For this assignment I used observed water quality points in Tampa Bay and a suite of interpolation techniques (Thiessen, IDW, and spline) to create a surface of water quality in Tampa Bay.
Thiessen interpolation creates a series of polygons based on the spatial distribution of observed points, then fills each of those polygons with one value. Though this produces a very rough-scale result, it can be accomplished by hand if needed, and can be useful for quickly identifying entire regions at a time. For this analysis I used the Create Thiessen Polygons tool in conjunction with the Feature to Raster tool, which filled those polygons with values.
Like Thiessen interpolation, Inverse Distance Weighted (IDW) interpolation is bound by the actual values entered, but uses the assumption that things close together must be more alike than things far away from eachother, and fills in the cells between values with estimates based on the nearby values. In IDW, values nearby are weighted higher for informing the estimated values. IDW creates a smooth raster that may contain hotspot-like nodes. The IDW analysis was easily completed by running the IDW tool on my existing datasets.
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| Inverse distance weighted (IDW) interpolation of biological oxygen demand in Tamp Bay |
Spline was the last technique examined, like IDW it takes existing values and creates estimates to fill the space between them, but where IDW uses a weight based on distance, Spline uses a constant weight from nearby points. The surface is "fitted" to the existing points as smoothly as possible, and the weight determines the allowable deviation. This means Spline may return values outside the range of the observed points, and will perform best with an evenly distributed sample set.
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