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1813 Ohio's 3rd congressional district special election

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A special election was held in Ohio's 3rd congressional district on May 10, 1813 to fill a vacancy left by the resignation of Duncan McArthur (DR) on April 5, 1813 before Congress assembled.[1]

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  • ✪ Political Geometry: The Mathematics of Redistricting | Moon Duchin || Radcliffe Institute
  • ✪ United States Presidents and The Illuminati Masonic Power Structure


[MUSIC PLAYING] - Thank you. Wow, well, it's an honor to be here at Radcliffe this year and to interact with all the other amazing fellows. And in fact, the thing that's the most painful about it is not being able to do everything. So it's wonderful and terrible to be talking to you about elections today, right after an election that's going to take a while to understand. So let me plunge right in and start with the preface. It's November 7. What just happened? Part of the answer is I don't totally know yet what my story will be in terms of how to think about it, but I want to take a moment to highlight some non-candidate outcomes that may have been under the radar for you. There were many. I'm particularly excited that here in Massachusetts by an emphatic margin we upheld anti-discrimination laws for trans people. [APPLAUSE] Florida-- complicated in other ways-- emphatically voted yes to restore voting rights for most people with felony convictions. [APPLAUSE] Arkansas and Missouri voted emphatically again to raise their minimum wage substantially. Medicaid expansion in states you might not expect it. And real modernization of registration policies in Maryland and Nevada and in Michigan. And redistricting reform seems to have won everywhere it appeared. Although, Utah is particularly slow to count its votes. So this brings me to the topic that I want to talk about today. What's going on in US redistricting? How can we-- how are we making progress? What are the inroads in understanding redistricting and how to do it better? OK, so I think what I'll do, what I'll definitely do, is invert maybe the usual logic of a talk. Usually, you start with the overview, and then you give your kind of detailed work. I'm going to go backwards and start with the math problem and come to the overview afterwards. So here we go. Here's the redistricting, seen through the lens of mathematics. So the power the power that comes with the ability to draw the lines, that's what we want to understand. So let's do the very simplest model. So in this picture, you see five districts. And I've drawn in the 50/50 line. And let's suppose that a state is made up of two kinds of voters, pink voters and orange voters. And what we'd like to do is understand if we have complete omniscience and omnipotence, and we can put those voters into districts, if we can just sort them out at will, with only the rule that there should be an equal number of voters in each district, what can we do? Well, it turns out we can do an extraordinary amount in terms of controlling the outcome. Orange can get no representation with 40% of the vote, or it can get 80% of the representation. So here are two pictures that illustrate that. So there is the same kind of volume of orange vote, 40%, in both pictures, or maybe 40% plus epsilon. And you can see that, on the left, because orange has 40% of every district, orange gets no representation. Is the schematic clear? OK, and on the other hand, if orange is able to arrange matters extremely efficiently, then that 40% can be converted into four seats out of five or 80% of the representation. OK, so at the outset we-- you know, at first blush, we should be kind of in awe of how much control you have when you get to organize the lines. But maybe this seems like a vastly oversimplified model because, in fact, you don't get to pick people up and put them in buckets that the districts represent. Instead, there's geography. There's demography. There's all kinds of complications and rules that delimit the problem. And I'll come to that, but let's stay simple for a moment. So the way that you can get such an extreme advantage is with two kind of venerated strategies called packing and cracking. And what those mean is this. Packing is when you take the power to draw the lines-- in this case, orange has the power to draw the lines-- and you take the other side, here pink, and stuff them into a few districts with wastefully high vote totals and then disperse them over the remaining districts. So packing is overstuffed districts. And cracking is when you have substantial, what some have called wasted votes, because there are a lot of votes being cast, but they're not being converted to representation. They're dispersed. All right, and in the other picture, it's orange that's been cracked. Right? So these are kind of the hallmarks of strategies to extract extra advantage from the ability to control the lines. OK, the next thing to note is that the outcome doesn't tell you everything about what it's like to live and vote in a society that's structured this way. So here are two different ways of getting proportional representation for orange-- two seats out of five or 40% of the representation. But in one world, it's a little bit more randomly aggregated. And in the other