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I have some data that is similar to rainfall, ie. a latitude, longitude, and a Z number. It is in a point layer that is a nice clean point grid. Many Z numbers are missing in this grid.

I want to interpolate to find reasonable Z numbers. TIN, IDW,or other methods. I do not want to go into a Raster first then back out the intersections from the raster onto the point grid.

I can perform an interpolation in QGIS, but it generates a raster which I'm wanting to avoid. The goal is to perform all computation within PostgreSQL for production purposes.

Is this possible?

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    It is doable. However, you need to clarify your expectation on what to be outputted from the query. For instance, do you want to show it as polygon grids and output as geojson? – wondim Sep 3 at 22:30
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    That seems feasible. Do a join between each grid point and the data points, perhaps limited by a distance (use ST_WithinDistance). Then compute the interpolated data value as the distance-weighted sum of the data values as in the IDW algorithm. – dr_jts Sep 4 at 4:25
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A naive IDW implementation, from the top of my head:

UPDATE <points> AS itp
  SET  "Z" = (
    SELECT SUM(z/d)/SUM(1/d)
    FROM   (
      SELECT smpl."Z" as z,
             ST_Distance(itp.geom, smpl.geom)^<P-value> AS d
      FROM   <point> AS smpl
      ORDER BY
             itp.geom <-> smpl.geom
      WHERE  smpl."Z" IS NOT NULL
      LIMIT  <sample_size>
    ) sq
    WHERE  ipt."Z" IS NULL
  ) q
;

Where

  • <P-value> is the factor applied to the inverse distance; you probably want to stay between 1.0 and 2.0

  • <sample_size> the amount of (known) sample points that needs to be considered in the (k) nearest neighborhood

It's been a while since I had a look at the IDW algorithm, so this may need tweaking; the subquery sq returns the list of inverse distances (when used as 1/<distance>^p), so you can go from there if you need to alter the actual IDW sums.

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