This dissertation is based on a project co-founded by the Health Market Quality Program (now Rozetta Institute) and the Australian Institute of Health and Welfare. The overall objective of this work is to provide a framework and a tool for classification and clustering of homogeneous geographic areas based on aggregated population data. Thus, to enable the presentation and reporting of comparable information of individual units with peers, I develop the Homogeneity and Location indices to measure respectively the dispersion and central tendency of a categorical ordinal distribution. The advantages of such indices include statistical efficiency and a simple presentation of results. Our approach is founded on the general theory of probability distributions, and our aim is to provide a natural benchmark for a homogeneity measure in terms of what is a "high" and "low" concentration of a probability distribution. Currently, there is no accepted benchmark that could be used to assess the homogeneity of a categorical ordinal variable. In this work, the proposed statistical indices are used to assess the socioeconomic homogeneity of the commonly used SA3 Australia census geography and analyse the variation of GP attenders in the metropolitan area of Sydney. The approach can be used to classify any geographic area and explore variation across any specified geographical boundaries. The SA3 dataset and scripts (R/Python) to develop these indices have been made available on my GitHub account: https://github.com/lpinzari/homogeneity-location-index