Interpolation methods are used to predict variables (water quality) in areas where it was not measured. To create a surface of water quality in Tampa Bay, I would use inverse distance weighted (IDW) interpolation. IDW determines the value of unknown locations from all the sample points. Points closer to the unknown location are given more weight. I chose this method because it does not require the data to be edited and
has the lowest standard deviation. It also creates a surface with smooth
transitions, unlike Thiessen. Thiessen interpolation uses polygons, making an abrupt line between each. Spline requires the data to be edited because it does not do well with sampling points close together (due to trying to fit a smooth line through points). The Tampa Bay data set has two sampling points almost overlapping. In order to use the Spline method, the field data would have to be modified.
IDW Map
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