Module 2.2: Surface Interpolation

Inverse Distance Weighting (IDW) Interpolation


This week’s assignment focused on using surface interpolation methods to visualize water quality in Tampa Bay. The goal was to estimate BOD concentrations across areas where sampling points were sparse, using techniques like IDW, spline, and Thiessen.

Interpolation methods are useful for creating continuous surfaces from discrete sampling points. IDW assumes that nearby points are more similar and produces smooth, gradual transitions. Spline generates very smooth surfaces that can sometimes exaggerate peaks and valleys. Thiessen divides the area into polygons where each location is assigned the value of the nearest point, creating distinct zones rather than smooth gradients. Comparing these methods shows that each represents the data differently, highlighting the importance of choosing an approach based on the data characteristics and analysis goals.

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