Available with 3D Analyst license.
Summary
Interpolates a raster surface from points using kriging.
Usage
Kriging is a processor-intensive process. The speed of execution is dependent on the number of points in the input dataset and the size of the search window.
Low values within the optional output variance of prediction raster indicate a high degree of confidence in the predicted value. High values may indicate a need for more data points.
The Universal kriging types assume that there is a structural component present and that the local trend varies from one location to another.
The Advanced Parameters allow control of the semivariogram used for kriging. A default value for Lag size is initially set to the default output cell size. For Major range, Partial sill, and Nugget, a default value will be calculated internally if nothing is specified.
The optional output variance of prediction raster contains the kriging variance at each output raster cell. Assuming the kriging errors are normally distributed, there is a 95.5 percent probability that the actual z-value at the cell is the predicted raster value, plus or minus two times the square root of the value in the variance raster.
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Some input datasets may have several points with the same x,y coordinates. If the values of the points at the common location are the same, they are considered duplicates and have no effect on the output. If the values are different, they are considered coincident points.
The various interpolation tools may handle this data condition differently. For example, in some cases, the first coincident point encountered is used for the calculation; in other cases, the last point encountered is used. This may cause some locations in the output raster to have different values than what you might expect. The solution is to prepare your data by removing these coincident points. The Collect Events tool in the Spatial Statistics toolbox is useful for identifying any coincident points in your data.
Syntax
Kriging_3d (in_point_features, z_field, out_surface_raster, semiVariogram_props, {cell_size}, {search_radius}, {out_variance_prediction_raster})| Parameter | Explanation | Data Type |
in_point_features |
The input point features containing the z-values to be interpolated into a surface raster. | Feature Layer |
z_field | The field that holds a height or magnitude value for each point. This can be a numeric field or the Shape field if the input point features contain z-values. | Field |
out_surface_raster |
The output interpolated surface raster. It is always a floating-point raster. | Raster Dataset |
semiVariogram_props kriging_model | The Semivariogram model to be used. There are two models for kriging: Ordinary and Universal. The Ordinary model has five types of semivariograms available. The Universal model has two types of semivariograms available. Each semivariogram has several optional parameters that can also be set.
The form of the semivariogram is a text string: "{semivariogramType},{lagSize},{majorRange},{partialSill},{nugget}" For example: "Circular, 2000, 2.6, 542" | KrigingModel |
cell_size (Optional) | The cell size at which the output raster will be created. This will be the value in the environment if it is explicitly set; otherwise, 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 |
search_radius (Optional) | Defines which of the input points will be used to interpolate the value for each cell in the output raster. There are two ways to specify the searching neighborhood: Variable and Fixed. Variable uses a variable search radius in order to find a specified number of input sample points for the interpolation. Fixed uses a specified fixed distance within which all input points will be used. Variable is the default. The syntax for these parameters are:
| Radius |
out_variance_prediction_raster (Optional) | Optional output raster where each cell contains the predicted semi-variance values for that location. | Raster Dataset |
Code sample
Kriging example 1 (Python window)
This example inputs a point shapefile and interpolates the output surface as a Grid raster.
import arcpy
from arcpy import env
env.workspace = "C:/data"
arcpy.Kriging_3d("ca_ozone_pts.shp", "OZONE", "c:/output/krigout",
"Spherical", 2000, "Variable 12")
Kriging example 2 (stand-alone script)
This example inputs a point shapefile and interpolates the output surface as a Grid raster.
# Name: Kriging_3d_Ex_02.py
# Description: Interpolates a surface from points using kriging.
# Requirements: 3D Analyst Extension
# Import system modules
import arcpy
from arcpy import env
# Set environment settings
env.workspace = "C:/data"
# Set local variables
inFeatures = "ca_ozone_pts.shp"
field = "OZONE"
outRaster = "C:/output/krigoutput02"
cellSize = 2000
outVarRaster = "C:/output/outvariance"
kModel = "CIRCULAR"
kRadius = 20000
# Check out the ArcGIS 3D Analyst extension license
arcpy.CheckOutExtension("3D")
# Execute Kriging
arcpy.Kriging_3d(inFeatures, field, outRaster, kModel,
cellSize, kRadius, outVarRaster)
Environments
Licensing information
- ArcGIS for Desktop Basic: Requires 3D Analyst or Spatial Analyst
- ArcGIS for Desktop Standard: Requires 3D Analyst or Spatial Analyst
- ArcGIS for Desktop Advanced: Requires 3D Analyst or Spatial Analyst