# Ordinary Kriging (Global)

(c) 2008 by O.Conrad
Ordinary Kriging for grid interpolation from irregular sample points. This implementation does not use a maximum search radius. The weighting matrix is generated once globally for all points.

## Parameters

Grid
Output Data Object
Variance
Output Data Object
Points
Input Shapes
Attribute
Table field
Create Variance Grid
Boolean
Target Grid
Choice
  Available choices: user defined, grid system, grid
Variogram Model
Choice
  Available choices: Spherical Model, Exponential Model, Gaussian Model, Linear Regression, Exponential Regression, Power Function Regression
Block Kriging
Boolean
Block Size
Floating point
Logarithmic Transformation
Boolean
Nugget
Floating point
Sill
Floating point
Range
Floating point
Additional Parameters
Node
Linear Regression
Floating point
  Parameter B for Linear Regression: y = Nugget + B * x
Exponential Regression
Floating point
  Parameter B for Exponential Regression: y = Nugget * e ^ (B * x)
Power Function - A
Floating point
  Parameter A for Power Function Regression: y = A * x ^ B
Power Function - B
Floating point
  Parameter B for Power Function Regression: y = A * x ^ B
Grid Size
Floating point
Fit Extent
Boolean
  Automatically fits the grid to the shapes layers extent.
X-Extent
Value range
  Minimum: 1.66036175244e-316; Maximum: 1.66031036962e-316
Y-Extent
Value range
  Minimum: 1.66047558517e-316; Maximum: 1.66043764093e-316
Grid System
Grid system
Grid System
Grid system
Grid
Input Grid
Variance
Input Grid