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Scale Effect and Spatial Data Aggregation

 In this lab we first looked at how the scale at which we analyze data can affect geometric properties. Looking at data at three different scales (1:1200, 1:24000, 1:100000) I was surprised by my final calculations. I thought that there would be a lineal proportional relationship along the scales, but I did not find this. It makes sense that the greatest resolution [1:1200] would have the most detail of geometric properties, but I was surprised that 1:100000 had greater geometric properties than the medium resolution.


I resampled the data using the Bilinear technique and found the following effect on DEM resolution. 


As DEM resolution increases the average slope in degrees decreases. This makes sense because as you get closer, more in detail by getting closer, you are zooming in as actively seeing less features altogether. 

In the last part of the lab, we looked into the Gerrymandering of districts in the USA. Gerrymandering essentially breaks up congressional districts in a manner that establishes an unfair advantage to a given political party. One can clearly see by the following example how oftentimes the way districts are broken up makes no sense whatsoever. In the following district in Michigan, this district is divided into 4 different sections that could have easily been absorbed by neighboring land. 






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