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Education

Are North Meck schools keeping pace with rapid growth?

Cornelius, Davidson, and Huntersville have grown quickly: their population age 3 and over rose 18% from 2018 to 2024. The number of residents enrolled in school grew far more slowly, up 5%. This section follows the data on who is in the classroom, how schools are performing, and where gaps are widest.

Overview Enrollment & Growth School Demographics Academic Performance Educational Attainment

import {Plot, stdPlot, chips, townChips, responsive, loadData, showTown, TOWNS, townColor, FONT} from "/js/nm-charts.js"

popK12 = loadData("pop-growth-k12-enrollment")
k12ByLevel = loadData("k12-enrollment-by-level")
bachTrend = loadData("bachelors-attainment-trend")
attainment2022 = loadData("attainment-breakdown-2022")
earningsByEd = loadData("earnings-by-education")
proficiency = loadData("grade-level-proficiency")
growth = loadData("school-academic-growth")
graduation = loadData("four-year-school-graduation")
econGap = loadData("school-achievement-and-economic-gap")
hsEconGap = loadData("highschool-achievement-economic-gap")
raceGap = loadData("school-race-gap")
schoolEconGap = loadData("school-economic-gap")
disabilityGap = loadData("school-disability-gap")

// The original education charts used the 12px chart font.
S = ({...FONT, fontSize: "12px"})
byTown = (rows, town) => town === "All" ? rows : rows.filter(d => d.town === town)
// Suppressed report-card values come through as text like ">95"; chart them at the bound.
withParsed = (rows, key) => rows.map(d => {
  if (d[key] != null) return d;
  const m = /^[<>]?\s*(\d+(\.\d+)?)/.exec(d[key + "_raw"] || "");
  return m ? {...d, [key]: parseFloat(m[1])} : d;
})
RACE_COLORS = ({"American Indian": "#440154", "Asian": "#3b528b", "Black": "#21908c", "Hispanic": "#5dc963", "Two or More Races": "#fde725"})
GROWTH_COLOR = ({legend: true, domain: ["Exceeded", "Met", "Not Met"], range: ["#2ca02c", "#ff7f0e", "#d62728"]})
ECON_COLOR = ({legend: true, domain: ["Economically Disadvantaged", "Not Economically Disadvantaged"], range: ["#d62728", "#4e79a7"]})

viewof masterTown = chips([{label: "All North Meck", value: "All"}, ...TOWNS], {label: "Area:", hint: "filters every chart"})

+5%

growth in North Meck residents enrolled in school since 2018, preschool through graduate school

ACS 2018–2024

~1 in 3

North Meck schools are federally designated Title I, meaning a high share of students come from low-income families

NC DPI, 2024

43.6 pts

average proficiency gap between students with and without disabilities across North Meck schools

NC DPI, 2024

~3 in 5

North Meck schools exceeded their expected academic growth target this year, a sign of strong year-over-year progress

NC DPI, EVAAS growth index, 2024

Total School Enrollment by Town (2018–2024)

All enrollment levels · preschool through graduate/professional

{
  const yMax = Math.ceil(Math.max(...popK12.map(d => d.n_enrolled_total)) / 1000) * 1000;
  const data = byTown(popK12, masterTown).slice().sort((a, b) => a.year - b.year);
  return responsive(width => stdPlot({
    width, height: 340, style: S,
    x: {label: "Year", tickFormat: d => String(d), ticks: [2018, 2019, 2020, 2021, 2022, 2023, 2024], insetLeft: 12, insetRight: 12},
    y: {label: "Total Enrollment", grid: true, interval: 1000, domain: [0, yMax]},
    color: townColor({legend: masterTown === "All"}),
    marks: [
      Plot.line(data, {x: "year", y: "n_enrolled_total", stroke: "town", strokeWidth: masterTown === "All" ? 2 : 3}),
      Plot.dot(data, {x: "year", y: "n_enrolled_total", stroke: "town", fill: "white", strokeWidth: 1.5, r: 4,
        tip: true, title: d => `${d.town}\n${d.year}: ${d.n_enrolled_total.toLocaleString()} enrolled`})
    ]
  }), {invalidation});
}

Grade-Level Proficiency by Title I status (2024)

Percent of students proficient at grade level, Title I designated schools compared to others

