More Obesity and other Health data

 

This study would greatly benefit from more detailed health data, especially obesity data, at a higher level than the current remoteness level.  Data available at LGA or suburb level would greatly enhance the accuracy and allow for deeper spatial and statistical analysis.  These data should be collected repeatedly and under the same conditions for several years so as to gauge any periodical chances or trends in Australia.  Also, more investigation into the large spike which is apparent in both Individual Median Income v Obesity and Non School Education v Obesity.  More spatial analysis with new data attributes would reveal the reason behind this spike.  This thesis was initially done based on the intention of observing at a suburb or LGA level which would utilise spatial analysis techniques to a maximum.  Therefore, a further study would be carried out using the techniques inherent in this thesis but richer and additional data such as fast-food eateries, activity centres, sport clubs and facilities, and etc.  Multiple overlays of these datasets and Obesity rates may lead to further discoveries of the contributing factors of obesity. 

 

Projecting Current Findings into Suburb level

Currently, this study has looked into Nation wide using State-wide Data divided into 3 Remoteness regions; Major cities, inner region and outer/other regions.  An application of this could be projecting the conclusions of the factors of obesity into more specific areas so to allow greater overview of areas of likely risks of obesity.  This may be trying to project at State-wide at suburb level to forecast suburbs of likely risks based on the findings that low income and education contributes to obesity.  However, this can only be done if the Income and Education data reflects the nation-wide data conditions, i.e. Income and Education are very closely correlated. 

A preliminary study has already been done to test if the conlusions of this thesis can be projected into more specific regions.  The case study is the State of New South Wales (NSW), trying to project at a suburb wide level.  As it is impractical to sample all the suburbs in NSW to gauge whether there is a correlation between Individual Median Income and Non School Qualifications, samples were used to represent the whole.  Samples suburbs were chosen at random from 3 remoteness categories, reflecting the nation-wide studies, refer to Figure 5.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5: Sample Suburbs from NSW

 

If these suburbs from each region reveals a correlation between Individual Median Income and Non School Qualifications, then a forecast can be run on the suburbs of NSW on areas of risk to obesity based on the conclusions of the nation-wide study.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Since only Major Cities and Inner Region shows a strong correlation between Individual Median Income and Non School Qualifications, suburbs in those areas can be forecasted.  Of course, more samples in each region would yield more accurate profile and this can be done as future studies.