Piece of Eden GIS Blog

 In this lab we first looked at how the scale at which we analyze data can affect geometric properties. Looking at data at three different scales (1:1200, 1:24000, 1:100000) I was surprised by my final calculations. I thought that there would be a lineal proportional relationship along the scales, but I did not find this. It makes sense that the greatest resolution [1:1200] would have the most detail of geometric properties, but I was surprised that 1:100000 had greater geometric properties than the medium resolution. I resampled the data using the Bilinear technique and found the following effect on DEM resolution.  As DEM resolution increases the average slope in degrees decreases. This makes sense because as you get closer, more in detail by getting closer, you are zooming in as actively seeing less features altogether.  In the last part of the lab, we looked into the Gerrymandering of districts in the USA. Gerrymandering essentially breaks up congressional districts in a manner tha

In this assignment, we learned about four different interpolation methods: Thiessen, IDW, and Spline Regularized/Tension. We used these methods to analyze the Biochemical Oxygen Demand (BOD) in milligrams per Liter in the Tampa Bay of Florida.  The first method, Thiessen interpolation, is a widely used method mainly because of its ease of use and high accuracy when using a large sampling density. Because this method assigns a value based on the nearest sample point, it does not do well with continuous data. However, because of this same attribute, this method can be highly beneficial when dealing with data that is oddly shaped or ends abruptly. Model 1: Thiessen IDW interpolation draws in the theory that points closer to each other are more alike.  Model 2: IDW The biggest difference in the Spline layer from the other two methods is it has a much smoother expression. This makes sense as the spline interpolation runs through each data point and aims to smooth out the surface elevation.