailand’s Provinces based on Socio-Economic Data

This study comprehensively analyzes Thailand’s 77 provinces, utilizing six socio-economic metrics, including the Gini index, Economic Complexity Index (ECI), population, population density, provincial budget, and Real Gross Provincial Product (RGPP) per capita. Employing three clustering algorithms–K-means, Hierarchical Agglomerative, and GMM. The optimal number of clusters is determined using Silhouette Score, resulting in 4 clusters for most years, except 2019, where 3 clusters are deemed optimal. Evaluation metrics highlight Hierarchical Agglomerative clustering as the most suitable algorithm. The identified clusters have significant implications for policymaking and regional development, providing nuanced insights for targeted interventions and strategies.