2c:null 2d:["$","title",null,{"children":"*Final Datavis Project - RUST BELT - Sam, Charlie, & Ainsley | 🪦 dis-2025-fall | Observable"}] 32:T522,# The Effects of Manufacturing Decline - Population It may not be immediately apparent how the decline of one industry could have such a significant impact on a large region of the United States. However, the graphs reveal that these thousands of factories fueled the local economies, enabling families to put food on the table and send their kids to school. In some areas, like Detroit, MI, the community was largely dependent on a single factory or company. When these companies started shutting down operations after WWII, the economy collapsed in many areas and left many people unemployed and families struggling to make ends meet. Moreover, while manufacturing is skilled labor, many workers suddenly found their skills obsolete, as most other factories had also left the area. This led to a collapse in many communities' tax bases, which closed down schools, public works, and infrastructure. Many communities were abandoned altogether as their inhabitants looked for better prospects elsewhere. Let's take a look at the population changes of the area from 1969 to 2023. **Interact Below:** Use the slider, click and drag the map, and hover over the counties to analyze the information below. Which areas increased or decreased in population over time? Which location has the greatest population change?33:T9da,viewof year4 = { const years = [1970, 1980, 1990, 2000, 2010, 2020]; // Scrubber const scrub = Scrubber(years, { format: d => d, autoplay: false, loop: false, delay: 1330 }); // --- PLAY BUTTON (20% smaller) --- const playBtn = scrub.querySelector("button"); if (playBtn) { playBtn.style.fontSize = "18px"; playBtn.style.padding = "8px 16px"; playBtn.style.borderRadius = "6px"; playBtn.style.border = "2px solid #444"; playBtn.style.background = "#111827"; playBtn.style.color = "#f9fafb"; playBtn.style.cursor = "pointer"; } // --- YEAR TEXT (20% smaller) --- const YEAR_SIZE = "26px"; function resizeText(node) { if (!node) return; node.querySelectorAll("span").forEach(s => { s.style.fontSize = YEAR_SIZE; s.style.fontWeight = "700"; s.style.padding = "2px 6px"; s.style.display = "inline-block"; }); node.querySelectorAll("output").forEach(o => { o.style.fontSize = YEAR_SIZE; o.style.fontWeight = "700"; o.style.padding = "2px 6px"; o.style.display = "inline-block"; }); node.childNodes.forEach(n => { if (n.nodeType === Node.TEXT_NODE && /\d{4}/.test(n.textContent)) { const wrap = document.createElement("span"); wrap.textContent = n.textContent; wrap.style.fontSize = YEAR_SIZE; wrap.style.fontWeight = "700"; wrap.style.padding = "2px 6px"; wrap.style.display = "inline-block"; n.replaceWith(wrap); } }); } resizeText(scrub); // --- OUTER BOX (same style as Place) --- const box = document.createElement("div"); box.style.padding = "12px 14px"; box.style.marginBottom = "8px"; box.style.borderRadius = "8px"; box.style.background = "#f5f5f8"; box.style.border = "1px solid #ccc"; box.style.display = "inline-block"; box.style.fontFamily = "system-ui, sans-serif"; const label = document.createElement("label"); label.textContent = "Year (press play or scroll)"; label.style.fontSize = "16px"; label.style.fontWeight = "600"; label.style.display = "block"; label.style.marginBottom = "6px"; box.append(label); box.append(scrub); // --- CRUCIAL PART: forward value + events to Observable --- Object.defineProperty(box, "value", { get() { return scrub.value; } }); scrub.addEventListener("input", () => { // bubble up to notify Observable box.dispatchEvent(new InputEvent("input", { bubbles: true })); }); return box; }34:T617,viewof county2 = { const input = Inputs.select( Array.from(new Set(employment.map(d => d.county))), { label: "County" } ); // Outer box to match your other controls const box = document.createElement("div"); box.style.padding = "12px 14px"; box.style.marginBottom = "8px"; box.style.borderRadius = "8px"; box.style.background = "#f5f5f8"; box.style.border = "1px solid #ccc"; box.style.display = "inline-block"; box.style.fontFamily = "system-ui, sans-serif"; // Label (clearer text) const label = document.createElement("label"); label.textContent = "County (click to change)"; label.style.fontSize = "16px"; label.style.fontWeight = "600"; label.style.display = "block"; label.style.marginBottom = "6px"; // Style the internal select a bit const select = input.querySelector("select"); if (select) { select.style.fontSize = "15px"; select.style.padding = "6px 8px"; select.style.borderRadius = "6px"; select.style.border = "2px solid #444"; select.style.cursor = "pointer"; select.style.minWidth = "220px"; } box.append(label); box.append(input); // Make the box behave like a proper viewof Object.defineProperty(box, "value", { get() { return input.value; } }); // Forward events so dependents update input.addEventListener("input", () => { box.dispatchEvent(new InputEvent("input", { bubbles: true })); }); input.addEventListener("change", () => { box.dispatchEvent(new