Cluster Analysis of Poverty Indicators Across Local Government Areas in Plateau State

Understanding the spatial distribution of poverty dimensions across local government areas enables more precise targeting of development interventions. This study applied hierarchical and K-Means clustering to multidimensional poverty indicator data across 17 Local Government Areas in Plateau State, North Central Nigeria. Data were sourced from the 2022 National Bureau of Statistics Multidimensional Poverty Index report, incorporating variables for education, health, living standard, and employment. Agglomerative hierarchical clustering with Ward linkage was applied alongside K-Means with four clusters, validated by silhouette analysis. Four poverty profiles emerged: severely deprived rural LGAs, moderately deprived agricultural zones, transitional peri-urban LGAs, and relatively developed urban cores. Jos North and Barkin Ladi LGAs dominated the urban core cluster with the highest health and education access scores. Wase and Mikang LGAs showed the deepest multidimensional poverty, driven primarily by severe education deprivation and limited electricity access. The clustering results aligned closely with geographic terrain, with LGAs in the southern Plateau zone consistently appearing in more deprived clusters. The hierarchical dendrogram visualisation provided intuitive groupings that could guide LGA-specific development plans. The study provides evidence that uniform development programming across Plateau State is inefficient and inequitable. Recommendations include cluster-specific resource allocation by the Plateau State Planning Commission, prioritisation of school infrastructure and teacher deployment in Wase and Mikang, and electrification investments in rural southern Plateau LGAs as foundational poverty reduction measures.

Keywords: cluster analysis, poverty indicators, local government areas, Plateau State, multidimensional poverty