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。
| 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
使用 SearchNeighborhoodStandard3D 和 Empirical 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", "")
使用 SearchNeighborhoodStandard3D 和 Empirical 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)