Special Topics in GIS - Module 6 - Scale Effect and Spatial Data Aggregation

The sixth and final module in Special Topics in GIS covered the topics of scale effect and spatial data aggregation. Understanding how we represent geographic data is crucial in GIS. In this lab, we explored scale effects on vector data, resolution effects on raster data, and how these concepts connect to the issue of gerrymandering.

Vector data (points, lines, and polygons) can appear very different depending on the scale. At a small scale (zoomed out), features may look simplified or smoothed. At a larger scale (zoomed in), more detail is visible, which can reveal complexities or errors not seen before. This affects how spatial relationships are interpreted, and can even change analysis results when aggregating or comparing areas.

Raster data stores information in grid cells. The resolution refers to the size of these cells, high resolution means smaller cells and more detail, while low resolution uses larger cells, which may miss important features or patterns. Resolution impacts the accuracy of measurements like land cover, elevation, or temperature, especially when zooming or resampling.

The final topic we explored was gerrymandering, which is the manipulation of political district boundaries to benefit a specific party or group. It often results in bizarrely shaped districts that dilute or over-concentrate voters. This undermines fair representation. In this part of the lab, the Modified Areal Unit Problem (MAUP) was explored in the context of political districts. According to ESRI, MAUP refers to a type of statistical bias that can arise during spatial analysis of aggregated data, where applying the same analysis to the same data yields different results depending on how the data is grouped or zoned.

One way to measure gerrymandering is through compactness. A common metric is the Polsby-Popper score, which compares a district's area to its perimeter. A lower score suggests a less compact (and potentially gerrymandered) shape. Below is a screenshot of a North Carolina District 12 that had the lowest Polsby-Popper score in the continental U.S. according to my calculations. Its irregular shape suggests it may have been drawn with intent beyond geographic or community boundaries, a potential sign of gerrymandering.

By examining the geometry of voting districts and understanding how scale and resolution affect spatial data, we can better identify and challenge distortions in political representation.

Overall, this lab was a great experience highlighting how the way we structure and visualize spatial data through scale, resolution, and boundary choices can deeply influence analysis and real-world outcomes. From population patterns to political fairness, understanding these effects is essential for responsible GIS work and informed decision-making.

Special Topics in GIS - Module 5 - Surfaces - Surface Interpolation

The fifth module in Special Topics in GIS introduced the topic of surface interpolation. In this lab, we explored the use of several interpolation techniques to create continuous surfaces of water quality across Tampa Bay. Interpolation is valuable because it allows us to estimate values between sampling points and better visualize spatial patterns. However, each technique approaches the problem differently and produces distinct results.

Thiessen polygons assign each location to the nearest sample point, which is simple to apply but results in abrupt boundaries that don’t reflect smooth changes in water quality. Inverse Distance Weighting (IDW) provides a more gradual surface, giving greater weight to nearby samples and reducing the blocky appearance of Thiessen. Spline goes further by fitting a smooth, curved surface through the data, producing a visually appealing result but sometimes creating unrealistic peaks or sinks in areas with clustered high values or sparse sampling. These differences highlight the importance of choosing an interpolation method that matches both the data characteristics and the purpose of the analysis. 

Below is a screenshot of my results using the Spline Tension technique:

Overall, this exercise showed that while all interpolation methods can create useful surfaces, their assumptions and behaviors vary widely. Understanding these differences is key to interpreting the results and making informed choices for an analysis. This lab gave me a stronger understanding of how interpolation works and why the method you choose matters. It was interesting to see how the same water quality data could look so different depending on the approach.