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SearchNeighborhoodStandard

Zusammenfassung

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

Learn more about search neighborhoods

Syntax

 SearchNeighborhoodStandard ({majorSemiaxis}, {minorSemiaxis}, {angle}, {nbrMax}, {nbrMin}, {sectorType})
ParameterErklärungDatentyp
majorSemiaxis

The distance, in map units, specifying the length of the major semi axis of the ellipse within which data is selected from.

Double
minorSemiaxis

The distance, in map units, specifying the length of the minor semi axis of the ellipse within which data is selected from.

Double
angle

The angle of the search ellipse.

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

Eigenschaften

EigenschaftErklärungDatentyp
angle
(Lesen und schreiben)

The angle of the search ellipse.

Double
majorSemiaxis
(Lesen und schreiben)

The distance, in map units, specifying the length of the major semi axis of the ellipse within which data is selected.

Double
minorSemiaxis
(Lesen und schreiben)

The distance, in map units, specifying the length of the minor semi axis of the ellipse within which data is selected.

Double
nbrMax
(Lesen und schreiben)

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

Long
nbrMin
(Lesen und schreiben)

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

Long
nbrType
(Nur lesen)

The neighborhood type: Smooth or Standard.

String
sectorType
(Lesen und schreiben)

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

String

Codebeispiel

SearchNeighborhoodStandard (Python window)

SearchNeighborhoodStandard with IDW to produce an output raster.

import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
arcpy.IDW_ga("ca_ozone_pts", "OZONE", "outIDW", "C:/gapyexamples/output/idwout", "2000", "2",
             arcpy.SearchNeighborhoodStandard(300000, 300000, 0, 15, 10, "ONE_SECTOR"), "")
SearchNeighborhoodStandard (stand-alone script)

SearchNeighborhoodStandard with IDW to produce an output raster.

# Name: InverseDistanceWeighting_Example_02.py
# Description: Interpolate a series of point features onto a rectangular raster
#              using Inverse Distance Weighting (IDW).
# 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 = "outIDW"
outRaster = "C:/gapyexamples/output/idwout"
cellSize = 2000.0
power = 2

# Set variables for search neighborhood
majSemiaxis = 300000
minSemiaxis = 300000
angle = 0
maxNeighbors = 15
minNeighbors = 10
sectorType = "ONE_SECTOR"
searchNeighbourhood = arcpy.SearchNeighborhoodStandard(majSemiaxis, minSemiaxis,
                                                       angle, maxNeighbors,
                                                       minNeighbors, sectorType)

# Execute IDW
arcpy.IDW_ga(inPointFeatures, zField, outLayer, outRaster, cellSize, 
             power, searchNeighbourhood)