SearchNeighborhoodStandardCircular

Summary

The SearchNeighborhoodStandardCircular class can be used to define the search neighborhood for Empirical Bayesian Kriging, IDW, Local Polynomial Interpolation, and Radial Basis Functions.

Syntax

SearchNeighborhoodStandardCircular ({radius}, {angle}, {nbrMax}, {nbrMin}, {sectorType})
 Parameter Explanation Data Type radius The distance, in map units, specifying the length of the radius of the searching circle. Double angle The angle of the search circle. This parameter will only affect the angle of the sectors. Double nbrMax Maximum number of neighbors, within the search ellipse, to use when making the prediction. Long nbrMin Minimum number of neighbors, within the search ellipse, to use when making the prediction. Long sectorType The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors. String

Properties

 Property Explanation Data Type angle(Read and Write) The angle of the search ellipse. Double radius(Read and Write) The distance, in map units, specifying the length of the radius of the searching circle. Double nbrMax(Read and Write) Maximum number of neighbors, within the search ellipse, to use when making the prediction. Long nbrMin(Read and Write) Minimum number of neighbors, within the search ellipse, to use when making the prediction. Long nbrType(Read Only) The neighborhood type: Smooth or Standard. String sectorType(Read and Write) The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors. String

Code sample

SearchNeighborhoodSmoothCircular (Python window)

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

import arcpy
arcpy.EmpiricalBayesianKriging_ga("ca_ozone_pts", "OZONE", "outEBK", "C:/gapyexamples/output/ebkout",
10000, "NONE", 50, 0.5, 100,
arcpy.SearchNeighborhoodStandardCircular(300000, 0, 15, 10, "ONE_SECTOR"),
"PREDICTION", "", "", "")
SearchNeighborhoodSmoothCircular (stand-alone script)

An example of SearchNeighborhoodStandardCircular 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
angle = 0
maxNeighbors = 15
minNeighbors = 10
sectorType = "ONE_SECTOR"