摘要
SearchNeighborhoodStandardCircular 类可用于定义以下邻域搜索方法：经验贝叶斯克里金法、反距离权重法、局部多项式插值法和径向基函数插值法。
语法
SearchNeighborhoodStandardCircular ({radius}, {angle}, {nbrMax}, {nbrMin}, {sectorType})| 参数 | 说明 | 数据类型 |
radius | 以地图单位表示的距离，用于指定搜索圆的半径长度。 | Double |
angle | 搜索圆的角度。此参数仅影响扇区角度。 | Double |
nbrMax | 要在进行预测时使用的搜索椭圆内的最大相邻要素数。 | Long |
nbrMin | 要在进行预测时使用的搜索椭圆内的最小相邻要素数。 | Long |
sectorType | 搜索椭圆可分为 1 个扇区，4 个扇区，4 个且偏移为 45º 的扇区，或 8 个扇区。 | String |
属性
| 属性 | 说明 | 数据类型 |
| angle （可读写） | The angle of the search ellipse. | Double |
| radius （可读写） | The distance, in map units, specifying the length of the radius of the searching circle. | Double |
| nbrMax （可读写） | 要在进行预测时使用的搜索椭圆内的最大相邻要素数。 | Long |
| nbrMin （可读写） | 要在进行预测时使用的搜索椭圆内的最小相邻要素数。 | Long |
| nbrType （只读） | 邻域类型：平滑或标准。 | String |
| sectorType （可读写） | The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors. | String |
代码示例
SearchNeighborhoodSmoothCircular（Python 窗口）
SearchNeighborhoodStandardCircular 与经验贝叶斯克里金法相结合生成输出栅格的示例。
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（独立脚本）
SearchNeighborhoodStandardCircular 与经验贝叶斯克里金法相结合生成输出栅格的示例。
# 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)