The PROCESS statement defines a point pattern for analysis. You must use a valid SAS variable name to define the process and you can described it by using variables that contain the and coordinates of the points within the point pattern. The variables must also be in the DATA=
data set. You can also specify patternoptions and processoptions. The patternoptions are related to different attributes of the observed point pattern that is read from the DATA=
data set. The processoptions represent different analyses that are associated with a point pattern. These analyses are usually helpful in characterizing
the underlying stochastic process that might have generated the point pattern. The PROCESS statement’s patternoptions are listed in Table 93.4. The PROCESS statement’s processoptions are listed in Table 93.5.
Table 93.4: Point Pattern Definition Options
Option

Description

AREA=

Specifies a rectangular study window

EVENT=

Specifies an EVENT variable that identifies individual point pattern events

MARK=

Specifies the MARK variable for the point pattern

You can specify the following patternoptions, which enable you to describe various aspects of a point pattern data set:

AREA=(xminnumber, yminnumber, xmaxnumber, ymaxnumber)

specifies parameters that define the study area bounds for the spatial point pattern. This option describes is a key attribute
that governs the intensity estimates that are obtained by different methods in PROC SPP. When you specify this option, you
must identify all the following area specifications:

xminnumber, the lower left limit for the x coordinate

yminnumber, the lower left limit for the y coordinate

xmaxnumber, the upper right limit for the x coordinate and

ymaxnumber, the upper right limit for the y coordinate
If there are BY groups in the DATA=
data set, then the explicit bounds remain the same across all BY groups. If you do not specify this option, then PROC SPP
estimates a default area based on the RipleyRasson window estimator. For more information about the RipleyRasson window
estimate, see the section RipleyRasson Window Estimator.

EVENT=variablename

specifies an event variable that is associated with instances (points) in this point pattern. If your DATA=
data set also contains information about covariates, use this option to identify the events in the point pattern.

MARK=variablename

specifies a character or quantitative variable from the DATA=
data set as a mark variable. Character variable marks are used for requesting distance function summary statistics across
different variable values.
Table 93.5: PROCESS Statement Options
Option

Description

F

Computes the emptyspace F function

G

Computes the G function

J

Computes the J function

K

Computes the K function to test for complete spatial randomness (CSR)

KERNEL

Obtains a nonparametric intensity estimate of the point pattern

L

Computes the L function

OUTSIM

Specifies an output data set to store the simulated data sets in computation of distance functions

PCF

Computes the PCF function

QUADRAT

Performs a quadrat based test for CSR

You can specify the following processoptions to study the point pattern data set and the underlying spatial point process that is likely to have generated this pattern:

F<GRID(valueNX,valueNY)>

performs a test for complete spatial randomness that is based on the emptyspace F function. For more information about the
F function and related functions see the section Statistics Based on SecondOrder Characteristics. You can specify the following suboption:

GRID(valueNX, valueNY)

specifies a reference grid for computing the emptyspace F function, where valueNX represents the number of horizontal divisions and valueNY represents the number of vertical divisions. By default, the SPP procedure uses a grid.

G

performs a test for complete spatial randomness that is based on the nearestneighbor G function.

J<GRID(valueNX, valueNY)>

performs a test for complete spatial randomness that is based on the J function. You can specify the following suboption:

GRID(valueNX, valueNY)

specifies a reference grid for computing the J function, where valueNX represents the number of horizontal divisions and valueNY represents the number of vertical divisions. By default, the SPP procedure uses a grid.

K

performs a test for complete spatial randomness that is based on the K function.

KERNEL<(kernelsuboptions)>

produces a nonparametric estimate of the firstorder intensity, or a nonparametric smoothed estimate of a quantitative mark
variable of the point pattern, depending on the kernelsuboptions. When you do not specify the kernelsuboptions, PROC SPP computes a nonparametric intensity estimate that is based on a default bandwidth and uses a Gaussian kernel. You
can specify the following kernelsuboptions.

TYPE=EPANECHNIKOV  GAUSSIAN  QUARTIC  TRIANGULAR  UNIFORM

specifies the kernel type for obtaining the nonparametric estimate. For more information about the different kernel types
that PROC SPP supports, see the section Nonparametric Intensity Estimation. By default, TYPE=GAUSSIAN.

B=value

specifies the value for the kernel bandwidth parameter. The bandwidth is a nonnegative number. By default, the SPP procedure uses a bandwidth
of , where is the CSR average intensity of the point pattern (Illian et al., 2008, p. 236).

ADAPTIVE

performs adaptive kernel estimation. Adaptive kernel estimation requires an initial bandwidth value to compute bandwidth estimates
for each data point. If you specify a bandwidth in the B=
kernelsuboption, then the SPP procedure uses this value as the initial bandwidth. Otherwise, it uses a default bandwidth value that is based
on the suggestion by Illian et al. (2008, p.236). For more information about adaptive kernel estimation, see the section Nonparametric Intensity Estimation.

OUT=SASdataset

specifies the name of a SASdataset to contain the kernel based nonparametric estimates.

GRID(valueNX, valueNY)

specifies a reference grid for computing the kernel estimate, where valueNX represents the number of horizontal divisions and valueNY represents the number of vertical divisions. By default, the SPP procedure uses a grid.

L

performs a test for complete spatial randomness that is based on the L function.

OUTSIM=SASdataset

specifies the name of a SASdataset to contain the results of simulations in distance functions. This option is ignored unless one of the distance functions
is specified in the PROCESS
statement.

