# SearchNeighborhoodSmoothCircular

## Summary

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.

## Syntax

`SearchNeighborhoodSmoothCircular ({radius}, {smoothFactor})`
 Parameter Explanation Data Type 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

## Properties

 Property Explanation Data Type radius(Read and Write) The distance, in map units, specifying the length of the radius of the searching circle. Double smoothFactor(Read and Write) Determines how much smoothing will be performed: 0 is no smoothing, and 1 is the maximum amount of smoothing. Double nbrType(Read Only) The neighborhood type: Smooth or Standard. String

## Code sample

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