The final module of Special Topics in GIS focused on scale / resolution, and data aggregation. The first portion of the lab explored two vector-based datasets [consisting of points, lines, and polygons] and how they were affected as the scale of the datasets was changed. As the scale / resolution of a dataset is enlarged, sample points will be eliminated as they space between is reduced. Therefore, lines and polygons will have less vertices, resulting in geometries that are over-generalized, or completely eliminated from the map. This is displayed on the map below, with the left-side map being delineated watersheds and the right-side map highlighting water bodies of the same swath of land located in North Carolina. The darkest shade of blue is the original dataset, the medium represents the same dataset at a 1:24,000 resolution, and the light blue represents the same dataset at a 1:100,000 resolution. It is evident that as the scale is increased, the number of line features decreases, eliminating line / polygon features from the dataset; hence, more line features / polygonal features are captured by the original and high-resolution [1:24,000] datasets. Due to this generalization effect, a careful consideration of appropriate scale must take place to ensure an accurate representation of the data.
The second portion of the lab assignment focused on spatial data aggregation, or specifically, the Modified Area Unit Problem. Essentially, the MAUP is when the same dataset can produce different results based on how the data is aggregated or shown at different scales. This is called the Zone Effect or Scale Effect, respectively. In the four maps below, the same dataset was used, but applied to different zones, or districts [this is spatial data aggregation]: census blocks, congressional house blocks, zip codes, and counties. A brief visual comparison of these four maps displays how the results can be dramatically different from one another, based on the type of aggregation used. Like the scale / resolution issue discussed in the first portion of this assignment, it is very important to acknowledge the Modified Area Unit Problem and carefully consider its potential effects on each spatial data analysis.
The third, and final portion of the lab demonstrated how spatial data can be used to manipulate results, especially in the realm of politics. Gerrymandering is a manipulation of political districting that creates an 'intentional bias' in order to favor one political party over another. To discover and combat this practice, the Polsby & Popper score can be used to determine the compactness of a political district; the Polsby & Popper score is a ratio between the area of the district and its perimeter, and is computed with the following equation:
PP = 4πA / P²
where A is the Area of the district and P is the perimeter; this score will fall within a range of 0 to 1, with 1 being perfect compactness [Morgan & Evans, 2018]. After running the Polsby & Popper test on all congressional districts in the continental United States, it was determined that the district in the map below scored the lowest, with a Polsby & Popper score of .02948!
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