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Jamie O'Hare

esigners really do love using circular charts don't they.

This is an interesting post, I particularly like your point about the large error bars and lack of observations undermining the certainty shown in the global analysis. This is particularly telling if you look at Kiribati which appears to have by far the highest levels when looking at the combined chart up until around 2005 where things tighten up a bit.

However when looking at the data for Kiribati alone these levels are based on no more than 3 observations and several age groups have 0. Most of these do show very high levels of smoking but the level of uncertainty is suprising given the combined chart.

Mike Freeman (@mf_viz)

Thanks so much for the constructive criticism of the tool – it will help me (and hopefully others) make better visualizations in the future. Here are a few responses:

On the sunburst (concentric circles) diagram: I agree that the “geography lesson” is a bit taxing, though we wanted to communicate results at aggregate regional levels while showing the hierarchy involved. The biggest concern that you point out is that the outer areas represent smaller numbers. This is an oddity that could be resolved with a shorter radius for outer segments (which might come at a considerable aesthetic loss).

On the line chart (“bumps”): The lines are obviously uninterpretable (you’re right, there are over 200). However, I find it useful that one can highlight them via clicking the map, or the menu below. This allows users to see time trends for countries of interest.

On uncertainty: You point out that there is considerable uncertainty in many estimates, which we’ve highlighted in the “data” and “country” tabs. In the “worldwide” tab, uncertainty is available through mouseover, though not symbolically built into any of the charts. Communicating this visually would be preferable, and I’d be interested to hear suggestions on effective ways to do that.

On “good data”: I’m torn about the idea that “no data is better than bad data”. These estimates are the only comprehensive and comparable estimates of tobacco usage patterns that are available. The models use data from adjacent countries/years to generate estimates (more info from the paper here: http://jama.jamanetwork.com/article.aspx?articleid=1812960). Should policy makers ignore these estimates in favor of using no data? I’m not sure. I think emphasizing uncertainty is a better alternative to not showing the data.


Hi Mike, always excited to hear from the designer himself! Also gives readers a chance to hear the thought process that went into designing these graphics.

While some readers will surely object, I prefer a chart with fewer lines, which means not showing some of the smallest countries. That partly takes care of the uncertainty issue because larger sample sizes lead to smaller error bars. You can also cluster the countries by the shape of their trajectories. If there are fewer lines on the chart, it would leave more room to showing the error bars.

On "no data is better than bad data", I want to clarify I don't mean throw out the entire data set. I just mean identify the weak points in the data set and hide them rather than treat them on equal terms as everything else.


The classic example of this is the right hand side of a survival curve where there are few subjects remaining so high error. Truncating the axis to the region where there is meaning flu data will produce something that is easier to understand.

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