Very nice! I was tired of making my own legends for each single things. And this one looks really nice and easy.
Though, one of my pet peeves: people, please don't use discrete color scales for continuous parameters. It distorts presentation for no good reason. For example two countries are colored the same way even though they are on the opposing edges of a bin (so the difference is being masked), or two countries are colored in a different way, even though the difference is minimal, but just passes an artificial threshold (then an artificial difference is being shown).
And the colormap should be easy to read with color blindness (so the blue/green one is not good), and also look good with various amounts of hue on different screens or print. For instance, printed in b&w. This page has a lot of color maps and how they look in greyscale in the bottom: http://matplotlib.org/1.4.1/users/colormaps.html As can be seen, the "jet" map often used is unreadable, luckily it is no longer default in matlab.
> And the colormap should be easy to read with color blindness (so the blue/green one is not good)
Well, blue/green or blue/red are actually the most acceptable scales to use, since most colorblind people only have problems distinguishing red and green, while for most of the others blue is very dark so the scale still works somewhat. The others are a very, very small minority that will already be used to use various tools to enhance their vision.
> people, please don't use discrete color scales for continuous parameters
There are arguments against using continuous scales:
* By using classes (e. g. quantiles) you can reduce the impact of outliers. On a continuous scale an outlier can cause everything else to be perceived as one color.
* Perception of color intensity is not strictly linear.
This is the exact wrong way round. Continuous colour scales are hugely problematic because of the fact that our colour perception is closely linked to context.
If precise comparisons are required you're better off not using colour at all. Colour e.g. in choropleth maps, is good for showing broad spatial trends or highlighting particular areas for selection. A bar chart is better for precise comparisons because we're much better at comparing lengths than colours.
It depends. Classes can greatly help the understanding of a map. It ia much easier to recognise and identify few classes than it is to interpret a vast continuous scale. But really, it totally depends on your data and intentions.
It is easy to arrive at wrong conclusions. But it is No.1 argument against discrete scales, not for it. It can happen by accident, but it is possible also to intentionally deceive - e.g. by setting such bins that Greece and Spain have the same color but Italy do not. Or that Spain and Italy have the same (but Greece different), etc.
Though, one of my pet peeves: people, please don't use discrete color scales for continuous parameters. It distorts presentation for no good reason. For example two countries are colored the same way even though they are on the opposing edges of a bin (so the difference is being masked), or two countries are colored in a different way, even though the difference is minimal, but just passes an artificial threshold (then an artificial difference is being shown).