world, you live only with people who vote like you. Right? And it's an open question which is sort of a healthier democratic state of affairs, but we can look at how tendencies have been trending in the US and think about what that means. OK, next point to make, we have thought-- it's been a kind of idea that's as old as the word gerrymander, which I'll return to, that bad shapes, that eccentrically shaped districts are really telling us that something nefarious is going. Some agenda is being advanced. And I actually want to speak against that narrative, somewhat, because what I want to argue is that, if you see the shapes, you don't know the story that they're telling unless you see what's underneath. Right? And so it's not the shapes of the district themselves. It's how they slice up the people that tells us how the outcome turns out and that ultimately tells us about the quality of the districting plan. OK, so let's look at some of the math up close, and we'll start with a very toy example. Instead of a big complicated city or state, suppose I want to redistrict just a 4 x 4 grid. So what you see here is all 22 ways to do that. Right? So in each of these pictures, I've taken a 4 x 4 grid, and I've cut it into four districts. I've sort of Tetris decomposed, right, the 4 x 4 grid. So they're 22 ways to do that. But that's up to symmetry. If I take some of these, and I rotate them and flip them, I get 117 different ways to do so. And that's it. That's all there are. But that's already a fairly complicated world to navigate if you're trying to understand the possibility space of districting plans. And it rapidly gets out of hand. Here's the actual current state of knowledge, which is fairly appalling, when it comes to the combinatorics or the number of ways to redistrict even these small grids. So here's what you're seeing in this picture. That 4 x 4 grid being districted into 117 ways is shown here because those were equal sized districtings. And by getting multiple different algorithms at work on some of the fastest computers available, this is how far we've been able to get. So the numbers rise fairly rapidly. By the time you want to redistrict a 9 x 9 grid, there are 700 trillion ways to do it. I would tell you how many ways there are to do a 10 by 10 grid, but that calculation has been running on the OpenStack machines at MIT for over a month without terminating. So you know, the sea of ignorance here is pretty startling. But of course, states are unimaginably bigger than this. So the problem is that, if you want to understand the landscape of ways to do redistricting, I would say that one of the challenges, historically, in approaching redistricting is that that possibility space is way too large to think about. So let's say you want to redistrict Pennsylvania. You have its census blocks, and you want to break them up into 18 districts. How many ways to do that? Well, if you started to enumerate this at 1 microsecond per plan, it would take until after the heat death of the universe to get to your response. You're just not going to do it. This is outside the realm of what is doable. The good news is-- and here's a little piece of magic. You heard a bit from Meredith about my dissertation, which it's true was about Teichmuller space. In fact, what I did in my dissertation was study random walks on a space of surfaces, a 6, 12, 18, 24, 30-dimensional space of surfaces. And the question was, if you start to modify things randomly, and you do that for a long time, what are the long-term trends. Well, in what feels to me like a piece of magic, that same kind of mathematics comes to bear on the redistricting problem. So here's what we'll do. We can't build out the whole space of possible districting plans, but we can study it. We can sample it at random. We can do random walks in the space of plans. So that's what I want to show you. OK, so I want to walk around in this space that I can't fully see. So I have to start somewhere. First, I need a way to make an initial plan. So there are lots of ways to do this, but one of them was, until recently, displayed in the art gallery over in Byerly. You can take a spanning tree approach and just form a spanning tree at random, whatever that is-- I'll show you pictures of one a little bit later-- and then split off districts of about equal size from it. And you get these bewitching beautiful neutral districting plans. Under the hood, there are fun computing algorithms for doing this for, building these spanning trees. And they have great properties. There's something called Wilson's algorithm that can sample uniformly from the space of spanning trees and do it really fast. And here's a kind of image of how, if you color code the individual steps, how you're winding around a space like this. OK, so we've got some way of starting with districting plans. And then here's the protocol. Just start modifying them at random. You apply some kind of proposal that tells you how to transform one plan to a different plan. And what you're effectively doing is walking around the space of possibilities. OK, so that's what you see happening in this picture. It's computationally very light. This is a capture of an in-browser demo. Your computer can do this randomly. It's extremely light computing. But it turns out that one of the research innovations that has come out of our group in the last