KrigingModelOrdinary

摘要

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

讨论

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.

语法

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

参数	说明	数据类型
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. (默认值为 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

属性

属性	说明	数据类型
semivariogramType （可读写）	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 （可读写）	The lag size to be used in model creation. The default is the output raster cell size.	Double
majorRange （可读写）	Represents a distance beyond which there is little or no correlation.	Double
partialSill （可读写）	The difference between the nugget and the sill.	Double
nugget （可读写）	Represents the error and variation at spatial scales too fine to detect. The nugget effect is seen as a discontinuity at the origin.	Double

代码示例

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")