SearchNeighborhoodStandard3D

摘要

SearchNeighborhoodStandard3D 类可用于为 3D 经验贝叶斯克里金法工具定义三维搜索邻域。

了解有关 3D 模式下的搜索邻域的详细信息

语法

 SearchNeighborhoodStandard3D ({radius}, {nbrMax}, {nbrMin}, {sectorType})
参数说明数据类型
radius

以地图单位指定搜索邻域的半径长度的距离。

Double
nbrMax

要在进行预测时使用的搜索半径内的最大相邻要素数。

Long
nbrMin

要在进行预测时使用的搜索半径内的最小相邻要素数。

Long
sectorType

搜索邻域的扇区类型。搜索邻域可以划分为 1、4、6、8、12 或 20 个扇区。扇区类型均基于正多面体。

  • ONE_SECTOR1 个扇区(球体)
  • FOUR_SECTORS4 个扇区(四面体)
  • SIX_SECTORS6 个扇区(六面体)
  • EIGHT_SECTORS8 个扇区(八面体)
  • TWELVE_SECTORS12 个扇区(十二面体)
  • TWENTY_SECTORS20 个扇区(二十面体)
String

属性

属性说明数据类型
nbrMax
(只读)

搜索邻域的最大相邻要素数。

Long
nbrMin
(只读)

搜索邻域的最小相邻要素数。

Long
radius
(只读)

搜索邻域的半径。

Double
sectorType
(只读)

搜索邻域的扇区类型。

String

代码示例

SearchNeighborhoodStandard3D(Python 窗口)

使用 SearchNeighborhoodStandard3DEmpirical Bayesian Kriging 3D 工具生成地统计图层。

import arcpy
arcpy.ga.EmpiricalBayesianKriging3D("my3DLayer", "Shape.Z", "myValueField", "myGALayer", "METER", "",
                                    "POWER", "NONE", 100, 1, 100, "NONE", "",
                                    "NBRTYPE=Standard3D RADIUS=10000 NBR_MAX=15 NBR_MIN=10 SECTOR_TYPE=ONE_SECTOR",
                                    "", "PREDICTION", 0.5, "EXCEED", "")
SearchNeighborhoodStandard3D(独立脚本)

使用 SearchNeighborhoodStandard3DEmpirical Bayesian Kriging 3D 工具生成地统计图层。

# Name: SearchNeighborhoodStandard3D_Example_02.py
# Description: Interpolates 3D points using a standard 3D neighborhood
# Requirements: Geostatistical Analyst Extension
# Author: Esri

# Import system modules
import arcpy

# Set local variables
in3DPoints = "C:/gapyexamples/input/my3DPoints.shp"
elevationField = "Shape.Z"
valueField = "myValueField"
outGALayer = "myGALayer"
elevationUnit = "METER"
measurementErrorField = "myMEField"
semivariogramModel = "LINEAR"
transformationType = "NONE"
subsetSize = 80
overlapFactor = 1.5
numSimulations = 200
trendRemoval = "FIRST"
elevInflationFactor = 20
radius = 10000
maxNeighbors = 15
minNeighbors = 10
sectorType = "FOUR_SECTORS"
searchNeighborhood = arcpy.SearchNeighborhoodStandard3D(radius, maxNeighbors, minNeighbors, sectorType)
outputElev = 1000
outputType = "PREDICTION"

# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")

# Execute Empirical Bayesian Kriging 3D
arcpy.ga.EmpiricalBayesianKriging3D(in3DPoints, elevationField, valueField, outGALayer, elevationUnit, myMEField,
                                    semivariogramModel, transformationType, subsetSize, overlapFactor, numSimulations,
                                    trendRemoval, elevInflationFactor, searchNeighborhood, outputElev, outputType)