Résumé
The SearchNeighborhoodStandard class can be used to define the search neighborhood for IDW, Local Polynomial Interpolation, and Radial Basis Functions.
Syntaxe
SearchNeighborhoodStandard ({majorSemiaxis}, {minorSemiaxis}, {angle}, {nbrMax}, {nbrMin}, {sectorType})| Paramètre | Explication | Type de données |
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 |
Propriétés
| Propriété | Explication | Type de données |
| angle (Lecture/écriture) | The angle of the search ellipse. | Double |
| majorSemiaxis (Lecture/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/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/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
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)
# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")
# Execute IDW
arcpy.IDW_ga(inPointFeatures, zField, outLayer, outRaster, cellSize,
power, searchNeighbourhood)