First, let’s acknowledge how impressive it is that William’s population equivalence score was below 1,000, despite having to draw 203 districts. Bravo. He did this without sacrificing his other two main goals, compactness and limiting county splits. His scores on those metrics ranked near the top of all mappers in both the House or Senate competitions. That is not an easy feat. William was also one of the few entrants to the legislative competitions who explicitly talked about the outreach that he had done. Mapping should be a communal affair, after all. We’ll look forward to seeing William in the DTL winner’s circle for the third time.
As a junior studying political science and history, my interest in ending gerrymandering is what all citizens should have— a universal one. Gerrymandering reinforces the worst influences of our political system in a way the founders never intended. With the U.S. Supreme Court reluctant to preside over the matter once and for all, the deck remains stacked in states that have been gerrymandered (PA, NC, etc).
With this map, I sought to embody several key principles: population equivalence, county splits, and compactness. Although the limitations for population variance were relaxed somewhat on state maps, compared to congressional,, I opted for a more difficult challenge— every district would remain within a thousand people of the target value. I would also attempt to stay within county lines as best as possible and keep districts aesthetically pleasing and compact.
With regard to these values, this map excels. The highest deviation from the target population is 999, or 1.59% deviation. The lowest deviation is 1 person from the target population. According to the compactness measure for the Dave's Redistricting App (DRA) platform, the average compactness is 69.
Population equivalence is a key element in ensuring that no citizen’s vote has a disproportionate effect on the outcome. Although for this particular contest I could have deviated 5-10% from the target population, the question would have been: Why do that? By aiming for as close to an even split as possible without utilizing a computer algorithm, this approach dramatically mitigates any political bias, packing, or other gerrymandering tactics that would attempt to discredit my map.
All this said, some may wonder why I did not factor in competitiveness or choose to let that guide my cursor as I painted districts. I thought about this throughout the process, and it reminded me of a familiar quote: “Lies, damned lies, and statistics.” In DRA, the statistics used for determining partisan bias is the 2016 presidential vote. While that is certainly a fine metric to utilize in anticipating district competitiveness, there are other ways to anticipate how competitive a district will be, such as the number of voters registered with each party.
With regard to outreach, my teammate Gregory Chang acted as a springboard for discussing our thoughts and rationalizing what map values to espouse in our entries. While we worked separately, he on the State Senate map and I on the State House, we nonetheless ensured that our two maps carry on the legacy of maps from Wilkes.
Lastly, when drawing this map, I had a painful realization: I should move to a less populous state and draw maps there. At roughly 63,000 people for each of the 203 districts in Pennsylvania, this was far more arduous than I had anticipated. Nonetheless, I pressed on and ensured that districts encompassed whole communities and counties as population restrictions would permit.