Edit: see @srean's excellent explanation why that won't do.
Edit: see @srean's excellent explanation why that won't do.
To appreciate why, consider strips along two constant latitudes. One along the Equator and the other very close to the pole. The uniformly random polar coordinates method will assign roughly the same number points to both. However the equatorial strip is spread over a large area but the polar strip over a tiny area. So the points will not be uniformly distributed over the surface.
What one needs to keep track of is the ratio between the infinitesimal volume in polar coordinates dphi * dtheta to the infinitesimal of the surface area. In other words the amount of dilation or contraction. Then one has apply the reciprocal to even it out.
This tracking is done by the determinant of the Jacobian.
This gives an algorithm for sampling from a sphere: choose randomly from a cylinder and then project onto a sphere. In polar coordinates:
sample theta uniformly in (0,2pi)
sample y uniformly in (-1,1)
project phi = arcsin(y) in (-pi,pi)
polar coordinates (theta, phi) define describe random point on sphere
Potentially this is slower than the method in the OP depending on the relative speeds of sqrt and arcsin.EDIT: Plotting it out as a point cloud seems to confirm your suspicion.
Without scaling: https://editor.p5js.org/spyrja/sketches/7IK_RssLI
Scaling fixed: https://editor.p5js.org/spyrja/sketches/kMxQMG0dj
One way to fix the problem is to sample uniformly not on the latitude x longitude rectangle but the sin (latitude) x longitude rectangle.
The reason this works is because the area of a infinitesimal lat long patch on the sphere is dlong x lat x cosine (lat). Now, if we sample on the long x sin(lat) rectangle, an infinitesimal rectangle also has area dlong x dlat x d/dlat sin(lat) = dlong x dlat cos (lat).
Unfortunately, these simple fixes do not generalize to arbitrary dimensions. For that those that exploit rotational symmetry of L2 norm works best.