Lab 14 - Aggregation, MAUP, and Gerrymandering
This week we covered one of the most important topics in Geographic Information Science: the effects of spatial data aggregation. We aggregate spatial datasets all the time! But did you know that limitations and consequences exist, which in turn can create varied results in spatial analysis? Two of the major implications of spatial data aggregation are: 1) Ecological Fallacy, and 2) the Modifiable Areal Unit Problem (or MAUP). What is important to remember is that in either of these implications, loss of detail will occur. And the reason why this will happen is due to analysis being completely dependent on the delineation of polygon boundaries. The good news is that there are some strategies to limit the effect: such as using smaller units when possible, and designing fair and consistent zones.
In our lab assignment, we also learned about gerrymandering. "In the process of setting electoral districts, gerrymandering is the practice intended to establish a political advantage for a particular party or group by manipulating district boundaries" (Wikipedia). For our lab exercises, we worked with contiguous U.S. Congressional District boundaries and County boundaries to determine how many districts were broken up into multiple, separate polygons, and then identified which ones could have easily been made into single parts.
Finally, we learned how to measure "compactness" and "community" of areal polygons. Compactness minimizes bizarre-shaped legislative districts; and community minimizes diving counties into multiple districts. These are guidelines set forth to achieve a most ideal boundary. For our last tasks in our lab assignment, I used geoprocessing tools such as Spatial Joins, Summarize, Field Calculator, and Calculate Geometry to come up with a list of Top 10 Worst Compactness and Community Districts that deviate from the ideal boundary guidelines. Two of my sample screenshots are provided below.
In our lab assignment, we also learned about gerrymandering. "In the process of setting electoral districts, gerrymandering is the practice intended to establish a political advantage for a particular party or group by manipulating district boundaries" (Wikipedia). For our lab exercises, we worked with contiguous U.S. Congressional District boundaries and County boundaries to determine how many districts were broken up into multiple, separate polygons, and then identified which ones could have easily been made into single parts.
Finally, we learned how to measure "compactness" and "community" of areal polygons. Compactness minimizes bizarre-shaped legislative districts; and community minimizes diving counties into multiple districts. These are guidelines set forth to achieve a most ideal boundary. For our last tasks in our lab assignment, I used geoprocessing tools such as Spatial Joins, Summarize, Field Calculator, and Calculate Geometry to come up with a list of Top 10 Worst Compactness and Community Districts that deviate from the ideal boundary guidelines. Two of my sample screenshots are provided below.
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