Will America’s latest redistricting cycle be even worse than the last?

幸せな木曜日,

In state capitals all across the United States, mapmakers are quietly beginning the hugely consequential process of drawing the political boundaries that will be in place for the next decade.

Using brand-new census data, these mapmakers are looking at demographics but also years of past election results as they draw their districts. Aided by sophisticated mapping software, they can craft districts that virtually guarantee particular election outcomes for years to come. It’s a huge boon for Republicans, who control the redistricting process in many states this year. 同時に, there’s increasing awareness of how this kind of map rigging, called gerrymandering, undermines the principle of free and fair elections.

David Daley, a journalist who has written extensively on gerrymandering, has warned that advances in technology enabled unprecedented levels of manipulation in the last redistricting cycle. “The data, the technology, and the ease and certainty with which [districts] can be manipulated if either side has the political will to do so are what made the post-2010 redistricting cycle fundamentally different from any other in the modern era,” he wrote in his 2016 本, Ratf**ked: Why Your Vote Doesn’t Count.

So over the last few months, I’ve asked mapmakers and other experts whether they think new technology will make gerrymandering this year more extreme than it was a decade ago. Their responses surprised me.

While the technology is better, 彼らは言った, they doubted that things would be much different than they were in the 2010s. Michael Li, a redistricting expert at the Brennan Center for Justice, compared the technology to iPhones. The iPhones a decade ago were pretty good – the ones we have now are better.

“What used to be a dark art is now a dark science,」 he told me. “Before, you weren’t sure about the data, but now you’re much more certain so you’re able to draw things in ways that can be more aggressive.”

I was also curious how the people who built the technology widely used for redistricting, a program called Maptitude, felt about the way their product was being used. Howard Slavin, the founder and president of Caliper, the company that makes Maptitude, told me he was caught off guard when politicians used it to severely gerrymander districts across the country a decade ago.

“We were horrified with what some people had done with our software," 彼は言った. “We were software guys, math guys. We were making tools and stuff. And we weren’t invested in, ええと, trying to make one side win against another or anything like that.”

One thing I did not expect to hear from experts is that they are somewhat optimistic that technology can be used to prevent extreme gerrymandering.

Unlike 10 数年前, there are now online tools that can immediately score proposed maps so the public can spot a gerrymander. There’s also fairly sophisticated mapping software that’s widely available to the public (you can try some of it ここに, ここに, そして ここに). With some training, ordinary citizens can use that software to create their own maps and keep a close eye on the maps that politicians are drawing.

If a regular citizen can draw an alternative district that doesn’t split up a community, or doesn’t stretch across the entire state, it puts more pressure on politicians to justify their strange-looking districts. Of course, a huge caveat in all of this is that lawmakers usually go to great lengths to prevent the public from seeing their maps until the last minute.

Going into this, I had assumed that maps drawn by algorithms – tools that can be used for “good or evil”, in the words of Moon Duchin, a mathematician at Tufts – would in fact be deployed in nefarious ways.

“If you want to do a maximal gerrymander and you want it to look pretty nice, and you want to respect county and municipality boundaries, then an algorithm can be helpful in identifying certain solutions that a human just might not stumble on to,” said Nicholas Stephanopolous, a law professor at Harvard.

But Stephanopolous and others believe that humans don’t really need algorithms to gerrymander – they’re good enough at it on their own. And they’re actually hopeful that algorithms will help observers discern how badly a map is gerrymandered in the first place.

That’s because experts can now tell an algorithm to generate thousands of maps that only weigh neutral criteria, and then compare those maps to the ones lawmakers are proposing. If the partisan outcomes in the proposed maps are far outside the range of expected possibilities from the thousands of sample maps, it becomes easier to spot a gerrymander.

A few years ago, the Pennsylvania supreme court said this kind of analysis was “perhaps the most compelling evidence” the state’s congressional map was so egregiously gerrymandered that it needed to be struck down.

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