Résumé
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})| Paramètre | Explication | Type 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
| Propriété | Explication | Type 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.EmpiricalBayesianKriging_ga("ca_ozone_pts", "OZONE", "outEBK", "C:/gapyexamples/output/ebkout",
10000, "NONE", 50, 0.5, 100,
arcpy.SearchNeighborhoodStandardCircular(300000, 0, 15, 10, "ONE_SECTOR"),
"PREDICTION", "", "", "")
SearchNeighborhoodSmoothCircular (stand-alone script)
An example of SearchNeighborhoodStandardCircular with Empirical Bayesian Kriging to produce an output raster.
# Name: EmpiricalBayesianKriging_Example_02.py
# Description: Bayesian kriging approach whereby many models created around the
# semivariogram model estimated by the restricted maximum likelihood algorithm is used.
# Requirements: Geostatistical Analyst Extension
# Author: ESRI
# 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 = "outEBK"
outRaster = "C:/gapyexamples/output/ebkout"
cellSize = 10000.0
transformation = "NONE"
maxLocalPoints = 50
overlapFactor = 0.5
numberSemivariograms = 100
# Set variables for search neighborhood
radius = 300000
angle = 0
maxNeighbors = 15
minNeighbors = 10
sectorType = "ONE_SECTOR"
searchNeighbourhood = arcpy.SearchNeighborhoodStandardCircular(radius,
angle, maxNeighbors,
minNeighbors, sectorType)
outputType = "PREDICTION"
quantileValue = ""
thresholdType = ""
probabilityThreshold = ""
# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")
# Execute EmpiricalBayesianKriging
arcpy.EmpiricalBayesianKriging_ga(inPointFeatures, zField, outLayer, outRaster,
cellSize, transformation, maxLocalPoints, overlapFactor, numberSemivariograms,
searchNeighbourhood, outputType, quantileValue, thresholdType, probabilityThreshold)