Research into geospatial patterns of health is often hampered using existing administrative spatial units which aggregate the results, mask spatial patterning, undermine analyses, and ultimately hinder local understanding.
Funded by the Australian Research Council (ARC) Linkage Infrastructure, Equipment and Facilities (LIEF) scheme, the objective of this new data infrastructure is to construct a series of openly available data sets for modelling the patterns and processes impacting the health of the Australian population. By calculating geographic access to services and facilities, this infrastructure will enable researchers, policy stakeholders and industry to better plan equitable resource allocation for the Australian population. Complementing this will be the creation of a synthetic population – this will preserve individual anonymity, while still providing detailed, geographically-located data across Australia.
To achieve the first step of calculating geographic access, this research utilises recent advances in modern data processing architecture to rapidly calculate distance/time metrics from all 11 million Australian residential dwellings to all Australian health facilities and relevant services. The core of the presentation describes the data requirements and computing environment needed to perform these calculations across the whole of Australia.
While the geospatial modelling will address a number of seminal and longstanding challenges faced by health geographers, it also guides and augments rapid translation in real world settings. A formal partnership between Deakin Rural Health (a University Department of Rural Health) and Grampians Health Service (located in a rural and regional setting delivering services to a catchment of >250,000 residents spanning >250 km) will deliver insights of direct relevance to the health system throughout the research process (e.g., by linking clinical datasets at address-level across geographically expansive areas). This will permit investigations such as dynamics between individual-level socio-economic status and service access, validation of synthetic populations, and scenario testing of health service use cases.