few weeks has been a much more efficient way to do it than this. So here again, I'm going to use this term spanning tree. And let me show you the picture of how to do it. At the side, you see the process. On top, I start with two districts, the orange and teal. And I'm going to do a move I call a recombination. And that's by analogy with biology, all right, where you're taking some things and glomming them together splitting them back up. And that's like what we're going to do here. So we start with these two districts. We merge them. We draw this random spanning tree. So that's a network with no cycles that visits every node. And then I look one by one at the edges of that. And I ask, if I cut this edge, will I get pieces of equal size. If there's no way to do that, I pick a new tree and proceed. So all of this can be done efficiently, quickly, at random. And then I cut at a balanced place, and I get a new pair of districts. Does that make any sense? Good, so let's see how they work. I'm going to illustrate this. Here's of course Pennsylvania. And here are two grids where I just want to show you the dynamical system of these random walks and what it looks like. So I've started the process on top. And in both of them, I began with an initial splitting into these column districts. Everybody with me? And then we're mutating the districts flip by flip. And what I want you to think about here is blind exploration of an unseen space. You've dropped down, and you've started making moves at random without knowing much about the landscape. All right? OK, as I talk, I'm letting this run, and you see it really is changing, but it hasn't forgotten that it used to be columns. Do you see that? It's got the memory of its initial seed. I was giving it a big head start so that I could try to impress you with the efficacy of this walk. So this is this new recombination move. It locomotes much more efficiently around the space of possibilities. And we have really good evidence that, after just a short amount of time, it can sample representatively from this unthinkably big space. That's big progress, and I'm scientifically very excited about it. And here it is on Pennsylvania. So this is a recombination random walk of Pennsylvania. OK, and that's a little bit of the mathematics that goes into this. I'll say there are some-- you know, in another piece of beautiful, magical synergy, it's not only that the kind of math that I know and love has something to say about this problem, but the problem gives back by raising spectacularly interesting new math questions. All right, so if you want to understand this walk, it raises all kinds of great questions about how spanning trees work. the probability that there is a balanced cut of a spanning tree? Can you characterize this Markov chain or random walk on the space of spanning trees? Lots of great questions, open questions-- this is kind of a research frontier of this particular application. That's the math model. Now let's talk about how it gets used, and then I want to zoom out to the big picture again. OK, so as you heard, I was very privileged recently to be asked to put this in action in Pennsylvania. This press release made me smile. So this is Governor Tom Wolfe who was just re-elected comfortably yesterday in Pennsylvania. And I think he just really wanted to say "enlist non-partisan mathematician." What could sound more objective than a mathematician? [LAUGHTER] But here was the problem. In Pennsylvania, it's one of the states with split control. It's been a Republican controlled legislature for some time. It has a Democratic governor, as you heard. But they have to agree on new maps. The state Supreme Court ultimately was asked to arbitrate a case that invalidated the plan that was enacted in 2011 as a partisan gerrymander. And that's the plan you see here. It's a fairly notorious plan. It's districts look pretty disgusting. I like to characterize shapes like this as tumors and fractals. It's got this notorious district here in the suburbs of Philadelphia. Some people call it Goofy kicking Donald Duck. All right, I'll show you a close up a little bit later. And it was thrown out by the courts. And then the task of the legislature and the governor was to agree on new maps. And what you see here is three different attempts at new maps. And other than tumors and fractals over there, I would say that, to my eyes, the other three look qualitatively fairly similar. I don't think their visual tells of nefarious intent in any one more than the others. Yet they perform very differently. So that's the task here is to use that math model that I described to do kind of forensics on these maps. As it turns out-- so that was the 2011 plan. This was the new plan floated by the legislature. Interestingly, they didn't pass it as a bill. They floated it on Twitter, true story. [LAUGHTER] This is the plan that was counter proposed by the governor's team. I want to emphasize that that was not my job. I was doing map analysis, not map creation. So this was one of the maps that I analyzed. And this was the plan that was ultimately enacted by the courts. OK, so the challenge is to understand what they're doing. OK, before I get back to that, let me talk about some research findings that are enabled by this math model. And one of them I'll try to illustrate on our home state of Massachusetts. And the