Kriging (3D Analyst)


Interpolates a raster surface from points using kriging.

Learn more about how Kriging works



Kriging_3d (in_point_features, z_field, out_surface_raster, semiVariogram_props, {cell_size}, {search_radius}, {out_variance_prediction_raster})
ParameterExplanationData Type

The input point features containing the z-values to be interpolated into a surface raster.

Feature Layer

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.


The output interpolated surface raster.

Raster Dataset

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.

  • Ordinary model semivariograms:
    • Spherical—Spherical semivariogram model. This is the default.
    • Circular—Circular semivariogram model.
    • Exponential—Exponential semivariogram model.
    • Gaussian—Gaussian (or normal distribution) semivariogram model.
    • Linear—Linear semivariogram model with a sill.
  • Universal model semivariograms:
    • LinearDrift—Universal Kriging with linear drift.
    • QuadraticDrift—Universal Kriging with quadratic drift.
  • After the semivariogram model is defined, the remaining parameters are common between Ordinary and Universal kriging. These are:
    • Lag size—The default is the output raster cell size.
    • MajorRange—Represents a distance beyond which there is little or no correlation.
    • PartialSill—The difference between the nugget and the sill.
    • Nugget—Represents the error and variation at spatial scales too fine to detect. The nugget effect is seen as a discontinuity at the origin.

The form of the semivariogram is a text string:


For example:

"Circular, 2000, 2.6, 542"

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

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 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:

  • Variable, number_of_points, maximum_distance, where:
    • number_of_points—An integer value specifying the number of nearest input sample points to be used to perform interpolation. The default is 12 points.
    • maximum_distance—Specifies the distance, in map units, by which to limit the search for the nearest input sample points. The default value is the length of the extent's diagonal.
  • Fixed, distance, minimum_number_of_points, where:
    • distance—Specifies the distance as a radius within which input sample points will be used to perform the interpolation. The value of the radius is expressed in map units. The default radius is five times the cell size of the output raster.
    • minimum_number_of_points—An integer defining the minimum number of points to be used for interpolation. The default value is 0.

      If the required number of points is not found within the specified distance, the search distance will be increased until the specified minimum number of points is found.

      When the search radius needs to be increased it is done so until the minimum_number_of_points fall within that radius, or the extent of the radius crosses the lower (southern) and/or upper (northern) extent of the output raster. NoData is assigned to all locations that do not satisfy the above condition.


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:
# 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

# Execute Kriging
arcpy.Kriging_3d(inFeatures, field, outRaster, kModel, 
                 cellSize, kRadius, outVarRaster)


Related Topics

Licensing Information

ArcView: Requires 3D Analyst or Spatial Analyst
ArcEditor: Requires 3D Analyst or Spatial Analyst
ArcInfo: Requires 3D Analyst or Spatial Analyst