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).
The document is linked HERE.
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.
The study can be found 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:
[TLDR : See this PDF for guidance: Ensemble Method Guidance ]
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.
The rest of the text can be found in this PDF document: Ensemble Method Guidance.
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.
The slides are available here.
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.
We will be hosting a conference on gerrymandering from 10am on Monday, March 2nd, until 1pm on Wednesday, March 4th. For details, see the link.
Today the state court chose to accept the Remedial Map which the legislature produced in response to the maps we have been using since 2016 being declared an illegal gerrymander in Harper v. Lewis.
Sadly this new map still has districts containing an abnormally large number of Democrats and others with comparably few. The result is a map which elects the same number of Democrats and Republicans over an abnormally large range of partisan election outcomes.
My analysis shows that the Remedial Map was much less sensitive to swings in the partisan vote fractions than the vast majority of the maps in the ensemble. The plots below, where the Remedial Plan is labeled HB 1029, show that under a uniform swing analysis the nonpartisan maps in the ensemble often produce 6 and sometimes 7 Democratic seats in election environments when the Democrats perform well (a statewide vote fraction in the low 50%) for many sets of votes, while the 2019 Remedial Map reliably produces 5 Democratic seats in most instances. Each of the different plots uses a different set of historical votes (Labels: USS16- US Senate 2016, AG16 – Attorney General 2016, USH16 – US House 2016, GOV16 – Governor 2016).
The important feature is the atypically large jump between the 6th and 5th most Democratic district. This is the same “signature of gerrymandering” jump which we saw in Common Cause v. Rucho. It makes the maps much less responsive to the votes cast. They produce the same results over a large number of election scenarios.
The fact that the court felt it did not have time to investigate this fully shows how the judicial pathway of preventing gerrymandering is limited. We need legislative reform. It seems the only way to ensure that the maps used in elections are responsive to shifts in the political sentiment of the electorate.
More details can be found in the report I submitted to the court which can be found here: Mattingly Nov. 26 Declaration.
We have revised our work quantifying gerrymandering in the N.C. Congressional Districts. The original work was presented in Common Cause v Rucho. We have taken the opportunity to use the improved code base which performs more sophisticated convergence test and update our discussion to better reflect our current perspective. We have also corrected a few inaccuracies and small errors. That being said, the results are fully in line with those from the original version.
The new text can be found HERE.
The data and code for this work can be found in this git repository: https://git.math.duke.edu/gitlab/gjh/nccongressionalensembles.git.