For this lab, I used several interpolation methods to model Biochemical Oxygen Demand (BOD) concentrations across Tampa Bay. Each method took a slightly different approach to estimating values between our 41 sample points.
The Thiessen method divided the bay into zones where each area takes the value of its nearest sample point. It’s simple and easy to visualize but produces blocky, unrealistic boundaries. The IDW (Inverse Distance Weighting) method improved on this by blending nearby values smoothly, assuming that locations closer together are more alike. It gave a natural-looking surface that still stayed within realistic ranges. The Spline methods (Regularized and Tension) created even smoother surfaces, but they sometimes exaggerated values in areas with unevenly spaced samples or outliers.
Overall, these techniques showed how interpolation can turn discrete sample data into continuous surfaces that help visualize water quality patterns. For this dataset, IDW provided the most balanced and realistic result, clearly showing how BOD concentrations vary throughout Tampa Bay.
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