The fifth module in Special Topics in GIS introduced the topic of surface interpolation. In this lab, we explored the use of several interpolation techniques to create continuous surfaces of water quality across Tampa Bay. Interpolation is valuable because it allows us to estimate values between sampling points and better visualize spatial patterns. However, each technique approaches the problem differently and produces distinct results.
Thiessen polygons assign each location to the nearest sample point, which is simple to apply but results in abrupt boundaries that don’t reflect smooth changes in water quality. Inverse Distance Weighting (IDW) provides a more gradual surface, giving greater weight to nearby samples and reducing the blocky appearance of Thiessen. Spline goes further by fitting a smooth, curved surface through the data, producing a visually appealing result but sometimes creating unrealistic peaks or sinks in areas with clustered high values or sparse sampling. These differences highlight the importance of choosing an interpolation method that matches both the data characteristics and the purpose of the analysis.
Below is a screenshot of my results using the Spline Tension technique:
Overall, this exercise showed that while all interpolation methods can create useful surfaces, their assumptions and behaviors vary widely. Understanding these differences is key to interpreting the results and making informed choices for an analysis. This lab gave me a stronger understanding of how interpolation works and why the method you choose matters. It was interesting to see how the same water quality data could look so different depending on the approach.
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