{
  const data = withParsed(byTown(proficiency, masterTown), "glp")
    .map(d => ({...d, status: d.is_title_1 ? "Title I" : "Not Title I"}))
    .filter(d => d.glp != null).sort((a, b) => b.glp - a.glp);
  return responsive(width => stdPlot({
    width, height: 90 + data.length * 22, marginLeft: 190, style: S,
    x: {label: "Grade-Level Proficiency (%)", domain: [0, 100], grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    color: {legend: true, domain: ["Title I", "Not Title I"], range: ["#21908c", "#5ec962"]},
    marks: [
      Plot.barX(data, {x: "glp", y: "school", fill: "status", rx: 3, tip: true,
        title: d => `${d.school} (${d.grade_span})\n${d.status}\nGrade-Level Proficient: ${d.glp}%`}),
      Plot.ruleX([0])
    ]
  }), {invalidation});
}

Proficiency gap for students with disabilities, by school (2024)

Percentage-point gap in grade-level proficiency, students with disabilities compared to students without

// school-disability-gap has no town field, so this chart doesn't follow the town filter.
{
  const data = disabilityGap.filter(d => d.gap != null).slice().sort((a, b) => b.gap - a.gap);
  return responsive(width => stdPlot({
    width, height: 90 + data.length * 22, marginLeft: 190, style: S,
    x: {label: "Proficiency gap (points)", grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    marks: [
      Plot.barX(data, {y: "school", x: "gap", fill: "#3b528b", rx: 3, tip: true,
        title: d => `${d.school}\nWith disabilities: ${d.swd_pct}%\nWithout: ${d.nswd_pct}%\nGap: ${d.gap} pts`})
    ]
  }), {invalidation});
}

Academic growth status by school (2024)

EVAAS growth index score, whether each school exceeded, met, or fell short of expected growth

{
  const data = byTown(growth, masterTown).slice().sort((a, b) => b.index_score - a.index_score);
  return responsive(width => stdPlot({
    width, height: 90 + data.length * 22, marginLeft: 190, style: S,
    x: {label: "Growth Index Score (0 = met expected growth)", domain: [-8, 8], grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    color: GROWTH_COLOR,
    marks: [
      Plot.barX(data, {x: "index_score", y: "school", fill: "status", rx: 3, tip: true,
        title: d => `${d.school} (${d.grade_span})\nStatus: ${d.status}\nGrowth Index: ${d.index_score}`}),
      Plot.ruleX([0])
    ]
  }), {invalidation});
}

Enrollment & Growth

North Meck's population is growing much faster than its school enrollment.

From 2018 to 2024, the population age 3+ in Cornelius, Davidson, and Huntersville grew 18%, while the number of residents enrolled in school (preschool through graduate school) rose 5% and K–12 counts rose 3–6%. K–12 counts in Cornelius and Huntersville peaked in 2021 and are down 7–9% since. How enrollment has shifted by school level matters for planning, bonds, and staffing decisions.

K–12 Enrollment Trend

Total residents enrolled in K–12 school · by town

{
  const data = byTown(popK12, masterTown);
  return responsive(width => stdPlot({
    width, height: 320, style: S,
    x: {label: "Year", tickFormat: d => String(d)},
    y: {label: "K–12 Enrollment", grid: true},
    color: townColor({legend: masterTown === "All"}),
    marks: [
      Plot.line(data, {x: "year", y: "n_enrolled_k12_total", stroke: "town", strokeWidth: masterTown === "All" ? 2 : 3}),
      Plot.dot(data, {x: "year", y: "n_enrolled_k12_total", stroke: "town", fill: "white", strokeWidth: 1.5, r: 4,
        tip: true, title: d => `${d.town}\n${d.year}: ${d.n_enrolled_k12_total.toLocaleString()}`})
    ]
  }), {invalidation});
}

Huntersville has added 311 K–12 students since 2018 — the largest absolute gain. Davidson is the smallest in total enrollment but has the highest overall enrollment rate relative to its population size (35% of residents age 3+ were enrolled in school in 2024, including college).

