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Because some people seemed to want this after I created a map of Fields medalists per country, here is a map of Fields medalists per million of citizens.

17 Answers

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Color choices could provide relative information, but your random ones don't.  You could use shades of a single hue to show gradients, or morph, say from dark reds, to oranges to yellows.
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I think these maps are fun to look at, thanks for the work.

As an aside, I'm not sure either map is more "fair" in terms of weighting to population size.  Of course larger populations will have higher chances of fields medalists, but you get diminishing returns once you hit a certain size due to how limited the resource of fields medalists is.

Putting it another way, there have been only 60 fields medalists to this date (I think).  If China won all of them, their density would be 0.0427960057, while if new Zealand won just one, their density would be 0.196695515.
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I’m surprised Russia doesn’t have more
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Thank you very much.
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You have a continuous measure but discrete, unordered color mapping. Always think of the dimensionality and topology of your data for good data viz.
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Brazil's strategy for increasing their ratio, is unconventional but effective. Looks like they would almost be boosted to pink
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> China with only one nominee and over a billion citizens has the lowest Fields medalist density.

This is wrong. China doesn't have the lowest density, as all countries with no Fields medals have the lowest density.
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The scales used for the colors is pretty cherry-picked as well lol. I still appreciate the map though, it's interesting.
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RIP Portugal.
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It's funny that people wanted to see it normalized to the population. I can't help but inject now that of course not all people in a country are realistically in the pool of people who could feasibly have the chance to do academic mathematics. Depending on the ratios of poverty and education there are people outside the effective pool. I'd now want to see this map normalized to middle class or maybe college educated people lol. The current map could largely (though not exclusively) be a map of inverse income inequality and average education standards.

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