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I am analyzing a postGIS point dataset in QGIS version 2.8.2, about 200k features total. To calculate weighted average values I am using the Inverse Distance Weighted interpolation technique. However, I am concerned that the "high" values I'm seeing in the outputs are the result of a handful of outlier values, not the basis of any meaningful spatial pattern.

Is there a way in the SAGA IDW plugin - or another IDW tool - to set a minimum number of points required for the raster surface to calculated the weighted averages, the same way it considers a maximum number of points?

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    Another approach I've tested is to make two rasters for the analysis, one using IDW to map attribute values, another using Kernel Density Estimation to map point density. Using Raster Calculator I set cells in the KDE raster below a certain density threshold to equal zero, all others equal to 1. Then I multiply the rasters together so that all areas below the specific density threshold are zero and removed from the resulting raster. Is this a sufficient workaround? – David Perlmutter Dec 10 '15 at 19:48
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The original SAGA IDW method does have a parameter for minimum number of points. But this parameter is not used in the QGIS version of the SAGA method.

As an alternative you could create a layer that contains only the points that have the minimum number of points within the search radius. Have a look at this answer for the code to make such a selection.

I hope this helps.

and here's hoping that Necromancer badge comes my way. ;-)

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