QGIS/python/plugins/sextante/saga/help/OrdinaryKriging(Global).html

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<h1 class='module'>Ordinary Kriging (Global)</h1>
<div class='author'>(c) 2008 by O.Conrad</div>
<div class='description'>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.</div>
<h2>Parameters</h2>
<dl class='parameters'>
<dt>Grid <div class='type'>Output Data Object</div></dt><dd> <div class='constraints'></div></dd>
<dt>Variance <div class='type'>Output Data Object</div></dt><dd> <div class='constraints'></div></dd>
<dt>Points <div class='type'>Input Shapes</div></dt><dd> <div class='constraints'></div></dd>
<dt>Attribute <div class='type'>Table field</div></dt><dd> <div class='constraints'></div></dd>
<dt>Create Variance Grid <div class='type'>Boolean</div></dt><dd> <div class='constraints'></div></dd>
<dt>Target Grid <div class='type'>Choice</div></dt><dd> <div class='constraints'>Available choices: user defined, grid system, grid</div></dd>
<dt>Variogram Model <div class='type'>Choice</div></dt><dd> <div class='constraints'>Available choices: Spherical Model, Exponential Model, Gaussian Model, Linear Regression, Exponential Regression, Power Function Regression</div></dd>
<dt>Block Kriging <div class='type'>Boolean</div></dt><dd> <div class='constraints'></div></dd>
<dt>Block Size <div class='type'>Floating point</div></dt><dd> <div class='constraints'></div></dd>
<dt>Logarithmic Transformation <div class='type'>Boolean</div></dt><dd> <div class='constraints'></div></dd>
<dt>Nugget <div class='type'>Floating point</div></dt><dd> <div class='constraints'></div></dd>
<dt>Sill <div class='type'>Floating point</div></dt><dd> <div class='constraints'></div></dd>
<dt>Range <div class='type'>Floating point</div></dt><dd> <div class='constraints'></div></dd>
<dt>Additional Parameters <div class='type'>Node</div></dt><dd> <div class='constraints'></div></dd>
<dt>Linear Regression <div class='type'>Floating point</div></dt><dd>Parameter B for Linear Regression:
y = Nugget + B * x <div class='constraints'></div></dd>
<dt>Exponential Regression <div class='type'>Floating point</div></dt><dd>Parameter B for Exponential Regression:
y = Nugget * e ^ (B * x) <div class='constraints'></div></dd>
<dt>Power Function - A <div class='type'>Floating point</div></dt><dd>Parameter A for Power Function Regression:
y = A * x ^ B <div class='constraints'></div></dd>
<dt>Power Function - B <div class='type'>Floating point</div></dt><dd>Parameter B for Power Function Regression:
y = A * x ^ B <div class='constraints'></div></dd>
<dt>Grid Size <div class='type'>Floating point</div></dt><dd> <div class='constraints'></div></dd>
<dt>Fit Extent <div class='type'>Boolean</div></dt><dd>Automatically fits the grid to the shapes layers extent. <div class='constraints'></div></dd>
<dt>X-Extent <div class='type'>Value range</div></dt><dd> <div class='constraints'>Minimum: 1.66036175244e-316; Maximum: 1.66031036962e-316</div></dd>
<dt>Y-Extent <div class='type'>Value range</div></dt><dd> <div class='constraints'>Minimum: 1.66047558517e-316; Maximum: 1.66043764093e-316</div></dd>
<dt>Grid System <div class='type'>Grid system</div></dt><dd> <div class='constraints'></div></dd>
<dt>Grid System <div class='type'>Grid system</div></dt><dd> <div class='constraints'></div></dd>
<dt>Grid <div class='type'>Input Grid</div></dt><dd> <div class='constraints'></div></dd>
<dt>Variance <div class='type'>Input Grid</div></dt><dd> <div class='constraints'></div></dd>
</dl>
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