Ryan wins yet another Draw the Lines award, building on previous honors by this time submitting a PA House map worthy of statewide honors. He balances population equivalance, compactness, and limiting splits, along with a descriptive essay, to earn his third DTL nod.
This is now my third round of submissions of maps into the Draw the Lines contest. Looking back at my previous submissions, I always start my essays out the same way. I want to get the point across that gerrymandering is bad and it has such a negative influence on our democracy. However, by this point, I think we all know that gerrymandering needs to be stopped. The reason anyone is reading this essay is because they know that we can eliminate gerrymandering by drawing good, fair maps. Therefore, I want to dedicate the majority of words in this essay discussing my map, not what we all already agree upon. Gerrymandering has been used as a tool for politicians to stay in power and choose their voters, rather than the other way around. Through careful and concise action, we can slowly start to give more power back to the people and make sure that our representatives actually represent us.
In the last round of submissions, my state House map did not win any awards. I had spent countless hours creating a map which I believed to cover all of my main goals. My main goal of the map was population equivalence, followed by compactness and then city/county splits. When considering how I would create my new map for this competition, I came to the conclusion of the same goals as my previous map. These goals were all explained in my previous submission, as I considered competitiveness to almost be the equivalent to gerrymandering, just on the other side of the spectrum. With this in mind, I decided to edit my old map and make it even better, rather than creating a new map. This approach led to some struggles, but also made other parts easier. I did not have to spend the time creating an entire new map. However, I believe that, particularly with the state House, editing a completed map is more difficult than starting from scratch. This is especially true when focusing on specific metrics. I found it extremely arduous to work towards a better population equivalence when, many times, nearby districts of the district I was currently working on had a population deviation that was matching in positivity or negativity. This problem required me to create a chain of many districts from which I was switching only one or two precincts each to even out the population for all of them. I had to maintain a written list of edited districts as I worked to make sure each district would contain the necessary population equivalence by the end of the editing. Compactness was almost just as difficult as population equivalence to optimize when editing a previous map instead of creating a new one. This is because instead of being able to use any needed space to create compact districts, when editing the map, each district that I tried to make more compact ended up negatively affecting an adjacent district. It took careful planning, editing, and geometry to figure out how to change multiple districts while making all of them more compact. My map from the previous competition, before any of the editing for this competition had been done, contained some strong metrics in population equivalence, compactness, and city/county splits. After I had finished refining my map, its metrics are even stronger and it has, in my opinion, a much better overall look to it and is much fairer.
My biggest goal for the map was population equivalence. This is measured by the district with the greatest deviation from the target population for a single district. The target population for the state House is 62,573 people per district. The original map I submitted had a greatest population deviance of 0.97%. This is already a very great score, but I challenged myself to do even better. I targeted my efforts towards the districts with the greatest population deviation. These were districts 77 and 189. After careful editing at the precinct level, I was able to get an even better greatest population deviation at only 0.93%. This is a decrease of approximately 4.12% in the greatest population deviation for my map, which is an even better score and accounts for the largest difference in population to be only roughly 584 people.
My secondary goal for the map was compactness. Compactness is measured through DRA in two main ways, the Polsby-Popper and Reock score. The original map I submitted scored a 0.4061 and 0.4212 for Polsby-Popper and Reock scores respectively. My strategy for increasing compactness scores was to target the districts that had the lowest individual compactness scores and try to increase those scores. I was able to increase the compactness scores for several districts, but significantly increased the scores for districts 100 and 23. For district 100, I increased the Polsby-Popper score from 0.3301 to 0.4847, the Reock score from 0.3999 to 0.6173 and the KIWYSI (Know It When You See It) score from 56 to 86. The KIWYSI score is a more advanced predictive model that takes many parameters, including the Polsby-Popper and Reock scores to determine if a district is compact through a 0-100 score. For district 23, I was able to increase the Polsby-Popper score from 0.2055 to 0.3440, the Reock score from 0.2161 to 0.2905, and the KIWYSI score from 19 to 51. Overall, along with some other changes, I was able to increase the overall Polsby-Popper score for my map from 0.4061 to 0.4066 and the Reock score from 0.4212 to 0.4223. Overall, these changes do not seem very notable. However, with a map full of 203 districts, this increase in compactness scores is significant and also hides the two more districts that went from very sprawled districts to compact districts. I am sure the members of those districts specifically would appreciate the changes to their districts, which represents approximately another 125,146 people satisfied with the map.
Lastly, the final metric I tried to work on was city/county splitting. In this case, I was not able to improve the metric. I found fixing city/county splits to be the hardest when editing a completed map. Whenever trying to fix a split county, multiple districts would have to be taken into account to avoid hurting my more important metrics of population equivalence and compactness. To increase the other metrics, my city/county splits actually increased just slightly, which is an unfortunate side effect I couldn’t do much about. My rating for splitting for my map went form an 87 to an 86. I think this displays one of the primary challenges with any political mapping. When you focus on one metric, normally the other metrics suffer. A perfect map that makes everyone happy simply does not exist. The best maps are able to find a tradeoff between metrics and work the best with their given goals. I am very happy I was somehow able to increase both my population equivalence and compactness scores in a map I created that already had scores that are much better than I had anticipated. In my opinion, this is now the best map I have ever created for any of these competitions.