サマリ
The SearchNeighborhoodStandardCircular class can be used to define the search neighborhood for Empirical Bayesian Kriging, IDW, Local Polynomial Interpolation, and Radial Basis Functions.
構文
SearchNeighborhoodStandardCircular ({radius}, {angle}, {nbrMax}, {nbrMin}, {sectorType})| パラメータ | 説明 | データ タイプ |
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 |
特性
| 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 |
| radius (読み書き) | The distance, in map units, specifying the length of the radius of the searching circle. | 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
SearchNeighborhoodSmoothCircular (Python window)
An example of SearchNeighborhoodStandardCircular 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 SearchNeighborhoodStandardCircular 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)