31:T481,# NC State Legislative Elections 2022 Here's an illustration of how the NC State legislature shapes up for the 2022 elections. The shading is based on partisan vote shares from recent elections, as computed by [The Princeton Gerrymandering Project for North Carolina](https://gerrymander.princeton.edu/reforms/NC). The recent elections used in this analysis are the statewide 2020 elections for US President, NC Governor, and US Senator for North Carolina. While those elections are quite different from the district elections of interest, there is still a very close correspondence between the results. And, of course, high quality polling data for elections at this level are extremely limited. *Of note*: The two parties are very closely split in vote shares: 49.8% Democratic to 50.2% Republican. The Republican party, though, has a much easier path to control of both houses. *Also of note*: There are really no competitive races in Western North Carolina. The closest such election is House District 115 where the Democratic party has 56% of the vote share. This is where Lindsey Prather will take on Pratik Bhakta in Southwest Buncombe County.32:Ta57,map = { let w = width < 1100 ? width : 1100; let h = 0.5 * width; let div = d3 .create("div") .style("width", `${w}px`) .style("height", `${h}px`) .style("overflow", "hidden"); let svg = div.append("svg").attr("width", w).attr("height", h); let projection = d3 .geoConicEqualArea() .parallels([35.64305, 34.69796]) .rotate([79.86097, 0]) .fitSize([w - 10, h], map_data.senate_districts); let path = d3.geoPath().projection(projection); let map = svg.append("g"); map .append("g") .selectAll("path") .data( map_data[chamber == "House" ? "house_districts" : "senate_districts"] .features ) .join("path") .attr("id", (d) => `district${d.properties.DISTRICT}`) .attr("data-dem_vote_share", (d) => d.properties.dem_vote_share) .attr("data-district", (d) => d.properties.DISTRICT) .attr("d", path) .attr("stroke", "#333") .attr("stroke-width", 0.7) .attr("fill", function (d) { if (d.properties.dem_vote_share < 0.5 - threshold) { return d3.interpolateRdBu(0.25); } else if (d.properties.dem_vote_share < 0.5 + threshold) { return d3.interpolateRdBu( 0.5 + 3 * (d.properties.dem_vote_share - 0.5) ); } else { return d3.interpolateRdBu(0.75); } }) .on("pointerenter", function (evt, d) { if ( true // 0.5 - threshold < d.properties.dem_vote_share && // d.properties.dem_vote_share < 0.5 + threshold ) { svg.selectAll("path").attr("stroke-width", 0.7); d3.select(this).attr("stroke-width", 2).raise(); } }) .on("pointerleave", function () { svg.selectAll("path").attr("stroke-width", 0.7); }) .nodes() .forEach(function (node) { if ( true // 0.5 - threshold < node.getAttribute("data-dem_vote_share") && // node.getAttribute("data-dem_vote_share") < 0.5 + threshold ) { tippy(node, { content: `

District: ${node.getAttribute( "data-district" )}

Dem Vote Share: ${d3.format( "0.0%" )(node.getAttribute("data-dem_vote_share"))}

Rep Vote Share: ${d3.format( "0.0%" )(1 - node.getAttribute("data-dem_vote_share"))}

`, theme: "light-border", allowHTML: true }); } }); return div.node(); }33:Tc8b,bar_chart = { let plot = Plot.plot({ marginLeft: 70, x: { label: "District count →" }, y: { label: "" }, marks: [ Plot.barX( bar_data, Plot.groupY( { x: "count" }, { y: "competitive_status", sort: (d) => d.rank, fill: "DISTRICT", stroke: "white", title: "DISTRICT" } ) ), Plot.ruleX([chamber == "House" ? 60 : 25], { stroke: "red" }), Plot.text([1], { x: () => (chamber == "House" ? 60 : 25), y: () => "Competitive", text: () => "Control" }) ] }); let rects = d3 .select(plot) .selectAll("rect") .attr("fill", "steelblue") .attr("data-id", function (_, idx) { let district_data = bar_data[idx]; return district_data.DISTRICT; }) .attr("data-competitive_status", function (_, idx) { let district_data = bar_data[idx]; return district_data.competitive_status; }); // The individual cells in the top row of the bar chart are hoverable. // Those cells are ordered according to increasing democratic vote share. // The following bit of code helps to set that up by ordering both the // competitive districts and the cells in the top row so we can match // them up. let competitive_districts = d3.sort( bar_data.filter((o) => o.competitive_status == "Competitive"), (o) => o.dem_vote_share ); let rect_nodes = rects.nodes(); let ymin = d3.min(rect_nodes.map((r) => +r.getAttribute("y"))); let top_row = rect_nodes.filter((r) => +r.getAttribute("y") == ymin); top_row.forEach((r, idx) => r.setAttribute("pos", idx)); rects.nodes().forEach(function (node) { let pos = parseInt(node.getAttribute("pos")); if (pos < competitive_districts.length) { let district_data = competitive_districts[pos]; if (district_data.competitive_status == "Competitive") { tippy(node, { content: `

