Densify Sampling Network (Geostatistical Analyst Tools)

Summary

Uses a predefined geostatistical kriging layer to determine where new monitoring stations should be built. It can also be used to determine which monitoring stations should be removed from an existing network.

Usage

The input geostatistical layer must be a kriging layer.
The case might arise where only a single new location is generated when more were requested. This happens when the same new location continues to be selected based on the selection criteria. This can be prevented by specifying a value for the Inhibition distance parameter. Using an inhibition distance is particularly important when using Lower quartile threshold or Upper quartile threshold (in Python, QUARTILE_THRESHOLD or QUARTILE_THRESHOLD_UPPER) as the selection criteria.
To decide which locations have the least influence on the prediction surface you may use the feature class that was used to create the kriging layer for the Input candidate point features parameter. If some monitoring stations need to be decommissioned, the locations with the least influence are good candidates for removal.

Label	Explanation	Data type
Input geostatistical layer	Input a geostatistical layer resulting from a Kriging model.	Geostatistical Layer
Number of output points	Specify how many sample locations to generate.	Long
Output point feature class	The name of the output feature class.	Feature Class
Selection criteria (Optional)	Methods to densify a sampling network. The Standard error of prediction option will give extra weight to locations where the standard error of prediction is large. The Standard error threshold, Lower quartile threshold, and Upper quartile threshold options are useful when there is a critical threshold value for the variable under study (such as the highest admissible ozone level). The Standard error threshold option will give extra weight to locations whose values are close to the threshold. The Lower quartile threshold option will give extra weight to locations that are least likely to exceed the critical threshold. The Upper quartile threshold option will give extra weight to locations that are most likely to exceed the critical threshold. When the Selection criteria is set to Standard error threshold, Lower quartile threshold, or Upper quartile threshold, the Threshold value parameter will become available, allowing you specify your threshold of interest. The equations for each option are: `Standard error of prediction = stderr Standard error threshold = stderr(s)(1 - 2 · abs(prob[Z(s) > threshold] - 0.5)) Lower quartile threshold = (Z<sub>0.75</sub>(s) - Z<sub>0.25</sub>(s)) · (prob[Z(s) < threshold]) Upper quartile threshold = (Z<sub>0.75</sub>(s) - Z<sub>0.25</sub>(s)) · (prob[Z(s) > threshold])` Standard error of prediction—Standard error of prediction criteria Standard error threshold—Standard error threshold criteria Lower quartile threshold—Lower quartile threshold criteria Upper quartile threshold—Upper quartile threshold criteria	String
Threshold value (Optional)	The threshold value used to densify the sampling network. This parameter is only applicable when Standard error threshold, Lower quartile threshold, or Upper quartile threshold selection criteria is used.	Double
Input weight raster (Optional)	A raster used to determine which locations to weight for preference.	Raster Layer
Input candidate point features (Optional)	Sample locations to pick from.	Feature Layer
Inhibition distance (Optional)	Used to prevent any samples being placed within this distance from each other.	Linear Unit

arcpy.ga.DensifySamplingNetwork(in_geostat_layer, number_output_points, out_feature_class, {selection_criteria}, {threshold}, {in_weight_raster}, {in_candidate_point_features}, {inhibition_distance})

Name	Explanation	Data type
in_geostat_layer	Input a geostatistical layer resulting from a Kriging model.	Geostatistical Layer
number_output_points	Specify how many sample locations to generate.	Long
out_feature_class	The name of the output feature class.	Feature Class
selection_criteria (Optional)	Methods to densify a sampling network. The `STERR` option will give extra weight to locations where the standard error of prediction is large. The `STDERR_THRESHOLD`, `QUARTILE_THRESHOLD`, and `QUARTILE_THRESHOLD_UPPER` options are useful when there is a critical threshold value for the variable under study (such as the highest admissible ozone level). The `STDERR_THRESHOLD` option will give extra weight to locations whose values are close to the threshold. The `QUARTILE_THRESHOLD` option will give extra weight to locations that are least likely to exceed the critical threshold. The `QUARTILE_THRESHOLD_UPPER` option will give extra weight to locations that are most likely to exceed the critical threshold. The equations for each option are: `Standard error of prediction = stderr Standard error threshold = stderr(s)(1 - 2 · abs(prob[Z(s) > threshold] - 0.5)) Lower quartile threshold = (Z<sub>0.75</sub>(s) - Z<sub>0.25</sub>(s)) · (prob[Z(s) < threshold]) Upper quartile threshold = (Z<sub>0.75</sub>(s) - Z<sub>0.25</sub>(s)) · (prob[Z(s) > threshold])` `STDERR`—Standard error of prediction criteria `STDERR_THRESHOLD`—Standard error threshold criteria `QUARTILE_THRESHOLD`—Lower quartile threshold criteria `QUARTILE_THRESHOLD_UPPER`—Upper quartile threshold criteria	String
threshold (Optional)	The threshold value used to densify the sampling network. This parameter is only applicable when Standard error threshold, Lower quartile threshold, or Upper quartile threshold selection criteria is used.	Double
in_weight_raster (Optional)	A raster used to determine which locations to weight for preference.	Raster Layer
in_candidate_point_features (Optional)	Sample locations to pick from.	Feature Layer
inhibition_distance (Optional)	Used to prevent any samples being placed within this distance from each other.	Linear Unit

Code sample

DensifySamplingNetwork example 1 (Python window)

Densify a sampling network based on a predefined geostatistical kriging layer.

import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
arcpy.ga.DensifySamplingNetwork("C:/gapyexamples/data/Kriging.lyr", 2,
                                "C:/gapyexamples/output/outDSN")

DensifySamplingNetwork example 2 (stand-alone script)

Densify a sampling network based on a predefined geostatistical kriging layer.

# Name: DensifySamplingNetwork_Example_02.py
# Description: Densify a sampling network based on a predefined geostatistical
#              kriging layer. It uses, inter alia, the Standard Error of
#              Prediction map to determine where new locations are required.
# Requirements: Geostatistical Analyst extension

# Import system modules
import arcpy

# Set environment settings
arcpy.env.workspace = "C:/gapyexamples/data"

# Set local variables
inLayer = "C:/gapyexamples/data/Kriging.lyr"
numberPoints = 2
outPoints = "C:/gapyexamples/output/outDSN"

# Run DensifySamplingNetworks
arcpy.ga.DensifySamplingNetwork(inLayer, numberPoints, outPoints)

Environments

Current Workspace, Extent, Geographic Transformations, Output Coordinate System, Parallel Processing Factor, Scratch Workspace

Licensing information