In this week's laboratory module, I utilized a multitude of interpolation techniques to display water quality metrics within the Tampa Bay. Interpolation is used to connect known data points and fill in gaps that would otherwise contain no specific data. Whilst it is a useful method in creating meaningful maps, it is a technique that comes with its own challenges. I found that each interpolation method was substantially different from the next, with each having their own unique set of pros and cons. Interpolating data can be tricky, as it often generalizes areas in incorrect ways, and this is important to account for when using certain data and techniques.
The results were very surprising, especially seeing how differently the output would appear between different methods. In order to go about deciding which interpolation technique I would use in future projects, I would run a similar setup to this lab. I believe that assessing the statistics each method provides and analyzing the visual accuracy of the produced interpolation are the best methods for seeing which interpolation should be utilized.
Upon completing each technique and comparing them amongst each other, I determined the best for this project to be the Spline (Tension) method. This particular technique showed the most fluid, smooth interpolation and was visually more realistic than the other methods. Also, the standard deviation and data range for this method was also relatively low, meaning it was more precise. Other methods, such as the Thiessen method, often over-generalized the data and created inaccurate interpolations for areas that would have different values than those produced.