District: ${ district_data.DISTRICT }

Dem Vote Share: ${d3.format( "0.0%" )(district_data.dem_vote_share)}

Rep Vote Share: ${d3.format( "0.0%" )(1 - district_data.dem_vote_share)}

`, theme: "light-border", allowHTML: true }); } } let district_data = competitive_districts[pos]; }); d3.select(plot) .selectAll("rect") .on("pointerenter", function (a) { // let title = d3.select(this).select("title").text(); if (d3.select(this).attr("data-competitive_status") == "Competitive") { let pos = parseInt(d3.select(this).attr("pos")); let id = competitive_districts[pos].DISTRICT; d3.select(map).select(`#district${id}`).attr("stroke-width", 3).raise(); } }) .on("pointerleave", function () { d3.select(map).selectAll("path").attr("stroke-width", 0.7); }) .select("title") .remove(); return plot; }34:[{"id":"795aab3f80218f3fe84a3544907730b79dbabfe2215042e7f0c27c5d93869c9f1c30e497d8cfabc3f14aed7885697be5f093328d6327e8305be388d70254cda6","href":"https://static.observableusercontent.com/files/795aab3f80218f3fe84a3544907730b79dbabfe2215042e7f0c27c5d93869c9f1c30e497d8cfabc3f14aed7885697be5f093328d6327e8305be388d70254cda6","name":"nc_leg_2022.json","type":"application/json","lastModified":1667315453182.655,"size":123163}] 2f:{"title":"NC State Legislative Elections 2022","cells":[{"id":0,"value":"$31","pinned":false,"hidden":false,"mode":"md"},{"id":17,"value":"viewof chamber = Inputs.radio([\"House\", \"Senate\"], {\n value: \"House\",\n label: \"Chamber:\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":444,"value":"

Lean:

${Swatches(\n d3.scaleOrdinal(\n [\"Democratic\", \"Republican\", \"Competitive\"],\n [d3.interpolateRdBu(0.75), d3.interpolateRdBu(0.25), d3.interpolateRdBu(0.5)]\n )\n)}

