KrigingModelOrdinary

Summary

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

Discussion

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.

Syntax

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

Parameter	Explanation	Data Type
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. (The default value is 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

Properties

Property	Explanation	Data Type
semivariogramType (Read and Write)	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 (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

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")