Spatial Statistics
Spatial point configurations are collections of points (or events) in space. Examples include the location of trees in a forest, the location of gold deposits, the location of stars, the location of earthquakes, crime locations, animal sightings, etc. Often the objective is to quantify trends in the density of points, presence clustering or regularity and to give models that can generate similar point patterns. Spatial point pattern and point process analysis is used in ecology, epidemiology, geoscience, astronomy, econometrics and crime research, etc. The Wolfram Language provides a complete toolkit for working with spatial point patterns and point processes, from exploratory analysis with visualization and descriptive statistics to modeling and simulation.
Spatial Point Collections »
SpatialPointData — create and represent spatial point data with observation region
ResourceData — spatial point datasets from a variety of sources
RandomPointConfiguration — simulate a point process to give a point collection
Location Measures
Mean ▪ SpatialMedian ▪ CentralFeature
Density Measures
MeanPointDensity — average number of points per area, volume, etc.
PointDensity — varying point density function 
HistogramPointDensity ▪ SmoothPointDensity ▪ PointDensityFunction
Counting Measures
PointCountDistribution — the distribution of point counts for any region
Homogeneity Measures »
RipleyK — expected number of points within distance r of each point 
EmptySpaceF — probability of finding another point within distance r of any location 
NearestNeighborG — probability of finding another point within distance r of a point 
PairCorrelationG — probability density of finding another point at distance 
of a point 
Hypothesis Tests
SpatialRandomnessTest — test whether data is uniformly distributed
PointProcessFitTest — test whether data follows a point process
Spatial Point Processes »
RandomPointConfiguration — simulate a point process to give a point collection
EstimatedPointProcess — estimate a point process from spatial point configurations
PointProcessFitTest — test whether data follows a point process
Independent Point Processes
PoissonPointProcess — constant intensity
InhomogeneousPoissonPointProcess — varying intensity
BinomialPointProcess — uniform distribution of 
points
Interaction Point Processes
HardcorePointProcess — hard-core with no point interaction within radius 
StraussPointProcess — soft-core with limited point interaction within radius 
StraussHardcorePointProcess — hard-core limited point interaction between two radii
PenttinenPointProcess — strength of interaction based on overlapping disk area
DiggleGrattonPointProcess — hard-core interaction with a gradual transition
DiggleGatesPointProcess — gradual hard-core to soft-core interaction
GibbsPointProcess — completely general point interaction
Clustered Point Processes
MaternPointProcess — cluster process with uniform (daughter) pattern in disk (isotropic)
ThomasPointProcess — cluster process with normal (daughter) pattern (isotropic)
CauchyPointProcess — cluster process with heavy (daughter) pattern (isotropic)
VarianceGammaPointProcess — cluster process with variance-gamma (daughter) pattern (isotropic)
NeymanScottPointProcess — general cluster process with inhomogeneous base point process and general point process as daughter process