Spatial Analysis
The goal of spatial data analysis is to derive insights from the location and context of real world phenomena such as crimes, accidents,
vegetation growth, availability of minerals, and so on. Spatial analysts are often concerned with why things happen where they do. They
are also concerned with how the occurrence of a phenomenon in a particular region affects nearby regions. More precisely, the “where” in
spatial data analysis usually refers to one of three possibilities:
 a set of randomly occurring discrete locations, such as places where a crime has been reported
 fixed places and their associated data, such as the measurements from sensors that detect pollutants or greenhouse gases
 a geographical region that has associated data, such as the average prices of homes in different counties
Application areas of spatial analysis include but not limited to, agriculture, forestry, crime analysis, public health, environmental monitoring, insurance, and, energy.
The SAS/STAT spatial analysis procedures include the following:
 KRIGE2D Procedure — Performs ordinary kriging or spatial prediction for spatial point referenced data.
 SIM2D Procedure — Produces a spatial simulation for a Gaussian random field with a specified mean and
covariance structure in two dimensions by using an LU decomposition technique
 SPP Procedure — Analyzes spatial point patterns
 VARIOGRAM Procedure — Computes variogram diagnostics to describe the spatial covariance structure in spatial point referenced data
KRIGE2D Procedure
The KRIGE2D procedure performs ordinary kriging or spatial prediction for spatial point referenced data.
The following are highlight's of the procedure's features:
 enables you to specify the correlation model by naming the form and supplying the associated parameters, or by using the
contents of an item store file that was previously created by PROC VARIOGRAM
 enables you to specify the locations of kriging predictions in a GRID statement, or they can be read from a SAS data set
 local kriging is supported through the specification of a radius around a grid point or the specification
of the number of nearest neighbors to use in the kriging system

 produces an output data set that contains the kriging predictions and associated standard errors for each grid
 produces an output data set that contains the neighborhood information for each grid point when you perform local kriging
 supports BY group processing, which enables you to obtain separate analyses on grouped observations
 automatically creates graphs by using ODS Graphics

For further details, see
KRIGE2D Procedure
SIM2D Procedure
The SIM2D procedure produces a spatial simulation for a Gaussian random field with a specified
mean and covariance structure in two dimensions by using an LU decomposition technique. The simulation
can be conditional or unconditional. The following are highlights of the SIM2D procedure's features:
 enables you to specify the mean structure as a quadratic function in the coordinates
 enables you to specify the covariance by naming the form and supplying the associated parameters,
or by using the contents of an item store file that was previously created by PROC VARIOGRAM
 enables you to specify the locations of simulation points in a GRID statement, or they can be read from a SAS data set

 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 writes the simulated values for each grid point to an output data set
 creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details, see
SIM2D Procedure
SPP Procedure
The SPP procedure analyzes spatial point patterns. The broad goal of spatial point pattern analysis is to describe the occurrence of
events (observations) that compose the pattern. The event locations are a discrete realization of a random spatial process. Therefore,
the analysis goal is to investigate and characterize the original spatial process that generated the events in the spatial point pattern.
The procedure's capabilities include the following:
 fits an inhomogeneous Poisson process model and produce a variety of residual diagnostics
 performs a simulation based goodnessoffit test for the fitted intensity model
 produces a nonparametric estimate of the firstorder intensity
 computes the F, G, J, K, L, and pair correlation functions and performs a test for complete spatial randomness based on these functions
 performs a quadrat based test for complete spatial randomness
 performs marked point pattern analysis by using character mark variables

 automatically computes the study area bounds for the spatial point pattern by using the RipleyRasson estimator
 performs covariate dependency tests that are based on an empirical distribution function
 manages duplicate records
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details, see
SPP Procedure
VARIOGRAM Procedure
The VARIOGRAM procedure computes variogram diagnostics to describe the spatial covariance structure in spatial point referenced data.
The following are highlights of the VARIOGRAM procedure's features:
 handles anisotropic and nested semivariogram models
 the following eight semivariogram models are supported:
 Gaussian
 exponential
 spherical
 power
 cubic
 pentaspherical
 sine hole effect
 Matern
 supports a single nugget effect

fits permissible theoretical models to the empirical semivariograms, so that you can use them in
subsequent analysis to perform spatial prediction
produces plots of the empirical semivariograms and the fitted models
provides isotropic and anisotropic measures
provides the Moran I and Geary c spatial autocorrelation statistics
enables you to save the context and results of the semivariogram model fitting analysis in an item store
performs BY group processing, which enables you to obtain separate analyses on grouped observations
creates a SAS data set that corresponds to any output table
automatically creates graphs by using ODS Graphics

For further details, see
VARIOGRAM Procedure