InputEvent("input", { bubbles: true })); }); return box; }35:T992,viewof year5 = { const years = [2003, 2013, 2023]; const scrub = Scrubber(years, { format: d => d, autoplay: false, loop: false, delay: 1330 }); // Play button (same style family as year4) const playBtn = scrub.querySelector("button"); if (playBtn) { playBtn.style.fontSize = "18px"; playBtn.style.padding = "8px 16px"; playBtn.style.borderRadius = "6px"; playBtn.style.border = "2px solid #444"; playBtn.style.background = "#111827"; playBtn.style.color = "#f9fafb"; playBtn.style.cursor = "pointer"; } // Year text (same ~26px size we settled on) const YEAR_SIZE = "26px"; function resizeText(node) { if (!node) return; node.querySelectorAll("span").forEach(s => { s.style.fontSize = YEAR_SIZE; s.style.fontWeight = "700"; s.style.padding = "2px 6px"; s.style.display = "inline-block"; }); node.querySelectorAll("output").forEach(o => { o.style.fontSize = YEAR_SIZE; o.style.fontWeight = "700"; o.style.padding = "2px 6px"; o.style.display = "inline-block"; }); node.childNodes.forEach(n => { if (n.nodeType === Node.TEXT_NODE && /\d{4}/.test(n.textContent)) { const wrap = document.createElement("span"); wrap.textContent = n.textContent; wrap.style.fontSize = YEAR_SIZE; wrap.style.fontWeight = "700"; wrap.style.padding = "2px 6px"; wrap.style.display = "inline-block"; n.replaceWith(wrap); } }); } resizeText(scrub); // Outer box styled like your other controls const box = document.createElement("div"); box.style.padding = "12px 14px"; box.style.marginBottom = "8px"; box.style.borderRadius = "8px"; box.style.background = "#f5f5f8"; box.style.border = "1px solid #ccc"; box.style.display = "inline-block"; box.style.fontFamily = "system-ui, sans-serif"; const label = document.createElement("label"); label.textContent = "Year (press play or scroll)"; label.style.fontSize = "16px"; label.style.fontWeight = "600"; label.style.display = "block"; label.style.marginBottom = "6px"; box.append(label); box.append(scrub); // viewof wiring: forward value + events Object.defineProperty(box, "value", { get() { return scrub.value; } }); scrub.addEventListener("input", () => { box.dispatchEvent(new InputEvent("input", { bubbles: true })); }); return box; 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Ainsley","thumbnail":"c80300c79530b0ca9253c280bebe7f1d223ea98be66b6864a81c7c4616276278","access_level":"public","create_time":"$D2025-11-19T14:49:19.119Z","update_time":"$D2025-12-10T11:29:14.673Z","view_time":null,"first_public_time":"$D2025-11-19T14:49:19.119Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"e9b2f7fde85a5f53","login":"dis-2025-fall","name":"🪦 dis-2025-fall","avatar_url":"https://avatars-next.observableusercontent.com/a95179728f7a208605a8a434afc1723fb8e19b4266664a0a24602d1c54817cd7","tier":"pro_2024"},"version":523,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"58b4bf6dc88792cc","login":"samls2","name":"SamLS","avatar_url":"https://avatars-next.observableusercontent.com/5af7af2dc826d1f90f4a8d79fd0de60a87003c067bc02c639778e08358d356dd","tier":null}],"members":[{"type":"team","id":"e9b2f7fde85a5f53","login":"dis-2025-fall","name":"🪦 dis-2025-fall","avatar_url":"https://avatars-next.observableusercontent.com/a95179728f7a208605a8a434afc1723fb8e19b4266664a0a24602d1c54817cd7","tier":"pro_2024","role":"editor"},{"type":"individual","id":"58b4bf6dc88792cc","login":"samls2","name":"SamLS","avatar_url":"https://avatars-next.observableusercontent.com/5af7af2dc826d1f90f4a8d79fd0de60a87003c067bc02c639778e08358d356dd","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":{"id":"37ce26c60a41a976","slug":"group-projects","title":"GROUP PROJECTS","owner":{"id":"e9b2f7fde85a5f53","login":"dis-2025-fall","name":"🪦 dis-2025-fall"}},"type":"notebook","body":{"title":"*Final Datavis Project - RUST BELT - Sam, Charlie, & Ainsley","cells":[{"id":0,"value":"# *Final Datavis Project - RUST BELT - Sam, Charlie, & Ainsley","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":" We chose to explore economic and population data related to the Rust Belt in the United States, which includes **Pennsylvania, Ohio, Michigan, Indiana, Illinois, Wisconsin, and part of West Virginia and New York.**\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":10,"value":"# To Begin - What is the Rust Belt?\nThe Rust Belt is a region surrounding the Great Lakes of America that was the country's industrial heartland. From around the 1880s to the 1980s, the region manufactured steel, oil products, cars, and other commodities at a dominant rate, and as a result became very wealthy. However, after WWII, the U.S. shifted focus from manufacturing to higher-paying industries and outsourced manufacturing to foreign countries like China and Germany. This heavily impacted the working Americans and industries in this region. As companies moved or closed, they left many people without jobs and the area became impoverished with many abandoned factories across the region, which is where the rust belt name originated.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":448,"value":"\n

