Empirical Bayesian Kriging (Geostatisical Analyst)
Summary
Empirical Bayesian Kriging is an interpolation method that accounts for the error in estimating the underlying semivariogram through repeated simulations.
Usage
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This kriging method can handle moderately nonstationary input data.
Only Standard Circular and Smooth Circular Search neighborhoods are allowed for this interpolation method.
A Smooth Circular 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.
Syntax
| Parameter | Explanation | Data Type |
in_features |
The input point features containing the z-values to be interpolated. | Feature Layer |
z_field |
Field that holds a height or magnitude value for each point. This can be a numeric field or the Shape field if the input features contain z-values or m-values. | Field |
out_ga_layer (Optional) |
The geostatistical layer produced. This layer is required output only if no output raster is requested. | Geostatistical Layer |
out_raster (Optional) |
The output raster. This raster is required output only if no output geostatistical layer is requested. | Raster Dataset |
cell_size (Optional) |
The cell size at which the output raster will be created. This value can be explicitly set under Raster Analysis from the Environment Settings. If not set, it is the shorter of the width or the height of the extent of the input point features, in the input spatial reference, divided by 250. | Analysis Cell Size |
transformation_type (Optional) |
Type of transformation to be applied to the input data.
| String |
max_local_points (Optional) |
The input data will automatically be divided into groups that do not have more than this number of points. | Long |
overlap_factor (Optional) |
A factor representing the degree of overlap between local models (also called subsets). Each input point can fall into several subsets, and the overlap factor specifies the average number of subsets that each point will fall into. A high value of the overlap factor makes the output surface smoother, but it also increases processing time. Typical values vary between 0.01 and 5. | Double |
number_semivariograms (Optional) |
The number of simulated semivariograms. | Long |
search_neighborhood (Optional) |
Defines which surrounding points will be used to control the output. Standard is the default. This is a Search Neighborhood class SearchNeighborhoodStandardCircular and SearchNeighborhoodSmoothCircular. StandardCircular
SmoothCircular
| Geostatistical Search Neighborhood |
output_type (Optional) |
Surface type to store the interpolation results.
| String |
quantile_value (Optional) |
The quantile value for which the output raster will be generated. | Double |
threshold_type (Optional) |
Determines whether the probability values exceed the threshold value or not.
| String |
probability_threshold (Optional) |
The probability threshold value. If left empty, the median of the input data will be used. | Double |
Code Sample
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", "", "", "")
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 = "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)