# 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.

## 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

 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