Introduction

Many societies around the world have undergone population growth, mass urbanization, and infrastructural development over the last few decades. To provide for growing populations, agricultural systems have become more productive and efficient, as well as increasing the import and export of consumer goods. Using this logic, while it is generally assumed that food insecurity is less prominent in recent years when comparing conditions from previous decades, not all places in the world have been developing equally to provide basic human needs. Several countries across the world are currently under civil unrest and health crises that have contributed to a reversion in their development and security.

In this report, undernourishment is defined as having a “caloric intake which is insufficient to meet the minimum energy requirements necessary for a given population” (FAO, 2020). The aim of this visualization is to highlight food insecurity across greater regions and specific nations, to compare food insecurity conditions across world regions, and to identify nations that have different food insecurity conditions from other countries in its region.

Methods and Process

This dataset on world undernourishment was downloaded from Kaggle, where the dataset contains data of all world countries and broader world regions as well as percentages of their population that suffers from undernourishment in specific years between 2000 to 2020. The dataset spans a total of 3,345 rows and includes 176 countries and regions.

I imported the Kaggle dataset into Tableau to create the following two charts that visualize world undernourishment. All columns in the dataset were used except for Code, which represented a 3-letter country code. All null values were excluded from the dataset.

The area chart visualization provides a broad overview of undernourishment prevalences in world regions from 2000 to 2020. All six major regions of the world were filtered from the list of country names, and their undernourishment prevalences can be compared to the world undernourishment statistics on the far right. An area chart was used instead of a line chart to highlight the extent of existing food insecurity by maximizing the colored area. Undernourishment minimum and maximum percentages are displayed to indicate that the latest year (2020) does not necessarily indicate the minimum undernourishment, as seen in East Asia and Africa.

There were world regions that did not present significant changes in undernourishment, such as the European Union and North America with the lowest rates. In these cases, data points were removed from years between 2000 and 2020 as the minimum and maximum would be identical, therefore the display of labels would overlap.

On the other hand, the map visualization chart helps to distinguish the undernourishment prevalence by distinct countries. As this map is not a time-based visualization, the prevalence of undernourishment is displayed as a total median from 2000 to 2020. The median was used instead of an average, as several countries have had large changes in undernourishment prevalences over the last 20 years (i.e. countries in East Asia), or have non-linear undernourishment trends as seen in the historical drop and recent rise of undernourishment in Latin America. This time, all countries were used from the list of country names and all major regions were excluded. Countries in grey were countries with null values in undernourishment percentages.

To present the highest visual contrast, the color palette used was “Red-Green-Gold Diverging” where green indicates a low prevalence of undernourishment, while yellow and red indicate a higher prevalence.

To decrease the homogenization of regions with the same color tone, as well as to increase the nuanced distinction between different countries, 21 steps were applied for this color palette. This way, we can more easily observe that two neighboring countries (i.e. Bolivia and Paraguay) can have greatly different food insecurity conditions despite being in the same region.

Findings and Analysis

Even though some world regions have high population percentages with undernourishment, not all countries within this region struggle with food insecurity. This is especially true for broader regions like Sub-Saharan Africa, where the prevalence of undernourishment is much higher than the world prevalence.

In 2020, the prevalence in Africa is 20.34% compared to the world percentage of 8.9%. Yet, there are individual countries within Sub-Saharan Africa, such as the Republic of Mauritius, where the prevalence of undernourishment is much lower than that of the world. The median prevalence of undernourishment in Mauritius is 5.4%.

A similar observation occurs in Latin America. It’s easy to overlook smaller outlying countries within a homogenous colored region. In this case, Haiti presents one of the highest median percentages of undernourishment in the world at 48%, despite being located in a region where undernourishment prevalences are lower than the world average. As 48% is an undernourishment median, it is important to be aware that this value could have been much higher at another year.

While separating world regions gives a relatively accurate overview of food insecurity, it does not completely account for specific outlying countries that fare better or worse than the average. It is therefore important to consider food insecurity while targeting specific countries, rather than making assumptions about broad regions with significantly different circumstances with public health, infrastructure, and human development.

Conclusions

Given more expertise with Tableau, I would have liked to make the map visualization a time-based representation rather than a display of medians for greater accuracy. For countries with high fluctuations in undernourishment over the span of 20 years, the median value does not account for either the low or high extremes, which unfortunately underrepresents the severity of their food insecurity.

Overall, the two visualizations for undernourishment prevalence provide new insights on food insecurity trends in a broader world context, as well as highlighting specific countries that are substantially more food insecure than the rest of the world.

References

Chauhan, A. (2022, September 13). Hunger and undernourishment data. Kaggle. Retrieved February 20, 2023, from https://www.kaggle.com/datasets/whenamancodes/hunger-and-undernourishment-data

Chhcalling. (n.d.). Global Food insecurity. Harris. Retrieved February 20, 2023, from https://www.heatonharris.com/global-food-insecurity

Resources. Public.tableau.com. (n.d.). Retrieved February 20, 2023, from https://public.tableau.com/app/resources/learn

Sustainable Development Goals. (n.d.). FAO. Retrieved February 28, 2023, from https://www.fao.org/sustainable-development-goals/indicators/211/en/#:~:text=Indicator%202.1.,normal%20active%20and%20healthy%20life.