finding is you don't get proportionality for free. Before I explain this picture, let me just say out loud what I mean. In Pennsylvania, it has been the case for some time that the partisan voting patterns are about 50/50, Democratic and Republican. But with about 50% of the vote, Republicans had been getting 13 out of 18 seats in the Congress, in the congressional delegation. And some people took that to be prima facie evidence of abuse. How can you be converting half of the votes into 72% of the seats? Right? So one of the findings of the model is that you'd be surprised at what happens when you neutrally redistrict. You shouldn't expect proportional outcomes to just come for free. And actually, one of the punch lines of this whole research enterprise and of the talk I hope is proportionality is complicated. If that's what we're after, we have a bad system for securing it. And there are others. I'll come back to that. OK, but I wanted to illustrate that with Massachusetts because here's a fairly dramatic example. So what you see in this picture is an image that comes from voting patterns in an actual election-- Kennedy versus Chase for US Senate in 2006. This election was an interesting geometry. What you see in the picture is. This chase got over 30% of the vote in that election. Now, of course, that was a Senate race. But now let's just imagine that, with people voting that way, let's imagine that I tried to district some into nine districts. Massachusetts currently has nine districts. How many seats would you expect-- in how many districts would you expect a Chase majority? Well, you might think, since this is about a third of the vote, I'd expect Chase to get up to a third of the seats or maybe more, right, maybe two or three out of the nine seats. Right? What I've done here is highlighted the most Chase-favoring possible collection of precincts. And I've just aggregated them until I get up to the size of a congressional district. So that's a ninth of Massachusetts' population. Everybody understand what I'm highlighting? OK, so Chase we think maybe should get two or three districts. This would be the first one, the most Chase-favoring district. Yet this collection still prefers Kennedy. OK, so I hope there's a little surprise there. And let me try to reiterate. Here it is. There are more plans for how to district Massachusetts than particles in the galaxy, literally. Yet every single one of them has a 9-0 Democratic delegation, every one. Right? So if you thought, based on the intuition that proportionality is natural or desirable, maybe you would have thought that Democrats are gerrymandering Massachusetts. Right? They're locking out Republicans. But it's should the math that's locking out Republicans in Massachusetts. So even if I let a district be scattered and discontiguous like this, I just couldn't get Republican representation here. OK, phenomena like this are interesting in every state. You see where the votes are, and then you have to see how districting handles those votes. And the answer isn't always what you expect. OK, second finding, shape signifies less than you think. Here are two plans of Pennsylvania. And what I'm going to do is use a common metric from political science called the mean-median gap to see if these plans are built to favor Democrats or Republicans. And here's what we get. So we ran this Markov chain for, in this case, a billion steps, a billion steps of the Markov chain. And here's the plan that was enacted in 2011. And that's a billion plans similar to it. And what's being measured here is how much the plan has a partisan skew in the Republican direction. So that's the Republican-favoring direction. And what you see is that the current plan is here. A billion similar plans are here. That plan is not just an outlier. It's not just in the thin part of the bell curve. It's in what I like to call the invisible part of the curve. OK, here's the new plan that was proposed by Republican legislators earlier this year. It's called TS, the Turzai-Scarnati plan. It looks just fine. But under the hood, it performs strikingly similarly. There's the plan. There's the billion comparison maps, invisible part of the curve. OK, even though there are surprises when you do analysis like this, it does still actually vary in whether it tells you that a plan is an outlier. So here's the plan of the governor's team, which, again, I wasn't a part of made. In fact, not only was I not part of it, I was in some suspense about how this test would evaluate this plan. And here's the answer. Not every plan is an outlier. Some plans sort of hit the meaty part of their distribution of comparisons. All right, does that make sense? OK, so what's going on here? We're learning that a test like this can tell you when a plan is very extreme or when it shares properties with most other things or many other things in this universe of alternatives. The way this is intended to be used is not to select a plan. It's to rule out the ones that are most egregiously outlying. OK, so that's the math model and an application of it. And now what I want to do is kind of look at the stakes, which we haven't really talked about yet. So before we can get to the stakes, we have to say something about the realistic rules. So what are the rules for redistricting? Well, plans must be population balanced. I'm going to quickly review