SearchNeighborhoodStandardCircular

Resumen

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

Learn more about search neighborhoods

Sintaxis

SearchNeighborhoodStandardCircular ({radius}, {angle}, {nbrMax}, {nbrMin}, {sectorType})

Parámetro	Explicación	Tipo de datos
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

Propiedades

Propiedad	Explicación	Tipo de datos
angle (Lectura y escritura)	The angle of the search ellipse.	Double
radius (Lectura y escritura)	The distance, in map units, specifying the length of the radius of the searching circle.	Double
nbrMax (Lectura y escritura)	Maximum number of neighbors, within the search ellipse, to use when making the prediction.	Long
nbrMin (Lectura y escritura)	Minimum number of neighbors, within the search ellipse, to use when making the prediction.	Long
nbrType (Sólo lectura)	The neighborhood type: Smooth or Standard.	String
sectorType (Lectura y escritura)	The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors.	String

Muestra de código

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
radius = 300000
angle = 0
maxNeighbors = 15
minNeighbors = 10
sectorType = "ONE_SECTOR"
searchNeighbourhood = arcpy.SearchNeighborhoodStandardCircular(radius,
                                                       angle, maxNeighbors,
                                                       minNeighbors, sectorType)
outputType = "PREDICTION"
quantileValue = ""
thresholdType = ""
probabilityThreshold = ""

# Execute EmpiricalBayesianKriging
arcpy.EmpiricalBayesianKriging_ga(inPointFeatures, zField, outLayer, outRaster,
                                  cellSize, transformation, maxLocalPoints, overlapFactor, numberSemivariograms,
                                  searchNeighbourhood, outputType, quantileValue, thresholdType, probabilityThreshold)