In August, Vox.com published The Truth about the Ice Bucket Challenge and included an infographic (“Where We Donate vs. Diseases That Kill Us”) that used proportionally-sized circles as its data visualization. The problem with this design was that the circle sizes didn’t match the values shown. This is a false visualization and significantly over exaggerated the smaller amounts of money contributed to each charity and the deaths attributed to each cause. The designer made the mistake of adjusting the diameter of circles to match the data instead of the area, which incorrectly sizes the circles dramatically.

To demonstrate, I designed a corrected version of the infographic and posted it on Cool Infographics®, which you can see here side-by-side next to the original. To stay close to the original, I only made three changes: corrected circle sizes, eliminated the color legend and added the connecting lines to help readers make the direct comparisons.

The Google Docs spreadsheet of the original data and correct circle area and diameter calculations is available here. To their credit, Vox.com has also published a corrected version of the infographic in the original article.

The first step was to get the bubble chart data visualization correct. Now that we have an infographic that matches the data presented, we can step back and ask the hard questions.

• Is a bubble chart the best way to visualize this information?

• Is this the right data to show when comparing money raised to deaths by diseases?

From Twitter, @indented recreated the visual as a scatterplot using HighCharts to more clearly show the large differences. (The interactive is available here.)

Jon Schwabish (@jschwabish) also created a scatterplot version, but changed the data to compare individual fundraising events to National Institute’s of Health funding, and then size the bubbles by the number of deaths. (The interactive is available here).

We (myself, @indented, and @jschwabish) had an interesting Twitter discussion about this visualization and the challenges of using the various data sources.

Looking for other options and options about how this data can be improved, or visualized better…

Additional resources:

This Bubble Chart Is Killing Me, David Mendoza

Where Should Our Money Go?, Aneesh Karve

One of the worst infographics ever, but people don’t care?, Phil Price

Ice buckets, research and the cost of disease, Scienceogram UK

NIH Spending Versus Diseases That Kill Us, Mohammed AlQuraishi

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**Update: Data with standard errors.

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This is a scatter of the actual change in state tax revenue, 2007-2009, per number of federal returns, against a hypothetical measure that we call revenue exposure, which is defined as the weighted sum of the change in AGI by income slice times the 2007 effective tax burden on that income slice. We are trying to graphically show how tight is the relationship between actual tax change and exposure; states way off the regression line include a few mineral states (ND, WY), states that offset their tax exposure by raising their tax rates (NY), or states with a lot of tax exporting (tourism, etc), that took a big hit during the recession.

Data (csv format)

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As for context, here is the abridged lead-in text to the Figure:
“To get a sense of the overall impact, Figure 1 plots the a(i) ratio [unit cost of product a of variety i] against its share in total imports within the sample for 2004. The most distorted goods tend to be a smaller share of imports. However, there are a number of goods that are relatively important with significant distortion.”

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