some of the common rules. Actually, before I do this, I should say the states have a lot of authority and a lot of latitude in how they make their own rules. Very few of them are universal. But here, this one is. It's almost the only guidance that's extracted from the Constitution. And it says that the plans should balance population. For legislative districts, they need to balance typically to within 10%. For congressional districts, the current practice, believe it or not, is to balance the census population of your districts down to one person deviation. OK, so that's insanely stringent. Two, compact, so this is a vague desideratum. And it says that the district should have a reasonable shape. And that's activating that intuition that bad shapes are red flags of nefarity. We see again Goofy kicking Donald Duck, maybe kind of flouting that compactness a little bit. The next requirement is that the district should be connected in a single piece. This is, but just. So there's actually two different cut points in this district, two different places where the district could be disconnected by the removal of a single building, true story, a hospital and a seafood restaurant. OK, next, the districting should avoid splitting counties or cities. Here's a picture to illustrate that that's hard. This is Columbus, Ohio and its county. And they actually overlap each other in an odd ways. So if you're a redistricter, and you try not to split that county and that city, that's actually complicated guidance. Avoid splitting communities of interest. This is a very interesting rule and a very slippery rule and a very lightly enforced rule that says that, when you have a community with a shared interest, you should try to keep them together. And then, finally and crucially, there's the federal Voting Rights Act of 1965 that discusses opportunity for minority groups to elect candidates of choice. And I'll come back to that. I put this slide up as a reminder of the humility with which you have to approach this problem as a modeler. Right, these rules are difficult to operationalize. They're difficult to prioritize. They're difficult to incorporate into your model. And yet, they're essential. So sometimes, you'll see modelers ignore the rules that they can't quantify. And that seems to me to be modeling malpractice. OK, so I think, when you want to make an applicable model, you want to think really hard about how all your rules interact. OK, so what are the stakes? Here's an image that shows you something about US redistricting. So this shows you about a dozen different districts around the country, face blends of their average district resident, a home at the actual median price in each district, a recent ballot initiative, and the representative downstream from all of that. And when you look at the texture of this, you really start to see the delicate dependence on line drawing for vastly different representational outcomes. So let's take a look at Southern California. So here are California 43 and 49, two districts very close to each other in Southern California. Because of how the lines are drawn, they have quite different district residences in terms of the demographic makeups of their districts, vastly different median home values and per capita incomes, in the case of 43, a median home value that's many, many times its per capita income. They vote differently on California's myriad ballot initiatives. And they get very different representation. So this is from the last cycle. And many of you may recognize the inimitable Maxine Waters and the also inimitable Darrell Issa. And this leads us back to this point. Here those two districts again on a map of the country. They're tiny, and they're delicately constructed. And it may give you a whole lot of bewilderment about exactly everything that flows out, but it's a reminder of the stakes of this line drawing process. OK, so under the hood a little more, let's look at Ohio. Here's how Ohio voted in 2016. They had just over half the votes went to Republicans in the congressional race, but 3/4 of the seats were to Republicans in the congressional race. This is a model that tries to understand what would happen if you model partisan swing towards one party or the other. So if you take the vote in the different districts, and you dial it gently up or down, this is how it would vary. So what's the conclusion of this? The conclusion is that that 12-to-4 delegation would have occurred-- you see this giant plateau here-- with Republicans getting anywhere from 49% to 79% of the vote. Is this wildly effective gerrymandering or an artifact of where people live? That's what the math model can do for you. Meanwhile, here are the stakes. OK, these are the 16 representatives of Ohio before yesterday. And now I'll show you the 16 representatives of Ohio after yesterday. Here they are again. OK, there wasn't really a close race. By contrast, Pennsylvania, which had its lines radically redrawn in the process from earlier this year, had enormous makeover of its congressional delegation. Interestingly, Pennsylvania's 18-person delegation was the largest in the country to be all-male until yesterday and elected four women for the first time. I think it's the year of the blonde woman in particular in Pennsylvania. OK, so the stakes of all the math are actual representatives who go to Washington and behave