KrigingModelOrdinary

Resumen

Defines the Ordinary Kriging model. The available model types are Spherical, Circular, Exponential, Gaussian, and Linear.

Debate

The KrigingModelOrdinary object is used in the Kriging tool.

Ordinary Kriging assumes the model:

 Z(s) = µ + ε(s)

The default value for lagSize is set to the default output cell size.

For majorRange, partialSill, and nugget, a default value will be calculated internally if nothing is specified.

Sintaxis

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

Parámetro	Explicación	Tipo de datos
semivariogramType	Semivariogram model to be used. SPHERICAL —Spherical semivariogram model. CIRCULAR — Circular semivariogram model. EXPONENTIAL — Exponential semivariogram model. GAUSSIAN — Gaussian (or normal distribution) semivariogram model. LINEAR —Linear semivariogram model with a sill. (El valor predeterminado es SPHERICAL)	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

Propiedades

Propiedad	Explicación	Tipo de datos
semivariogramType (Lectura y escritura)	Semivariogram model to be used. SPHERICAL—Spherical semivariogram model. CIRCULAR—Circular semivariogram model. EXPONENTIAL—Exponential semivariogram model. GAUSSIAN—Gaussian (or normal distribution) semivariogram model. LINEAR—Linear semivariogram model with a sill.	String
lagSize (Lectura y escritura)	The lag size to be used in model creation. The default is the output raster cell size.	Double
majorRange (Lectura y escritura)	Represents a distance beyond which there is little or no correlation.	Double
partialSill (Lectura y escritura)	The difference between the nugget and the sill.	Double
nugget (Lectura y escritura)	Represents the error and variation at spatial scales too fine to detect. The nugget effect is seen as a discontinuity at the origin.	Double

Ejemplo de código

KrigingModelOrdinary example 1 (Python window)

Demonstrates how to create a KrigingModelOrdinary 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"
kModelOrdinary = KrigingModelOrdinary("CIRCULAR", 70000, 250000, 180000, 34000)
outKrigingOrd1 = Kriging("ca_ozone_pts.shp", "ELEVATION", kModelOrdinary, 2000, RadiusVariable(),"")
outKrigingOrd1.save("C:/sapyexamples/output/kordinary1")

KrigingModelOrdinary example 2 (stand-alone script)

Calculates a Kriging surface using the KrigingModelOrdinary object.

# Name: KrigingModelOrdinary_Ex_02.py
# Description: Uses the KrigingModelOrdinary 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/ovariance2"

# Create KrigingModelOrdinary Object
lagSize = 70000
majorRange = 250000
partialSill = 180000
nugget = 34000
kModelOrdinary = KrigingModelOrdinary("CIRCULAR", lagSize, majorRange,
                                         partialSill, nugget)

# Check out the ArcGIS Spatial Analyst extension license
arcpy.CheckOutExtension("Spatial")

# Execute Kriging
outKrigingOrd2 = Kriging(inPointFeature, "ELEVATION", kModelOrdinary, 2000,
                     RadiusFixed(200000, 10), outVarRaster)

# Save the output 
outKrigingOrd2.save("C:/sapyexamples/output/kordinary2")