","pinned":false,"hidden":false,"mode":"html"},{"id":20,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":247,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":429,"value":"computed_comment = {\n if (chamber == \"House\") {\n return md`The Princeton Gerrymandering Project classifies an election of as \"Competitive\", if neither party has more than ${\n 100 * (0.5 + threshold)\n }% of the vote share. Of the ${\n house_counts.total\n } districts in the NC House,\n\n - ${house_counts.dem_count} lean Democratic,\n - ${house_counts.rep_count} lean Republican , and\n - ${house_counts.toss_up_count} are relatively competitive.\n`;\n } else if (chamber == \"Senate\") {\n return md`Of the ${senate_counts.total} districts in the NC Senate,\n\n - ${senate_counts.dem_count} lean Democratic,\n - ${senate_counts.rep_count} lean Republican, and\n - ${senate_counts.toss_up_count} are relatively competitive.\n`;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":607,"value":"My own logistic regression model indicates that, if a party has 51% of the vote share, they are already 96% likely to win the district. You can use the radio selector below to see what this all the above looks like with this more stringent competitive threshold.","pinned":false,"hidden":false,"mode":"md"},{"id":595,"value":"viewof threshold = Inputs.radio([0.035, 0.01], {\n label: \"Competitive threshold:\",\n value: 0.035,\n format: (x) => d3.format(\"0.1%\")(0.5 + x)\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":613,"value":"## Data","pinned":false,"hidden":false,"mode":"md"},{"id":285,"value":"bar_data = {\n let bar_data =\n chamber == \"House\" ? map_data.house_districts : map_data.senate_districts;\n bar_data = bar_data.features.map((o) => o.properties);\n bar_data.forEach(\n (o) =>\n (o.competitive_status =\n o.dem_vote_share < 0.5 - threshold\n ? \"Republican\"\n : o.dem_vote_share > 0.5 + threshold\n ? \"Democratic\"\n : \"Competitive\")\n );\n bar_data = d3.sort(bar_data, (o) => o.dem_vote_share);\n bar_data.forEach((o, i) => (o.rank = i));\n // d3.sort(\n // bar_data.filter((o) => o.cometitive_status == \"Democratic\"),\n // (o) => o.dem_vote_share\n // ).forEach(function (o, i) {\n // let oo = bar_data.filter((ooo) => ooo.DISTRICT == o.DISTRICT)[0];\n // oo.rank = i;\n // });\n return d3.sort(bar_data, (o) => o.DISTRICT);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":206,"value":"## Data","pinned":false,"hidden":false,"mode":"md"},{"id":100,"value":"house_counts = {\n let total = map_data.house_districts.features.length;\n let rep_count = map_data.house_districts.features.filter(\n (o) => o.properties.dem_vote_share < 0.5 - threshold\n ).length;\n let dem_count = map_data.house_districts.features.filter(\n (o) => o.properties.dem_vote_share > 0.5 + threshold\n ).length;\n\n let toss_ups = map_data.house_districts.features.filter(\n (o) =>\n 0.5 - threshold < o.properties.dem_vote_share &&\n o.properties.dem_vote_share < 0.5 + threshold\n );\n let dem_vote_share_extent = d3.extent(\n map_data.house_districts.features.map((o) => o.properties.dem_vote_share)\n );\n\n return {\n total,\n rep_count,\n dem_count,\n dem_vote_share_extent,\n toss_ups,\n toss_up_count: toss_ups.length\n };\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":89,"value":"senate_counts = {\n let total = map_data.senate_districts.features.length;\n let rep_count = map_data.senate_districts.features.filter(\n (o) => o.properties.dem_vote_share < 0.5 - threshold\n ).length;\n let dem_count = map_data.senate_districts.features.filter(\n (o) => o.properties.dem_vote_share > 0.5 + threshold\n ).length;\n\n let toss_ups = map_data.senate_districts.features.filter(\n (o) =>\n 0.5 - threshold < o.properties.dem_vote_share &&\n o.properties.dem_vote_share < 0.5 + threshold\n );\n let dem_vote_share_extent = d3.extent(\n map_data.senate_districts.features.map((o) => o.properties.dem_vote_share)\n );\n\n return {\n total,\n rep_count,\n dem_count,\n dem_vote_share_extent,\n toss_ups,\n toss_up_count: toss_ups.length\n };\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":6,"value":"map_data = {\n let map_file = await FileAttachment(\"nc_leg_2022.json\").json();\n let senate_districts = topojson.feature(\n map_file,\n map_file.objects[\"NC-S22-Dist1\"]\n );\n let house_districts = topojson.feature(\n map_file,\n map_file.objects[\"NC-H22-Dist1\"]\n );\n return {\n senate_districts,\n house_districts\n };\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":208,"value":"## Imports","pinned":false,"hidden":false,"mode":"md"},{"id":123,"value":"import { Legend, Swatches } from \"@d3/color-legend\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":627,"value":"require.resolve(`tippy.js/themes/light-border.css`)","pinned":true,"hidden":false,"mode":"ojs"},{"id":45,"value":"tippy_style = html``","pinned":true,"hidden":false,"mode":"ojs"},{"id":43,"value":"tippy = require(\"tippy.js@6\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":176,"value":"// Princeton Gerrymandering project indicates the competive range should be +/- 3.5%,\n// though my logistic regression analysis indicates more like +/- 1%.\n// threshold = 0.035","pinned":true,"hidden":false,"mode":"ojs"},{"id":41,"value":"https://www.wunc.org/politics/2022-10-04/2022-midterm-elections-north-carolina-breaking-nc-general-assembly-races","pinned":false,"hidden":false,"mode":"md"}],"files":"$W34","stdlib":"1","titling":"auto"}