# 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)

# Execute Kriging
outKrigingOrd2 = Kriging(inPointFeature, "ELEVATION", kModelOrdinary, 2000,