GIS5935 LAB 2.2

 

The goals of this week's lab were to practice with different surface interpolation techniques including Theissen Polygons, Inverse Distance Weighted Interpolation, and Spline Interpolation including tenson and regularized. Additional goals were to interpret and compare and contrast the results from the interpolation techniques. A dataset containing sampling points with the corresponding level of Biochemical Oxygen Demand (BOD) were provided, and the lab gave instructions on performing different interpolation techniques. Beginning with the non-spatial interpolation technique, which was simply looking at the attribute table using the statistics tool to determine the data's statistics, like maximum, minimum, average and standard deviation. A benefit of this method is that it is user-friendly, while a con is a lack of accuracy compared to other methods. Moving on to the first spatial interpolation technique, Thiessen Polygons. Thieseen Polygons draw on neighboring datasets to create values around each sampling point. The second interpolation technique was the Inverse Distance Weighted Interpolation, which uses "inverse proportion to the distance" so that datasets further away have less value (Bolstad, 531). The final datasets were Spline Interpolation, regularized and tension, which required a modification in the original dataset because the values were too close together and the outcome was inaccurately showing high BOD concentrations with no surrounding sample points. Ultimately, I enjoyed learning about the different interpolation methods, but did find the Spline Interpolation changes to the dataset to be a bit confusing. 

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