Clustering of Countries Through UMAP and K-Means: A Multidimensional Analysis of Development, Governance, and Logistics

Enrique Delahoz-Domínguez (), Adel Mendoza-Mendoza and Delimiro Visbal-Cadavid
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Enrique Delahoz-Domínguez: Statistical and Quantitative Methods Research Group (GEMC), Universidad del Magdalena, Santa Marta 470004, Colombia
Adel Mendoza-Mendoza: Industrial Engineering Program, Universidad del Atlántico, Barranquilla 080001, Colombia
Delimiro Visbal-Cadavid: Industrial Engineering Program, Faculty of Engineering, Universidad del Magdalena, Santa Marta 470004, Colombia

Logistics, 2025, vol. 9, issue 3, 1-18

Abstract: Background : Growing disparities in development, governance, and logistics performance across countries pose challenges for global policymaking and Sustainable Development Goal (SDG) monitoring. This study proposes a classification of 137 countries based on multiple structural dimensions. The dataset for 2023 includes six components of the Logistics Performance Index (LPI), six dimensions of the Worldwide Governance Indicators (WGIs), and four proxies of the Human Development Index (HDI). Methods : The Uniform Manifold Approximation and Projection (UMAP) technique was used to reduce dimensionality and allow for meaningful clustering. Based on the reduced space, the K-means algorithm was employed to group countries with similar development characteristics. Results : The classification process allowed the identification of three distinct groups of countries, supported by a Hopkins statistic of 0.984 and an explained variance ratio of 87.3%. These groups exhibit structural differences in the quality of governance, logistics capacity, and social development conditions. Internal consistency checks and multivariate statistical analyses (ANOVA and MANOVA) confirmed the robustness and statistical significance of the clustering. Conclusions : The resulting classification offers a practical analytical tool for policymakers to design differentiated strategies aligned with national contexts. Furthermore, it provides a data-driven approach for comparative monitoring of the SDGs from an integrated and empirical perspective.

Keywords: UMAP; K-means clustering; country classification; multidimensional analysis (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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