GIS5935 M2.2 - Surface Interpolation

 Interpolating Water Quality Data for Tampa Bay

In this exercise, we applied various interpolation techniques to explore water quality conditions in Tampa Bay, Florida, using a dataset of samples collected over a short period. The primary focus was on Biochemical Oxygen Demand (BOD) concentrations, measured in milligrams per liter, a key indicator of water quality. By using methods like Thiessen, IDW, and Spline interpolation, I was able to generate surfaces that estimate BOD levels across Tampa Bay, providing insight into the spatial distribution of pollution. Each method offered a different approach—some emphasizing sharp transitions between points and others providing smoother, more continuous surfaces that better represent gradual changes in water quality conditions.

Performing Interpolation Analysis 

  • Thiessen interpolation assigns each location the value of the nearest point, resulting in abrupt boundaries that may not be suitable for continuous phenomena like pollution.
  • Inverse Distance Weighting (IDW) interpolation weighs values based on distance, providing a good balance by capturing local variations but sometimes resulting in sharp transitions between points. 
  • Spline interpolation creates a smoother, more continuous surface, using a mathematical function that minimizes overall surface curvature, making it ideal for representing gradual transitions in water quality. There are two types:
    • Regularized creates a smooth, gradually varying surface, but it may produce values that fall outside the range of the original sample data. This method is ideal for capturing smooth transitions but can sometimes overestimate or underestimate values in areas with sparse data. 
    • Tension adjusts the surface's stiffness based on the data's characteristics, resulting in a less smooth surface but more closely aligned with the actual sample values. This method is better suited when preserving the data range is required as it minimizes the risk of producing unrealistic extremes.

Screen Capture of Tension Spline Interpolation

Each method has its strengths, but choosing the right one depends on the specific needs of the analysis—whether you prioritize local accuracy or smooth transitions. Below is an example of the Tension Spline surface used to model BOD concentrations in Tampa Bay since it seemed to combine the data range while still creating a smooth surface that better reflects the BOD concentration gradient.


Two coincident points in the high-concentration area influenced the Spline interpolation, creating a hotspot in a region without sufficient sample points. Initially, I removed both points, but this expanded the area of high concentration, exacerbating the issue. Removing only one of the coincident points was a more effective solution upon reevaluation. I chose to delete the lower concentration point, guided by my background in fisheries and wildlife. This decision reflects real-world conditions where urban development typically results in runoff containing pollutants such as fertilizers and sewage entering waterways through stormwater or industrial discharge. By retaining the higher concentration point, the interpolation better represents the environmental impact of such urban pollution sources in Tampa Bay.





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