A medieval map that made cartography into a science
newscientist.com
newscientist.com
Importantly, the map is "upside down", since it's oriented to have south at the top.
[0]: https://upload.wikimedia.org/wikipedia/commons/1/1b/FraMauro...
https://upload.wikimedia.org/wikipedia/commons/4/48/Hereford...
> A new world map published in 2021 by physicists J. Richard Gott, David Goldberg and Bob Vanderbei minimised such distortions.
Interested, I read the article and try to understand what makes their Gott-Wagener projection more accurate than others. Apparently this is determined by calculating 6 types of distortions, and then summing the squared error of those distortions, but only after normalising on the Equirectangular projection.
And I'm wondering: why that normalisation step? And why Equirectangular? Does that not introduce its own distortions? In particular, Mercator scores very poorly because in the category where it scores worst, Equirectangular scores pretty good, creating a big difference that gets emphasised by squaring it.
So I recalculate the summed squares without the normalisation, and I get very different results:
Normalised: not normalised:
Equirectangular: 6 0.09959
Mercator: 8.308 0.46488
Lagrange: 6.556 0.32536
Briesemeister: 6.193 0.49412
Eckert IV : 5.8474 0.408
Winkel-Tripel: 4.563 0.18284
Gott-Wagener: 4.497 0.10969
So funny thing: Equirectangular is now suddenly the best, when it used to be average. Gott-Wagener is still better than Winkel-Tripel and only just after Equirectangular. Mercator is still bad, but Briesemeister is slightly worse.So I've got serious doubts about this normalisation procedure. I suspect you get very different results depending on which projection you use to normalise on.
They optimize for least squares distance errors for all pairs of distances across the map, but they consider points which are at opposite sides of a map across a discontinuity (but nearby on the globe) to have extremely high error, and therefore their optimization routine radically tugs those points towards each-other. The result is really scrunched up edges.
They'd get dramatically better results by either (a) ignoring very distant points altogether, (b) figuring out how to measure distance across a discontinuity, or (c) comparing distances on the map to distances on the globe if you go the "long way" around, i.e. following a similar path to the one implied to be shortest by the map.
Having not seen this map in person, it's not clear to me which of these two has the more accurate white balance.
"The transformative OpenStreetMap project turned cartography into a community-driven exploration."