GIS 4035 Module 1

 Visual Interpretation of Aerial Photographs Lab

Our lab was divided into three parts this week. The first exercise was focused on identifying Tone and Texture.  Tone is determined by the brightness or darkness of an area whereas texture involves the smoothness or coarseness of the area. We were create a a feature class Tone and another for Texture. We then selected five good examples to show the different levels in each feature class. See map below:

The next part of the lab focused on identifying features based on four criteria: shape/size, shadow, pattern, and association. In this section we used points instead of polygons. We selected three examples of each (only 2 for association) and created a map layout.

The third and last portion of the lab involved comparing the color difference for feature in a TrueColor image and a False Color Infrared image.  We selected five points on the TrueColor map to make direct comparison easier. For me the difficulty was where the area colors on the TrueColor image where mixed or contained off shades. This made the color comparisons with the False Color Infrared less straight forward.

   

GIS 5935 Scale Effect and Spatial Data Aggregation

 Lab 6

This last lab of the course was very challenging, we covered scale effect on vector and raster data, the MAUP (Modifiable Area Unit Problem, gerrymandering, compactness and multipart features. Lets first examine the effects of scale on vector data. We found that when using smaller scales (higher resolution) the more generalized the features are. Therefore, smaller scales (generalized features) are good for faster rendering of data but for a more accurate analysis finer details are needed. With rasters, increasing cell size (resolution), the average slope is underestimated and decreases.

In the last part of our lab we examined gerrymandering, specifically in congressional districts in the US. Gerrymandering is "drawing a district shape with intentional bias (benefiting one party over another)" (Morgan and Evans, 2018).  There is more than one method to measure gerrymandering. In this lab we used the Polsby-Popper test for compactness. It evaluates the compactness of the shape, in this case a congressional district, mathematically and give it a score. A score of 0 indicates a lack of compactness and a score of 1 indicates maximum compactness. This is the Polsby-Popper formula:

D is the district, P(D) is the perimeter of the district, and A(D) is the area of the district.

 In this lab we examined congressional districts only in the continental United States and see which areas (districts)  were the worst offenders of gerrymandering. Below is a screenshot for the worst offender, District 12:

References:

Morgan, J.D. and Evans, J. (2018). Aggregation of Spatial Entities and Legislative RedistrictingLinks to an external site.. The Geographic Information Science & Technology Body of Knowledge (3rd Quarter 2018 Edition), John P. Wilson (Ed.). DOI:10.22224/gistbok/2018.3.6