Day 3: Polygons

Gerrymandering in 🇺🇸

The Gerrymandering Project is an initiative that aims to bridge the gap between mathematics and the law to achieve fair representation through redistricting reform. The project uses a unique set of analytics to grade each state’s newly-drawn maps during the redistricting process, which happens every 10 years to account for changes in population.

The project’s methodology is based on several key aspects. Their site explains all this, but I can drop some deets here.

The Redistricting Report Card is a tool developed by the Gerrymandering Project to help the public identify gerrymandered maps. It uses a powerful and unique set of analytics to grade each state’s newly-drawn maps during the redistricting process. The grading system is based on competitiveness, geographic features, and most robustly, partisan fairness.

The report card uses an algorithm that generates 1 million districting plans for each state, comparing maps against the full range of possibilities and issuing grades. The grades are posted as each state produces maps during their redistricting processes.

Partisan gerrymandering is a process where a political party’s power is amplified beyond what it deserves based on their vote share alone. The Gerrymandering Project uses mathematical tests to detect this. One such test is the Lopsided Wins test (direct PDF), which identifies a pattern of wins for both the perpetrator and the victim parties, wherein the victim party wins its few seats by overwhelming margins and the perpetrating party wins its many seats by considerably lower margins.

The project also uses the ensemble method to generate a large set of alternative districting plans that follow traditional redistricting criteria. The resulting distribution of Democratic seat shares for each of the maps in the ensemble gives a baseline for the naturally occurring seat shares for a state given its political geography and redistricting rules.

The Polsby-Popper (PP) measure is a ratio of the area of the district to the area of a circle whose circumference is equal to the perimeter of the district. A district’s Polsby-Popper score falls within the range of and a score closer to 1 indicates a more compact district.

The Reock Score is the ratio of the area of the district to the area of a minimum bounding circle enclosing the district. A higher Reock score indicates a more compact and therefore less gerrymandered district.

District Competitiveness

Both major parties in America engage in this unfair practice. As such, I find the “competitiveness” score to be a useful neutral indicator to look at to see which districts are worse than others in a given state without a red/blue lens.

The map below is an embedded Observable notebook that uses data from The Gerrymandering Project to visualize the competitiveness of the districts of a selected state. The map displayed was the most recent “official” or “enacted” in each state’s “SCORED MAPS” section on GP’s site.

If you’re from the U.S., see how well (or, poorly) your state did.

Credit: Gerrymandering in 🇺🇸 by boB Rudis

Why the notebook embedding vs Quarto OJS blocks?

Quarto does not embed the latest libraries so I cannot use className field available in the most recent Observable Plot (which I need to use for targeting some CSS).

FIN

There’s scads more information to explore (and you can get the data!) over at the Gerrymandering Project.

# Day 3: Polygons {.unnumbered} <style> h2 { border: 0px; margin-bottom: 0px; } figure h3 { margin-top: 0px; } </style> ## Gerrymandering in 🇺🇸 The [Gerrymandering Project](https://gerrymander.princeton.edu/) is an initiative that aims to bridge the gap between mathematics and the law to achieve fair representation through redistricting reform. The project uses a unique set of analytics to grade each state's newly-drawn maps during the redistricting process, which happens every 10 years to account for changes in population. The project's methodology is based on several key aspects. Their site explains all this, but I can drop some deets here. The [Redistricting Report Card](https://gerrymander.princeton.edu/redistricting-report-card/) is a tool developed by the Gerrymandering Project to help the public identify gerrymandered maps. It uses a powerful and unique set of analytics to grade each state's newly-drawn maps during the redistricting process. The grading system is based on competitiveness, geographic features, and most robustly, partisan fairness. The report card uses an algorithm that generates 1 million districting plans for each state, comparing maps against the full range of possibilities and issuing grades. The grades are posted as each state produces maps during their redistricting processes. Partisan gerrymandering is a process where a political party's power is amplified beyond what it deserves based on their vote share alone. The Gerrymandering Project uses mathematical tests to detect this. One such test is the [Lopsided Wins test](http://www.stanfordlawreview.org/wp-content/uploads/sites/3/2016/06/3_-_Wang_-_Stan._L._Rev.pdf) (direct PDF), which identifies a pattern of wins for both the perpetrator and the victim parties, wherein the victim party wins its few seats by overwhelming margins and the perpetrating party wins its many seats by considerably lower margins. The project also uses the ensemble method to generate a large set of alternative districting plans that follow traditional redistricting [criteria](https://gerrymander.princeton.edu/redistricting-report-card-methodology). The resulting distribution of Democratic seat shares for each of the maps in the ensemble gives a baseline for the naturally occurring seat shares for a state given its political geography and redistricting rules. The [Polsby-Popper (PP) measure](https://en.wikipedia.org/wiki/Polsby%E2%80%93Popper_test) is a ratio of the area of the district to the area of a circle whose circumference is equal to the perimeter of the district. A district’s Polsby-Popper score falls within the range of and a score closer to 1 indicates a more compact district. The [Reock Score](https://en.wikipedia.org/wiki/Reock_degree_of_compactness) is the ratio of the area of the district to the area of a minimum bounding circle enclosing the district. A higher Reock score indicates a more compact and therefore less gerrymandered district. ## District Competitiveness Both major parties in America engage in this unfair practice. As such, I find the "competitiveness" score to be a useful neutral indicator to look at to see which districts are worse than others in a given state without a red/blue lens. The map below is an embedded Observable notebook that uses data from The Gerrymandering Project to visualize the competitiveness of the districts of a selected state. The map displayed was the most recent "official" or "enacted" in each state's "SCORED MAPS" section on GP's site. If you're from the U.S., see how well (or, poorly) your state did. <div id="observablehq-viewof-v-5321d40f"></div> <div id="observablehq-map-5321d40f"></div> <div id="observablehq-sty-5321d40f"></div> <p>Credit: <a href="https://observablehq.com/@hrbrmstr/gerrymandering-in@39">Gerrymandering in 🇺🇸 by boB Rudis</a></p> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@observablehq/inspector@5/dist/inspector.css"> <script type="module"> import {Runtime, Inspector} from "https://cdn.jsdelivr.net/npm/@observablehq/runtime@5/dist/runtime.js"; import define from "https://api.observablehq.com/@hrbrmstr/gerrymandering-in@39.js?v=4"; new Runtime().module(define, name => { if (name === "viewof v") return new Inspector(document.querySelector("#observablehq-viewof-v-5321d40f")); if (name === "map") return new Inspector(document.querySelector("#observablehq-map-5321d40f")); if (name === "sty") return new Inspector(document.querySelector("#observablehq-sty-5321d40f")); return ["m","s"].includes(name); }); </script> ### Why the notebook embedding vs Quarto OJS blocks? Quarto does not embed the latest libraries so I cannot use `className` field available in the most recent Observable Plot (which I need to use for targeting some CSS). ### FIN There's scads more information to explore (and you can get the data!) over at the [Gerrymandering Project](https://gerrymander.princeton.edu/).