Comparative clustering and visualization of socioeconomic and health indicators: A case of 47 counties in Kenya

Abstract

Kenya as a country is still faced with uneven regional development seen across various economic sectors. The regions experienced inequalities in economic diversity, economic development infrastructure, human development levels, social structure and living conditions, as well as political representation and participation in decision-making. The aim of this article is to examine the dynamics of marginalization and regional inequalities in Kenya by grouping counties based on socioeconomic and health indicators. We also analyzed aggregated data from the World Bank country website, online publications, and county government websites. The data includes 24 variables and metrics describing counties; Fertility, mortality , social, education, population and development and access to social services. Principal component analysis was used to visualize socioeconomic similarities between counties and identify the indicators of maximum variation. Agglomerative hierarchical and K-means clustering were then used to identify county groups with similar socioeconomic and health performance. The grouped counties were then visualized on a geographic map. Five clusters were identified that may be useful to county and state governments in future plans to promote inclusive and sustainable economic development.