quite differently. So I want to devote this next section of the talk to kind of revisiting the redistricting problem as a nexus of concerns from many different disciplinary traditions and for many different fields. OK, history matters for understanding how redistricting works. So as many of you probably recognize, here is the original gerrymander. So here we are. This is a picture from the North Shore of Boston. And it's a districting plan that was approved in 1812 by then governor Elbridge Gerry. Some of you may notice, as you tool around town, around Cambridge, how many things are named after him around town. So there's Gerrys Landing Road down when you're trying to get from Mem Drive from around the corner onto Soldiers Field. There's Gerry Lane right up four blocks from here. Who was he? Besides the governor of Massachusetts, he was sort of one of the giants, the so so-called founding fathers. He was a representative. He was a, well, governor until 1812. He actually was voted out in this election and then became Vice President. So he sort of has his fingerprints all over really America. What's going on in this picture is a congressional district that was thought to favor Gerry's Democratic-Republican Party over the rival Federalists. For those of you who've had the pleasure of seeing Hamilton, which I recently did, you got to see these two rival factions, in the persons of Hamilton and Jefferson, battle it out. Here's the political cartoon-- this one's from 1813 in the Salem Gazette-- that kind of brought that to life. And I want to actually show you a little bit of its text because I think it's fascinating. "Federalists, followers of Washington, again behold and shudder at the exhibition of this terrific dragon brought forth to swallow and devour your liberties and equal rights. Unholy party spirit and inordinate love of power gave it birth. Your patriotism and hatred of tyranny must by one vigorous struggle strangle it in its infancy." A little over the top, perhaps. What's the argument that it's making? It's that, from its shape, as exaggerated here by its claws and its wings, Gerry's salamander, or the gerrymander, is giving you evidence of its unfair and bad design. So that's kind of the partisan terrain in gerrymandering has been fought out since at least 1812. But the 20th century gives us a different history of gerrymandering. So here's the gerrymander of the 1950s. What we see here is Tuskegee, Alabama, which redrew its own city lines in 1957. So that made its way-- that was challenged and made its way up to the Supreme Court in a landmark case from 1960, Gomillion v. Lightfoot. So before the redrawing, Tuskegee was larger. It voluntarily shrank itself. It used to be the green square that you see here and then redrew itself to be this 28-sided eccentric looking polygon. Why? You won't be surprised to hear before the redrawing Tuskegee was nearly 80% black, and then it became actually 100% white after the redrawing. So there's such pervasive and dramatic residential racial housing segregation that it's possible with surgical precision to draw yourself a one-race city. This particular case was fairly easy for the Supreme Court to handle because it's a matter of disenfranchisement. If black voters here are drawn out of the city, they're in unincorporated territory in Alabama. They have no more mayor, city council, city services. They've lost a vote. So the Supreme Court threw this out. This was fairly easy. The congressional case is a lot harder because, when you're drawn out of one congressional district, you've drawn into another. So it becomes a matter of weight of votes and not of having or not having the vote. And to this moment, the court has kind of continued to punt. The Supreme Court has continued to punt on how to handle that. OK, onward into the 1960s, here's a picture of Mississippi. Now the black population in Mississippi is concentrated here in the delta region, and Jackson is around there. Here are some districting plans of Mississippi before the 1960 census. And here are the three plans that the Mississippi legislature considered after that census. Look what's happening. Whereas the delta used to be preserved in a single district, it's split two or three ways in each of the new plans, guaranteeing zero majority black districts. So this is the fundamental context of all of the legal framework that we have around gerrymandering today. So it's the civil rights moment that gives birth to the biggest tool that we have for voting rights. And that's the Voting Rights Act of 1965. Here's an image from the signing ceremony. So this is a powerful bill that was originally aimed at eliminating so-called devices blocking the black vote-- poll taxes, literacy tests, and so on. Some historical hot spot regions around the country were bound by Section 5 of the Voting Rights Act to request preclearance from the federal government before they could make any changes to their voting rules. Actually, I want to emphasize how important this is. This isn't just you need prior permission if you want to change the districts. It's all changes to your voting related rules-- early voting hours, polling locations, all of that. You need prior approval from the federal government. In 2013, that was swept away in a Supreme Court decision called Shelby v. Holder. What that's left in its wake is a situation where multiple