描述
SearchNeighborhoodSmoothCircular 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（仅在使用 INVERSE_MULTIQUADRIC_FUNCTION 关键字的情况下使用）。该类接受输入的搜索圆半径和平滑系数。
语法
SearchNeighborhoodSmoothCircular ({radius}, {smoothFactor})| 参数 | 说明 | 数据类型 |
radius | 以地图单位指定搜索圆的半径长度的距离。 | Double |
smoothFactor | 确定要执行的平滑量：0 为无平滑；1 为最大平滑量。 | Double |
属性
| 属性 | 说明 | 数据类型 |
| radius (可读写) | 以地图单位指定搜索圆的半径长度的距离。 | Double |
| smoothFactor (可读写) | 确定要执行的平滑量：0 为无平滑，1 为最大平滑量。 | Double |
| nbrType (只读) | 邻域类型：平滑或标准。 | String |
代码示例
SearchNeighborhoodSmoothCircular 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",
100000, "NONE", 50, 0.5, 100,
arcpy.SearchNeighborhoodSmoothCircular(300000, 0.5),
"PREDICTION", "", "", "")SearchNeighborhoodSmoothCircular 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
smooth = 0.6
searchNeighbourhood = arcpy.SearchNeighborhoodSmoothCircular(radius, smooth)
outputType = "PREDICTION"
quantileValue = ""
thresholdType = ""
probabilityThreshold = ""
# Execute EmpiricalBayesianKriging
arcpy.EmpiricalBayesianKriging_ga(inPointFeatures, zField, outLayer, outRaster,
cellSize, transformation, maxLocalPoints, overlapFactor, numberSemivariograms,
searchNeighbourhood, outputType, quantileValue, thresholdType, probabilityThreshold)