KrigingModelUniversal

Disponible avec une licence Spatial Analyst.

Résumé

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.

Syntaxe

 KrigingModelUniversal ({semivariogramType}, {lagSize}, {majorRange}, {partialSill}, {nugget})

Paramètre	Explication	Type de données
semivariogramType	Semivariogram model to be used. LINEARDRIFT—Universal Kriging with linear drift. QUADRATICDRIFT— Universal Kriging with quadratic drift. (La valeur par défaut est 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

Propriétés

Propriété	Explication	Type de données
semivariogramType (Lecture et écriture)	Semivariogram model to be used. LINEARDRIFT—Universal Kriging with linear drift. QUADRATICDRIFT—Universal Kriging with quadratic drift.	String
lagSize (Lecture et écriture)	The lag size to be used in model creation. The default is the output raster cell size.	Double
majorRange (Lecture et écriture)	Represents a distance beyond which there is little or no correlation.	Double
partialSill (Lecture et écriture)	The difference between the nugget and the sill.	Double
nugget (Lecture et écriture)	Represents the error and variation at spatial scales too fine to detect. The nugget effect is seen as a discontinuity at the origin.	Double

Exemple de code

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)

# Execute 
outKrigingUni2 = Kriging(inPointFeature, "ELEVATION", kModelUniversalObj, 2000,
                           RadiusFixed(200000, 10), outVarRaster)

# Save the output 
outKrigingUni2.save("C:/sapyexamples/output/kuniversal2")