In this lab I learned how scale and resolution can change the way spatial data looks and behaves. For the vector data section, I saw how features like water bodies and hydrographic lines change depending on the map scale. At a larger scale the boundaries were more detailed and accurate, but as the scale got smaller, the shapes became simpler and lost detail. This showed how using the wrong scale can affect measurements like area and perimeter.
For the raster data section, I created several digital elevation models (DEMs) at different resolutions. The high-resolution rasters showed steeper slopes and more precise elevation changes, while the low-resolution rasters smoothed out the terrain and lowered the average slope values. This made it clear that higher resolution captures more variation but can also increase processing time and file size.
In the final part of the lab, I studied gerrymandering and district compactness. Gerrymandering happens when political boundaries are drawn in a way that gives one group an advantage over another. It can be measured using compactness formulas like the Polsby-Popper score, which compares a district’s area to its perimeter. A score close to 1 means the district is compact, and lower numbers show that it’s more stretched or oddly shaped.
After running the compactness analysis, I found that districts 12, 3, 5, 1, and 7 had the lowest Polsby-Popper scores, meaning they were the least compact. Even though these districts looked irregular, none of them seemed to be intentionally gerrymandered. The shapes mostly followed natural or geographic boundaries. Below, I included one of these low-compactness districts as my screenshot example of an “offender.”
This lab helped me understand how map scale, raster resolution, and boundary delineation can all impact spatial data analysis and interpretation. It also showed how statistical tools can help detect patterns like gerrymandering, but visual and contextual review are still important to understand the full picture.
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