GIS Blog: Melanie A. Sutton

In this lab, we examined the effects of scale and resolution on the properties of spatial data. On vector data, as scale becomes finer, we expect to see increases in the number of areas and volumes, more detail in boundaries, and more homogeneity in the features (Goodchild, 2011, pp. 6). This occurs because coarser scales (e.g., 1:100000) are more generalized, and thus a smaller number of polygons capture the most significantly sized features, while omitting smaller features. As more details are captured at finer scales (e.g., 1:1200) compared to coarser scales, geometric characteristics such as the sum of total lengths for hydrographic features will increase, based on the addition of the captured smaller features. On raster data, as resolution becomes coarser (e.g., going from 1x1m to 90x90m cells), the image becomes more smoothed, as represented by increasingly smaller average slopes as steep regions are averaged into surrounding non-steep terrain regions. Kienzle observed this whe

  In this lab we examined various interpolation methods to create a surface of water quality in Tampa Bay. Each method received as input a set of sample points containing Biochemical Oxygen Demand (BOD) values in milligrams per liter that were recorded from various points throughout the study region. The interpolation methods used this case study included the following: Thiessen IDW Spline - Regularized Spline - Tension This interpolation method is a special case of IDW where only the nearest water sample is used to estimate the BOD value at an unsampled location. This interpolation method uses a preset number of nearest neighbors to estimate the BOD value at an unsampled location, and inversely weights the contributing strength of these neighbors based on their distances from the unsampled location. Spline interpolation methods work to fit a smooth surface exactly touching the BOD sampled points, while