IDW Interpolation
In this week's lab, we learned about different interpolation techniques and applied to water quality sampling points of Tampa Bay area. Three different interpolation techniques were used which were Thiessen, IDW, and Spline. The numerical results of both Thiessen and IDW were very similar; however the patterns of the results were quite difference from each. Thiessen retained it's polygon features when converting into a raster dataset unlike IDW which does not having any polygon features or outlines. The Spline interpolation technique shows the BOD concentration’s minimum is in the
negatives and is higher than the previous interpolation techniques that were
applied to this data. By having a higher minimum, the surface curvature minimum
is that particular area to be extremely high since there are no sampling points
in this area to create a smooth surface for the Spline interpolation. The
Spline interpolation relies heavily on the input points to create a smooth
surface. Lack of points in a particular location of the data will create a
distorted when using Spline interpolation. To remedy this problem, deleting a
point from the wq_bod data will reduce the minimum value of the points which
will create a smooth surface.
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