A point process for modeling location of trees in an infinite forest? I am looking for an example of a stationary, infinite point process on $\mathbb R^n$ with a few simple properties. I would not be surprised to discover that there is a well-studied, canonical process with these features, but I don't know the field very well and have had no success in my search thus far.
The most important property I want is for the points to be repulsive and in the sense that there is a characteristic distance $r> 0$ between any two nearby points, and attractive in the sense that there is zero probability of finding a ball of radius say, $10r$, in which there are no points. Finally, the process should be stationary so that the distribution is unchanged by translation. Isotropy (invariance under rotations) would be nice, but I don't really care. It is crucial for my purposes that it be an infinite process, defined on all of $\mathbb R^n$, and in dimension $n\geq 2$. I believe that in one dimension it is easy enough to construct such an example.
The idea is to model, for example, the location of trees in a forest.  
Is there some well-known point process I am informally describing (or is it easy enough to construct one?), or is there some good reason I am having trouble finding one?
 A: All your requirements are satisfied by the Poisson-Disk process.  It's the limit of a uniform sampling process with a minimum-distance rejection criterion.  The easiest way to describe it is as the limit of the following process: uniformly sample points in the area of interest, rejecting any points that are less than $r$ from an existing point.  Keep sampling points until there is no area not within $r$ of a point, and you're done.  
This process is popular in computer graphics and image processing because its Fourier Transform has some nice properties.
This process generates a distribution without any big holes, so it may not be the right thing for a forest where small clearings are allowed but big ones are not.
The process as described is very slow to converge.  Some Googling for "Poisson Disk" suggests  there are far more efficient modern algorithms than this for generating a Poisson-Disk distribution.  But I can't guide you to that literature; I last generated a Poisson Disk distribution in 1982, and we did it the hard way.
