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This dissertation is based on a project co-founded by the Health MarketQuality Program (now Rozetta Institute) and the Australian Institute of Healthand Welfare. The overall objective of this work is to provide a framework and atool for classification and clustering of homogeneous geographic areas based onaggregated population data. Thus, to enable the presentation and reporting ofcomparable information of individual units with peers, I develop theHomogeneity and Location indices to measure respectively the dispersion andcentral tendency of a categorical ordinal distribution. The advantages of suchindices include statistical efficiency and a simple presentation of results.Our approach is founded on the general theory of probability distributions, andour aim is to provide a natural benchmark for a homogeneity measure in terms ofwhat is a "high" and "low" concentration of a probability distribution.Currently, there is no accepted benchmark that could be used to assess thehomogeneity of a categorical ordinal variable. In this work, the proposedstatistical indices are used to assess the socioeconomic homogeneity of thecommonly used SA3 Australia census geography and analyse the variation of GPattenders in the metropolitan area of Sydney. The approach can be used toclassify any geographic area and explore variation across any specifiedgeographical boundaries. The SA3 dataset and scripts (R/Python) to developthese indices have been made available on my GitHub account:https://github.com/lpinzari/homogeneity-location-index

Development of Tools for the Classification of Peer Groups Geographies in the Analysis of Health Care Variation