Module 6: Scale Effect and Spatial Data Aggregation

During this week's module, we learned about scale effect and spatial data aggregation. Map data with a large scale typically has a lot of detailed information in it, but small-scale maps usually do not.  We also learned about spatial data aggregation and the Modified Area Unit Problem, which is a statistical bias present when conducting spatial data analysis, which can affect statistical results.

We were provided with US Congressional Districts and asked to determine if gerrymandering had occurred.  Gerrymandering is the process of changing voting district boundaries for political advantage. I learned about the purpose of the Polsby Popper score, which is calculated using the following formula:  PP= 4πA/P²  , Where A= Area ; P=Perimeter.  If the resulting number is closer to 1, that means that the area is compact.  If the resulting number is low (or closer to zero), this generally indicates that the area is non-compact. 

I calculated the Polsby Popper scores for all US Congressional Districts, which indicated that North Carolina’s District 12 had the least compactness score.  It appears that the district is spread out, and not contiguous with neighboring districts.

North Carolina - District 12 (Least Compactness Score)