Demography, Disease and Health Service Infrastructure: A Spatial Data Visualization Model

Authors: Joanne Travaglia*, University of Technology Sydney
Topics: Medical and Health Geography, Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: health services, disease, demography, systems
Session Type: Paper
Day: 4/12/2018
Start / End Time: 1:20 PM / 3:00 PM
Room: Estherwood, Sheraton, 4th Floor
Presentation File: No File Uploaded

Data visualization is an increasingly popular and utilised aspect of the emerging ‘big data’ era. Geography and cartography are central to spatial data analysis but such methods are only gradually being incorporated into visualization software packages. The potential of spatial data visualization is growing for complex service environments such as health and medical care.

In this project a seamless approach to complex spatially integrated data visualization for a diverse user audience. The objective is to improve health services provision through the timely analysis and visualization (using Tableau) of population-level epidemiology and health service infrastructure, because both are highly spatially contingent. Data modelling was conducted at the Australian SA2 geographic level, with the capacity to aggregate data visualizations upwards to SA3 and SA4 level.

The result is a linked data environment extending from a conventional Excel data modeling platform (demography and disease) through a GIS and on to Tableau visualization. The focus here i population ageing and its consequences, with specific health services (hospitals, ambulance services etc.) included to provide area-based spatial accessibility measures. The result is an interactive data environment that combines tables, charts and graphs with corresponding spatial data visualization.

Spatial data analysis and visualization is increasingly integral to complex human systems environments such as healthcare. Data modelling and analysis are key to health system responses to dynamic health problems, such as age-related chronic disease. This work connects three key components to produce a practical data visualization environment that incorporates geography as central.

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