# EBK Regression Prediction

Mit der Geostatistical Analyst-Lizenz verfügbar.

## Zusammenfassung

EBK Regression Prediction is a geostatistical interpolation method that uses Empirical Bayesian Kriging with explanatory variable rasters that are known to affect the value of the data that you are interpolating. This approach combines kriging with regression analysis to make predictions that are more accurate than either regression or kriging can achieve on their own.

## Verwendung

• This tool only supports prediction map outputs. To create standard error, quantile, or probability maps, output a geostatistical layer and convert it to a raster (or multiple rasters) using GA Layer To Rasters.

• This kriging method can handle moderately nonstationary input data.

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

• If any of your Input explanatory variable rasters have many NoData cells, the Output geostatistical layer may fail to visualize in the map. This is not a problem, and the calculations have been performed correctly. To visualize the output, convert your geostatistical layer to a raster using GA Layer To Rasters or GA Layer To Grid. You can also choose to output a raster directly from this tool using the Output prediction raster parameter.

• If the Input dependent variable features are 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 What is Empirical Bayesian Kriging help topic.

## Syntax

`EBKRegressionPrediction(in_features, dependent_field, in_explanatory_rasters, out_ga_layer, {out_raster}, {out_diagnostic_feature_class}, {measurement_error_field}, {min_cumulative_variance}, {in_subset_features}, {transformation_type}, {semivariogram_model_type}, {max_local_points}, {overlap_factor}, {number_simulations}, {search_neighborhood})`

## Codebeispiel

EBKRegressionPrediction example 1 (Python window)

Interpolates a point feature class using explanatory variable rasters.

``````import arcpy
arcpy.EBKRegressionPrediction_ga("HousingSales_Points", "SalePrice",
["AREASQFEET", "NUMBATHROOMS", "NUMBEDROOMS","TOTALROOMS"],
"out_ga_layer", None, None, None, 95, None, "LOGEMPIRICAL",
"EXPONENTIAL", 100, 1, 100, None)``````
EBKRegressionPrediction example 2 (stand-alone script)

Interpolates a point feature class using explanatory variable rasters.

``````# Name: EBKRegressionPrediction_Example_02.py
# Description: Interpolates housing prices using EBK Regression Prediction
# Requirements: Geostatistical Analyst Extension
# Author: Esri

# Import system modules
import arcpy

# Set environment settings
arcpy.env.workspace = "C:/gaexamples/data.gdb"

# Set local variables
inDepFeatures = "HousingSales_Points"
inDepField = "SalePrice"
inExplanRasters = ["AREASQFEET", "NUMBATHROOMS", "NUMBEDROOMS","TOTALROOMS"]
outLayer = "outEBKRP_layer"
outRaster = "outEBKRP_raster"
outDiagFeatures = "outEBKRP_features"
inDepMeField = ""
minCumVariance = 97.5
outSubsetFeatures = ""
depTransform = ""
semiVariogram= "K_BESSEL"
maxLocalPoints = 50
overlapFactor = 1
numberSinulations = 200

# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")

# Execute EBKRegressionPrediction
arcpy.EBKRegressionPrediction_ga(inDepFeatures, inDepField, inExplanRasters,
outLayer, outRaster, outDiagFeatures, inDepMeField, minCumVariance,
outSubsetFeatures, depTransform, semiVariogram, maxLocalPoints,
overlapFactor, numberSinulations, searchNeighbourhood)``````

## Lizenzinformationen

• Basic: Erfordert Geostatistical Analyst
• Standard: Erfordert Geostatistical Analyst
• Advanced: Erfordert Geostatistical Analyst