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:
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