states had restrictive new voting laws cued up and ready to go for this decision. Three or more states passed new voting restrictions the day this decision came down. And now, instead of being blocked in advance, those have to be litigated after the fact. All right, so it's changed the landscape considerably. But the Voting Rights Act for now lives on and is still a very powerful tool. It's been frequently renewed and expanded in interesting ways that I won't get into. That's the subject of a whole other story. But it's still on the books. It's still important. And it's still, in some ways, the only legal tool that we. Next, a reminder of all the ways that this matters-- so here we are in Massachusetts where we currently have nine congressional districts, but we have all kinds of other lines and zones. We have a state Senate with districts, a state house with districts, a governor's council. We have school districts. And here in Boston, I think that should have a specially resonant history because, in the 1970s, it was specifically not just the idea of busing, but how that busing was implemented in terms of zones, maps, and who would be bused where, that triggered racist riots and gave Boston a long enduring reputation as a racially problematic city among many others. Interestingly, when legislators can't agree on a map, courts throw it to an outside expert. That expert is called, in a fairly resonant term, a special master. So it was a special master who drew the school busing map here in Boston that cross-bused between Charlestown and Dorchester, particularly, some say igniting the powder keg that became the riots. And it's a special master who redrew Pennsylvania's map. It's a special master who's been called on to redraw Virginia's map. So this is still very much a system that's in effect. This slide is also intended to remind us, in terms of the history and the stakes, that it's not just Congress, but redistricting also has a very local texture. And where the rules are more constrained when it comes to congressional districts, they can be much more up for grabs when it comes to everything local. OK, these narratives intertwine. So I'm going to end this talk with just four vignettes about the power of these kinds of forensic techniques to show us how redistricting works around the country in a way that combines these various elements. Notorious district, Illinois 4, the earmuffs-- anyone recognize it? OK, those of you from Chicago may recognize it better now. Here it is sitting inside the city, connecting Pilsen, a predominantly Mexican neighborhood on the south side, with Humboldt Park, which is the center of the city's Puerto Rican community on the north side, strung together via a highway. What effects does the construction of a district like this have? Packing. So Luis Gutierrez did represent this district-- he's decided to move on-- but from 1992 continuously to 2016, never getting less than 75% of the vote. OK, so this looks like a textbook case where Latinos have been packed in inefficient numbers into a district to dilute their voting strength. But in an interesting twist, this turns out to be friendly packing, endorsed and actually aided by civil rights groups. An actual image for you from John Oliver's show of a real-life wedding cake of two redistricting legal experts-- "Nick Ruth, Combining Communities of Interest"-- because they both worked on this district. So what's the story of the district? It's a Latino district that was built to interlock with three African-American districts also locking in and heading towards the south side. What I find fascinating about this is that it's a well-meaning gerrymander in a sense. It's predicated on the idea that, in order to comply with the Voting Rights Act, you want separate opportunity districts for different racial groups. I'm happy to say that today, in 2018, we're starting to see some of that logic break down, as civil rights organizations, like the Lawyers' Committee and others, are bringing for the first time coalition voting rights claims. So right here in Massachusetts, the Lawyers' Committee has a case in Lowell, which is a coalition Latino-Cambodian voting rights claim. OK, and I think this is a new and important frontier in the history of voting rights litigation where, instead of creating separate and kind of calcified, ossified racial categories with separate districts, there's more coalitional thinking going forward. Ironies of segregation-- here's an image that comes from a white paper that our research group put together because we were asked to look at redistricting in Santa Clara, California. Before I tell you what's happening in the picture, let me give you a bit of back story on Santa Clara. It's about 40% Asian and was sued by Asian civil rights groups because its six-member city council had never had an Asian member, OK, with a 40% Asian population in the city the city, interestingly, said you're right. Our system is terrible and racist. Let's replace it. And OK, they didn't in those words. And the interesting contestation became replace it with what. OK, so we used our models to try to understand how districting would work because that's the go-to remedy when you throw out an at-large city council system is to move to districts. And here's an irony that we found. So in this simple model, I'm showing you 40% of the population either