Available with Spatial Analyst license.

## 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

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")
```

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,
RadiusFixed(200000, 10), outVarRaster)
# Save the output
outKrigingOrd2.save("C:/sapyexamples/output/kordinary2")
```