Dalton Inman

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 relatio...

For this lab, we used three different interpolation methods: Thiessen , Inverse Distance Weighting (IDW) , and Spline , to map Biochemical Oxygen Demand (BOD) across Tampa Bay, and compared how each method handles spatial variation and data distribution. The easiest and most simple method was Thiessen interpolation , which assigns each location the value of its nearest sample point, which means the interpolated surface matches the exact values at the sample sites. This wasn't the best type of interpolation though as it creates abrupt transitions between the polygons which made it not as good at modeling the continuous environmental data like BOD. Spline interpolation fits a curved surface that must pass directly through all of the input points. This created a smoothed, and honestly, very visually appealing interpolation, but seemed to be overestimating or underestimating values in areas with low data. This is what was happening in certain splots of the study area which is wh...