Health Expenditure Per Capita

Brittney Stewart

Choosing which graph to use depends on which conclusive outcomes you are trying to reach. For all of my visualizations I have chosen the same data set which contained numerous information. Too put all of the information onto one graph would make the information more difficult to understand. So from this one data set I used different aspects of the data to create visualizations based on my desired outcomes.

http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/263c4a164bb411e1ac21000255111976/comments/263e8ec04bb411e1ac21000255111976

This visualization highlights the health expenditure per capita of all the UN member states categorized by region, income, and OECD membership. The original data set consisted of the current information as well as the per capita expenditure of each country. When creating the graph I didn’t want to include both sets of data, it would be too repetitive. Also, the original data set also provided information from 2006-2009. I chose to focus just on 2009 because the resulting graph would be too complicated and dense to fully understand. I initially tried to create a bubble chart for only the countries. Using a bubble chart is helpful when you would like to compare how the value of one variable is to another. Bigger bubbles mean bigger values and smaller bubbles mean smaller values. The way the bubbles are structured has no impact on the interpretation of the data, it is only the bubble size that matters. From the reading we became aware of the importance of coloring in interpreting results. This is true because if all of the circles were the same color it would be harder for the audience to distinguish each country. Coloring provides differentiation. The resulting chart for the countries was too difficult to understand. Individually, each country’s per capita was too small to distinguish and all the circles ended up being the same size. This visualization provided no benefit to the audience in understanding and comprehending the data set. I then decided to use the bubble chart using the country categories. This made the use of a bubble chart more acceptable as the data became larger. The biggest circle is NAC, so from the legend you know that North America spends the most money per capita. When you find the smallest circle you see it is LIC and from the legend you know that the low income countries spend the least amount of money per capita. This resulting graph does an excellent job of highlighting which categories have the highest per capita health expenditure by providing a visual understanding comparing each region.

http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/15acb75a4bb211e19efb000255111976/comments/15b6305a4bb211e19efb000255111976

Compared to the previous visualization, this chart is most suited for using the country data set. This graph provides the audience with an excellent understanding of health expenditure by regions and countries. From the various shades on the graph, you can automatically tell that North America, Australia, Europe and Japan are different from the rest of the world. Then by looking at the legend you see that the darker the shade the higher the per capita expenditure. Now, instead of spending many minutes organizing your original data set to organize each country you were able to find out in a few seconds. This is the purpose of graphs; to not only make the information easier to understand but to also allow the reader to understand the information quicker.

http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/08153e3e4bb011e1967d000255111976/comments/081829784bb011e1967d000255111976

This visualization is similar in reasoning and purpose as visualization 1. I chose to focus on country categories as oppose to the individual because the chart would make the data more difficult to understand because it would be too large and confusing. Nevertheless this graph is better than the bubble if you are really interested in observing the information from all of the countries. To see the smaller bubbles in visualization 1, you have to scroll over each bubble which is clustered together. Here all of the categories can be seen and you can accurately see how each category compares to each other. As well, the actual numbers are listed at the top of the each bar so you can see the numerical values as well. It was more difficult to see this with the bubble chart where you would have to move your mouse over each bubble to see its numerical value. To clarify, both graphs display the same information but if you wish to focus on the highest and lowest spending categories, the bubble chart provides an overview of the results. This chart is ideal if you wish to see the data set but in a more understandable and easy to process format.

http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/517e8e284baf11e18021000255111976/comments/5180bf9a4baf11e18021000255111976

This visualization uses the original data from 2006-2009 but you are only able to compare three years at a time so I have chosen 2007 -2009. This chart is ideal if you are interesting in comparing countries within themselves and their yearly progresses (or depreciations) in health spending. As with the other visualizations this graph uses the categories instead of the individual countries because it would otherwise be counterproductive. This graph also shows the importance of color in differentiation that would otherwise be too confusing without. So from this chart you can also visually observe which categories provide the most and least amount of spending but you can also compare a country’s spending over the years.

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