Module 6 - Scale Effect and Spatial Data Aggregation

 In this lab, we were tasked with understanding the effects of scale on vector data and resolution on raster data, understanding the effect of the modifiable area-unit problem (MAUP) using OLS analysis, identifying multipart features, and being able to measure the compactness of features. 

The first thing I want to discuss is the scale effect on vector data. The smaller the scale, the more detailed the vector data will be. Specifically in the lab, we were given six data sets of hydrographic features in Wake County at varying degrees of scale: 1:1200, 1:24000, and 1:100000. By comparing each dataset, it was clear that the 1:1200 scale had more detail of the hydrographic features within the county compared to the 1:100000 scale. The same goes for the resolution of raster data. The smaller the cell size, the more detailed the raster will be. We compared the resolution of a DEM at 1 meter with that of 90 meters of resolution. What was observed is that at the 1-meter resolution, significantly more features within the DEM were visible than what was visible with the 90-meter resolution.

Another part of the lab we explored is the problem that is gerrymandering. If you don't know what gerrymandering is, it is the manipulation of voting districts to favor a certain party more than another party. The best way it can be measured is by visually looking at it or by calculating the compactness of a district by calculating the district's Polsby-Popper score. To calculate the score, I utilized this formula:

The closer a district is to the number 1, the more compact a district is. The lower the compactness score, the worse the district is gerrymandered. The worst offending district in the country, with a low compactness score, is Congressional District 12 in North Carolina, with a score of 0.03:

Module 5 - Surface Interpolation

 In this week's lab, we were tasked with carrying out different surface interpolation techniques in GIS, critically interpreting the results from the interpolation techniques, and then comparing and contrasting the different interpolation techniques. Those surface interpolation techniques we explored are Theissen interpolation, Inverse Distance Weighting (IDW) interpolation, and Spline interpolation (Regularized and Tension). The area we created the surface interpolation for was Tampa Bay water quality, specifically, Biological Oxygen Demand (BOD). Each method of interpolation conducted resulted in a different output for the same data. 

IDW is a method of surface interpolation that estimates cell values by averaging out all of the data from the sample points, resulting in this output:

Spline is a method of interpolation that estimates values using a mathematical function to minimize surface curvature, resulting in a smooth surface that directly passes and connects points together. However, there are two methods of spline, regularized and tension. Regularized creates the smooth surface common with splines by changing surface values that may lie outside the set data range set by the collected points. Here is what a regularized output may look like:

A tension spline primarily controls the stiffness of a surface. This results in a less smooth surface that is constrained to the set data range, resulting in this output:

The last method is the Theissen Method, which is the simplest form of interpolation. It assigns each cell location the same value as the nearest point, resulting in precise measurements for the cells. It is done by first converting the points to Thiessen polygons, then converting those polygons to a raster by utilizing the feature to raster tool, resulting in this output:

Overall, this lab definitely opened my eyes to how complex GIS can be, and I learned a lot about different methods of interpolation I can now use at my place of employment.