The difference is that each top-level grid "square" (to your point, not actually square, but that's what they're called) is 20° longitude by 10° latitude represented by two letters. While the computation of a 4 or more character grid locator code is complex enough that most people can't quite do that in their head, because of them being treated as if they were coordinate "rectangles", it is simple enough to translate lat/lon coordinates to a grid code and vice versa with pen and paper if needed in the case of an emergency, if you know the algorithm or have a reference sheet. The 10° latitude size means that the parallels are the same in the Maidenhead system (for the first two letters of a code) as in this post. It also has the added benefit of knowing that DN is north of DM, which are both west of EM. I've gotten to where I can roughly place someone I hear on the radio based on a mental map of grid codes, and have memorized many grid codes of large population centers.
The beauty of the grid code system is that you can further refine an area by adding on subsequent numbers and letters, much like degrees/minutes/seconds in coordinates, but requiring significantly less characters to read to others over the radio. And, you can use phonetics for the letters, i.e. "delta mike seven niner" (DM79) is roughly the entire Denver metro area. Fort Collins, CO on this parent post falls under DN, above that 10°-sized parallel.
More info on the Maidenhead system: https://en.wikipedia.org/wiki/Maidenhead_Locator_System
Map with two-letter grid codes: https://www.mapability.com/ei8ic/maps/gridworld.php
Some of those squares have zero-length edges.
They certainly are. Parallels and meridians always meet at 90 degrees.
According to Wikipedia [1], "in spherical geometry, angles are defined between great circles". Meridians are great circles, but parallels are not. Possibly the angle is simply not defined for circles that are not great circles?
Here's an example: https://www.desmos.com/calculator/is7mush1ma
The red and blue circles are both perpendicular to the green circle at the origin.
However, parallels, while smooth, are not straight, they "curl" toward the nearest pole (i.e., non-zero second derivative, relative to the earth's surface). This accounts for the non-"rectangular" shape of quadrangles and can be replicated on a 2D plane.
Take the lines tangent to the meridian & parallel at the point of intersection. Observe these lines are perpendicular. Hence the meridian & parallel meet at a right angle.
How is that relevant regarding “Parallels and meridians always meet at 90 degrees“?
You can also subdivide the faces of a cube into smaller squares with a quadtree, e.g. https://s2geometry.io/
The Disdyakis triacontahedron's faces are probably too long and skinny for most geospatial applications though.
I guess it ends up in the same square with Moscow and thus doesn't show up but either the map doesn't show the shoreline of the sea very accurately at all or it's wrong about the location of St. Petersburg.
Helsinki has a population of 650,058 and is located at 60°10′15″N 24°56′15″E while;
Saint Petersburg has a population of 5,351,935 and is located at 59°56′15″N 30°18′31″E
Saint Petersburg is one quadrant south and one quadrant east of Helsinki, or the same quadrant as Moscow as you said.
The bay gets a little thin before Saint Petersburg, and there is a parallel passing exactly through it. I’m guessing that’s whats making Saint Petersburg appear more inline then it is, or—more accurately—makes the shoreline appear to be more to the west then it actually is.
I guess a triangle. Or one of these:
https://upload.wikimedia.org/wikipedia/commons/thumb/6/67/Sp...
https://upload.wikimedia.org/wikipedia/commons/thumb/9/9b/Sp...
For just shapes the above is a nice visual start.
However a striking feature of the linked article is how frequently there's a major city right near a cell edge.
A more useful list might be constructed by some process involving assigning a "city" an imprint size based on population, and a surrounding 'metro area' based on absorbing any weaker cities that overlap until the process either repeats or is matched by a neighboring city.
A starting point for results that are already similar to that: https://en.wikipedia.org/wiki/Metropolitan_area
The US specific list: https://en.wikipedia.org/wiki/List_of_metropolitan_statistic...
I like that idea of building the regions bottom-up.
It would be interesting to see an interactive version with a free choice of meridians.
If you are thinking of drawing arbitrary rectangles on arbitrary projections, then you'd have to come up with a rigorous way to define your rectangles. If you don't, then you're just drawing random shapes on other random shapes. Either way, yes, a different projection with the same rectangles drawn over it would yield different answers, and this should be immediately obvious.
This bears considering when looking at the map, because some of those regions are much, much smaller than others.