\n Steel plant in Pittsburgh, PA (US Steel, 1967). U.S. Steel alone employed over 100,000 people in the city in the 1960s. \n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":457,"value":"\n

\n Such factories may have been ugly, but they genuinely upheld the region's middle class. Above is a Detroit factory worker, his home, and his family in the 1950s. Manufacturing jobs made this lifestyle available to millions of people in what is now the Rust Belt. \n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":13,"value":"# The Decline of the Rust Belt - Manufacturing Earnings, 1969-2019\nLet us analyze the region's economic decline through the income of manufacturing industries per county, from 1969 to 2019.\n\n**Interact Below:**\nUse the slider and hover the mouse over the map below to analyze the percentage of decline in manufacturing per county. Which counties struggled the most? How does to see the change in county income over the decades? Which counties have the highest income? What decade has the least income across the region?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":59,"value":"coolMap","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"# The Decline of Manufacturing Earnings\n\nLooking at the data with a different metric, what changes about the map depending on the percentage of manufacturing decline? The following map features the same color-coded counties, but analyzes the data with through percentage with the wages adjusted for inflation in 2025.\n\n**Interact Below:** Use the slider and hover the mouse over the map below to analyze the percentage of decline in manufacturing per county. Which counties struggled the most? How does it compare to the map above? What story are you seeing?\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":104,"value":"coolMap2","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"\n

\n The outsourcing of the manufacturing industry to other countries caused employers to simply abandon large manufacturing plants. This steel mill in Bethlehem, Pennsylvania, was too expensive to demolish and remains unused. Such sights inspired the name Rust Belt. These factory closures took away millions of good jobs in the area and in many places caused economic stagnation. \n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":139,"value":"populationMap","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":478,"value":"

