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})
| Name | Explanation | Data type |
|---|---|---|
|
semivariogramType (Optional) |
Semivariogram model to be used.
The default value is SPHERICAL. |
String |
|
lagSize (Optional) |
The lag size to be used in model creation. The default is the output raster cell size. |
Double |
|
majorRange (Optional) |
Represents a distance beyond which there is little or no correlation. |
Double |
|
partialSill (Optional) |
The difference between the nugget and the sill. |
Double |
|
nugget (Optional) |
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
| Name | Explanation | Data type |
|---|---|---|
|
semivariogramType (Read and Write) |
Semivariogram model to be used.
|
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 run 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)
# Run Kriging
outKrigingOrd2 = Kriging(inPointFeature, "ELEVATION", kModelOrdinary, 2000,
RadiusFixed(200000, 10), outVarRaster)
# Save the output
outKrigingOrd2.save("C:/sapyexamples/output/kordinary2")