Resumen
The SearchNeighborhoodSmoothCircular class can be used to define the search neighborhood for Empirical Bayesian Kriging, IDW, Local Polynomial Interpolation, and Radial Basis Functions (only when the INVERSE_MULTIQUADRIC_FUNCTION keyword is used). The class accepts inputs for the radius of the searching circle and a smoothing factor.
Sintaxis
SearchNeighborhoodSmoothCircular ({radius}, {smoothFactor})| Parámetro | Explicación | Tipo de datos |
radius | The distance, in map units, specifying the length of the radius of the searching circle. | Double |
smoothFactor | Determines how much smoothing will be performed. 0 is no smoothing; 1 is the maximum amount of smoothing. | Double |
Propiedades
| Propiedad | Explicación | Tipo de datos |
| radius (Lectura y escritura) | The distance, in map units, specifying the length of the radius of the searching circle. | Double |
| smoothFactor (Lectura y escritura) | Determines how much smoothing will be performed: 0 is no smoothing, and 1 is the maximum amount of smoothing. | Double |
| nbrType (Sólo lectura) | The neighborhood type: Smooth or Standard. | String |
Muestra de código
SearchNeighborhoodSmoothCircular (Python window)
An example of SearchNeighborhoodSmoothCircular with Empirical Bayesian Kriging to produce an output raster.
import arcpy
arcpy.EmpiricalBayesianKriging_ga("ca_ozone_pts", "OZONE", "outEBK", "C:/gapyexamples/output/ebkout",
100000, "NONE", 50, 0.5, 100,
arcpy.SearchNeighborhoodSmoothCircular(300000, 0.5),
"PREDICTION", "", "", "")
SearchNeighborhoodSmoothCircular (stand-alone script)
An example of SearchNeighborhoodSmoothCircular 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
smooth = 0.6
searchNeighbourhood = arcpy.SearchNeighborhoodSmoothCircular(radius, smooth)
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)