Data Visualization and Analysis, Part 1/3 – World Bank Indicator

By | January 16, 2016

Author: Qi Chen

1. Introduction

Nowadays, unprecedented volume of data is available from books, radio, television, Internet and so on. From these data, useful knowledge can be discovered. However, data can be in any format such as numbers, text, or sound, which increases the difficulty of discovering useful knowledge. Some data formats, such as numbers, are sometimes too abstract and not friendly to people. As one of the techniques to address this issue, data visualization organizes different formats of data and presents it in a more easily understandable way.

In this article, we will demonstrate some examples of data visualization with Tableau. Specifically, we are interested in the relationship between GDP and life expectancy among different countries based on the World Bank database. We also want to know the development of different countries during the period of 2000-2010. The dataset has been aggregated and could be downloaded from this link:

https://uwmadison.box.com/s/okpk3mg51kmzzkvb754sgcdikhquhmxh

2. Analysis

2.1 Software used

In this article, we will demonstrate the use of Tableau and Excel for data visualization.

2.2 Dataset

The dataset contains many interesting variables such as passage cars per 1000 people, mobile phone subscribers in 10 years of period, health expenditure percentage of GDP, life expectancy for different countries and the like. For demonstration purpose, we will show the process of plotting GDP and healthcare expenditure for different countries in 10 years and the relationship between GDP and life expectancy.

The original dataset is complicated. For example, there are a lot of countries in the world and it is somewhat difficult to present quantitative value (such as GDPs) and geographic information simultaneously just by literal description and tables. But with data visualization techniques, we could easily show multi-dimensional information in one figure.

2.3 Approaches

Tableau could generate built-in longitudinal and latitudinal data and plot figures on the world map. With this function, we can easily plot the GDP of all countries on the world map. Here, a larger circle size indicates a greater GDP of the country. What’s more, we use different colors to represent the healthcare expenditure percentage of the total GDP for different countries.

Graph 1: GDP of All Countries in 2000

WorldBank1

Graph 1 shows the GDP of different countries in 2000. With the color closer to blue, more investment is made in healthcare, and with the color closer to red, vice versa. We could see that in the year of 2000 the US spent a great amount of money in healthcare and Japan’s GDP was the largest in the Asia.

Graph 2: GDP of All Countries in 2010

WorldBank2

After 10 years, we could see changes of the GDP investment in Graph 2 above. Countries in Europe also put a lot of effort to make more investment into the field of healthcare and we could see China’s GDP increased a lot in the 10 years of period.

We can also explore the relationship between GDP per capita and life expectancy for different countries with data visualization in Graph 3. Let X-axis be the average life expectancy, Y-axis be GDP per capita, we could see that GDP per capita would have a positive relationship with life expectancy. Similar to the figures above, different circle sizes are used to represent the total GDP for different countries. One may notice that for some countries like China and India, although their overall GDP is very high, because of their large population, the GDP per capita is relatively low, as well as the life expectancy.

Graph 3: GDP per Capita vs. Life Expectancy

WorldBank3

3. Summary

In this demonstration article, two examples of data visualization are discussed. The first example presents the GDP, health expenditure percentages, and geographic information of all the countries in one figure. And the second example demonstrates the relationship between life expectancy and GDP for all the countries.

This article is based on a course project in Industrial Data Analytics offered by Prof. Kaibo Liu in University of Wisconsin-Madison in Spring 2015. Thank Prof. Liu for his instruction and also thank Alyssa Krueger and Vito Freese for their initial work.

 

Source of data: http://data.worldbank.org/

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8 thoughts on “Data Visualization and Analysis, Part 1/3 – World Bank Indicator

  1. Pingback: Data Visualization and Analysis, Part 2/3 – United States Department of Agriculture | Industrial Engineering Era

  2. Pingback: Data Visualization and Analysis, Part 3/3 – Binge Drinking | Industrial Engineering Era

  3. Michael Baer

    I think this is a great visualization of healthcare data. While it can be seen that certain countries have a larger GDP and may be spending a larger percentage on healthcare, it does not always guarantee a larger life expectancy. This is a perfect example of just how imperfect the healthcare system is and how there are many other extraneous variables that affect life expectancy.

  4. Erik Pechnick

    This is a very interesting way to visualize health care. However, It would also be interesting to see a correlation between a nations GDP and the amount of people who go to that country to become doctors. I only say this because USA gets a bad rap for spending so much in health care and being low on the life expectancy list, but the USA also has the most technologically advanced health care system in the world

  5. Eric Fleming

    This is a cool way to visualize this data. This article highlights some of the things that are possible with Tableau. I bet 10 different people could take that dataset and come up with 10 unique and interesting ways to represent it using that software!

  6. Dylan Weber

    Very interesting article. I think it is interesting to note the relationship between GDP per capita and average life expectancy. Very intriguing that many countries with a substantially lower GDP per capita have the same or greater life expectancy than the US.

  7. David Wilkins

    I initially wanted to do an analysis regarding life expectancy including the use of GDP/capita. However this analysis lacks the depth to draw real conclusions. There are many countries that are ‘poorer’ but have greater life expectancies than the United States. Healthcare, lifestyle, and ethnic trends of the countries would be necessary in order to draw real conclusions. For example, Japan has a lower GDP per capita, but has a better funded healthcare system, make healthier lifestyle decisions, and are a fairly homogeneous ethnic country which makes healthcare a little easier to deal with.

  8. Pooja Shivale Patil

    It is interesting to see a comparison between life expectancy and GDP. From the viewpoint of the data, it looks like there is no correlation between life expectancy. It would be better if we were to draw some conclusion on aspects that GDP would impact.

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