Interpolation Methods

Steve Henderson



Inverse Distance Weighted Method


Inverse weighted distance interpolation using a variable radius, and inverse squared weighting and a minimum of 7 points to estimate heights.  Overlaid onto this raster are the random point (purple crosshairs) that the estimated topography were created from.  The surface created is heavily influenced by the location of the points.


Inv Dist Weighted


A smaller portion of the raster above, overlaid onto this raster are the random point (purple crosshairs) and variable color contour lines, with reds representing errors near negative 300 feet and dark blue positive 300 feet, with greens and yellows representing smaller error in between.  The largest errors, characterized by blue and red contours can be found farthest away from the points used to create the estimate.   This method tended to shave off peaks and fill in valleys.


Straight Line Distance Allocation






Simple Kriging


Isotropic Semivariogram




Anisotropic Semivariogram




The Anisotropic semivariogram allows the analysis of the trends of the data to consider that the elevation leading from each of the points is likely to be different in different directions.  The Anisotrpic map seems to mimic general trends of the landform more closely, although the limited number of points over such are large area do not make those improvements overly obvious.