SearchNeighborhoodSmoothCircular

Résumé

The SearchNeighborhoodSmoothCircular class can be used to define the search neighborhood for Empirical Bayesian Kriging, IDW, Local Polynomial Interpolation, and Radial Basis Functions (only when the INVERSE_MULTIQUADRIC_FUNCTION keyword is used). The class accepts inputs for the radius of the searching circle and a smoothing factor.

Learn more about smooth interpolation

Syntaxe

SearchNeighborhoodSmoothCircular({radius}, {smoothFactor})

Nom	Explication	Type de données
radius (Facultatif)	The distance, in map units, specifying the length of the radius of the searching circle.	Double
smoothFactor (Facultatif)	Determines how much smoothing will be performed. 0 is no smoothing; 1 is the maximum amount of smoothing.	Double

Propriétés

Nom	Explication	Type de données
radius (Lecture et écriture)	The distance, in map units, specifying the length of the radius of the searching circle.	Double
smoothFactor (Lecture et écriture)	Determines how much smoothing will be performed: 0 is no smoothing, and 1 is the maximum amount of smoothing.	Double
nbrType (Lecture seule)	The neighborhood type: Smooth or Standard.	String

Exemple de code

SearchNeighborhoodSmoothCircular (Python window)

An example of SearchNeighborhoodSmoothCircular with Empirical Bayesian Kriging to produce an output raster.

import arcpy
arcpy.ga.EmpiricalBayesianKriging("ca_ozone_pts", "OZONE", "outEBK", "C:/gapyexamples/output/ebkout",
                                  100000, "NONE", 50, 0.5, 100,
                                  arcpy.SearchNeighborhoodSmoothCircular(300000, 0.5),
                                  "PREDICTION", "", "", "")

SearchNeighborhoodSmoothCircular (stand-alone script)

An example of SearchNeighborhoodSmoothCircular with Empirical Bayesian Kriging to produce an output raster.

# Name: EmpiricalBayesianKriging_Example_02.py
# Description: Bayesian kriging approach whereby many models created around the
#   semivariogram model estimated by the restricted maximum likelihood algorithm is used.
# Requirements: Geostatistical Analyst extension

# Import system modules
import arcpy

# Set environment settings
arcpy.env.workspace = "C:/gapyexamples/data"

# Set local variables
inPointFeatures = "ca_ozone_pts.shp"
zField = "ozone"
outLayer = "outEBK"
outRaster = "C:/gapyexamples/output/ebkout"
cellSize = 10000.0
transformation = "NONE"
maxLocalPoints = 50
overlapFactor = 0.5
numberSemivariograms = 100
# Set variables for search neighborhood
radius = 300000
smooth = 0.6
searchNeighbourhood = arcpy.SearchNeighborhoodSmoothCircular(radius, smooth)
outputType = "PREDICTION"
quantileValue = ""
thresholdType = ""
probabilityThreshold = ""

# Run EmpiricalBayesianKriging
arcpy.ga.EmpiricalBayesianKriging(inPointFeatures, zField, outLayer, outRaster,
                                  cellSize, transformation, maxLocalPoints, overlapFactor, numberSemivariograms,
                                  searchNeighbourhood, outputType, quantileValue, thresholdType, probabilityThreshold)