Health Demonstrator Tool Briefs & AURIN Portal Tour

This tutorial contains two parts. In Part One, we demonstrate how to use the Health Demonstrator Tool to identify geographical distribution patterns of different health data layers for the North-West Melbourne region. Part Two gives you quick tour around the AURIN portal, such as data searching, visualization, and saving your project in “My AURIN”.

Health Demonstrator Tool

This demonstrator tool was developed in 2013. AURIN and CSDILA give an extensive explanation of the project background, research questions and values.

To begin, open the Health Demonstrator Tool. The user interface is concise. The left panel contains the five health indicator layers, which are:

  • Socio-Economic Indexes for Areas (SEIFA),
  • Type 2 Diabetes,
  • Depression,
  • Obesity and
  • Smoking.

The panel on the right side is a set of filters for searching health services (i.e. GP clinics, hospitals).
The bottom part is the map area. By clicking the small arrow button sitting above the right panel, we can collapse these two panels and make more space for the map. 

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On the right side of map, we can switch colored indicator layers on and off. For example, if we tick the “Type 2 Diabetes”, the related layer will be loaded. The light yellow areas on the map are those areas with low “Type 2 Diabetes Prevalence Rate”, while the dark red indicates high rate.

 

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We can do a quick test on the tool to confirm this. Let’s only check the “Type 2 Diabetes” and set the match criteria to “<= 3”  and then click the “Run” button.

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The following 3-frame gif animation shows exactly that the light yellow areas match the results (blue polygons) of our selection criteria.

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A general rule for understanding colors on this map is: light yellow indicates positive (high SEIFA score, low Diabetes rate, low depression rate, etc.) , while dark red indicates negative (low SEIFA score, high Diabetes rate, high depression rate, etc.)

If multiple indicators are selected in the left panel, the selection criteria are constructed with logic AND. For example, in the following criteria combination, the result will be those areas that meet both “SEIFA>=8” AND “Diabetes Rate<=3”.
Particular attention should give to SEIFA, which is an ordinal scale and the number showing along the SEIFA slider bar here is a decile. For example, an area with a number 10 is not twice as advantaged as an area with a number of 5. The rest indicators are in ratio scale.

 

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Ok, let’s try to use this tool to answer some social science questions. For example, is it true that wealthy people (socio-economic advantaged) usually have less depression? The two graphs below may give you some rough ideas, at least visually. Based on Pareto principle, we can assume that SEIFA>=8 indicates a wealthy (less disadvantaged) population. We’ll also take the a value of 7.5 to separate depression into 2 groups, less depression and more depression.
Since we are working on a subject called ‘Analytical Methods’, drawing a solid conclusion needs far more than two images. We can perform a quantitative analysis to get a more confident answer based on the downloadable results.

Tips: You can open multiple tabs in a browser, run the tool in each tab with different parameter settings and compare different calculation results. Once the calculation is done, the results is also ready for download.

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We can use the right panel to set up search criteria for health service. The first  filter “No access to GP within XXX Meter” does an interesting job. It will exclude areas which have access to a GP within a certain walk distance in the output.

Let’s take a closer look at process behind the scenes. This next section illustrates the spatial analysis methods carried out by the back-end software. We’ll be revisiting these techniques later with the walkability tools.

Step1. Assume there is a GP clinic (red dot) sitting among SEIFA polygons (SA1).

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Step2. Then we load a piece of road network (lime lines) around the GP.

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Step3. By running a walkability algorithm (described here: Walkability Tools in AURIN), we can get a buffer area (orange color) which is reachable within 500m walk distance from the GP.

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Step4.  Then we intersect walkability buffer with background SEIFA polygons and assume that all intersected polygons are within a reasonable proximity to the GP.

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Step5. Exclude these intersected polygons from the SEIFA polygons, and we get the final output.

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The following 2-frame gif animation gives you a better understanding of the impact of this filter.

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Now, for instance, you should be able to identify disadvantaged communities, with a high incidence of diabetes, who do not have adequate access to medical treatment. This sort of tool/technique is known as a ‘Multiple Criteria Decision Model’ (MCDM).

AURIN Portal Tour

Have a look at the introductions to the AURIN portal – this will enable you to retreive and study urban research data in more depth. To begin visit the AURIN Tutorials and MyAURIN Introduction. Please follow these links. Hope you find AURIN portal is useful for your research.