Densify Sampling Network (Geostatisical Analyst)
Récapitulatif
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.
Utilisation
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The input geostatistical layer must be a kriging layer.
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The selection criteria and the equations they are based upon are listed here:
STDERR = Standard Error of Prediction (stderr) STDERR_THRESHOLD = stderr(s)(1 - 2 · abs(prob[Z(s) > threshold] - 0.5)) QUARTILE_THRESHOLD = (Z0.75(s) - Z0.25(s)) · (prob[Z(s) < threshold]) QUARTILE_THRESHOLD_UPPER = (Z0.75(s) - Z0.25(s)) · (prob[Z(s) > threshold])
The STERR_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 STERR_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.
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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 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 QUARTILE_THRESHOLD or QUARTILE_THRESHOLD_UPPER as the selection criteria.
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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. If some monitoring stations need to be decommissioned, the locations with the least influence are good candidates for removal.
Syntaxe
| Paramètre | Explication | Type de données |
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 (Facultatif) |
Methods to densify a sampling network. | String |
threshold (Facultatif) |
The threshold value used to densify the sampling network, applicable only when STDERR_THRESHOLD, QUARTILE_THRESHOLD, or QUARTILE_THRESHOLD_UPPER selection criteria is used. | Double |
in_weight_raster (Facultatif) |
A raster used to determine which locations to weight for preference. | Raster Layer |
in_candidate_point_features (Facultatif) |
Sample locations to pick from. | Feature Layer |
inhibition_distance (Facultatif) |
Used to prevent any samples being placed within this distance from each other. | Linear unit |
Exemple de code
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)