SearchNeighborhoodSmoothCircular (arcpy)

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.

Smooth interpolation

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.env.workspace = "C:/gapyexamples/data"
arcpy.LocalPolynomialInterpolation_ga(
    "ca_ozone_pts", "OZONE", "outLPI", "C:/gapyexamples/output/lpiout", "2000",
    "2", arcpy.SearchNeighborhoodSmooth(300000, 300000, 0, 0.5), "QUARTIC", 
    "", "", "", "", "PREDICTION")

SearchNeighborhoodSmoothCircular (stand-alone script)

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

# Name: LocalPolynomialInterpolation_Example_02.py
# Description: Local Polynomial interpolation fits many polynomials, each 
#              within specified overlapping neighborhoods. 
# 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 = "outLPI"
outRaster = "C:/gapyexamples/output/lpiout"
cellSize = 2000.0
power = 2
kernelFunction = "QUARTIC"
bandwidth = ""
useConNumber = ""
conNumber = ""
weightField = ""
outSurface = "PREDICTION"

# Set variables for search neighborhood
majSemiaxis = 300000
minSemiaxis = 300000
angle = 0
smoothFactor = 0.5
searchNeighbourhood = arcpy.SearchNeighborhoodSmooth(majSemiaxis, minSemiaxis,
                                                     angle, smoothFactor)


# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")

# Execute LocalPolynomialInterpolation
arcpy.LocalPolynomialInterpolation_ga(inPointFeatures, zField, outLayer, outRaster,
                                      cellSize, power, searchNeighbourhood,
                                      kernelFunction, bandwidth, useConNumber,
                                      conNumber, weightField, outSurface)