This week we investigated how scale has an impact on both raster and vector data. We also looked at other issues such as the modifiable areal unit problem and how to measure gerrymandering.
To explore the impact on vector data, I compared three sets of data of streams and bodies of water at three different scales. The larger scale data set had a much greater level of detail showing all the crenelations of each tiny stream, while the smaller scale data set provided a much greater generalization with a lower level of detail.
For the raster data, we were provided with a DEM LiDAR data set. I then resampled the data at multiple resolutions ranging from 1 meter cells to 90 meter cells. Then on those resampled layers, I ran the slope tool for each one and looked at the median slope. The slope value decreases as the resolution becomes lower. The lower resolution a data set, the less information it captures.
Gerrymandering is the act of drawing a political district to include certain populations and exclude others, in an effort to create a district that is more likely to vote in favor of the party delineating the district's boundaries. Gerrymandering can be measured using the Polsby-Popper score which is calculated using 4π(Area of the District)/(Perimeter of the District^2). This measures the level of compactness the district has. The more compact it is, the closer to 1 the score will be. Conversely, the closer to zero the score is the worse the boundary is.
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| The Congressional District 7 of Pennsylvania has a very low Polsby-Popper score of 0.04099574. This district was so blatantly gerrymandered that in 2018 the Supreme Court of Pennsylvania ruled that the boundaries had to be changed. |
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