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	The distance, in map units, specifying the length of the radius of the searching circle.	Double
angle	The angle of the search circle. This parameter will only affect the angle of the sectors.	Double
nbrMax	Maximum number of neighbors, within the search ellipse, to use when making the prediction.	Long
nbrMin	Minimum number of neighbors, within the search ellipse, to use when making the prediction.	Long
sectorType	The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors.	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)