\n The loss of so many jobs gutted the populations of many Rust Belt cities. In Detroit, once the center of the world's car production, many once-thriving neighborhoods now look like this. \n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":24,"value":"# Revitalization - Successful Counties\nThe decline of the Rust Belt's economy hurt many communities and the region will likely never be as prosperous as it once was. However, certain areas of the Rust Belt are adapting to changing economic conditions. To examine how this happens, let's look at some successful counties. \n\n**Interact Below:** Click the button below to view some of the success stories of the revitalized Rust Belt, and click and drag to move the map. Is there a pattern you see with successful surviving areas?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":390,"value":"highlightRustBeltSpecial","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"# Areas of Revitalization: Chicago, Pittsburgh, Madison, and Columbus\n\nThese are areas that have been able to adapt their economies and survive the manufacturing downturn. While Chicago and Pittsburgh have lost population from their height in the 1960s, these losses have stabilized in recent years, and today the cities are hubs for finance and technology. \n\nBetween 2019 and 2023, Madison and Columbus actually grew faster than the national average, a rare achievement in the region. The influx of population and stabilization of their economies has many causes, but let's examine two tools for revitalization: education and new industries. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":480,"value":"

\n Chicago's financial district. The city was once a manufacturing powerhouse but has since pivoted to being a finance and tech center.\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":29,"value":"# Education\nIn the peak of the Rust Belt, many middle-class workers worked in manufacturing, which was usually unionized, well-paid, and taught on the job. The decline of these high-paying manual labor jobs led to a growth in white-collar jobs that require college education, once fairly rare in the region. \nThe decline of manual labor jobs made room for more high-paying service or knowledge-based jobs, known as \"white collar\" jobs, which pushed more people to get higher education. Below, you can examine the share of the population with a bachelor's degree from the 1970s to the present in all four cities: **Chicago, Columbus, Madison, and Pittsburgh**.\n\n**Interact Below:** Compare the growth of education in the workforce between these four counties. Select the \"**place**\" and \"**year**\", or press **\"play\"**. What is the trend in college education? How might this contribute to economic **revitalization** in these counties? ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":170,"value":"viewof county = {\n // Create the original input\n const input = Inputs.select(\n Array.from(new Set(tidy.map(d => d.county))),\n {\n label: \"Place (click to change)\",\n format: c => {\n const city = cityByCounty[c];\n return city ? `${city} (${c})` : c;\n }\n }\n );\n\n // Add styles to make it more obvious\n input.style.padding = \"10px 12px\";\n input.style.marginBottom = \"8px\";\n input.style.borderRadius = \"8px\";\n input.style.background = \"#f5f5f8\";\n input.style.border = \"1px solid #ccc\";\n\n const label = input.querySelector(\"label\");\n if (label) {\n label.style.fontSize = \"15px\";\n label.style.fontWeight = \"600\";\n label.style.marginBottom = \"4px\";\n }\n\n const select = input.querySelector(\"select\");\n if (select) {\n select.style.fontSize = \"15px\";\n select.style.padding = \"6px 8px\";\n select.style.borderRadius = \"6px\";\n select.style.border = \"2px solid #444\";\n select.style.cursor = \"pointer\";\n }\n\n return input;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":211,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":168,"value":"cap","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":484,"value":"

\n Graduates from the Carnegie Mellon computer engineering school in Pittsburgh, 2024. Most of them will likely earn a higher salary than the unionized factory workers in Pittsburgh in the 1960s.\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":490,"value":"

\n Ohio State University in Columbus, one of the nation's largest schools. Many OSU graduates will populate the Columbus workforce with well-educated, well-paid positions.\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"# New Industries: Healthcare, Finance, and Tech\n\nAnother source of prosperity for Rust Belt counties is a paradigm shift in the main industries of the region. With manufacturing employers leaving the area, new skills-based industries became prominent and began to attract the rising number of college graduates in the area. Healthcare, a steady sector, has seen massive growth in recent decades. Cities like Chicago and Pittsburgh are also strong in finance, another growing market. Information and technology, which wasn't reported in census data until 2000, has also grown meteorically in the United States. Explore how these industries have grown relative to manufacturing since the year 2003.\n\n**Interact Below:** Pick a county, year, or press \"play\", and analyze the industry buildings change over time. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":220,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":218,"value":"buildingsChart","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":493,"value":"