PCF<B=value>

performs a test for complete spatial randomness that is based on the pair correlation function (PCF) function. The pair correlation
function is calculated only when you specify EDGECORR=ON
in the PROC SPP statement. You can specify the following suboption:

B=value

specifies the bandwidth value to use in the kernel density estimation inside the pair correlation function. The value must be a nonnegative real number. Otherwise, it is assigned a default value of , where is the CSR average intensity of the point pattern or of the current categorical mark type (Illian et al., 2008, p. 236).

QUADRAT<(<valueNX,valueNY> </DETAILS>)>

performs a test for complete spatial randomness. You can specify valueNX and valueNY to provide a quadrat specification that includes the number of horizontal and vertical divisions. If you do not specify the
number of horizontal and vertical divisions, PROC SPP computes a default quadrat of . By default, the QUADRAT option displays only the Pearson chisquare test for CSR. If you also specify the DETAILS suboption,
then PROC SPP displays the quadrat count in addition to the Pearson residual information.
When you specify an F, G, J, K, L, or PCF processoption (shown in Table 93.5), you can also specify the following distancefunctionoptions.
Table 93.6: Distance Function Options
Option

Description

BYTYPE

Requests categorical mark typewise calculation of distance functions

CROSS

Requests crosstype distance function analysis that is based on the categorical mark that is specified in the MARK=
option

MAXDIST=

Specifies the ending distance for distance functions

MINDIST=

Specifies the starting distance for distance functions

NDIST=

Specifies the number of distances to use for different distance functions

NSIM=

Specifies the number of simulations to compute the CSR envelope

BLOCKS

Specifies the block size for calculation of confidence intervals for distance functions


BYTYPE(ALLvaluelist)

requests distance function calculation by values of the mark variable. This option produces individual distance function calculations
for each mark type. You can specify the following options:

ALL

requests distance function calculation for all available character mark variable values in the DATA=
data set.

valuelist

requests distance function calculation for certain formatted mark variable values, which you specify as quoted strings in
the valuelist.

CROSS=TYPES(valuelist1<,valuelist2>)

requests crosstype distance function analysis between different mark values. For crosstype analysis, you must specify a
mark variable in the point pattern definition by using the MARK=
patternoption. The CROSS= option applies only to any requested distance functions K, L, G, J, or PCF. You must specify the TYPES suboption
as follows:

TYPES(valuelist1<,valuelist2>)

requests crosstype analysis only among types that are specified in valuelist1 and an optional valuelist2. If you specify only valuelist1, then PROC SPP performs crosstype analysis within all the types that are specified in valuelist1. If you also specify the additional valuelist2, PROC SPP performs crosstype analysis across both lists. For valuelist1 and valuelist2, specify quoted strings that correspond to values of the variable that is specified in the MARK=patternoption.

MAXDIST=value  MAX  AUTO  CUT

specifies the option to be used for computing the maximum distance for different distance functions. You can specify the following
options:

value

specifies a value for the maximum distance for performing distance function calculations. The value must be positive and larger than the value of the MINDIST=value
option. You can specify any positive value for the maximum distance. However, values that are too large might produce artifacts
that do not reflect the true underlying process.

MAX

uses the maximum possible distance, based on the suggestion by Baddeley and Turner (2013). The maximum possible distance is calculated as follows:

For the K, L, and PCF functions, the maximum possible distance is calculated as
where is the intensity of the point pattern in the study area and the ranges of x and y are computed over the minimum bounding rectangular window of the study area.

For the F and G functions, the maximum possible distance is calculated as
where is the intensity of the point pattern in the study area and is the minimum bounding rectangular window of the study area.

For the J function, the maximum possible distance is calculated as .

AUTO

uses the maximum possible distance as follows:

for the F and G functions, uses the distance at which the upper confidence limit is greater than 1

for the J and PCF functions, uses the distance at which the variance for the confidence interval is 0

for the K and L functions, uses the distance that is based on the rule in the MAX
.

CUT

uses the maximum distance at certain cutoff values that are recommended by Baddeley (2014). The cutoff values are as follows:

for the F and G functions, the distance at which the F or G value reaches 0.9

for the J function, the distance at which the F or G value in the calculation of the J function reaches 0.9

for the PCF function, the distance that corresponds to the MAX
option that is applied to individual subdivisions of the study area for computing the confidence interval of the PCF statistic

for the K and L functions, the distance that corresponds to theMAX
option that is applied to the entire study area
By default, PROC SPP uses the value of MAXDIST is CUT.

MINDIST=value

specifies a positive number for the minimum distance (or starting) distance for all distance function calculations. The value of this option cannot be more than the value of MAXDIST=
option.

NDIST=value

specifies the number of distance bins with which to compute all the specified distance functions. This is a global option
that applies to all specified distance functions. When you specify a value for this option, the SPP procedure uses this value instead of others for distance function calculations.

NSIM=value

specifies a positive integer for the number of simulations to be used to compute envelopes for the CSR tests in all distance
functions. When you specify this option, it applies to all specified distance functions.

BLOCKS(NX, NY)

specifies the block size that is required for calculating the confidence intervals of distance functions, where NX specifies the number of horizontal blocks and NY specifies the number of vertical blocks. The block size should be neither too small nor too large for this option to behave
reasonably. For more information about estimating the confidence intervals for distance functions see the section Confidence Intervals for Summary Statistics. The default block size is .