Introduction
When I moved to New York City, I was struck not only by the cultural and ethnic diversity of its population but also by the incredible variety of foods available on every corner. From global street eats to gourmet desserts, NYC introduced me to the richness of food as cultural expression—but also sparked a question: How does what we eat shape us, collectively and physically?
That curiosity led me to investigate sugar consumption around the world. Sugar is often a common ingredient in diverse cuisines, yet it is also one of the most controversial components of modern diets. In this project, I set out to map global patterns of sugar consumption and examine how they might relate to obesity rates.
Datasets
To explore this topic, I used a dataset from Kaggle, which provides figures on global sugar consumption trends from 1960 to 2023. Specifically, I focused on three variables: Total sugar consumption (metric tons): a measure of national sugar intake on a macro level. Average daily sugar intake (grams per person): a more individual-level consumption view. Obesity rate (%): used as a proxy for health outcomes potentially related to sugar intake.
The dataset includes both developed and developing nations, allowing for cross-comparison by geography, economic status, and health metrics. While it offers a solid foundation, I also acknowledge its limitations: not all countries have consistent data across years, and obesity as a health metric is influenced by many factors beyond sugar consumption alone.
Process: From Raw Data to Narrative
I began with a broad research question: What global patterns emerge when sugar consumption and obesity rates are visualized geographically?
To frame my research, I conducted a brief literature review on sugar and public health, referencing reports from the CDC that link the relationship between sugar intake and chronic disease, including obesity. Building on this foundation, I structured the visual narrative by region—comparing continents and selected countries—to examine whether high sugar consumption aligned with elevated obesity rates. In designing the visualizations, I used Tableau to create maps for international total sugar consumption and make another map to highlight correlations between total consumption and average daily sugar intake.
Visualization Results
These visualizations mainly show the relationship between sugar consumption and obesity rates across different countries. The map provides a clear geographical representation of average daily sugar intake, highlighting regions with higher consumption levels. I tried to identify which countries are consuming sugar at notably high or low levels. With these maps, I can see the co-relationship between total sugar consumption, daily intake, and obesity rate. Some countries with high sugar consumption but relatively low obesity rates could indicate the influence of other factors, such as physical activity levels, dietary patterns, or how the country is developing. This visualization can give a very basic level of insights to research how public health initiatives can be affected by food or dgree of development.
Reflection
This project raised more questions than it answered—which I see as a good thing. While some visualizations show a clear link between sugar consumption and obesity, others suggest more complex patterns. One limitation is that my dashboard focuses only on sugar, without considering other factors that affect public health. For example, some countries don’t follow the expected trend, which made me wonder about the impact of things like sugar taxes, food labels, or access to healthcare. It also doesn’t show how development levels—like income, education, or urban living—might relate to diet and health. In the future, I’d like to include these kinds of data to better understand how public health is shaped by more than just sugar consumption.
Resources
Get the Facts: Added Sugars https://www.cdc.gov/nutrition/php/data-research/added-sugars.html
Relationship between Added Sugars Consumption and Chronic Disease Risk Factors: Current Understanding https://pmc.ncbi.nlm.nih.gov/articles/PMC5133084/