Source: ACS 2018–2024 · Table B14001 · counts residents enrolled in school, not CMS school-level enrollment

Population growth vs. K-12 Enrollment

Indexed to 2018 = 100 · Population (age 3+) vs. K–12 enrollment · by town

{
  const towns = masterTown === "All" ? TOWNS : [masterTown];
  const enrollIndex = towns.flatMap(t => {
    const rows = popK12.filter(d => d.town === t).sort((a, b) => a.year - b.year);
    if (!rows.length) return [];
    const base = rows[0];
    return rows.flatMap(d => [
      {town: t, year: d.year, series: "Total population (3+)", index: d.total_pop_3_plus / base.total_pop_3_plus * 100, raw: d.total_pop_3_plus},
      {town: t, year: d.year, series: "K-12 enrollment", index: d.n_enrolled_k12_total / base.n_enrolled_k12_total * 100, raw: d.n_enrolled_k12_total}
    ]);
  });
  const vals = enrollIndex.map(d => d.index);
  const domain = [Math.floor(Math.min(...vals) / 5) * 5 - 5, Math.ceil(Math.max(...vals) / 5) * 5 + 5];
  return responsive(width => stdPlot({
    width, height: 300, marginRight: 20, style: S,
    x: {label: "Year", tickFormat: "d", ticks: [2018, 2020, 2022, 2024]},
    fx: {label: null},
    y: {label: "Index (2018 = 100)", domain, grid: true},
    color: {legend: true, domain: ["Total population (3+)", "K-12 enrollment"], range: ["#b5495b", "#3b6ea5"], label: null},
    facet: {data: enrollIndex, x: "town"},
    marks: [
      Plot.ruleY([100], {stroke: "#ccc", strokeDasharray: "3,3"}),
      Plot.line(enrollIndex, {x: "year", y: "index", stroke: "series", strokeWidth: 2.5}),
      Plot.dot(enrollIndex, {x: "year", y: "index", fill: "series", r: 3}),
      Plot.tip(enrollIndex, Plot.pointer({x: "year", y: "index", stroke: "series",
        title: d => `${d.town}, ${d.year}\n${d.series}\n${d.raw.toLocaleString()} (${d.index.toFixed(1)} vs 2018)`}))
    ]
  }), {invalidation});
}

K-12 student counts rose only 3–6% while town populations grew 14–26% since 2018.

Source: U.S. Census Bureau, ACS 5-Year Estimates, Table B14001. Population shown is age 3+, the universe for the ACS school enrollment question.

Enrollment by School Level

Residents enrolled by grade band · Grades 1–4, 5–8, and High School

{
  const LEVEL_COLORS = {"Grades 1–4": "#aec6e8", "Grades 5–8": "#6baed6", "High School": "#2171b5"};
  const LEVELS = Object.keys(LEVEL_COLORS);
  const years = [...new Set(k12ByLevel.map(d => d.year))].sort((a, b) => a - b);
  const rows = byTown(k12ByLevel, masterTown);
  const sum = (yr, key) => rows.filter(r => r.year === yr).reduce((s, r) => s + r[key], 0);
  const data = years.flatMap(yr => [
    {year: yr, level: "Grades 1–4", count: sum(yr, "elem14")},
    {year: yr, level: "Grades 5–8", count: sum(yr, "elem58")},
    {year: yr, level: "High School", count: sum(yr, "hs")}
  ]);
  return responsive(width => stdPlot({
    width, height: 340, style: S,
    x: {label: "Year", tickFormat: d => String(d)},
    y: {label: "Enrollment", grid: true},
    color: {domain: LEVELS, range: LEVELS.map(l => LEVEL_COLORS[l]), legend: true},
    marks: [
      Plot.areaY(data, Plot.stackY({x: "year", y: "count", fill: "level", order: LEVELS, fillOpacity: 0.85})),
      Plot.lineY(data, Plot.stackY({x: "year", y: "count", stroke: "level", strokeWidth: 1.5, order: LEVELS}))
    ]
  }), {invalidation});
}

Across North Meck, grades 1–4 enrollment is roughly flat (+2% since 2018), but that total hides opposite trends by town: Cornelius -17%, Davidson +6% and Huntersville +11%. Grades 5–8 (+28%) and high school (+7%) enrollment have grown.

