SearchNeighborhoodStandardCircular

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。

了解有关搜索邻域的详细信息

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)	搜索椭圆的角度。	Double
radius (可读写)	以地图单位指定搜索圆的半径长度的距离。	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)	搜索椭圆可分为 1 个，4 个，4 个且偏移为 45º，或 8 个分区。	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 = ""

# Execute EmpiricalBayesianKriging
arcpy.EmpiricalBayesianKriging_ga(inPointFeatures, zField, outLayer, outRaster,
                                  cellSize, transformation, maxLocalPoints, overlapFactor, numberSemivariograms,
                                  searchNeighbourhood, outputType, quantileValue, thresholdType, probabilityThreshold)