Meaning
Repulsive models represent point patterns where a repulsive force prevents individuals from appearing too close to one another. Using a strauss process allows for the modeling of inhibition by assigning a penalty for every pair of points that falls within a specified interaction radius. This creates a pattern that is more regular than a random one but less rigid than a perfect grid.
Hardcore Distance
Interaction parameter ranges from zero to one. This hardcore distance represents the minimum separation where no two points can exist if the parameter is zero.
Probability Density
Likelihood of a specific configuration depends on the number of close pairs. When the interaction parameter is one, the model becomes a poisson process with no interaction. As the parameter decreases toward zero, the likelihood of finding points near each other drops considerably.
This makes the model useful for describing physical systems where objects take up space and cannot overlap. Maximum likelihood estimation is typically used to find the radius and the interaction strength from a set of observed coordinates.
Statistical Fit
Convergence of the model depends on the density of the points. A statistical fit measures how well the inhibited model matches the observed spacing.
