Topic 3 - Module 1: Scale Effect and Spatial Data Aggregation

 Scale Effects on Vector Data:

In vector data, the scale at which features are represented can dramatically affect spatial analysis results. Larger scales (zoomed in) capture more detail, while smaller scales (zoomed out) generalize features. For example, analyzing counties versus ZIP codes can produce different statistical outcomes because aggregation over larger areas smooths local variability, a phenomenon known as the Modified Area Unit Problem (MAUP).

Resolution Effects on Raster Data:

Raster data are made of grid cells, and the cell size determines the spatial resolution. High-resolution rasters capture fine details but require more storage and processing power, whereas low-resolution rasters generalize information and may hide local variation. This affects analyses like slope, elevation, or population density surfaces, where resolution can change both visual and statistical results.

Gerrymandering:

Gerrymandering is the practice of drawing political district boundaries to favor a particular party or group, often resulting in irregular, non-compact districts. It can be measured using compactness metrics, such as the Polsby–Popper score, which compares the area of a district to the square of its perimeter. Scores closer to 1 indicate compact districts, while scores closer to 0 indicate elongated or bizarrely shaped districts. Other methods include analyzing the number of non-contiguous polygons and counting splits across counties.

The screenshot below shows one of the least compact districts identified using the Polsby–Popper score. Its irregular shape and elongated boundaries make it a clear example of a potential gerrymandering “offender.”

Congressional District 12: 



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