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 |
Ejemplo de código
SearchNeighborhoodSmoothCircular (Python window)
An example of SearchNeighborhoodSmoothCircular with Empirical Bayesian Kriging to produce an output raster.
import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
arcpy.LocalPolynomialInterpolation_ga(
"ca_ozone_pts", "OZONE", "outLPI", "C:/gapyexamples/output/lpiout", "2000",
"2", arcpy.SearchNeighborhoodSmooth(300000, 300000, 0, 0.5), "QUARTIC",
"", "", "", "", "PREDICTION")
SearchNeighborhoodSmoothCircular (stand-alone script)
An example of SearchNeighborhoodSmoothCircular with Empirical Bayesian Kriging to produce an output raster.
# Name: LocalPolynomialInterpolation_Example_02.py
# Description: Local Polynomial interpolation fits many polynomials, each
# within specified overlapping neighborhoods.
# 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 = "outLPI"
outRaster = "C:/gapyexamples/output/lpiout"
cellSize = 2000.0
power = 2
kernelFunction = "QUARTIC"
bandwidth = ""
useConNumber = ""
conNumber = ""
weightField = ""
outSurface = "PREDICTION"
# Set variables for search neighborhood
majSemiaxis = 300000
minSemiaxis = 300000
angle = 0
smoothFactor = 0.5
searchNeighbourhood = arcpy.SearchNeighborhoodSmooth(majSemiaxis, minSemiaxis,
angle, smoothFactor)
# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")
# Execute LocalPolynomialInterpolation
arcpy.LocalPolynomialInterpolation_ga(inPointFeatures, zField, outLayer, outRaster,
cellSize, power, searchNeighbourhood,
kernelFunction, bandwidth, useConNumber,
conNumber, weightField, outSurface)