Sample from an InhomogeneousPoissonPointProcess:

Sample from an InhomogeneousPoissonPointProcess defined on the surface of the Earth:

Visualize the points:

Sample from a nonparametric point density:

Sample from a binned density:

Define a point process with the computed point density function and check if it is valid:

Simulate multiple point configurations:

Simulate several realizations:

Sample from any valid RegionQ, whose RegionEmbeddingDimension is equal to its RegionDimension:

Check the region conditions:

Sample points:

Gaussian scattering is an example of isotropic inhomogeneous Poisson point process:

Simulate the process over a rectangle:

PointCountDistribution is invariant with respect to a rotation about the origin:

Point count distribution in the rotated region:

The distributions are the same as identified by equal means:

Define piecewise density:

Define process:

Sample from the process:

Point process with density depending on the distance to a line, like a fault line:

Define the point process:

Simulate the process:

Simulate possible point pattern of seeds fallen around a tree:

Define the point process:

Simulate the process:

Simulate the seed pattern:

Inhomogeneous Poisson point process with constant density autoevaluates to PoissonPointProcess:

The expected number of points in a region for InhomogeneousPoissonPointProcess follows a PoissonDistribution:

Compute the point count distribution over a rectangle:

Over a disk:

Over an implicit region:

Compute void probabilities for an inhomogeneous Poisson point process:

For a rectangle:

For the rectangle translated:

Inhomogeneous Poisson point process is not stationary—the density depends on the location:

Point count distribution in a subregion:

Point count distribution in the translated subregion:

The region measures are the same:

The densities as expressed via PointCountDistribution differ:

InhomogeneousPoissonPointProcess with a constant density function is PoissonPointProcess:

The point count distribution in a disk:

Point count distribution for a corresponding Poisson point process in the same region:

In higher dimension:

The point count distribution in a ball:

Point count distribution for a corresponding Poisson point process in the same region: