While redistricting seldom finds its way into the forefront of voters’ minds, its reach touches every aspect of the political systems, oftentimes determining the very direction of a country’s future for years if not decades to come.
Every 10 years in Canada and the US, designated groups redraw the boundaries of every statewide or provincial, and congressional district in the ways in which they see best fit. These districts are the ones in which everyday constituents vote for their representatives in the Canadian House of Commons and US House of Representatives, and yet, this process is entrenched in delays, corruption, and errors.
And while human commissions and legislatures are often great at drawing up decent maps, the sheer scale of the operation and the number of the variables make parts of the process more suited to computer algorithms.
"Redistricting is like an election in reverse. It's a great event. Usually the voters get to pick the politicians. In redistricting, the politicians get to pick the voters." - Thomas Hofeller, once referred to as the “the master of the modern gerrymander”$^{[1]}$
In January of 2023, I first pitched my idea of an algorithm that creates “perfectly redistricted” districts to the FUSION class and teachers to mixed reactions.
It was clear in my mind what I wanted to create, although at times, it felt as if I hadn’t fully considered where my solution stood in politics.
Was it actually beneficial? Is this something really that my audience wants? Why bother?
At the time, I didn’t have answers to any of those questions I posed to myself, but after some time researching, I was able to develop a potential audience, problem definition statement, and constraints to judge my solution.
<aside> <img src="/icons/document_purple.svg" alt="/icons/document_purple.svg" width="40px" /> Here’s what I had to say about my potential audience back in February:
The primary audience for this project, given that it’s about redistricting, are the independent commissions that currently work on redrawing the lines in Canada and the US. While I had originally intended to create a solution to target partisan Gerrymandering in the US, as I have done more research, I’ve come to realize that such extreme cases of gerrymandering can only be resolved by the same people who put those unfair districts into play (and given how effective those have been, that is unlikely).
Independent districts, used in all Canadian provinces and some US states, are generally well-meaning, and are most likely to use such algorithms/systems to the benefit of the people rather than political ambitions.
Beyond the redistricting process, such tools may also be helpful to those who are directly or in-directly affected by said lines. While drawing districts may not be of use for voters and politicians, the other two tools may be interesting to play with. Constituents can use evaluation tools to look at the why their district was put together in the way that it was, and see the important factors that went into the decision. Incumbents can look at their adjusted districts, and use simulation tools to predict what an election may look like.
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Despite all the work that I’ve done, I think this still stands true; since then, I’ve cut back on how ambitious my solution is, but the intent is still the same. I still want to create that redistricting tool that bolsters existing redistricting commissions, and I still want to utilize such a tool as a method of informing constituents and incumbents alike as a secondary audience.
How successful I was in creating a product for these audiences remain to be seen, but they have not changed much since the beginning of this process.