GIS5935 Mod6: Scale Effect and Spatial Data Aggregation


 In this lab, I had the chance to dive deep into how scale and resolution affect spatial data and how spatial aggregation can impact analysis outcomes. I started with hydrographic vector data for Wake County at multiple scales—from 1:1,200 to 1:100,000. Comparing the total lengths, areas, and perimeters of rivers and lakes, it was clear that larger-scale data captures far more detail, while smaller-scale data tends to generalize features and omit smaller elements. This exercise highlighted how scale influences geometric properties, which is critical when making decisions or interpreting spatial patterns.

Next, I examined raster data by working with LIDAR-derived DEMs at various resolutions. As expected, coarser resolutions smoothed out the terrain and lowered the average slope, while finer resolutions captured subtle topographic variations. This illustrated the importance of choosing the right resolution for accurate terrain modeling and slope analysis—too coarse and you lose important details, too fine and processing can become cumbersome.

Finally, I explored gerrymandering by analyzing the compactness of US congressional districts using the Polsby-Popper score. This metric measures how irregular or elongated a district is, with lower scores indicating less compactness. Identifying the worst offenders provided a tangible example of how political boundaries can be manipulated, showing that the way boundaries are drawn can have profound political and social implications.

Overall, this lab tied together several fundamental GIS concepts: scale, resolution, spatial aggregation, and the consequences of boundary design. It was a great exercise in understanding how spatial data properties directly impact analysis results and, ultimately, real-world decisions in urban planning, environmental management, and political geography.

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