Available with Spatial Analyst license.
Summary
Defines the Universal Kriging model. The available model types are Linear with linear drift and Linear with quadratic drift.
Discussion
The KrigingModelUniversal object is used in the Kriging tool.
The Universal Kriging types (Linear with linear drift and Linear with quadratic drift) assume that there is a structural component present and that the local trend varies from one location to another.
Universal Kriging assumes the model:
Z(s) = µ(s) + ε(s)A default value for lagSize is initially set to the default output cell size.
For majorRange, partialSill, and nugget, a default value will be calculated internally if nothing is specified.
Syntax
KrigingModelUniversal ({semivariogramType}, {lagSize}, {majorRange}, {partialSill}, {nugget})| Parameter | Explanation | Data Type |
semivariogramType | Semivariogram model to be used.
(The default value is LINEARDRIFT) | String |
lagSize | The lag size to be used in model creation. The default is the output raster cell size. | Double |
majorRange | Represents a distance beyond which there is little or no correlation. | Double |
partialSill | The difference between the nugget and the sill. | Double |
nugget | Represents the error and variation at spatial scales too fine to detect. The nugget effect is seen as a discontinuity at the origin. | Double |
Properties
| Property | Explanation | Data Type |
| semivariogramType (Read and Write) | Semivariogram model to be used.
| String |
| lagSize (Read and Write) | The lag size to be used in model creation. The default is the output raster cell size. | Double |
| majorRange (Read and Write) | Represents a distance beyond which there is little or no correlation. | Double |
| partialSill (Read and Write) | The difference between the nugget and the sill. | Double |
| nugget (Read and Write) | Represents the error and variation at spatial scales too fine to detect. The nugget effect is seen as a discontinuity at the origin. | Double |
Code Sample
KrigingModelUniversal example 1 (Python window)
Demonstrates how to create a KrigingModelUniversal object and use it in the Kriging tool within the Python window.
import arcpy
from arcpy import env
from arcpy.sa import *
env.workspace = "C:/sapyexamples/data"
kModelUniversal = KrigingModelUniversal("LINEARDRIFT", 70000, 250000, 180000, 34000)
outKrigingUni1 = Kriging("ca_ozone_pts.shp", "ELEVATION", kModelUniversal, 2000, RadiusVariable(),"")
outKrigingUni1.save("C:/sapyexamples/output/kuniversal1")
KrigingModelUniversal example 2 (stand-alone script)
Calculates a Kriging surface using the KrigingModelUniversal object.
# Name: KrigingModelUniversal_Ex_02.py
# Description: Uses the KrigingModelUniversal object to execute the Kriging tool.
# Requirements: Spatial Analyst Extension
# Import system modules
import arcpy
from arcpy import env
from arcpy.sa import *
# Set environment settings
env.workspace = "C:/sapyexamples/data"
# Set local variables
inPointFeature = "ca_ozone_pts.shp"
outVarRaster = "C:/sapyexamples/output/uvariance2"
# Create KrigingModelUniversal Object
lagSize = 70000
majorRange = 250000
partialSill = 180000
nugget = 34000
kModelUniversalObj = KrigingModelUniversal("LINEARDRIFT", lagSize, majorRange,
partialSill, nugget)
# Check out the ArcGIS Spatial Analyst extension license
arcpy.CheckOutExtension("Spatial")
# Execute
outKrigingUni2 = Kriging(inPointFeature, "ELEVATION", kModelUniversalObj, 2000,
RadiusFixed(200000, 10), outVarRaster)
# Save the output
outKrigingUni2.save("C:/sapyexamples/output/kuniversal2")