GIS 5935 - Surface Interpolation

 During this week's module we were introduced to and learned about interpolation. While we were made aware that there are many interpolation techniques available, we went more in depth when learning about Thiessen, Inverse Distance Weighted and Spline techniques. We were then tasked with performing these techniques. 

We were required to use a dataset of water quality samples from the Tampa Bay area to show where there were concentrations of the Biochemical Oxygen Demand (BOD). The Thiessen interpolation method was used first and produced a raster on polygons. This was not deemed suitable based on the abrupt changes that take place at the edge of polygons versus the fluid and continuous nature of water. The IDW and Spline methods (regularized and tension) both produced a continuous surface which was more suitable. Additionally the spline method proved to be helpful because it made an anomalous area noticeable. Ultimately, however, I decided that the IDW was most suitable from my perspective. I believe this to be the case because the IDW result better highlights areas of concentration given that the sample values stay true to location in the interpolated surface, with estimates of unknown values made based on the distance from the known samples, and the samples are limited to a short timeframe, as stated in the exercise. Below you can see a screenshot of the IDW interpolation result. 

IDW Interpolated Surface of Water Quality in the Tampa Bay Area

 

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