The availability of high quality geographically referenced information on populations, health outcomes, and environmentalrisk factors is important for spatial epidemiological studies. Much of this research relies on data aggregated at the arealevel which is impacted by the chosen set of aggregation units (the modifiable areal unit problem - MAUP), and can obscurethe communication of occurrence rates. This thesis examines how spatially aggregated data impacts epidemiological andhealth related association analysis, often used to plan for health resource allocation, while illustrating the utility of the overlayaggregation method (OAM) for addressing the MAUP.
|Qualification||Doctor of Philosophy|
|Award date||19 Apr 2022|
|Publication status||Unpublished - 2021|