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)