SearchNeighborhoodStandard

Résumé

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

Syntaxe

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

Nom	Explication	Type de données
majorSemiaxis (Facultatif)	The distance, in map units, specifying the length of the major semi axis of the ellipse within which data is selected from.	Double
minorSemiaxis (Facultatif)	The distance, in map units, specifying the length of the minor semi axis of the ellipse within which data is selected from.	Double
angle (Facultatif)	The angle of the search ellipse.	Double
nbrMax (Facultatif)	Maximum number of neighbors, within the search ellipse, to use when making the prediction.	Long
nbrMin (Facultatif)	Minimum number of neighbors, within the search ellipse, to use when making the prediction.	Long
sectorType (Facultatif)	The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors.	String

Propriétés

Nom	Explication	Type de données
angle (Lecture et écriture)	The angle of the search ellipse.	Double
majorSemiaxis (Lecture et écriture)	The distance, in map units, specifying the length of the major semi axis of the ellipse within which data is selected.	Double
minorSemiaxis (Lecture et écriture)	The distance, in map units, specifying the length of the minor semi axis of the ellipse within which data is selected.	Double
nbrMax (Lecture et écriture)	Maximum number of neighbors, within the search ellipse, to use when making the prediction.	Long
nbrMin (Lecture et 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 et écriture)	The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors.	String

Exemple de code

SearchNeighborhoodStandard (Python window)

SearchNeighborhoodStandard with IDW to produce an output raster.

import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
arcpy.ga.IDW("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)

# Run IDW
arcpy.ga.IDW(inPointFeatures, zField, outLayer, outRaster, cellSize,
             power, searchNeighbourhood)