GIS 311 Lecture Notes - Lecture 4: Inverse Distance Weighting, Interpolation, Distance Decay
30 Nov 2017
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Every grid point would be within these polygons, and could inherit that value. An estimated point (new point) is interpolated using the voronoi polygons. This is looking for unsampled locations by leveraging the known points around the sampled point. Based on weights within the voronoi polygons, rather than idw"s distances from polygons: inverse distance weighting divides each of the observations by a distance it is from the target point raised to a power. The search radius can be fixed or variable, and it is not necessary. Arcgis sets: the power function parameter to model the rate of distance decay + output cell size: advantages common, quick, easy to understand, disadvantages interpolated values limited by rang of data. Extent contraints (can"t interpolate values outside the observed range of z values). And the classic bulls-eye" look to the output: kriging data points are representative sample of data.
