A. Starling's GIS Blog

       An important consideration in mapping to take into account is scale. The larger the scale value, the more detailed a map may be (showing a smaller area). For example, if we were to make a map of airports with airports represented as points (vector data) and we zoomed in significantly, we would be able to see many more details (polygon features indicating building, lines indicating runways). As a note, vector data consists of points, lines, and polygons.      Raster data, on the other hand, is comprised of cells. The smaller the cells, the more details/higher resolution. The bigger the cells, the lower the resolution. The level of detail relates to the processing time of the data cells, so the higher the resolution, the more processing time is necessary.      Gerrymandering is manipulation of districts to give an advantage to one political party over another. One way to determine if a district is undergoing gerrymandering is to...

     In this lab, we sought to use different interpolation methods to determine which would be the best to most accurately depict the BOD (Biochemical Oxygen Demand) within the Tampa Bay area in Florida. We explored Thiessen, Inverse Distance Weighted (IDW), and Spline (Regularized and Tension) methods. Thiessen interpolation advantages include the interpolated surface equaling the values of each given sample point, which means there isn’t a difference between the true and interpolated values. Disadvantages include the abrupt change of between values at the edges of the polygons this interpolation method outputs. While Thiessen interpolation is good for sampled locations, it may not be as accurate around unsampled locations. IDW averages values and determines the weight based off point cell proximity. This method assumes points closer to one another are more alike and is good for more evenly distributed, uniform, dense points. Spline interpolation will estimate va...