Densify Sampling Network (Geostatisical Analyst)
Zusammenfassung
Uses a predefined geostatistical kriging layer to determine where new locations are required or which can be removed.
Verwendung
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The input geostatistical layer must be a kriging layer.
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The selection criteria and the are equations they are based upon are listed here:
STDERR = O0(s) O0(s) = Standard Error of Prediction (stderr) STDERR_THRESHOLD = O1(s) O1(s) = stderr(s)(1 - 2 · abs(prob[Z(s) > threshold] - 0.5)) QUARTILE_THRESHOLD = O2(s) O2(s) = (Z0.75(s) - Z0.25(s)) / (prob[Z(s) > threshold])
The STERR_THRESHOLD and 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 QUARTILE_THRESHOLD option should be preferred to STERR_THRESHOLD when you are interested in areas that do not exceed the critical threshold.
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The case might arise where only a single new location is generated when more were requested. This happens when the same new location keeps being selected based on the selection criteria. This can be prevented by specifying a value for the Inhibition distance parameter.
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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.
Syntax
| Parameter | Erläuterung | Datentyp |
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. | String |
threshold (optional) |
The threshold value used to densify the sampling network, applicable only when STDERR_THRESHOLD or QUARTILE_THRESHOLD selection criteria are 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 |
Codebeispiel
Densify a sampling network based on a predefined geostatistical kriging layer.
import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
arcpy.DensifySamplingNetwork_ga("C:/gapyexamples/data/Kriging.lyr", 2,
"C:/gapyexamples/output/outDSN")
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"
# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")
# Execute DensifySamplingNetworks
arcpy.DensifySamplingNetwork_ga(inLayer, numberPoints, outPoints)