Module 3.1: Scale Effect and Spatial Data Aggregation
The Modifiable Area Unit Problem (MAUP) essentially says that there are various results for data that is created at different scales. Specifically, scales that are smaller (i.e ratios of 1:100000) have different vector and raster results than at scales that are larger (i.e ratios of 1:1200). An example showing water features at different scales are shown below:
Each line color represents the same water feature that was created at different scales. The red line contains more details than the pink, which is more generalized and does not include the smaller tributary lines.
The same idea also applies to raster data. Examples showing a 1m resolution LiDAR DEM (Digital Elevation Model) (top) shows much more detail than a raster with a 90m resolution (bottom)
Gerrymandering is one example that takes advantage of the MAUP effect. Gerrymandering manipulates the boundaries of voting districts to favor one political party or reduces the voting power of ethnic or minority groups. The results of gerrymandering may result in oddly shaped boundaries or districts that are split into multiple smaller parts. In order to score the compactness, or amount of gerrymandering that has occurred, the Polsby-Popper score may be calculated using the equation below:
AD represents the area of the voting district while PD represents the area of a circle whose circumference is equal to the perimeter of the district. This module calculated the Polsby-Popper score for the Congressional Districts with the continental U.S. Values that are closer to 1 are more compact (i.e. indicate less gerrymandering practices). The top 5 worst offenders of gerrymandering and their calculated scores included:
1. District 1 = 0.008353
2. District 6 = 0.009188
3. District 5 = 0.009332
4. District 3 = 0.010891
5. District 4 = 0.010998
This shows a great example of how spread out and broken up one voting district can be due to gerrymandering.
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