Special Topics Week 6: Scale Effect and Spatial Data Aggregation

In our final lab we explored the effects of scale on vector and raster data, the Modifiable Areal Unit Problem, and compactness of gerrymandered congressional districts. For vector data, data at a larger scale is more detailed, resulting in longer polylines and polygons with larger perimeters and areas. For raster data (specifically DEMs), higher resolutions result in higher average slopes. Higher resolution rasters have more cells, meaning they have more data points and capture more variation, which results in higher slopes in the case of elevation.

District 12 in North Carolina

We then explored the topic of gerrymandering, which is the creation of intentionally distorted political districts to favor one party by splitting constituencies between districts or concentrating them in certain districts. The Polsby-Popper score is a way to measure gerrymandering by measuring how compact a district is on a scale of 0 to 1, in which 1 is the most compact. The formula for the Polsby-Popper score is 4πA/P² , where A is the area and P is the perimeter of the district (Morgan & Evans, 2022). I calculated the Polsby-Popper scores of all the congressional districts in the US in 2014 and found that the least compact, with a Polsby-Popper score of 0.029, is District 12 in North Carolina. This district is a famous example of gerrymandering and has been redrawn multiple times over the past few decades. I grew up less than a mile from District 12 in Winston-Salem, and I can see on the map how it was drawn to include neighborhoods with a majority of black residents and exclude neighborhoods with a majority of white residents, like my family’s.

References:

Morgan, J.D. and Evans, J. (2022). Aggregation of Spatial Entities and Legislative Redistricting. The Geographic Information Science & Technology Body of Knowledge (1st Quarter 2022 Edition), John P. Wilson (Ed.). DOI:10.22224/gistbok/2022.1.5.