SearchNeighborhoodStandard3D

Summary

The SearchNeighborhoodStandard3D class can be used to define the three dimensional search neighborhood for the Empirical Bayesian Kriging 3D tool.

Learn more about search neighborhoods in 3D

Syntax

 SearchNeighborhoodStandard3D ({radius}, {nbrMax}, {nbrMin}, {sectorType})

Parameter	Explanation	Data Type
radius	The distance, in map units, specifying the length of the radius of the search neighborhood.	Double
nbrMax	The maximum number of neighbors, within the search radius, to use when making the prediction.	Long
nbrMin	The minimum number of neighbors, within the search radius, to use when making the prediction.	Long
sectorType	The sector type of the search neighborhood. The search neighborhood can be divided into 1, 4, 6, 8, 12, or 20 sectors. Each sector type is based on a Platonic solid. ONE_SECTOR—1 Sector (Sphere) FOUR_SECTORS—4 Sectors (Tetrahedron) SIX_SECTORS—6 Sectors (Cube) EIGHT_SECTORS—8 Sectors (Octahedron) TWELVE_SECTORS—12 Sectors (Dodecahedron) TWENTY_SECTORS—20 Sectors (Icosahedron)	String

Properties

Property	Explanation	Data Type
nbrMax (Read Only)	The maximum number of neighbors of the search neighborhood.	Long
nbrMin (Read Only)	The minimum number of neighbors of the search neighborhood.	Long
radius (Read Only)	The radius of the search neighborhood.	Double
sectorType (Read Only)	The sector type of the search neighborhood.	String

Code sample

SearchNeighborhoodStandard3D (Python window)

Use SearchNeighborhoodStandard3D with the Empirical Bayesian Kriging 3D tool to produce a geostatistical layer.

import arcpy
arcpy.ga.EmpiricalBayesianKriging3D("my3DLayer", "Shape.Z", "myValueField", "myGALayer", "METER", "",
                                    "POWER", "NONE", 100, 1, 100, "NONE", "",
                                    "NBRTYPE=Standard3D RADIUS=10000 NBR_MAX=15 NBR_MIN=10 SECTOR_TYPE=ONE_SECTOR",
                                    "", "PREDICTION", 0.5, "EXCEED", "")

SearchNeighborhoodStandard3D (stand-alone script)

Use SearchNeighborhoodStandard3D with the Empirical Bayesian Kriging 3D tool to produce a geostatistical layer.

# Name: SearchNeighborhoodStandard3D_Example_02.py
# Description: Interpolates 3D points using a standard 3D neighborhood
# Requirements: Geostatistical Analyst Extension
# Author: Esri

# Import system modules
import arcpy

# Set local variables
in3DPoints = "C:/gapyexamples/input/my3DPoints.shp"
elevationField = "Shape.Z"
valueField = "myValueField"
outGALayer = "myGALayer"
elevationUnit = "METER"
measurementErrorField = "myMEField"
semivariogramModel = "LINEAR"
transformationType = "NONE"
subsetSize = 80
overlapFactor = 1.5
numSimulations = 200
trendRemoval = "FIRST"
elevInflationFactor = 20
radius = 10000
maxNeighbors = 15
minNeighbors = 10
sectorType = "FOUR_SECTORS"
searchNeighborhood = arcpy.SearchNeighborhoodStandard3D(radius, maxNeighbors, minNeighbors, sectorType)
outputElev = 1000
outputType = "PREDICTION"

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

# Execute Empirical Bayesian Kriging 3D
arcpy.ga.EmpiricalBayesianKriging3D(in3DPoints, elevationField, valueField, outGALayer, elevationUnit, myMEField,
                                    semivariogramModel, transformationType, subsetSize, overlapFactor, numSimulations,
                                    trendRemoval, elevInflationFactor, searchNeighborhood, outputElev, outputType)