In Lab 8 I explored various interpolation methods. The lab covered thiessen polygons, inverse distance weighted (IDW), regularized spline, and tension spline. The picture above is an example of tension spline used to interpolate water quality in Tampa Bay, FL. Thiessen polygons take each point and matches it to the closest data point. This creates uniform areas that don't easily display anything subtle. IDW weighs the distance from data points and uses that to interpolate. The farther away a data point is the less effect it has on how an area is interpreted. The two types of spline both use similar formulas to create a "sheet" that best fits the slope the data points create. Regularized splines create smoother more gradually changing surfaces but the values can lay outside of the data's original range. Tension splines are a little bit stiffer and values are more constrained by the data's original values.
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