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KrigingModelOrdinary

サマリー

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

ディスカッション

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.

構文

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

名前 説明 データ タイプ

semivariogramType

(オプション)

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

(オプション)

The lag size to be used in model creation. The default is the output raster cell size.

Double

majorRange

(オプション)

Represents a distance beyond which there is little or no correlation.

Double

partialSill

(オプション)

The difference between the nugget and the sill.

Double

nugget

(オプション)

Represents the error and variation at spatial scales too fine to detect. The nugget effect is seen as a discontinuity at the origin.

Double

プロパティ

名前 説明 データ タイプ

semivariogramType

(読み取り/書き込み)

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

(読み取り/書き込み)

The lag size to be used in model creation. The default is the output raster cell size.

Double

majorRange

(読み取り/書き込み)

Represents a distance beyond which there is little or no correlation.

Double

partialSill

(読み取り/書き込み)

The difference between the nugget and the sill.

Double

nugget

(読み取り/書き込み)

Represents the error and variation at spatial scales too fine to detect. The nugget effect is seen as a discontinuity at the origin.

Double

コードのサンプル

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 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")