Generally, econometric studies on socio-economic inequalities consider regions as
independent entities, ignoring the likely possibility of spatial interaction between them. This
interaction may cause spatial dependency or clustering, which is referred to as spatial
autocorrelation. This paper analyzes for the first time, the spatial clustering of income,
income inequality, education, human development, and growth by employing spatial
exploratory data analysis (ESDA) techniques to data on 98 Pakistani districts. By detecting
outliers and clusters, ESDA allows policy makers to focus on the geography of socioeconomic
regional characteristics. Global and local measures of spatial autocorrelation have
been computed using the Moran’s I and the Geary’s C index to obtain estimates of the spatial
autocorrelation of spatial disparities across districts. The overall finding is that the
distribution of district wise income inequality, income, education attainment, growth, and
development levels, exhibits a significant tendency for socio-economic inequalities and
human development levels to cluster in Pakistan (i.e. the presence of spatial autocorrelation is
confirmed).