SearchNeighborhoodSmoothCircular

Resumen

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

Sintaxis

SearchNeighborhoodSmoothCircular ({radius}, {smoothFactor})

Parámetro	Explicación	Tipo de datos
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

Propiedades

Propiedad	Explicación	Tipo de datos
radius (Lectura y escritura)	The distance, in map units, specifying the length of the radius of the searching circle.	Double
smoothFactor (Lectura y escritura)	Determines how much smoothing will be performed: 0 is no smoothing, and 1 is the maximum amount of smoothing.	Double
nbrType (Sólo lectura)	The neighborhood type: Smooth or Standard.	String

Muestra de código

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)