Exploring Undernourishment: Part 6 — Research Area 3: Surprising Trends
By: Chris Mahoney
Tags: R Shiny, Machine Learning, UN, R, Data Exploration

Exploring Undernourishment: Part 6 — Research Area 3: Surprising Trends

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 6 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

Part 6 — Research Area 3: Surprising Trends ← Selected page

Part 7 — Research Area 4: Most Influential Indicator

Part 8 — Recommendations and Conclusions

Research Area 3: Surprising Trends

Three different facts were chosen for inclusion in this section. That is not to say that there are no other surprising trends in the data; for there are. However, these three trends were perhaps the most prominent, and were ones which stood out during the completion of other sections of this Exploration Analysis.

Increasing Trends per Country

There have been a number of countries which have had a negative influence on their Prevalence of Undernourishment. The plot here shows the top 20 countries that have had an increase in their POU score.

There are some interesting features about this plot, including:

There are six countries that have a Prevalence of Undernourishment score of over 0.2 in 2018: Afghanistan , Central African Republic , Madagascar , Yemen , Uganda , and Niger .

score of over in 2018: , , , , , and . There appears to be a general trend to have a decrease in the years 2010–2013, with an increase thereafter.

These trends are surprising as they are not regional specific, but instead there are multiple countries from many different regions on this plot. While there may have many different influences, it is interesting that multiple different countries have this depressive trend in the middle. It would therefore warrant further exploration and research in to this area.

Figure 14: Surprising Trends: Increasing Countries

Arable Land per Country

As a naïve approach to addressing the problem of undernourishment, one might say something along the lines of ‘Why can’t the countries just grow more food?’ This response in and of itself will not solve the problem. Firstly, the countries must have the arable land available for farming, then there are a myriad of other systemic influences involved in establishing a viable supply chain and food security.

What this plot shows is the Percentage of Arable Land on the X-Axis, and the Prevalence of Undernourishment on the Y-Axis. Each dot is coloured according to its country, and the attributes can be displayed when hovering over a data point.

The following information can be drawn from inspecting this plot:

Even countries with 100% arable land still have a very high PoU score. For example: Djibouti , Sao Tome and Principe , Ecuador .

arable land still have a very high PoU score. For example: , , . In some countries, an increase in arable land has actually led to an increase in PoU. For example: Lao , Timor-Leste , Haiti .

, , . In some instances, the amount of arable land has had no effect on PoU at all. For example: Afghanistan , Guatemala , DPRK .

These results are surprising, because given land that is suitable for farming, it would be logical to use this land to help to address the issue. But this is not the case. There must therefore be other influences that can contribute to improving the Prevalence of Undernourishment for a country. Perhaps focus should then be on international trade, supply chain relationships, economic viability, and the political stability of the country.

Figure 15: Surprising Trends: Arable Land per Country

Change in GDP

One of the best means of measuring the economic viability of a country is by reviewing their Gross Domestic Product ( GDP ). By comparing one country’s GDP to another, it is easy to see which one is being more successful. The inputs that are used to calculate the GDP cover a range of social, political, trade, and financial factors.

The figure to the right plots the GDP score on the X-Axis, and the PoU score on the Y-Axis. One would expect that as the GDP increases for a given country, their associated PoU score would decrease. Each country has a different colour, and the majority of countries have been faded out to the background. Attention should be drawn to the four highlighted countries. The plot has been cut, to focus on countries with a GDP of less than 20,000 .

The following information can be concluded:

For Zambia , as the GDP increased, so did the PoU score. This was up to a certain point, then the POU decreased accordingly. The resultant shape is conical in nature, and indicates that the GDP in this instance did not influence the PoU.

, as the GDP increased, so did the PoU score. This was up to a certain point, then the POU decreased accordingly. The resultant shape is conical in nature, and indicates that the GDP in this instance did not influence the PoU. For India , the shape has been primarily flat, with a hook-like shape at the lower end of the GDP score range. Due to the flatness of these scores, it indicates that the increase in GDP has not had a corresponding decrease in the PoU score.

, the shape has been primarily flat, with a hook-like shape at the lower end of the GDP score range. Due to the flatness of these scores, it indicates that the increase in GDP has not had a corresponding decrease in the PoU score. The trend for Eswatini is majoritively upwards, indicating that an increase in GDP has had an overall negative influence in the PoU, and has pushed it in an upward direction.

is majoritively upwards, indicating that an increase in GDP has had an overall negative influence in the PoU, and has pushed it in an upward direction. The correlation for Timor-Leste is the most interesting of the highlighted trends, because it is the most sporadic. This country shows some very substantial increases in GDP, and this is quite positive to see. However, the corresponding change in PoU has not always been influenced in a downward direction as hoped. Instead, there are instances where the score increases, and some where there is a sharp decrease. The PoU score appears to be unstable, when compared to GDP, and this indicates that there are many other societal and socio-economic influences on the Prevalence of Undernourishment in this country, other than GDP.

Figure 16: Surprising Trends: GDP vs PoU

Findings

There were three surprising trends that were found during the completion of this Analysis. Including:

There were a number of countries that saw a substantial increase in their Prevalence of Undernourishment score over time; including Central African Republic , Madagascar , and Uganda . Moreover, this same data showed that there was a distinct depression in the scores around the years 2010–2014, which seemed to be consistent over multiple countries in various regions. There does not seem to be a correlation between the Prevalence of Undernourishment available in a particular country, and their Percentage of Arable Land score. Some countries with high PoU and high PAL include: Djibouti , Sao Tome and Principe and Ecuador . One would hope that it would be possible to implement farming principles to help to address the level of PoU, but this data suggests otherwise. The trend for the large proportion of countries indicates that an increase in Gross Domestic Product is associated with a decrease in Prevalence of Undernourishment . However, there are a number of countries in which this trend is not consistent; including: Zambia , India , and Eswatini . This indicates that there is either some other systemic societal attributes which are influencing this score more than GDP, or that there is a substantial level of political instability, or that these countries simply do not fit the trend like the other countries.

Read On:

Previous section: Research Area 2: Most Successful Countries

Next section: Research Area 4: Most Influential Indicator