In this lab, I learned to examine the effects of scale and resolution and became familiar with the Modifiable Area Unit Problem (MUAP). The MUAP is the bias that comes from point data being aggregated into areas. It is affected by the shape of the area and scale it was aggregated at. Scale effects vector data by determining the amount of detail. A polygon digitized at a larger scale will have more detail. However, there is no good way to represent this scale. Scholars have used the smallest polygon or shortest segment as an approximation. Resolution (cell size) effects raster data by determining the scale. An object has to be at least as large as the cell size to be recognizable in the raster.
In the last lab section, I had to measure gerrymandering. Gerrymandering is "drawing a district shape with intentional bias (benefiting one party over another)" (Morgan and Evans, 2018). It can be measured by compactness, contiguity, and based on constituents (Morgan and Evans, 2018). I had to measure the compactness of the districts in the contiguous US states in this lab. I determined that District 12 in North Carolina has the lowest compactness score (0.0295). Below is a screenshot of the district:
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