Comparative clustering and visualization of socioeconomic and health indicators: A case of Kenya

Evans Kiptoo Korir

Socio-Economic Planning Sciences, 2024, vol. 95, issue C

Abstract: In this study, we used principal component analysis (PCA) to reduce the dimensionality of the data and used a hierarchical and K-means clustering technique to stratify counties in Kenya into five clusters. The grouped counties were then projected onto a geographic map to understand the relationship between their location and socioeconomic and health indicators. The results obtained may be useful to the county and state governments in future plans to promote inclusive and sustainable economic development.

Keywords: Cluster analysis; Socioeconomic and health indicators; Counties; Dimensionality reduction; Principal component analysis; Hierarchical and K-means clustering (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:95:y:2024:i:c:s0038012124001605

DOI: 10.1016/j.seps.2024.101961

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