サマリー
The SearchNeighborhoodStandard class can be used to define the search neighborhood for IDW, Local Polynomial Interpolation, and Radial Basis Functions.
構文
 SearchNeighborhoodStandard ({majorSemiaxis}, {minorSemiaxis}, {angle}, {nbrMax}, {nbrMin}, {sectorType})| パラメーター | 説明 | データ タイプ | 
| 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 | 
プロパティ
| プロパティ | 説明 | データ タイプ | 
| angle (読み書き) | The angle of the search ellipse. | Double | 
| majorSemiaxis (読み書き) | The distance, in map units, specifying the length of the major semi axis of the ellipse within which data is selected. | Double | 
| minorSemiaxis (読み書き) | The distance, in map units, specifying the length of the minor semi axis of the ellipse within which data is selected. | Double | 
| nbrMax (読み書き) | Maximum number of neighbors, within the search ellipse, to use when making the prediction. | Long | 
| nbrMin (読み書き) | Minimum number of neighbors, within the search ellipse, to use when making the prediction. | Long | 
| nbrType (読み取り専用) | The neighborhood type: Smooth or Standard. | String | 
| sectorType (読み書き) | The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors. | String | 
コードのサンプル
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 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)