Source: ACS 2018–2023 · Table B14001 · No 2020 estimate

School Academic Growth Across North Mecklenburg (2024-25)

Growth index score by school · 0 = met expected growth

viewof growthTown = townChips(masterTown)
{
  showTown(growthTown, "#ed-growth-host");
  const data = byTown(growth, growthTown).slice().sort((a, b) => b.index_score - a.index_score);
  return responsive(width => stdPlot({
    width, height: 100 + data.length * 22, style: S,
    x: {label: "Growth Index Score (0 = met expected growth)", domain: [-8, 8], grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    color: GROWTH_COLOR,
    marks: [
      Plot.barX(data, {x: "index_score", y: "school", fill: "status", tip: true,
        title: d => `${d.school} (${d.grade_span})\nStatus: ${d.status}\nGrowth Index: ${d.index_score}`}),
      Plot.ruleX([0])
    ]
  }), {invalidation});
}

School Demographics

Who is in the classroom, and how has that changed?

The racial, ethnic, and economic composition of North Meck schools reflects, and sometimes diverges from, the communities they serve. Tracking these patterns over time reveals how growth, attendance zones, and school choice interact with equity.

A note on terminology: "proficient" means a student scored Level 3 or higher (of 5) on NC's state exams, a passing but not top-tier score. See the glossary for details.

Proficiency Gap by Race, Average Across Schools

Percentage-point gap vs. white students' proficiency rate · North Meck schools

{
  const byGroup = {};
  raceGap.forEach(d => { if (d.gap != null) (byGroup[d.comparison_group] = byGroup[d.comparison_group] || []).push(d.gap); });
  const avgData = Object.keys(byGroup).filter(g => byGroup[g].length >= 3).map(g => ({
    comparison_group: g,
    avg_gap: byGroup[g].reduce((a, b) => a + b, 0) / byGroup[g].length,
    n: byGroup[g].length
  })).sort((a, b) => b.avg_gap - a.avg_gap);
  return responsive(width => stdPlot({
    width, height: 260, marginLeft: 150, style: S,
    x: {label: "Avg. proficiency point gap", grid: true},
    y: {label: null, domain: avgData.map(d => d.comparison_group)},
    marks: [
      Plot.barX(avgData, {y: "comparison_group", x: "avg_gap", fill: d => RACE_COLORS[d.comparison_group], rx: 3}),
      Plot.text(avgData, {y: "comparison_group", x: "avg_gap", text: d => d.avg_gap.toFixed(1) + " pts (" + d.n + " schools)", dx: 24, fontSize: 12})
    ]
  }), {invalidation});
}

This shows an achievement gap, not enrollment composition: a 20-point gap means that group's proficiency rate is 20 points below white students' rate at the same schools, not that the group makes up 20% of enrollment. American Indian students are excluded from this average — only 1 school reported a value, too few to average meaningfully.

Source: NC School Report Cards, NC DPI

Schools With the Largest Race-Based Proficiency Gap

Largest reported gap per school, top 12 · colored by comparison group

{
  const bySchool = {};
  raceGap.forEach(d => {
    if (d.gap == null) return;
    if (!bySchool[d.school] || d.gap > bySchool[d.school].gap) bySchool[d.school] = d;
  });
  const top = Object.values(bySchool).sort((a, b) => b.gap - a.gap).slice(0, 12);
  return responsive(width => stdPlot({
    width, height: 420, marginLeft: 190, style: S,
    x: {label: "Largest reported gap (points)", grid: true},
    y: {label: null, domain: top.map(d => d.school)},
    color: {domain: Object.keys(RACE_COLORS), range: Object.values(RACE_COLORS), legend: true},
    marks: [
      Plot.barX(top, {y: "school", x: "gap", fill: "comparison_group", rx: 3, tip: true,
        title: d => `${d.school}\n${d.comparison_group}: ${d.gap} point gap`})
    ]
  }), {invalidation});
}

Source: NC School Report Cards, NC DPI

Economic Disadvantage: Proficiency Gap by School

Economically disadvantaged students' proficiency rate vs. their peers, by school

{
  const data = schoolEconGap.filter(d => d.gap != null && (masterTown === "All" || d.town_name === masterTown)).slice().sort((a, b) => b.gap - a.gap);
  return responsive(width => stdPlot({
    width, height: masterTown === "All" ? 480 : Math.max(160, data.length * 26 + 60), marginLeft: 190, style: S,
    x: {label: "Proficiency gap (points)", grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    marks: [
      Plot.barX(data, {y: "school", x: "gap", fill: "#21908c", rx: 3, tip: true,
        title: d => `${d.school}\nEcon. disadvantaged: ${d.econ_disadv}%\nNot disadvantaged: ${d.not_disadv}%\nGap: ${d.gap} pts`})
    ]
  }), {invalidation});
}

