Expenditure on Public Health

Erika Hang

My original plan was to compare expenditure of public health vs. life expectancy at birth of various different countries. However, I found it very difficult to visualize that data in an understandable way. I decided that it would be better to focus simply on “Expenditure on Public Health.” The data reveals how much countries around the world spent on public health – which consists of current and capital spending from government budgets, external borrowings and grants, and social health insurance funds – in 2009. I think that this data is an important indicator of how countries are developing in terms of their ability to fund health care for everyone. The ability of a country to spend a good portion of their money on their citizen’s well-being gives a sense of the life those citizens have and the type of aids available. The data revels that countries like the United States, Ireland, Switzerland, etc. are spending more than countries like Congo, Myanmar, Ethiopia, Madagascar, etc. However, the data fails to recognize if the countries utilize the money effectively or equally among their people.

Visualization #1:


For my first visualization, I started with a simple bar graph created on Many Eyes. The x-axis represents all the different countries and the y-axis represents expenditure on public health. The data reveals a wide disparity in expenditure on health. You can easily view each country individually. The data is simple and easily understood by anyone even if they have no background on the topic. However, as a simple bar graph, the data appears lost in numbers and is not very impactful. The graph has no sense of revelation or intensity in terms of its presentations – it resembles something you would glance at in a textbook and skip over. I think that today it is important to present data in an engaging manner to lure people into being interested.

Visualization #2:

The second visualization is a world map based visualization that groups countries together based on levels of expenditure. This visualization improves upon Visualization #1. We can notice the countries that spend the most by looking at the color code: pink being the most. The use of color aids the reader in understanding the data by giving them more cues and a sense of where these countries are located. We can also start to generalize continents or certain regions – thus we can compare more developed or less developed countries. The visualization lends itself to easy categorization and would be more engaging than a simple bar graph.

Visualization #3:

The third visualization mainly focuses on color to indicate expenses on health – just like my second visualization. The advantage of this visualization was that I was able to embed and keep the interactive quality of it. Each country can be hovered over to view the exact value. You can clearly spot the differences with this visualization by comparing the shade of green. Again, it helps you categorize certain regions and make your own conclusions without any text necessarily needed. Compared to the other map visualization that I made, the colors are more prominent that the “pins” and could be easier to view for the audience.

Visualization #4:

I really enjoyed this visualization website that I found called Better World Flux. My problem with it was that it was difficult to extract. In order to view this visualization, I embedded it as an iFrame. Simply, click done on the Indicator and Countries tab – it has already been preset to Health Expenditure. The tool allows you to track the progress of countries over the years, view which country lives in a “better world” vs. a “bad world”, and gives a bar graph to go along with the graphic. I like that this visualization gave you a lot of different options and allowed me to best understand the data. By clicking on different sections of the flux graphic, you can view what countries exist on the better world spectrum – thus, you can see that the United States, Ireland, Monaco, Luxembourg, Switzerland, and Norway spend the most funds on health. Also, the size of the section indicates the number of people in that world section, which indicate how many people exist in this better or bad world. It’s an interesting and abstract way to understand in terms of a “better world influx” and something you don’t usually see.

Conclusion:

I gradually worked my way from a simple visualization to more complex and interesting visualizations. I found that the more interactive, the more color utilized, and the more components to a visualization, the more effective and comprehensive the data became. My data did not illuminate a lot about health issues. I think, however, that if this data was presented alongside a talk about how effective countries are using their expenditures on public health – it would aid in the responses from the audience or listeners. This project reinforced the importance of color in Tufte’s article as well as the importance of visualizing health statistics to increase education on health topics, raise awareness, and spread information.

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