JCLambert GIS Enterprise: Lab 5 M2.2 Surface Interpolation

This week’s lab focused on water quality in Tampa Bay, officially Surface Interpolation. It is always interesting to learn how there are different ways of studying data and interpreting results. We worked with different ways of interpolating data, specifically Thiessen, Inverse Distance Weighted (IDW), and Spline (Regularized and Tension). The data (BOD_MGL) for the study used BOD (Biochemical Oxygen Demand) in MGL (Milligrams Per Liter ) to measure data points for water quality in Tampa Bay (the body of water). We needed to determine areas with low and high water quality based on the results using different interpolation techniques.

The techniques we used to interpolate gave somewhat similar results. The Thiessen offered the same results as the non-spatial information. The IDW was very similar to Thiessen, only offering a difference in standard deviation. Spline was the interpolation technique that offered the greatest variation from the others. Interpolation offers a way to study the spatial distribution of phenomena across a wide range of points. These are a few of those options.

Thiessen-This interpolation technique contains only a single point having any location within the output polygon closer than any other point, it defines an area around a point. It divides areas into proximal zones or polygons. Thiessen polygons are also called Voronoi polygons or Voronoi diagrams.

Inverse Distance Weighted (IDW)-As the name suggests it relies on inverse distance from points with emphasis placed on the nearest ones. The mapped variables have decreased influence as distance increases from the sampled location.

Spline-This technique has two types: Regularized and Tension. Regularized offers a smooth changing surface and has values that may be outside of its range. Tension offers a less smooth surface but has data that adheres closer to sample data ranges. Both can be altered in the number of points and the weight when running the tool.