\n The Epic Systems campus in Madison, WI. Epic, which produces healthcare technical systems, makes $5.7B in annual revenue and employs 13,000 Madison residents.\n

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":496,"value":"

\n Google employees in Pittsburgh. While housing prices have risen, many Pittsburgh tech workers can live the same middle-class life their predecessors did, trading steel work for programming. \n

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":509,"value":"

\n Breaking ground on a new factory in Buffalo, NY.\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":507,"value":"Of course, these cities are success stories, but many counties are unlikely to fully recover from the deindustrialization of the Rust Belt. However, as we can see, there is certainly hope for this region. Only time will tell. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":499,"value":"

\n A very poetic symbol of Rust Belt revitalization: recently, a quantum computing company purchased an abandoned Chicago steel mill and plans to replace it with the above data-center complex that may generate as much as $60 billion in economic impact. \n

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":75,"value":"# Imports\n\nSome graph sources are borrowed from other works. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":216,"value":"import {buildingsChart} with {county2 as county2, year5 as year5, years5 as years5, tooltip as tooltip} from \"@dis-2025-fall/buildings-engineering\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"import {Scrubber} from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":63,"value":"years = [1969, 1979, 1989, 1999, 2009, 2019]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"import {coolMap} with {year as year, years as years} from \"@dis-2025-fall/final-project\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":388,"value":"import {highlightRustBeltSpecial} from \"@dis-2025-fall/population-columns\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"dataByYear = new Map(\n years.map(y => [\n y,\n new Map(\n data\n .filter(d => +d.Year === y && Number(+d.Value))\n .map(d => [d.FIPS, d.Value])\n )\n ])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"years2 = Array.from(new Set(data2.map(d => d.Year))).sort(d3.ascending)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":327,"value":"dataByYear2 = new Map(\n years.map(y => [\n y,\n new Map(\n data\n .filter(d => +d.Year === y && Number(+d.Value))\n .map(d => [d.FIPS, d.Value])\n )\n ])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"data = FileAttachment(\"profile.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"// Build a name lookup from your long table\n// rows has: FIPS, County, State (as \"County, State\"), Year, Value\nnameByFips = new Map(\n data.map(d => [\n String(Math.trunc(+d.FIPS)).padStart(5, \"0\"),\n d[\"County, State\"] ?? d.County ?? d.Name ?? \"\"\n ])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"profile = await FileAttachment(\"profile.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"profile","database":"$undefined"},{"id":85,"value":"mutable hoveredFips = null ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":124,"value":"data2 = rows.map(d => ({\n FIPS: String(d[\"FIPS\"] || d[\"FIPS\"] || \"\")\n .replace(/\\D/g, \"\")\n .padStart(5, \"0\"),\n CountyState: d[\"County, State\"] || d[\"County, State\"] || \"\",\n Year: +String(d[\"Year\"] || d[\"Year \"] || \"\")\n .replace(/\\D/g, \"\"),\n Value: +String(d[\"Value\"] || d[\" Value\"] || \"\")\n .replace(/[^\\d.-]/g, \"\")\n}))\n.filter(d => d.FIPS && Number.isFinite(d.Year) && Number.isFinite(d.Value));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":126,"value":"rows = FileAttachment(\"percentIncomeChange.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":128,"value":"percentincomechange = await FileAttachment(\"percentIncomeChange.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"percentincomechange","database":"$undefined"},{"id":102,"value":"import {coolMap2} with {year2 as year2} from \"@dis-2025-fall/percent-decline\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":146,"value":"years3 = Array.from(\n new Set(populationRaw.map(d => +d.Year))\n)\n.filter(y => y !== 2023) // <-- remove 2023\n.sort((a, b) => a - b)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":148,"value":"populationRaw = FileAttachment(\"county_population.