China’s Exponential Rise in Tomato Production in Last Two Decades

Step one: the visualization

The data visualization I selected is titled “Biggest Tomato Producers”, created with sjvisualizer and sourced from Food and Agriculture Organization of the United Nations, FAOSTAT. The original visualization can be accessed here: https://www.linkedin.com/feed/update/urn:li:activity:7038221922265915394/ Dataset Link: https://data.world/makeovermonday/2023w17

I chose this visualization because of its dynamic bar chart design, which immediately captures attention by showing how different countries compare in tomato production. China, for example, overwhelmingly dominates global production with more than 66 million tonnes, while India and Turkey follow at a much lower scale. The use of bright national flags, bold numeric values, and a moving chart had alot of scope for improvement.

This particular visualization intrigued me because I also did internship at United Nations HQs, and was curious to find out about FAOSTAT. Static numbers in a spreadsheet often fail to convey scale or impact, but visualizations like this make global agricultural patterns intuitive and relatable.

Screenshot 2025-09-18 at 11 39 06 AM

Step two: the critique

The FAOSTAT visualization on “Which country produces the most tomatoes?” was engaging due to its dynamic nature and attention-grabbing heading. It effectively highlighted key insights such as China’s rise and the U.S.’s decline in tomato production. Readable labeling and the use of flag-inspired colors were positives.

However, the design suffered from visual clutter—overuse of colors, unnecessary repetition of text, distracting flags, and an irrelevant pie chart. The background image of tomatoes further reduced clarity. Additionally, the omission of potatoes from the dataset limited completeness and reduced its research utility.

The primary audience is likely researchers, policymakers, and UN/food organizations, yet the LinkedIn post format without explanatory context limited accessibility for this group.

Redesign Priorities:

Remove flags and repetitive labels.

Use a calmer, consistent color scheme with reduced brightness/opacity.

Eliminate the redundant pie chart.

Simplify the background to improve readability.

Explore a line chart (years on X-axis, tonnage on Y-axis) to show production trends more intuitively.

Step three: Sketch a solution

For Step 3, I sketched out my redesign by hand, keeping Stephen Few’s ideas about clarity in mind. I wanted to move away from the clutter of the original FAOSTAT chart and focus on something simple and direct. My plan is to use a line chart to show tomato production over time, which makes trends easier to spot at a glance. Inspired by Ben Garvey’s sketching approach, I shared my rough draft with friends through a Google Form and got feedback on colors, layout, and readability.

Step four: Test the solution

For Step 4, I shared my sketch with peers and collected feedback from three people to test clarity and effectiveness. All three correctly understood that the visualization showed China as the rising tomato producer between 1981–2021. They noted that the handwritten heading was appealing, but overlapping lines and bold colors made the graph cluttered. Several suggested using lighter colors for other countries and highlighting China more clearly, perhaps by labeling it at the end of the line. The intended audience was identified as both researchers and data analysts. Overall, the main feedback pattern emphasized improving clarity, reducing visual noise, and focusing more directly on China’s production trend.

Google Form Link : https://docs.google.com/forms/d/e/1FAIpQLSc2Xzh4BANoHPOsFStqvrVMrDur2HC4EHcqNqHjGXqGksU7ag/viewform?usp=header

Synthesis:

A clear pattern across the feedback was that while the overall message—China as the largest tomato producer—was understood, the visual clarity needed improvement. Participants found the handwritten heading engaging but pointed out that overlapping lines and bold colors made interpretation difficult. Another consistent theme was the suggestion to tone down colors for other countries and emphasize China more clearly, such as by labeling it at the end of the line. Audience identification also showed consensus that both data analysts and researchers are the most likely users.

From this, I learned that simplicity and focus are key. My final redesign will highlight China’s production trend while using lighter, muted colors for other countries, reduce overlapping by simplifying the chart, and improve labeling to guide interpretation without extra explanation.

Step five: build the solution

For my final redesign, I focused on simplifying the visualization while clearly communicating the key message: China is the largest tomato producer over time. I replaced the original cluttered bar and pie charts with a line chart showing production trends from 1981–2021, with years on the X-axis and tonnage on the Y-axis. China’s line is highlighted in a bold, easily distinguishable color, while other countries are shown in muted, lighter tones to reduce visual noise. I removed the unnecessary pie chart, background images, and flag icons, and ensured labeling was consistent and placed strategically at the end of each line for clarity.

The redesign process included hand sketches, peer feedback, and iterative refinements. Feedback revealed that while the original chart’s story was understandable, overlapping lines, bold colors, and a cluttered design made it harder to interpret. Participants suggested emphasizing China and simplifying other elements, which directly influenced my color choices, labeling strategy, and chart selection.

Overall, this process reinforced the importance of clarity, focus, and audience-centered design in data visualization. Iterating through sketches and gathering peer feedback allowed me to test whether the design communicates effectively without explanation, which ultimately strengthened the final visualization.

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References

https://www.linkedin.com/feed/update/urn:li:activity:7038221922265915394/ https://www.fao.org/faostat/en/#data/QCL https://data.world/makeovermonday/2023w17 https://docs.google.com/forms/d/1mP-50jqGxZo6qnacaFcd8uiLjGmnxJh9G3-Eax5T_9o/edit#responses

AI acknowledgements

AI used for step by step guidance on tableu- Copilot AI also used for summarising my own initial thoughts, especially for senetence formation, and to check grammatical errors.

Shishank_CMU