Module 2.2: Interpolation

In this week's module, we delved into various surface interpolation techniques and how to determine the most appropriate method based on specific factors. The dataset at hand involved multiple water samples taken from various points across Tampa Bay for water quality analysis. These interpolation methods were employed to generate continuous surface estimates for the areas between the sample points. The methods used included Thiessen, Inverse Distance Weighting (IDW), as well as Regularized and Tension Splines. Each of these techniques yielded different results owing to variations in data collection and point density. Some of the outputs exhibited anomalies; for instance, the Spline (Regularized) output displayed irregularities in the northern section, while the Thiessen method produced a somewhat jagged surface. The IDW method provided the best result based on my opinion. As shown below, the IDW interpolation method generated a smooth surface with coherent estimates and values.