This project is the study of me organizing and visualizing various kind of dataset of correlationship between mortality rate due to unsafe water, life expectancy, and health expenditure that helps people to understand numerical dataset at a glance.​​​​​​​
While I graphed the mortality rate attributed to unsafe water, sanitation and hygiene services I realized that countries with high rates had corresponding low life expectancies.  Which led me to question whether or not the amount of money a country spends on health related resources affects these numbers. Adding the variable of Health Expenditure as % of total GDP to the graph I saw that there are some correlation between health expenditure and life expectancy or mortality rate. I believe that the size, population and total GDP each country possess skews the perspective of this graph. If I refer to the second graph where I show the percentages converted to amounts I can see that in general countries that spend less amount of money have the higher mortality rate due to unsafe water and lower life expectancies. [All data comes from the UNDP]
Figuring out the unknown relationship among three subjects- Mortality rate, health expenditure, and life expectancy- is the main key for the project. Ultimately, analyzing those textual data into effective visual is the crucial goal.​​​​​​​
I first gathered all the necessary the precise dataset from the UNDP in excel files and I organized them into excel file format by percentage, date, size, and age range. Then, I analyzed what kind of data is corresponding to each other, and I assumed that there can be a pretty high relationship between mortality rate due to unsafe water with the total health expenditure of each country. From there, I started to research about many different kinds of graph method that I can use to design my data visualization the most effectively. Consequently, I came up with a decision where the bubble graph might be the best way to deliver the data , due to the reason that it is very effect and understandable way to show multiple data-set that contains size variations.
I first had to think how I should organize all the necessary and unnecessary informations that I collected. I imported them in excel format and had to trim a lot of portions.
I did not know there are tons of graphing method that people can use to illustrate and organize informations, but after all the research I found what method can best suite for my data visualization; the bubble chart and the bar graph can be the most effective way to deliver the information that I gathered and interpreted.
Revised version after months.
I decided to color coded/ranged the circle to show the density of the mortality rate.
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