Module 2.2: Surface Interpolation

This week we covered topics in surface interpolation techniques in GIS, including Theissen, Inverse Distance Weighted (IDW), and Spline. We critically interpreted the results from the techniques to compare and contrast them. The lab consisted of exploring water quality data for Tampa Bay, FL in the Biochemical Oxygen Demand (BOD) in milligrams per liter. Data consisted of 41 sample points in assuming random locations. Determining the best way to accurately represent the data was a bit up to us. Theissen technique uses polygons to define an area of influence around its sample point, so that any location inside the polygon is closer to that point than any of the other sample points. IDW assumes that things that are close to one another are more alike than those that are farther apart. Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. After looking at the statistics of the data for each technique and the overall output for any anomalies I chose IDW interpolation as my image to display below. This is because it is an exact interpolator, good use for water data because of how it works, and there were no adjustments needed to make the data work for the technique. It was the sufficient and accurate way to go for this particular data set in my opinion to show the water quality conditions in Tampa Bay.