Results and



Results for this thesis are only available at nation-wide study at a state level.  They only stand true when viewing Australia on a state level. 


The obesity level for Australia at a Nation-wide State level can be seen in Figure 2.





















Figure 2: Obesity rates of Australia


Two main factors were identified as a contributing factor to Obesity; Individual Median Income and Non School Education, refer to Figure 3 and Figure 4 respectively.
























Figure 3: Median Individual Income overlaid against obesity rates

























Figure 4: Non School Education overlaid against Obesity rates.

Weighted overlays


Weighted overlays were calculated first with sums of 30% Non High School Qualifications and 70% Individual Median Income.  A R Squared value of just below 0.3 was obtained, refer to Graph 1.  The converse was also calculated, refer to Graph 2 with surprising results.  The two graphs, although the sum of contrasting weightings, were very similar.  There was a large peak which was emulated almost exactly in both graphs.


















Graph 1: Regression Model for 30% Qual. and 70% Income                          Graph 2:Graph 1: Regression Model for 70% Qual. & 30% Income


This suggests that Individual Median Income and Non School Qualifications are highly dependent upon each other.  That is, a change in one will be almost exactly mirrored in the other. Therefore, there is no need to find out how each impact obesity, any combination of any weights for these two factors will result in the same outcome.



This study suggests that;

· High income and High Education  persons are less likely to be obese than  persons of lower income and education.

· There is no interaction between of the amount of green spaces and obesity.

· No conclusive results were found on the link between Diabetes Mellitus and Heart Disease in relation to Obesity.

· With except to South Australia, Inner regions and Major cities of Australia have higher portions of people suffering from Heart Disease and Diabetes Mellitus.

· Remote areas of Australia have higher obesity rates than Major Cities and Inner Regions of Australia. 

· New South Wales and South Australia have the highest obesity rates whilst Queensland has the lowest.

· In Australia, level of income and education are directly related and dependent of each other.

· Spatial Analysis is a useful tool when presented with problems with geographical significance and location.  However, when coupled with statistical analysis, more results and deeper understanding can be identified.