Here's a quick R script for bar plots comparing all the listed places and the resulting image:

Thanks Berry.

I am having a very hard time seeing how the pie chart solves this problem in a way that a stacked bar chart does not.

I think you've perhaps given too much credence to their attempt to be clever as well - unless I've missed something, they haven't done anything to the axis labels, they've simply added data labels that present a different measure than the axis labels do.

I certainly agree that the original chart is not ideal to represent this information. I also remade the chart as a better version of a stacked bar, here:

https://jsfiddle.net/jlbriggs/nb4cmmzu/

If I really needed to present the specific different percentages, I would add a table.

What am I missing that makes the pie chart better?

jlbriggs: Nice idea with the vertical US average lines!
However, we have a different understanding of the original numbers (not our fault!).
Using the US-average as example: I understood delinquent to be 10.1% of 31.4% underwater, hence 3.2% of total mortgage owners.
Same with the cities... You can see how the values in our charts differ. I have Las Vegas total underwater at 71%, in your graph it is displayed at slightly over 80%.

@berry - good catch. Just sloppy math on my part while trying to throw this together between work tasks :)

Ideally I would also add something to the chart that explained the breakdown precisely. Some brackets along the top or bottom bar with a "31% of homes underwater, of which 3.2% are also delinquent", for instance.

While I wouldn't have a problem with a single pie chart displaying this type of relationship, I don't think 18 such pie charts would be a very effective way of getting this message across.

Berry/jlbriggs: Good discussion and thanks for the visuals. I agree that having an entire wall of pie charts is an eyesore. And I agree a stacked bar chart version is competitive. For more than three segments, stacked bar charts also have issues from the perspective of presenting multiple series of proportions with different bases.
What we learned from this discussion is that the key improvements are (a) adding back the "missing" segment (the not-underwater mortgages) and (b) fixing the labels to describe the segments accurately.

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