Special Topics in GIS, Module 3.1

The topic of this lab assignment was scale and spatial data aggregation. Scale affects the level of detail present in both vector and raster data. The lab demonstrated that the number and size/length of features in vector data is influenced by scale, which can in turn affect any calculations performed using a given dataset. Raster resolution also impacts analysis; the example used in the lab assignment was calculation of slope from DEMs of varying resolutions. Lower resolutions had a smoothing effect on the slope calculations, resulting in decreasing average slope values. 

The second part of the lab dealt with the Modifiable Areal Unit Problem (MAUP), which is the phenomenon where different approaches to data aggregation (e.g. ZIP codes vs. census tracts) results in statistical differences, and gerrymandering, which is the practice of drawing legislative boundaries in a way that intentionally favors one group over another. There are several ways to measure gerrymandering, including by evaluating the compactness of a district, its contiguity, and/or the demographic makeup of its constituents.

The lab assignment involved calculating a compactness statistic (the Polsby-Popper score), which showed North Carolina's Congressional District 12 to the be the least compact in the contiguous US:

Special Topics in GIS, Module 2.2

This week's lab assignment was about interpolation. In the first portion of the lab, we worked with DEMs, and in the second portion we used several different interpolation methods to create surface rasters depicting water quality in Tampa Bay. The methods used were Thiessen, inverse distance weighting (IDW), and spline. Spline additionally has two possible techniques, regularized and tension.

Thiessen interpolation first requires Thiessen polygons, which are geometrically calculated "neighborhoods" where each polygon contains one input point and any location within the polygon is closer to that input point that to any other in the dataset. To create a raster from this, each cell is assigned the same value as the input point in its neighborhood. IDW calculates each cell value based on the values of the closest X number of input points, and gives more weight to points that are closer to the cell being calculated. In the spline methods, smooth curves are drawn to connect the input points and cell values are based on the cell's position on the curve. Tension spline is more constrained by the input values than regularized spline, though both methods preserve the input values at their individual locations.

Here is the output of the tension spline interpolation for the Tampa Bay data (with the original sample points classified similarly to the surface raster):