GIS4930 - Module 3.1 - Scale Effect and Spatial Data Aggregation

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For this final module, we were tasked to look at how scale and resolution affect vector and raster data, as well as how the Modifiable Areal Unit Problem (MAUP) affects spatial analysis.

For the vector portion, we had to analyzed two types of hydrographic features (Waterbodies and Flowlines) in Wake County at three different scales: 1:1,200, 1:24,000, and 1:100,000. The larger scale data had much more detail, while the smaller scales were more simple and missing some details like smaller streams and water bodies.

In the raster analysis, we had to resample a 1m LIDAR DEM into coarser resolutions at 2m, 5m, 10m, 30m, and 50m, using. I used bilinear interpolation over Cubic or Neighbor, since it made the most realistic elevation surface. The average slope decreased when coarser resolutions were used (30m and 50m) with the terrain was becoming less detailed. The finer resolutions kept steeper and more detailed slopes.

 

We then looked at how the relationship between poverty and race changed when data was aggregated from block groups to ZIP codes, house districts, and counties. Correlations weakened as the area units became larger, showing that Modifiable Areal Unit Problem (MAUP) uses larger boundaries to average out the local differences between groups which reduces spatial detail.

For the final part, we explored gerrymandering by analyzing U.S. congressional districts. Gerrymandering is the manipulating of district boundaries to give unfair political advantage to governmental parties. I found 15 multipart districts in the continental United States, many of which were split by geography such as water, while others were divided for seemingly no cause. Using the Polsby-Popper score to measure compactness (Perimeter)24π×Area​), we were tasked to find the least compact or “worst offender” districts.

 

Least Compact District in NC

 

 

 

 

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