Module 2.2: Interpolation Methods- Tampa Bay Water Quality
Interpolation involves estimating unknown data based on measured or known collection data points that are located nearby. There are multiple methods of interpolation, but this module evaluated the following methods:
- Thiessen
- Spline
- Inverse Distance Weighing (IDW)
In this scenario, we wanted to interpolate water quality data for the entire Tampa Bay area. Water quality data included measured biochemical oxygen demand (BOD) in milligrams per liter at 41 sampling points distributed within Tampa Bay. This same dataset was used in all three methods and the results were compared.
The CREATE THIESSEN POLYGONS tool was used to create the Thiessen method interpolation results. This method essentially assigns a value based on the closest sample point. This is a simple method and easy to interpret, but the boundaries are too abrupt to accurately estimate the natural flow of water in Tampa Bay.
The Spline method creates a smooth surface by passing through each sample point and the surface is determined based on the values at each point. The spline method has two additional option types, a regularized option and a tension option. The regularized method produces a smoother surface than the tension method, with values that may be outside the data range. An example of the interpolated water quality using the spline-regularized method is below, in which BOD ranged from high (red/pink) to low (green). This method seemed to be the most accurate at interpolating the water quality data.
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