I see that bounty at the bottom, so tossing away my chances here, but this visualization is just asking to be mapped onto a Hilbert Curve. [0] When you "stripe" the data like this, points that are sorted close together could end up pretty far apart, since a distance in the Y axis skips an entire row of data as you move down, rather than a distance in the X axis which is 1-to-1 with the source data.
If you map it onto a hilbert curve, the X and Y axis mean nothing, but visually points that are close together in the sorted list, will be visually close together in the output image.
Since the first part of an ISBN is the country, then the second part is the publisher, and the third part is the title, with a check sum at the end, I would remove the checksum and sort them each as a big number. (no hyphens)
You should end up with "islands", where you see big areas covered by big publishing countries, with these "islands" having bright spots for the publisher codes.
Bonus points for labeling these areas!
I set up something a while ago [1] for an interview that does this with weather data. It makes the seasons really obvious since they're all grouped together.
[0] https://en.wikipedia.org/wiki/Hilbert_curve
[1] https://graypegg.com/hilbert (https://github.com/graypegg/hilbertcurveplayground code if anyone wants to go for the prize using this! Please at least mention me if you decide to reuse this code, but I can't stop ya lol)
What property makes the Hilbert curve desirable compared to, say, a snake pattern, with which neighbouring ISBNs are also neighbours in the visualisation?
The worry I have with Hilbert curves is that they make the result look like there are distinct "squares" of data [0] when really this is just an artifact of how Hilbert curves work. In that sense, the current visualization is more useful, because it's straightforward to identify the location of each country in it.
[0] https://raw.githubusercontent.com/jakubcerveny/gilbert/maste...
And there's a generalized Hilbert curve, the Gilbert curve, for non powers of two rectangular regions [0] (online demo [1]).