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 analyses 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 socio-economic 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).