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Exploring Undernourishment: Part 5 — Research Area 2: Most Successful Countries

A Visual Data Exploration Research Project to Better Understand the Nuances of Our Global Nutrition

Image Source: Food and Agriculture Organisation of the United Nations

Contents

This is Part 5 of an 8-Part research project aiming to better understand the nuances of our global nutrition. It explores this topic through the utilisation of data visualisation and data science techniques. It is complimented by a Web App: ExploringUndernourishment, which is freely available to the public.

Part 1 — Introduction and Overview
Part 2 — Literature Review
Part 3 — Data Exploration
Part 4 — Research Area 1: General Trend
Part 5 — Research Area 2: Most Successful Countries ← Selected page
Part 6 — Research Area 3: Surprising Trends
Part 7 — Research Area 4: Most Influential Indicator
Part 8 — Recommendations and Conclusions

Research Area 2: Most Successful Countries

The challenge with identifying the most successful countries is primarily as a result of missing data. Some countries do not have any data for Prevalence of Undernourishment, and some countries only have records for a few years; these countries were excluded from this section.

In order to adequately explore the top successful countries, they are plotted and explored in contrast to one another. The user interaction added to this plot allows for flexible and easy exploration of any number of top countries, in any region. Next, the full data is included in a table, so one can see the numeric values for each country for each year. Lastly, individual countries can be selected, and all of their features can be explored, in order to establish what it is that allows them to be successful.

Top Countries Plot

Credit where it is due, there are some very impressive reductions in the Prevalence of Undernourishment over time. This data can be explored in the chart to the right. To see the details of each line, simply hover over it.

Countries like Angola, Ethiopia, Myanmar, and Dominican Republic have begun with a score of over 0.2, and have each reduced their score by at least 50% of their original value. This is an impressive effort, and it is good to see that these changes have been successful and sustainable.

Figure 12: Most Successful: Top Successful Countries

All Countries Table

This table provides a pivoted overview of each country per year, including their overall score.

Noting the following:

  • Each line represents a different country.
  • Each year is included as a different country, reading from left to right, oldest to newest.
  • The overall improvement for each country is calculated as a percentage difference between the first column (2001), and the last column (2018). This score is included in the ‘improvement’ column.
  • The data is then ordered by this ‘improvement’ column, showing the countries with the highest decrease at the top, and countries with the least amount of decrease in PoU at the bottom.

Predictor Features by Country

Again looking at the scores for each country, the below plots allow for easy exploration of the data for each country.

Note that:

  • Each plot is a different feature of the original data. Which is effectively a different column of the original data.
  • Each plot shows the x-axis as time.
  • Each of the colours are simply to help easy differentiation between the variables.
  • All of the plots are only showing for one country, which can be changed using the drop-down box.
Figure 13: Most Successful: Individual Features per Country

Findings

Some of the countries that have improved the most over the last 20 years include Angola, Ethiopia, Myanmar and Dominican Republic. In 2018, these countries recorded a Prevalence of Undernourishment score that was at least 50% lower than their 2001 score. There were some gaps in the data, particularly when some countries had begun their PoU recordings later than others, or had stopped after a few years, or were missing all together. This level of missing-ness should be addressed in the data collection strategies, and should be the responsibility of the individual countries to collect, and the FAO to follow-up on.

Looking at the individual attributes of each country, it can be seen that the decrease in their Prevalence of Undernourishment score is correlated with an increase in attributes such as Access to Basic Drinking Water, Access to Basic Sanitation Services and Gross Domestic Product Per Capita Ppp, among others. Looking at these individual attributes, per country, is helpful to understand the differences between countries, and the individual circumstances that many find themselves in. There is no one solution to this problem, as it is a systemic, complex, and societal/socio-economic issue.

Read On:

Previous section: Research Area 1: General Trend
Next section: Research Area 3: Surprising Trends

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Chris Mahoney

Chris Mahoney

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I’m a keen Data Scientist and Business Leader, interested in Innovation, Digitisation, Best Practice & Personal Development. Check me out: chrimaho.com