Collection of my maps on u/4lmeme and Kevin’s random tweet generator 🔰🧦
I started making election maps long before I had the confidence to post them. During quarantine, I found out about Dave’s Redistricting (DRA) and added it to the 800+ maps saved as of now. My first “published” maps were DRA exports into QGIS and Inkscape (colored manually and NOT clipped to the coastline). So my start on mapping was definitely humble, but we all start somewhere.
Even though these maps look pretty similar, this is where I learned a lot about QGIS and Inkscape (check out the progress in Texas!)
My maps started to get a lot better when I started to make imaginary election maps. I got more comfortable with QGIS and Python to simulate elections and make prettier visuals. Election Lore is definitely not my strong suit; my process was “map now, write later” for most of them.
These two New York maps were the most fun to make. Creating the districts and regions, simulating results, and creating the vector images was time-consuming but it was no doubt worth it. These are my favorite hypothetical elections, and I’m probably not topping them.
Although the style of the map didn’t change much, my processes shifted a lot. I improved the way I collected district demographic data in QGIS (thanks pyQGIS) and started to explore Census shapefiles for this Texas map.
I shifted my focus on modeling and predictions as the 2022 midterms got closer. I coded up a pretty simple model based on national polling and past election results to create a 2022 house model. This was also when I started posting on Twitter a lot more than on Reddit (thank god).
After the terrible failure of my models and the election analysis as a whole last November, I shifted again to making just demographic maps. But behind the scenes, I was trying to collect data for a much more complete model in 2024.
In this data-wrangling effort, I got lost on a tangent on the density of US cities and urban areas. Despite losing focus, I am really glad to see how the analysis and data turned out. But the real work had to start, which is where I am at now with getting election results by block group and mastering the hell that is pyQGIS.
These two maps have definitely taken the most time to complete, fighting with QGIS and getting consistent for wayyy too long. But they represent my triumph in connecting my Python scripts to QGIS mapping.
As the 2024 election approaches, my goal is to create a dynamic, interconnected model for the Presidency, Senate, and House of Representatives. So I’ve got a lot of work cut out for me, but I’m glad to be back on ET and making maps.