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"import {populationMap} with {year3 as year3} from \"@dis-2025-fall/population-columns\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":150,"value":"county_population = await FileAttachment(\"county_population.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"county_population","database":"$undefined"},{"id":163,"value":"refGrowthTable = [\n { from: 1969, to: 1979, value: 0.1148 }, // ~11.48%\n { from: 1979, to: 1989, value: 0.0978 }, // ~9.78%\n { from: 1989, to: 1999, value: 0.1315 }, // ~13.15%\n { from: 1999, to: 2009, value: 0.0971 }, // ~9.71%\n { from: 2009, to: 2019, value: 0.0735 }, // ~7.35%\n { from: 2019, to: 2023, value: 0.0260 } // ~2.6% (2019→2023)\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":166,"value":"import {cap} with {county as county, year4 as year4} from \"@dis-2025-fall/grad-cap\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":172,"value":"cityByCounty = ({\n \"Cook County, IL\": \"Chicago\",\n \"Franklin County, OH\": \"Columbus\",\n \"Dane County, WI\": \"Madison\",\n \"Allegheny County, PA\": \"Pittsburgh\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":174,"value":"\ntidy = data4.flatMap(row =>\n years.map((year, i) => ({\n county: row.A,\n year,\n rate: row[cols[i]] // already 0–1, e.g. 0.0916\n }))\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":176,"value":"cols = [\"B\", \"C\", \"D\", \"E\", \"F\", \"G\"]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":183,"value":"workbook2 = FileAttachment(\"College Ed Pivoted.xlsx\").xlsx()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":201,"value":"workbook = FileAttachment(\"College Ed Pivoted.xlsx\").xlsx()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":205,"value":"data5 = workbook.sheet(0, {\n headers: false,\n // range: \"A1:J10\"\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":185,"value":"data4 = workbook2.sheet(0, {\n headers: false,\n // range: \"A1:J10\"\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":198,"value":"workbook1 = FileAttachment(\"College Ed Pivoted.xlsx\").xlsx()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":186,"value":"data4","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":196,"value":"years4 = [1970, 1980, 1990, 2000, 2010, 2020]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":222,"value":"// one row per county–industry–year\nemployment = employmentRaw.flatMap(row =>\n industries.flatMap(ind =>\n years.map(year => ({\n county: row[\"County\"],\n industry: ind.name,\n year,\n value: parseMoney(row[`${ind.key}, ${year}`])\n }))\n )\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":224,"value":"function parseMoney(x) {\n if (!x) return 0;\n return +x.replace(/\\$/g, \"\").replace(/,/g, \"\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":261,"value":"industries = [\n { name: \"Manufacturing\", key: \"Manu\" },\n { name: \"Finance\", key: \"Finance\" },\n { name: \"Information\", key: \"Info\" },\n { name: \"Health\", key: \"Health\" }\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":259,"value":"\nyears5 = [2003, 2013, 2023]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":257,"value":"function getValue(county, industry, year) {\n const row = employment.find(d =>\n d.county === county &&\n d.industry === industry &&\n d.year === year\n );\n return row ? row.value : 0;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":"maxBase2003 = d3.max(\n employment.filter(d => d.year === 2003),\n d => d.value\n)\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":253,"value":"\n\nheightScale = d3.scaleLinear()\n .domain([0, maxBase2003])\n .range([80, 360]) // min + max building height for 2003 baseline","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":317,"value":"tooltip = {\n const div = html`

`;\n return div;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":251,"value":"fmtMoney = d3.format(\"$,.0f\") // e.g. $34,687,698\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"fmtPct = d3.format(\"+.1%\") // e.g. +23.6%","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":272,"value":"rustbeltindustryemployment2 = await FileAttachment(\"rustBeltIndustryEmployment@2.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"rustbeltindustryemployment2","database":"$undefined"},{"id":226,"value":"employmentRaw = FileAttachment(\"rustBeltIndustryEmployment@2.csv\").csv({\n typed: false\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 31:"$30:body"