We analyze the enacted North Carolina congressional plan (CST-13), the enacted North Carolina House General Assembly plan (SL 2021-175), and the currently proposed North Carolina Senate General Assembly plan (SST-13).
We compare these three districting plans with the ensembles we generated from non-partisan redistricting policies. See here for the congressional analysis and here for the general assembly analysis; these documents contain information on the non-partisan criteria considered along with the resulting ensemble of maps.
As an example, we look at the number of elected Democrats that would have occurred under a range of historic elections. Under each election, we compare the ensemble of non-partisan maps with the enacted map. The enacted congressional plan elects 4 Democratic representatives under a large range of statewide Democratic vote fractions. This level of non-responsiveness is not seen in the ensemble of plans.
We have performed an analysis of the geopolitical landscape of the North Carolina State using the 2020 Census data. We provide a
summary plot derived from an explicit distribution on redistricting
plans. The distribution favors plans with compact districts which keep counties intact.
As the character of the votes considered swing from more Republican to more Democrat, we see a gradual increase in the number of seats won by the democratic party. This responsiveness to the changing opinion of the electorate is consistent with what has been observed when maps are drawn without partisan considerations; either by sampling or by bipartisan committees.
We have investigated two possible policies for the current redistricting cycle in North Carolina. Both policies respect the county cluster rule, respect one-person-one-vote, and prioritize compact districting plans. One of the policies preserves municipalities when redistricting and the other does not consider municipal preservation. This is in response to the North Carolina Joint Redistricting Committee’s criteria in which they state they “may” consider.
We use these policies to generate two probability distributions (i.e. we quantify preferences between redistricting plans), and we reveal the typical partisan behavior of non-partisan maps drawn according to these policies.
As discussed in our previous post, county clusters are used in the North Carolina General Assembly districts to reduce splitting counties. With the release of the 2020 census data, we can now establish all possible county clusters.
We report our findings in a document written with Christopher Cooper (Western Carolina University), Blake Esselstyn (FrontWater LLC and Mapfigure Consulting), and Rebecca Tippett (Carolina Demography, UNC at Chapel Hill).
To summarize, we find that 36 of 50 state Senate districts will reside county clusters that are now completely determined. The remaining districts reside in four regions of the state, each with two possible choices of county clusters. When a county cluster only contains a single district, the district is the cluster. In the Senate, ten of the 50 districts are determined via clustering.
In the state House, 107 of 120 districts will reside in county clusters that are completely determined. The remaining districts reside in three regions of the state, each with two possible choices of county clusters. Eleven of the 120 districts are determined via clustering.
We have also examined incumbency within the new clusters. Unless incumbents change address, we find that 4 Senate districts must contain two incumbents (i.e. 4 districts will “double bunk” incumbents). We also find that 5 House districts must contain two incumbents.
We stress that the county clustering process adds a constraint to the redistricting process. Largely, the county clusters do not determine districts and there is still room for the legislature to draw either fair or gerrymandered maps.
The decennial redistricting cycle is an important moment in our democracy. Drawing districts can have a dramatic effect on how our elections are interpreted and the people’s ability to hold their representatives accountable.
Today, roughly 1 month before the 2020 Census is release, we and 3 other North Carolinians are releasing an analysis around the coming 2020 Census and how it will affect the county clusters used to draw the legislative districts for the N.C. State Legislature.
For more information on what county clusters are and how they affect districting, see here and here.
We do not intend our analysis to have much predictive value. A main takeaway from the study is that the clusters are very sensitive to fluctuations in the county populations; the official county populations will not be released until mid-august. Nonetheless, it is clear that the current county clusters will change in the coming redistricting cycle and that there will likely be multiple options for which clusters we implement. We hope that our note will inform public discussion around this process.
Quoting from the study, the main takeaways are:
We expect that county clusters based on the 2020 Census population will be very different from those used in the last decade.
There is significant variation in the county clusters across the different population estimates for 2020. Hence, caution should be exercised when drawing conclusions from the specifics of the maps we have included. We note the few clusters which seem to be stable across the population estimates.
Using the 2010 population figures we found 2 possible clusterings for the N.C. Senate. The number of clusterings for the 2020 population estimates ranged from 12 to 33.
Using the 2010 population figures we found 4 possible clusterings for the N.C. House. The number of clusterings for the 2020 population estimates ranged from 2 to 16.
Some members of the study team will be releasing further commentary on what we have seen here during the coming month. We expect to release a similar discussion once the official 2020 Census population figures are released. Please watch this site and the following venues for further commentary:
As the country gears up for the next redistricting cycle we have attempted to collect our thoughts and guidance on using the ensemble method to critique a particular redistricting plan. This summarizes our current thinking stretching back to 2013-2014 as one of the originators of the Ensemble Method; it reflects our experience gained through providing expert guidance, including live testimony, to the court in a number of cases.
The document begins with the following summary:
The ensemble of maps translates the specific redistricting design criteria into an understanding of what a redistricting plan should look like if the specified criteria are followed without ulterior motive. The criteria are formalized in a distribution on redistricting plans from which a sufficiently rich ensemble is drawn to capture the properties of the distribution. If the criteria are non-partisan then the ensemble gives a non-partisan normative collection against which other plans can be compared for their partisan and non-partisan properties.
With the upcoming redistricting cycle, there are a number of questions about how ensembles can, will, and should be used to audit and restrict gerrymandering.
We have prepared a slide deck illuminating how the ensembles have been used to legally challenge plans during the last redistricting cycle. We have also examined some of the new indices that are being put forward as measures of gerrymandering. Under a certain set of votes, it is possible to “game” these indices, in the sense that plans can be typical of an ensemble according to multiple indices while still being gerrymanders.
Finally, for the North Carolina general assembly, we demonstrate the fragility and possible choices when it comes to county clustering.
We have recently posted a pre-print of a Multi-Scale Merge-Split sampling algorithm. The work’s central contribution is to provide a multi-scale representation of the state space which can be used to efficiently sample redistricting plans containing both fine and coarse-scale details. Because of the coarse-graining, this algorithm promises fast scaling on problems with fine-scale graphs. It also is capable of naturally preserving nested communities of interest (such as precincts made up of census blocks, and counties made up of precincts) without creating prohibitive energetic barriers which slow mixing. This work builds on our Merge-Split algorithm, which, in turn, builds on the ReCom algorithm.