These two data visualizations share the story of poverty: the tales of various countries and the struggles they tell through the numbers of deaths, infant mortality, and life expectancy rates.

Sheet 1

Incorporating Nathan Yau’s first point of visual cues, I aimed to map and locate where each of the data statistics regionally belonged to. In a sense, by taking what was once just pure numeric values and placing it on a map, it humanizes the numbers. It’s rather easy to brush off numbers, especially the more there are or the larger they get, as the viewer starts to glaze over the masses. Yet, by putting the numbers in the locations, it makes it suddenly become familiar– the viewer comes to terms that these are numbers of deaths/births of others in locations that even they reside in.

Furthermore, I strived to play with encoding “values to shapes, sizes, and colors” through the size of the dot in each location, which represents the death rates of each country, and the color, which represents the birth rates. This way, the audience can see a direct correlation between these two rows of information in relativity to other countries.


Sheet 5


Something else that data visualization highlighted that may not have been able to be seen in the data alone is the ability to instantaneously see the direct correlation of what the GNP (Gross National Product), also understood as the country’s “market value”, and the standard of living. In the bar graph above, the countries are ranked by their GNP in increasing order from left to right. With that, one can clearly see a depletion in Infant Mortality rates as the GNP rises, as well as the life expectancy for both males and females to rise. This visualization gives the information to the audience to draw conclusions within themselves on the influences between socio-economic relationships and patterns.