Uncertainty, and the Visualization thereof

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Nathan Yau has a nice roundup of the different ways in which we can show #Uncertainty, including Ranges, Distributions, Multiple Outcomes, and Obscurity.
Wait what?
Obscurity? WTF is Obscurity in this context?
It’s actually quite straightforward — the more uncertain the data, the more difficult it is to see — which you can do via blurring, transparency, colors, etc. — something that sounds really obvious in retrospect, no? 😌
The upside is clear — reliable data is made prominent. The downside, well, it has to do with what Obscurity actually means to us — do we actually understand gradations? If so, how many levels are feasible — 3 levels of blurriness? 13? Or is it just “blurry or not” in our minds? It turns out that there is surprisingly little research into this 😞
Anyhow, a pretty cool tool for #Visualization , and something I hadn’t seen before…

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