Module 3.1: Scale & Spatial Data Aggregation

 Module 3.1: Scale & Spatial Data Aggregation

This weeks module was about the Modifiable Areal Unit Problem (MAUP) which involve the scale and zonal effects of data. According to Manley (2014), the scale effect shows that there are hierarchies that are put into our work that if we are not aware can skew the data. Also, the scale effect is attributed to variation in numerical results owing strictly to the number of areal units used in the analysis of a given area (Openshaw and Taylor 1979). Lastly, when thinking about MAUP it is important to know that your data can be skewed but if you make changes in how you correlate your data such as with scale it can help make sure you are getting answers that make sense.

Another issue that arose this week was the basic resolution effects on raster data. When talking about raster data the most common are digital elevation models (DEM) which focuses on the grid cell size that has significant effects on derived terrain variables such as slope, aspect, and curvature. Knowing that the grid size changes the look of the maps helps to choose the correct raster grid cell size. Such as when trying to carry out realistic terrain analysis is limited by the quality of the DEM applied (Kienzle 2007). There are three important aspects to keep in mind when trying to use DEM to analysis data: 

• "The accuracy and distribution of the elevation points used to interpolate the DEM.
• The interpolation algorithm used to generate a continuous DEM.
• The chosen grid cell size."

Knowing all this information can help the GIS analyst understand how to use the data to get the results they might want for terrain analysis (Kienzle 2007).

The last issue that was in this weeks module was how gerrymandering of political boundaries can cause issues because of boundary definition. These boundaries can change to make it to where one political party has more of an advantage in that congressional district. When trying to determine the compactness of a district I learned about the Polsby-Popper score and the formula. First, we needed to get the area and perimeter of all the districts. I did it in kilometers to keep it consistent. The formula was not working for me to use it all at once, so I split it to three different attributes. I did the formula for the top (4 * pi * Area). Then another formula for the bottom (Perimeter^2). Lastly, I created a formula where I divided the top number by the bottom number. The worst offender was North Carolina Congressional District 12 with the score 0.029476. It is seen in the picture below.