KrigingModelUniversal

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. LINEARDRIFT —Universal Kriging with linear drift. QUADRATICDRIFT — Universal Kriging with quadratic drift. (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. LINEARDRIFT —Universal Kriging with linear drift. QUADRATICDRIFT —Universal Kriging with quadratic drift.	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")