Surface Interpolation

 For this week's module, we learned about different methods to use for surface interpolation. The three different methods explored were Thiessen Polygons, IDW, and Spline (Regularized/Tension). Thiessen interpolation method assigns interpolated value equal to the value found at the nearest sample location. Some advantages of the Thiessen interpolation method are that the polygons are only created once and it’s the easiest method to conceptualize and apply. Some disadvantages are that topography is not considered and boundaries are often oddly shaped (not smooth and continuous like spline). IDW interpolation determines values by using a linear-weighted set of sample points. The weight assigned is a function of the distance from the sample point from output cell location. The further away, the less weight that is assigned to the sample point. The spline interpolation estimates values using a mathematical function that minimizes overall surface curvature and shows smooth surfaces that pass through each sample point.

We used the various surface interpolation methods to explore the water quality conditions in Tampa Bay. The dataset of sample points were gathered and the water quality was determined by measuring the Biochemical Oxygen Demand (BOD) of each sample. After analyzing all of the methods, I chose the Spline with Tension Interpolation method to develop a good description of the BOD concentrations in Tampa Bay. Spline interpolation surfaces are smooth and easier to read than IDW. It appears that the sample points were taken in a uniform distance and amount so I feel better about any distortions skewing the surfaces. This is highlighted between the Spline Regular and the Spline with Tension. Once the two data points that were too close together were moved, it depicted the overall data better.