This week's lab covered the topic of spatial data aggregation. The lab this week looked into how congressional districts are within the United States. The first part of the lab looked at how compacted the districts are throughout the country. Compactness is based on the shape of the polygon. For example, oddly shaped polygons are not considered compact.
To determine the top 10 worst offenders of least compactness, I had to calculate the area and perimeter of each polygon. The odder the shape, the more likely the longer the perimeter. As the screenshot below will show, the district has a weird shape that clearly shows the district is covering across a lot of space to get certain groups in the district.
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| Compactness |
The other aspect of gerrymandering is community. It is ideal to have the least amount of districts in each county. Having more than one district to cover a county show that certain areas of the county could be select to achieve certain results. I made sure to exclude counties with large populations which would need multiple districts and then looked at how many districts fell in a county. Below are the results of the analysis.
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| Community |
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