Well, it's not all as simple as that, I don't remember the details,
it's been about a year since I last touched GeoDjango stuff, but the
base assumption you're making that is wrong is that "a point is
defined by latitude and longitude".
I reality, there's a LOT of different projection systems to match the
real world with a map. The latitude-longitude system (which has a code
that I don't remember) is the most accurate one, but also the most
expensive, because it implies spherical calculations, so the distance
between two points is not just sqrt((a-b).x**2 + (a-b).y**2) since
it's a 3D spherical surface.
The solution for this performance issue is to generate a bazillion
different projection systems each of which work well with a particular
area of the world. NYC has it's "optimal" projection, and Buenos Aires
has another. There's a different one that averages best on
country-scale level for the US, and another one for brazil, get the
idea?
So one of your first choices should be your projection system. postgis
supports all of them (they can be loaded).
And conversion between system is one of the points where GDAL comes
into play. Some if it's utilities are used for conversion between
different systems.
Likewise, there're are other operations that the utilities you mention
help with: intersection, substraction, addition between two areas must
return an area. Containment, area vs point, distance between two
areas, etcetera. All these are complicated operations that all the
toolset help you achieve. I don't remember the details, as I said, but
the documentation should help you
> To view this discussion on the web visit
>
https://groups.google.com/d/msg/geodjango/-/nJ1zYjAfcTMJ.
--
"The whole of Japan is pure invention. There is no such country, there
are no such people" --Oscar Wilde
|_|0|_|
|_|_|0|
|0|0|0|
(\__/)
(='.'=)This is Bunny. Copy and paste bunny
(")_(") to help him gain world domination.