I have a set of points (shown as little black circles) which ideally form a hexagonal lattice shape, each point having an equal distance to all of its neighboring points. (Sorry for my drawing, some of the line intersections are off, but I hope you get the idea).

(These points are actually acquired through image processing, by calculating the center of gravity of some circular blobs in an image. And I am trying to figure out how close these points are to being an ideal.)
My question is: Is there a way to find the "best fit" lattice so that the total distances of points to their ideal position is minimized? I honestly don't know how a lattice is expressed, but I'm thinking if I have a distance d (the equal distance between all lattice points), a slope m, and an X,Y displacement, I should be able to calculate the ideal point corresponding to each real point.
I think it would be easier to analyze if you apply the (inverse) Fourier transform to get the spacial frequency (complex) spectrum, known as the reciprocal lattice of your "crystal", as in http://en.wikipedia.org/wiki/X-ray_crystallography#Diffraction_theory. That picture will describe the lattice fully, including main direction and spacing, whether it's indeed hexagonal, and how big the possible deviations from a perfect lattice. I realize that this replaces one problem with another, but then there must be a well developed physical methodology for analyzing diffraction images, maybe even to automatically determine the lattice type and spacing.