# Empirical Bayesian Kriging

Disponible con una licencia de Geostatistical Analyst.

## Resumen

Empirical Bayesian kriging is an interpolation method that accounts for the error in estimating the underlying semivariogram through repeated simulations.

What is Empirical Bayesian Kriging?

## Uso

• This kriging method can handle moderately nonstationary input data.

• Only Standard Circular and Smooth Circular Search neighborhoods are allowed for this interpolation method.

• The Smooth Circular option for Search neighborhood will substantially increase the execution time.

• The larger the Maximum number of points in each local model and Local model overlap factor values, the longer the execution time. Applying a Data transformation will also significantly increase execution time.

• To avoid running out of memory, the software may limit the number of CPU cores that can be used for parallel processing.

• If the input data is in a geographic coordinate system, all distances will be calculated using chordal distances. For more information on chordal distances, see the Distance calculations for data in geographic coordinates section of the on the What is Empirical Bayesian Kriging help topic.

## Sintaxis

`EmpiricalBayesianKriging_ga (in_features, z_field, {out_ga_layer}, {out_raster}, {cell_size}, {transformation_type}, {max_local_points}, {overlap_factor}, {number_semivariograms}, {search_neighborhood}, {output_type}, {quantile_value}, {threshold_type}, {probability_threshold}, {semivariogram_model_type})`

## Muestra de código

EmpiricalBayesianKriging example 1 (Python window)

Interpolate a series of point features onto a raster.

``````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", "", "", "", "LINEAR")``````
EmpiricalBayesianKriging example 2 (stand-alone script)

Interpolate a series of point features onto a raster.

``````# 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 = "EMPIRICAL"
maxLocalPoints = 50
overlapFactor = 0.5
numberSemivariograms = 100
# Set variables for search neighborhood
smooth = 0.6
outputType = "PREDICTION"
quantileValue = ""
thresholdType = ""
probabilityThreshold = ""
semivariogram = "K_BESSEL"

# Execute EmpiricalBayesianKriging
arcpy.EmpiricalBayesianKriging_ga(inPointFeatures, zField, outLayer, outRaster,
cellSize, transformation, maxLocalPoints, overlapFactor, numberSemivariograms,
searchNeighbourhood, outputType, quantileValue, thresholdType, probabilityThreshold,
semivariogram)``````

## Información sobre licencias

• Basic: Requiere Geostatistical Analyst
• Standard: Requiere Geostatistical Analyst