Source: NC School Report Cards, NC DPI

Students With Disabilities: Proficiency Gap by School

Students with disabilities' proficiency rate vs. their peers, by school

{
  const data = disabilityGap.filter(d => d.gap != null).slice().sort((a, b) => b.gap - a.gap);
  return responsive(width => stdPlot({
    width, height: 480, marginLeft: 190, style: S,
    x: {label: "Proficiency gap (points)", grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    marks: [
      Plot.barX(data, {y: "school", x: "gap", fill: "#3b528b", rx: 3, tip: true,
        title: d => `${d.school}\nWith disabilities: ${d.swd_pct}%\nWithout: ${d.nswd_pct}%\nGap: ${d.gap} pts`})
    ]
  }), {invalidation});
}

Source: NC School Report Cards, NC DPI

Academic Performance

How are students performing, and which schools are improving?

NC end-of-grade proficiency, college and career readiness, and graduation rates paint a picture of academic outcomes across North Meck schools, and of the gaps between groups of students.

A note on terminology: "proficient" (or GLP) means a student scored Level 3 or higher (of 5) on NC's state exams, a passing but not top-tier score. "College/Career Ready" (CCR) is the stricter Level 4 or 5 standard. "Economically disadvantaged" and "Title I" are related but distinct designations, and growth is measured by the EVAAS Growth Index rather than a passing score. Graduation figures use the cohort graduation rate. See the glossary for details.

Grade Level proficiency rate by school (2024-2025)

% students at or above grade level

viewof glpTown = townChips(masterTown)
{
  const data = withParsed(byTown(proficiency, glpTown), "glp").filter(d => d.glp != null).sort((a, b) => b.glp - a.glp);
  return responsive(width => stdPlot({
    width, height: 100 + data.length * 22, style: S,
    x: {label: "Grade-Level Proficiency (%)", domain: [0, 100], grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    marks: [
      Plot.barX(data, {x: "glp", y: "school", fill: "#4e79a7", tip: true,
        title: d => `${d.school} (${d.grade_span})\nGrade-Level Proficient: ${d.glp}%\nCollege/Career Ready: ${d.ccr}%`}),
      Plot.ruleX([0])
    ]
  }), {invalidation});
}

4-year graduation rate — North Meck High Schools

Cohort graduation rate · 7 North Meck high schools

viewof gradTown = townChips(masterTown)
{
  showTown(gradTown, "#ed-grad-host");
  const data = withParsed(byTown(graduation, gradTown), "grad_4yr").filter(d => d.grad_4yr != null).sort((a, b) => b.grad_4yr - a.grad_4yr);
  return responsive(width => stdPlot({
    width, height: 100 + data.length * 22, style: S,
    x: {label: "Four-Year Cohort Graduation Rate (%)", domain: [0, 100], grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    color: {legend: true, domain: ["90%+", "80–90%", "Below 80%"], range: ["#2ca02c", "#ff7f0e", "#d62728"]},
    marks: [
      Plot.barX(data, {x: "grad_4yr", y: "school", fill: d => d.grad_4yr >= 90 ? "90%+" : d.grad_4yr >= 80 ? "80–90%" : "Below 80%", tip: true,
        title: d => `${d.school}\n4-Year Grad Rate: ${d.grad_4yr_raw}`}),
      Plot.ruleX([0])
    ]
  }), {invalidation});
}

School Achievement Gap By Economic Status in North Meck Schools (2024-25)

