Module 2.2 Surface Interpolation

For this week's Lab we really focused on Surface Interpolation.

Some of this week's learning outcomes were the ability to carry out different surface interpolation techniques in GIS, including Theissen, IDW, and Spline. As well as having the ability to critically interpret the results from surface interpolation techniques and understanding how to compare and contrast different surface interpolation techniques.

For this week's exercise I used various techniques to explore water quality conditions in Tampa Bay. Using dataset of samples provided to us from UWF taken over a short time in Tampa Bay. The water quality parameter we were tasked with exploring was Biochemical Oxygen Demand (BOD) in milligrams per liter.

 Each method used offered a different perspective on how spatial data can be interpreted. Thiessen interpolation assigns each location the value of its nearest sample point, creating sharp boundaries that reflect zones of influence. Spline interpolation fits a smooth surface through all points, but can exaggerate values in under sampled areas. In contrast, Inverse Distance Weighting (IDW) produces a balanced surface by weighting nearby points more heavily, resulting in a smooth gradient that respects spatial proximity without overfitting.

Among these, I felt IDW provided the most realistic and spatially coherent representation of BOD concentrations. It captured local variation while avoiding artificial peaks or abrupt transitions. Below is a map of the IDW surface, showing how BOD levels vary across the bay in a way that aligns with sampling density and spatial logic.

Choosing the right method depends on the nature of the data, the sampling design, and the patterns we expect to find.