Data Visualization: Poverty Statistics

I explored the data set on poverty statistics, found here, which details the birth and death rates, infant morality rates, life expectancy rates of men and women, and GNP of 97 countries in 1990. One of the first things I noticed was that each country had been assigned a specific “region,” indicated by a number 1-6. Eastern European countries, such as Albania and Romania, were assigned region 1. South American countries, such as Brazil and Columbia were assigned region 2. Region 3 was compiled with mostly Western European countries such as France and Germany, but also included, interestingly, North America (U.S.A and Canada) and Japan. Middle Eastern countries such as Turkey and Israel were assigned region 4, and Asian countries, excluding Japan, made up region 5. Lastly, region 6 contained African countries, such as Kenya and Uganda.

I predicted that regions 3, 4, and 5, which described Western Europe, the Middle East, Asia, and North America, would likely have the highest GNP, while regions 1,2, and 6, which described Eastern Europe, South America, and Africa, would likely have the lowest GNP. I made many different visualizations to show the GNP of each region, but ultimately decided to use Raw to create a scatter plot. While all of the other visualizations I made were “cooler” looking, I chose this one because Nathan Yau said it was most important to choose a visualization that had the right visual cue. In the plot below, the GNP is located on the y-axis and the regions are located along the x-axis.

After seeing this, I took it a step further and estimated that the countries with the highest GNP (regions 3,4,5), likely had the highest life expectancy rates, and that countries with the lowest GNP (regions 1,2,6), likely had the highest birth, death, and infant morality rates. I used Google Fusion Tables to create data visualizations to see if my predictions were correct.

The graph above shows the average birth rates, death rates, and infant mortality rates across the regions, with the average rates located on the y-axis and the regions located along the x-axis. The visualization shows that the region 6, which contains the countries with the lowest GNPs, clearly has a higher average infant mortality rate, and a considerably larger average birthrate, but does not have a notably larger average death rate. In fact, the regions do not vary much in average death rate. Region 5, which has the 3rd highest average GNP, actually has some of highest birth, death, and infant morality rates, which I was not expecting.

The graph above shows average male and female life expectancies across regions, with the regions located on the y-axis and the ages located along the x-axis. This graph also makes region 6 stand out, but this time for its low life expectancies. Region 3 has a noticeably higher life expectancy than the rest of the regions, but isn’t too far ahead of region 1. This also surprised me, because region 1 has the second lowest GNP.

While looking at the data set, I assumed that I would be able to guess which countries had which rates with fairly high accuracy, but after looking at the data visualizations I can see that the lines are not so clearly drawn. It is clear, however, that in 1990, those living in Western Europe, North America, and Japan had much higher life expectancy rates and far lower death, birth, and infant mortality rates than those in other countries, while those living in Africa had almost the exact opposite.