Grade-level proficiency: economically disadvantaged vs. non-disadvantaged students

viewof econGapTown = townChips(masterTown)
{
  showTown(econGapTown, "#ed-econgap-host");
  const data = byTown(econGap, econGapTown).slice().sort((a, b) => b.gap - a.gap);
  return responsive(width => stdPlot({
    width, height: 100 + data.length * 22, style: S,
    x: {label: "Grade-Level Proficient (%)", domain: [0, 100], grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    color: ECON_COLOR,
    marks: [
      Plot.link(data, {x1: "econ_disadv", x2: "not_disadv", y: "school", stroke: "#cccccc", strokeWidth: 2}),
      Plot.dot(data, {x: "econ_disadv", y: "school", fill: "#d62728", r: 5, tip: true,
        title: d => `${d.school}\nEconomically Disadvantaged: ${d.econ_disadv}%\nNot Disadvantaged: ${d.not_disadv}%\nGap: ${d.gap} points`}),
      Plot.dot(data, {x: "not_disadv", y: "school", fill: "#4e79a7", r: 5})
    ]
  }), {invalidation});
}

High School College & Career Readiness: North Meck (2024-25)

Grade-Level Proficient vs. College/Career Ready · schools that serve high school grades

viewof ccrTown = townChips(masterTown)
{
  showTown(ccrTown, "#hs-ccr-host");
  const HS_SPANS = ["09-12", "09-11", "11-13", "0K-12"];
  const METRICS = ["Grade-Level Proficient", "College/Career Ready"];
  const data = withParsed(byTown(proficiency, ccrTown), "glp")
    .filter(d => HS_SPANS.includes(d.grade_span) && d.ccr != null)
    .sort((a, b) => b.ccr - a.ccr);
  const long = data.flatMap(d => [
    {school: d.school, metric: METRICS[0], value: d.glp},
    {school: d.school, metric: METRICS[1], value: d.ccr}
  ]).filter(d => d.value != null);
  return responsive(width => stdPlot({
    width, height: 60 + data.length * 46, style: S,
    x: {label: "Percent of students (%)", domain: [0, 100], grid: true},
    y: {axis: null, domain: METRICS},
    fy: {label: null, domain: data.map(d => d.school)},
    color: {legend: true, domain: METRICS, range: ["#a0cbe8", "#4e79a7"]},
    marks: [
      Plot.barX(long, {x: "value", y: "metric", fy: "school", fill: "metric", tip: true, title: d => `${d.school}\n${d.metric}: ${d.value}%`}),
      Plot.ruleX([0])
    ]
  }), {invalidation});
}

Includes K–12 comprehensive schools (grade span 0K–12), whose Grade-Level Proficient and College/Career Ready rates reflect their entire student body, not high schoolers alone — treat these figures as directional rather than high-school-specific.

Achievement Gap in North Meck High Schools by Economic status (2024-25)

Grades 9–12 grade-level proficiency: economically disadvantaged vs. not

viewof hsGapTown = townChips(masterTown)
{
  showTown(hsGapTown, "#ed-hsgap-host");
  const data = byTown(hsEconGap, hsGapTown)
    .filter(d => d.econ_disadv != null && d.not_disadv != null && d.gap != null)
    .sort((a, b) => b.gap - a.gap);
  return responsive(width => stdPlot({
    width, height: 100 + data.length * 22, style: S,
    x: {label: "Grade-Level Proficient (%)", domain: [0, 100], grid: true},
    y: {label: null, domain: data.map(d => d.school)},
    color: ECON_COLOR,
    marks: [
      Plot.link(data, {x1: "econ_disadv", x2: "not_disadv", y: "school", stroke: "#cccccc", strokeWidth: 2}),
      Plot.dot(data, {x: "econ_disadv", y: "school", fill: "#d62728", r: 5, tip: true,
        title: d => `${d.school}\nEconomically Disadvantaged: ${d.econ_disadv}%\nNot Disadvantaged: ${d.not_disadv}%\nGap: ${d.gap} points`}),
      Plot.dot(data, {x: "not_disadv", y: "school", fill: "#4e79a7", r: 5})
    ]
  }), {invalidation});
}

Educational Attainment

What is the education level of the adults who live here?

Resident educational attainment shapes the workforce, economic mobility, and civic life of a community. In North Meck, attainment varies meaningfully across the three towns — and has shifted as the population has grown.

