Brooke's GIS Blog

 The objectives of the final week's lab were to analyze vector data to determine effects on scale, and analyze raster data to determine the effects on resolution. Additionally, another task was to learn the effects of the Modifiable Area Unit Problem (MAUP) using Ordinary Least Squares (OLS), as well as identifying multipart features and measuring compactness to view Congressional Districts impacted by gerrymandering. Beginning the post with vector data scale and raster resolution, as scale increases, vector data becomes less detailed. As scale decreases, vector data becomes more detailed because the map is more "zoomed" in. Considering raster resolution, as the resolution increases, the raster becomes less detailed and more pixelated, making it difficult to make out independent visual features, like elevational differences on a DEM. Gerrymandering is the process of modifying or redistricting congressional districts for political gain. This can be measured by calculating ...

  The goals of this week's lab were to practice with different surface interpolation techniques including Theissen Polygons, Inverse Distance Weighted Interpolation, and Spline Interpolation including tenson and regularized. Additional goals were to interpret and compare and contrast the results from the interpolation techniques. A dataset containing sampling points with the corresponding level of Biochemical Oxygen Demand (BOD) were provided, and the lab gave instructions on performing different interpolation techniques. Beginning with the non-spatial interpolation technique, which was simply looking at the attribute table using the statistics tool to determine the data's statistics, like maximum, minimum, average and standard deviation. A benefit of this method is that it is user-friendly, while a con is a lack of accuracy compared to other methods. Moving on to the first spatial interpolation technique, Thiessen Polygons. Thieseen Polygons draw on neighboring datasets to cre...