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

The post Donations vs. Disease appeared first on HelpMeViz.

So, is the bubble chart the best way to show the data? Does the interactivity help? How can it be improved?

I pulled the data from this Google spreadsheet provided by OpenSecrets.org in a write-up posted yesterday.

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the story you want to tell…and the one your data shows

I was working on a makeover for a recent workshop when it became apparent that the story being told wasn’t quite right, or at least wasn’t exactly the story I would tell after looking at the data in a couple of different ways. In the following post, I’ll walk you through an anonymized version of the makeovers and my corresponding thought process. Continue Reading…

The post Remake: Product Column Chart appeared first on HelpMeViz.

Most entries in the Gallery will be re- or cross-posted from other websites; others will be HelpMeViz originals produced by experts in the field of data visualization. For those re- or cross-posts, introductory text will also appear with a “Continue Reading” button that will bring you to the original remake. The “before” images will be posted on the HelpMeViz main page with a blue “Remake” banner. Entries will also appear in the “Remake Gallery” category for easier filtering.

If you would like your remake to appear in the HelpMeViz Remake Gallery, please send an email to HelpMeViz@gmail.com.

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I saw this chart a few weeks ago and immediately felt that it was a terrible way to show the data. (Let’s ignore for the moment that with only four numbers, a graphic may not actually be necessary and that having annual data would be helpful). When I sat down and started sketching alternatives, I didn’t love any of them, so I’m seeking help on visualizing just these four numbers–100 years & 4.4 gigatons; 3 years & 6.6 gigatons.

Alternatively, one could convert the numbers to gigatons per year and just plot it as a simple column chart, etc, but I think the four-number challenge is a bit more interesting.

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We’ve been struggling with how to make this table reader friendly for the non-data literate.  Please help!  This table is included in a larger report that will be distributed to community agencies with varying levels of data literacy. Please provide help using Excel—we don’t have access to other software programs (unless they are free!).

Data are available here.

The post Table Design Help appeared first on HelpMeViz.

I am aiming to compare two categories (both in percentage), one of which can and does have negative values for particular locations, and then grouped into regions. There is also a 75% threshold that I would like to show – the 75% is relevant only for the blue category.

What I hope to show is the relationship between the blue (% variance to budget) and orange (% variance FTEs) categories. For example, when the % variance to budget values exceed the 75% threshold–which is good–is that because the %FTEs are positive percentages (i.e., they used more staff than budgeted)? Or does the % variance to budget exceed 75% (again, the good result) when fewer staff are budgeted (negative % FTE)? While the current graph has the two bars side by side is there a better way to show the relationship? Any other general observations would also be appreciated as I don’t know how to avoid having some location labels overlapping the negative bars.

The Excel file is available here.

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As we begin to explore years of data on women’s empowerment from the World Bank and United Nations, we want to ask the question: Do countries that significantly improve the status of women also see lower rates of stunting? Women are the primary caregivers in the family. Research from countries around the world has shown that when women are empowered to earn more and have a greater say in home finances, they are more likely than men to invest the additional money in promoting the welfare of their children — through nutritious food, for example. In this project, Bread for the World Institute is interested in exploring whether and where women’s empowerment is associated with improvements in stunting and, if so, over what period of time. The answer to the question of whether the two indicators coincide is most likely to be “sometimes yes, sometimes no.”

Data and background information can be found in the “Challenge 2″ in this Google drive folder.

Cleaned IFPRI Data

Cleaned Female Stunting Data

Cleaned Male Stunting Data

Cleaned Female Stunting Data (updated)

Cleaned Male Stunting Data (updated)

Final Cleaned Full Data (updated)

Final Data Cleaned again

The post Hackathon: Women’s Empowerment & Nutrition appeared first on HelpMeViz.

New research shows that even the most advanced current efforts to gauge women’s empowerment still miss critical elements of what it takes for women to be empowered in the developing world. Far too many of the indicators used to measure women’s empowerment, for far too many countries, are based on data that is largely unreliable, old, or inconsistent. This compromises the accuracy and integrity of the assessments and makes them less reliable for policy makers who base decisions on them. In its upcoming 2015 Hunger Report, Bread for the World Institute will identify key missing data and explain why better data are essential to continued progress.

Data and background information can be found in the “Challenge 1″ in this Google drive folder.

The post Hackathon: Exposing Gaps in Data on Women’s Empowerment appeared first on HelpMeViz.

I’ve built an open-source Chrome extension that replaces Chrome’s default history page with a visually oriented interface. It captures and displays in Pinterest-style format all the pages you’ve visited and it let’s you search the content of all saved pages.

For privacy and performance, the extension stores everything locally using the browser’s built-in NoSQL store (IndexedDB) and it can store up to 10,000 pages including screenshots and full text, which comes to about 5GB of disk space, but this limit is arbitrary. It will be possible for the user to set a much lower or much higher value in the next version.

The extension is capable of recording the time you spend each day on each URL (how long in total the URL is loaded in a visible tab/window excluding idle time) and it can therefore tell you things like how much time you’ve spent on Hacker News or Gmail over any period. However, the visualizations can be a lot more powerful and meaningful if the user opts in to share their anonymized browsing history. With that, we can tell you if you’re spending too much time on Hacker News or on the D3 mailing list, as compared to the total population of users.

It can do another thing by gathering anonymized browsing data, per opt-in permission, which is to use that to draw correlations between seemingly unrelated sites, which users can visualize, may be in a network graph where edges grow thicker or thinner depending on link strength and nodes grow bigger or smaller depending on popularity of the given site among all users, but it can be a chord diagram, too. I’m not a visualization expert.

The extension can also monitor things like rapid back and forth switching between open tabs, where the sites are not correlated at all, as it may indicate ADHD behavior (but that’s just an idea that I personally find fascinating, although it may be something that users don’t wish to analyze — where to draw the line…)

Given all of the potential for Personal Web Analytics, the type of input I’m asking for is all about the imagination, not so much concrete ideas. I just want to hear what people think and what kind and variety of personal web analytics they would find useful.

There is also some Hacker News discussion of the existing, non-data-visualization features of the extension, where, for improved privacy, I promised to add a “Do Not Capture” list of hostnames (e.g. mail.google.com) that users can declare. This feature has been added in the latest version.

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