In this week’s lab, we worked with Biochemical Oxygen Demand (BOD) data points for Tampa Bay. We tested 4 different interpolation techniques to determine which technique most accurately created a water quality surface. We used a non-spatial technique to summarize the BOD data in the attribute table. This summarized the values of the data points. We used the Thiessen technique to create a raster of the attribute table data. We used inverse distance weighted (IDW) interpolation on the data points. This interpolation assumed value for each point based on how close the point was to another point on the map.
In this instance, we needed to interpolate the data based on the value of each point. Finally, we used the Spline interpolation technique to calculate the value of the points. The Spline interpolation technique considers the density of the point data when calculating the output. This technique created a more accurate map showing the point data values. We also compared both options of spline type by using the Regularized and Tension options in the Geoprocessing tool. The Regularized option creates a smoother surface that is more generalized, while the Tension option creates a coarser surface that more closely reflects the point data values. My example of the Spline interpolation technique with the Tension option is posted above.
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