SearchNeighborhoodSmoothCircular

サマリー

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

構文

SearchNeighborhoodSmoothCircular ({radius}, {smoothFactor})

パラメーター	説明	データ タイプ
radius	The distance, in map units, specifying the length of the radius of the searching circle.	Double
smoothFactor	Determines how much smoothing will be performed. 0 is no smoothing; 1 is the maximum amount of smoothing.	Double

プロパティ

プロパティ	説明	データ タイプ
radius (読み書き)	The distance, in map units, specifying the length of the radius of the searching circle.	Double
smoothFactor (読み書き)	Determines how much smoothing will be performed: 0 is no smoothing, and 1 is the maximum amount of smoothing.	Double
nbrType (読み取り専用)	The neighborhood type: Smooth or Standard.	String

コードのサンプル

SearchNeighborhoodSmoothCircular (Python window)

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

import arcpy
arcpy.EmpiricalBayesianKriging_ga("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
# Author: ESRI

# 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 = ""

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