Using Interpolation Methods to Create a Surface of Water Quality in Tampa Bay

In this exercise, I explored different interpolation methods to create a surface of Biochemical Oxygen Demand (BOD) concentrations for Tampa Bay. The primary goal was to compare how each technique handles spatial variability in the data, ultimately determining which approach offers the best representation of water quality across the bay.

The methods I used included Thiessen Polygons, Inverse Distance Weighting (IDW), and Spline interpolation. 

• Thiessen Polygons: This method creates zones around each sampling point where every location within a zone is assigned the same value as the closest point. 
• Inverse Distance Weighting (IDW): IDW calculates values based on the distance from surrounding points, resulting in a surface that reflects local variations more accurately. 
• Spline Interpolation: Spline creates a smooth surface that minimizes sharp transitions. 

Overall, IDW provided the most balanced surface, capturing localized changes without overgeneralizing. It’s my preferred method for mapping water quality in this case, as it maintains a good balance between precision and smoothness.

Each of these methods has its use depending on the context, but it’s crucial to consider the nature of the data and the spatial arrangement of sampling points before selecting an interpolation technique.