Bachelor's+ Rate Trend

% adults 25+ with a bachelor's degree or higher · 2018–2023

{
  const data = byTown(bachTrend, masterTown);
  return responsive(width => stdPlot({
    width, height: 320, style: S,
    x: {label: "Year", tickFormat: d => String(d)},
    y: {label: "Bachelor's+ (%)", grid: true, domain: [20, 85]},
    color: townColor({legend: masterTown === "All"}),
    marks: [
      Plot.line(data, {x: "year", y: "bach_plus", stroke: "town", strokeWidth: masterTown === "All" ? 2 : 3}),
      Plot.dot(data, {x: "year", y: "bach_plus", stroke: "town", fill: "white", strokeWidth: 1.5, r: 4,
        tip: true, title: d => `${d.town}\n${d.year}: ${d.bach_plus.toFixed(1)}%`})
    ]
  }), {invalidation});
}

Davidson (home to Davidson College) has the highest bachelor's-or-higher rate of the three towns in some years and the lowest in others: its estimates swing between 33% and 76% because of the town's small ACS sample. Cornelius has climbed overall, from 54.5% in 2018 to nearly 60% by 2023.

Source: ACS 2018–2023 · Table B15003 · adults 25+ · Note: Davidson figures show year-to-year variation typical of small-population ACS estimates · Huntersville 2023 excluded (data anomaly) · No 2020 estimate

Attainment Breakdown, 2022

Distribution of educational attainment for adults 25+ · by town

{
  const LEVEL_COLORS = {"Less than HS": "#d73027", "HS/GED": "#fc8d59", "Some College": "#fee090", "Associate's": "#e0f3f8", "Bachelor's": "#74add1", "Graduate/Prof": "#4575b4"};
  const LEVELS = Object.keys(LEVEL_COLORS);
  const data = byTown(attainment2022, masterTown);
  const towns = [...new Set(data.map(d => d.town))];
  return responsive(width => stdPlot({
    width, height: 60 + towns.length * 60, style: S,
    x: {label: "Share (%)", tickFormat: d => d + "%", grid: true},
    // An explicit domain lets stdPlot size the left margin (the original clipped "Huntersville").
    y: {label: null, domain: towns},
    color: {domain: LEVELS, range: LEVELS.map(l => LEVEL_COLORS[l]), legend: true},
    marks: [
      Plot.barX(data, Plot.stackX({x: "pct", y: "town", fill: "level", order: LEVELS, tip: true, title: d => `${d.level}: ${d.pct.toFixed(1)}%`})),
      Plot.ruleX([0])
    ]
  }), {invalidation});
}

Cornelius and Huntersville show similar attainment profiles. Davidson's 2022 snapshot reflects ACS sample variability — its 2023 figure (75.6% bachelor's or higher) better reflects the long-run pattern.

Source: ACS 2022 · Table B15003 · 2022 used for consistent cross-town comparison · Note: Davidson 2022 shows ACS sampling artifact; 2023 value is 75.6%

Median Earnings by Education Level, 2024

Annual median earnings for full-time year-round workers 25+ · by education level and town

{
  const LEVEL_ORDER = ["Less than HS", "HS Diploma", "Some College", "Bachelor's", "Graduate/Prof"];
  const data = byTown(earningsByEd, masterTown);
  return responsive(width => stdPlot({
    width, height: 360, style: S,
    x: {label: "Education Level", domain: LEVEL_ORDER},
    y: {label: "Median Earnings", grid: true, tickFormat: d => "$" + Math.round(d / 1000) + "k"},
    color: townColor({legend: true}),
    marks: [
      Plot.dot(data, {x: "level", y: "earnings", stroke: "town", fill: "town", r: 7, fillOpacity: 0.85, tip: true,
        title: d => `${d.town}\n${d.level}\n$${d.earnings.toLocaleString()}`}),
      Plot.ruleY([0], {strokeOpacity: 0})
    ]
  }), {invalidation});
}

In both Cornelius and Huntersville, a bachelor's degree is worth about $44,500 more per year than a high school diploma (Davidson's high-school figure is suppressed). Davidson's graduate-degree earners have median earnings over $150k a year.

Source: ACS 2024 · Table S1501 · median earnings for full-time year-round workers 25+ · Davidson HS-diploma figure suppressed by Census (small sample)

Are North Meck schools keeping pace with rapid growth?

North Meck's population is growing much faster than its school enrollment.

Who is in the classroom, and how has that changed?

How are students performing, and which schools are improving?

What is the education level of the adults who live here?

Glossary