One thing it says that in recent court decisions shape is not enough. Partisan demographics and racial demographics are enough to determine that a gerrymander has happened to the point that a court can throw it out. I think this implicitly declares that in order to do it right the first time, these features of demography would have to be accounted for in the initial district drawing after a Census. Party affiliation isn't part of the Census, but it's State data in the voter registration files.
Designing districts towards demographic ends is at best gerrymandering for good or shoddy proportional representation.
I still think that if we want proportional representation then we should actually do that and not fake it with badly drawn districts. I think we need an answer to the question: what is a district for? I think it is for representing a locality or a region. But, in practice, maybe it's for electing a representative. And we want our representatives to follow our population; and that means some kind of proportionality. So, if locality or region doesn't matter, and we have to have districts, then draw the district however is needed to meet the demographic goals. And now my logic is eating its own tail and I'm back to the conclusion I started with, we want proportional representation, and we should do it right.
Paper ballots. This makes ranked ballots you can print and fill out on paper; also a tabulation sheet for counting with Condorcet’s method. (Instructional video is planned.)
Online votes. I linked to an silly example voting between four flavors of ice cream. But make any poll you like there. Requires fb or google log in to create a poll or vote. Doesn’t spam anyone, just make a link and send the link to people you want to vote.
Nine and a half minutes.
Delivered at TEDx Cambridge, 2016 June 9.
Some time in 2005 I started tinkering on working out a solver for impartial compact redistricting. There was one big false start around trying to use genetic algorithms that worked okay at zip-code level data but didn't scale up to the finest resolution Census data. Now I have two different algorithms implemented that seem to work pretty well. There was a phase of using a mesh triangulation package to fake up adjacency between census block centers, but eventually I downloaded the full geographic data with the lat,lon coordinate shapes of everything in the country and processed that to get real adjacency. I took all that geometric data and wrote my own rasterizer because other packages seemed cumbersome and inefficient when dealing with 600,000 polygons of 4-20 edges each. There was a bug in that rasterizer that went unsolved for about six years. I wrote what could have been used as a distributed client, but I only ever ran it on one computer and that turned out to be enough. I had scripts collecting the best solutions I found and had a bug in which one they presented that went undetected for around five years. I got a few shout outs from minor tech bloggers and one article in a law journal. In 2014 I got cited by a washington post blogger declaring, "This Computer Programmer Solved Gerrymandering In His Spare Time". And most recently I got invited to speak at TEDx Cambridge where I gave a ten minute talk on gerrymandering in the US and an impartial alternative.
I'm not sure what's next, but there are a few things to try before 2020.