SearchNeighborhoodStandardCircular (arcpy)

Récapitulatif

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

Syntaxe

SearchNeighborhoodStandardCircular ({radius}, {angle}, {nbrMax}, {nbrMin}, {sectorType})
ParamètreExplicationType de données
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

Propriétés

PropriétéExplicationType de données
angle
(Lecture/écriture)

The angle of the search ellipse.

Double
radius
(Lecture/écriture)

The distance, in map units, specifying the length of the radius of the searching circle.

Double
nbrMax
(Lecture/écriture)

Maximum number of neighbors, within the search ellipse, to use when making the prediction.

Long
nbrMin
(Lecture/écriture)

Minimum number of neighbors, within the search ellipse, to use when making the prediction.

Long
nbrType
(Lecture seule)

The neighborhood type: Smooth or Standard.

String
sectorType
(Lecture/écriture)

The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors.

String

Exemple de code

SearchNeighborhoodSmoothCircular (Python window)

An example of SearchNeighborhoodStandardCircular 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 SearchNeighborhoodStandardCircular 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)
4/26/2014