SearchNeighborhoodSmoothCircular—帮助

摘要
语法
属性
代码示例

摘要

SearchNeighborhoodSmoothCircular 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（仅在使用 INVERSE_MULTIQUADRIC_FUNCTION 关键字的情况下使用）。该类接受输入的搜索圆半径和平滑系数。

了解有关平滑插值法的详细信息

语法

SearchNeighborhoodSmoothCircular ({radius}, {smoothFactor})

参数	说明	数据类型
radius	以地图单位指定搜索圆的半径长度的距离。	Double
smoothFactor	确定要执行的平滑量：0 为无平滑；1 为最大平滑量。	Double

属性

属性	说明	数据类型
radius (可读写)	以地图单位指定搜索圆的半径长度的距离。	Double
smoothFactor (可读写)	确定要执行的平滑量：0 为无平滑，1 为最大平滑量。	Double
nbrType (只读)	邻域类型：平滑或标准。	String

代码示例

SearchNeighborhoodSmoothCircular（Python 窗口）

SearchNeighborhoodSmoothCircular 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",
                                  100000, "NONE", 50, 0.5, 100,
                                  arcpy.SearchNeighborhoodSmoothCircular(300000, 0.5),
                                  "PREDICTION", "", "", "")

SearchNeighborhoodSmoothCircular（独立脚本）

SearchNeighborhoodSmoothCircular 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
smooth = 0.6
searchNeighbourhood = arcpy.SearchNeighborhoodSmoothCircular(radius, smooth)
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)