GIS Portfolio - Michael Malone

  The focus of this lab was to identify features in aerial images using various techniques. The top map used shows features that were identified using one of four methods, pattern, shape and size, association, and shadows. The bottom identified areas using texture and tone from fine to course and light to dark.

 This final lab focused on the way scale and spatial data aggregation can affect the quality of data. The resolution of raster data can greatly affect the variation seen in the final results, reducing the resolution tends to smooth out data resulting in more consistent results through generalization. Altering the scale of vector data can diminish the impact of data depending on the way it's aggregated. Specific data sets that may stand out on their own may be diminished when combined with larger areas making the final impact negligible, this is most famously seen in gerrymandering when political districts are redrawn, sometimes in outlandish fashion, in order to give a specific party a political advantage. This is done by splitting up areas that may contain high density of one party into areas that contain high densities of the opposing party, essentially negating the impact of the opposing votes. The outlandishness of the districts can be quantified using the Polsby-Poplar score w

  This weeks module focused on identifying the best interpolation method for modeling the air quality over Tampa Bay. Four methods were tested using the same set of sample points Thiessen, Inverse Weighted Distance (IDW), Tensioned Spline (seen above), Regularized Spline. Thiessen Interpolation assigns all cells in the raster with the value of the nearest sample point. IDW calculates the value of all cells by considered multiple sample points nearby and giving closer points a higher weight than further points. Both Spline methods create a smooth surface over the sample points but the regularized version creates a smooth curvature regardless of the range of values in the sample meaning cell values can end up both above and below the minimum and maximum values found in the sample. The tension model attempts to fix this by constricted the curvature of values to the ranges found in the sample points.