Module 3.1 Scale Effect and Spatial Data Aggregation

Here we are at the finish line in Special Topics in Geographic Science with the last module! For this week's lab we covered several topics such as scale effects, basic resolution, and gerrymandering, with two main objectives: To examine the effects of scale and resolution on the properties of spatial data and become familiar with the Modifiable Area Unit Problem (MAUP). Some of the learning outcomes for this week were being able to understand of the effect of the Modifiable Area Unit Problem (MAUP) using OLS analysis. Understanding the effects of scale on vector data and being able to explain the effects of resolution on raster data. As well as knowing how to Identify multipart features and how to measure compactness.

Vector data points, lines, and polygons are highly sensitive to scale. At smaller scales (zoomed out), features are generalized to reduce complexity and improve performance. This can lead to simplification of boundaries, omission of minor features, and distortion of shapes. At larger scales (zoomed in), more detail is preserved, but the data becomes heavier and more granular. This scale dependency affects spatial analysis outcomes, especially when comparing features across datasets with inconsistent levels of generalization.

Raster data is defined by its resolution the size of each pixel in real world units. High resolution rasters  capture fine detail but require more storage and processing power. Low resolution rasters are more efficient but may obscure critical spatial patterns. Resolution directly influences classification accuracy, edge detection, and the reliability of derived products like slope or land cover.

Gerrymandering is the deliberate manipulation of political district boundaries to favor a particular party or group. It often results in oddly shaped districts that dilute voting power or split communities. The term originated in 1812 when Massachusetts Governor Elbridge Gerry approved a district shaped like a salamander hence, “Gerry-mander.”

One way to quantify gerrymandering is through compactness metrics, which assess how geometrically “regular” a district is. The Polsby-Popper score is a widely used measure:

\text{PP\_Score} = \frac{4\pi \times \text{Area}}{\text{Perimeter}^2}

A score closer to 1 indicates a compact, circular shape.

A score closer to 0 suggests a sprawling, irregular boundary a red flag for gerrymandering.

Below is a screenshot of one of the worst offenders based on its Polsby-Popper score. This district exhibits extreme boundary irregularity, with fragmented shapes and unnatural extensions that defy geographic logic.