Special Topics Week 5: Surface Interpolation

In this week's lab we explored different interpolation methods for water quality in Tampa Bay. Given a set of points measuring Biochemical Oxygen Demand (BOD), we interpolated the surface using Thiessen interpolation, Inverse Distance Weighting (IDW), and both regularized and tension Spline methods. Thiessen interpolation generated a surface of discrete polygons of varying sizes, each with the value of the nearest sample point. IDW generated a smooth surface based on nearby sample points weighted by proximity. The spline methods interpolated along smooth curves based on calculated polynomial functions. Both the spline methods were heavily influenced by several outlier points in the data, which caused them to predict some negative values and some unrealistically high values for the BOD surface. After removing these outliers from the original dataset, the spline methods improved, although the regularized spline method still had negative values.

IDW interpolation of BOD in Tampa Bay, with sample points

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