scattered in a regular pattern, semi-clustered, or clustered as in a kind of more segregated way around the city. Then we run our model. The computers go boop, boop, boop, boop. And they tell us what kind of representational outcomes we should expect. And what we found surprised me quite a bit. What we found was this is not surprising. If you're very checkerboarded around a city, you're not going to do very well under districts because you'll have a minority of every district. Does that make sense? That should be kind of obvious. On the other hand, segregation does at least ensure a strong possibility of representation. But here's the surprise. As you kind of interpolate between these extremes, it takes a long time for your representation to kick in. So here's our kind of semi-clustered configuration. And check it out. It performs just as badly under districting as the checkerboard. So from a mathematical point of view, there's a kind of phase transition that occurs as you cluster more. And representation doesn't kick in until you're fairly segregated. So I call this ironies of segregation. Just a few more images, this is ongoing work modeling the situation happening right now in Virginia. So here are three maps of Virginia in the wake of its district court throwing out its house of delegates' plan as a racial gerrymander. This is the enacted Republican plan, a democratic counter proposal, and an independent third party group. And what we were able to do is, using our ensemble methods, building big piles of comparison plans, we can see how each of these performs in the distribution of black voting age population compared to neutrally computer generated maps. And what you see is here's a zone of black population where you're empirically likely to be able to elect a black representative. And I hope this gives you kind of a forensic glimpse into the statistical guts of these plans. It's showing you that, for instance, this Republican plan elevated or packed black population in these districts so much that it's depressed in the next batch or two of districts, costing opportunity for African-Americans to elect candidates of choice. But the analysis shows you more. Here's the zone where you're more likely to elect white Democrats. And that's where a lot of blue dots fall. OK, so an analysis like this can show you whether districts are geared towards black representation or, perhaps, maybe optimized to elect more white Democrats. Finally, let's come full circle to Mississippi. And I'll just say, with our methods of generating plans, we were able to generate millions of alternative Mississippis. Today, Mississippi has four congressional districts. One of them is about 2/3 black, and the others are 1/3 black or less. It elects one black Democrat and three white Republicans. And what you see, as you randomly explore that vast space of possibilities, is that it doesn't need to be so. And Mississippi could have a vastly different congressional delegation and a vastly different balance in its districts, and it takes computers really just a second to find other ways to do so. OK, so I'll close with the punch lines. What are the bottom lines about the lines? Small picture, the rules interact in complicated ways, but the models can help us figure that out. The eyeball test is dead. It no longer can tell us when something's been gerrymandered. Deviations from proportionality are more complicated than we thought. And there's maybe even a new normative story here about gerrymandering. So what is this outlier test bringing to us as a matter of fairness? It's asking the question, does a map behave as though drawn only from the stated principles. And where it deviates, is there a reason? These are the kinds of questions that this outlier analysis can bring you. In the big picture, I hope the takeaways are that single-member plurality districts are a legacy. They're an artifact of many layered histories that I hope I've outlined. You can't tell the story of redistricting without the story of the black vote in particular and the violent and genteel efforts at suppressing it throughout US history. There's a kind of perverse irony in play that districts work best to secure representation for a numerical minority in the presence of marked segregation. And so less segregated populations are much harder to represent with a districting system. This is true not only for Asians in Santa Clara, but for Republicans in Massachusetts. All right, this is true for any numerical minority. Republicans are just not segregated enough here in Massachusetts. The Voting Rights Act is a fundamental tool, best that we have, but it calcifies racial categories. And then the math model, what does it do? It gives us a baseline for neutral redistricting. It reveals forensics of map design. And it lets us challenge our intuitions that something is being gamed. Fairness or justice does not equal neutrality. And that's another matter. And so computers should never draw our maps for us. They should be tools that rein in the most extreme abuses. Thank you. [MUSIC PLAYING] [APPLAUSE]

Election results

Candidate Party Votes[2] Percent
William Creighton, Jr. Democratic-Republican 538 59.8%
Abraham Claypool 361 40.2%

Creighton took his seat June 15, 1813[1]

See also


This page was last edited on 21 October 2019, at 16:03
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