Long term population projections

A rough reproduction of the projections for world wide long term population growth (and decline) as described in this NYTimes article and this academic paper. I guess it’s all a little …bleak.

Plot.plot({
  marginLeft: 70,
  width: 1100,
  r: { type: "linear", domain: [0, 1100], range: [0, 5000] },
  x: { tickFormat: d3.format("d") },
  y: { tickFormat: (p) => d3.format("d")(p / 10 ** 9) + " Billion" },
  marks: [
    Plot.line(data, {
      x: "year",
      y: "population",
      z: "tfr_scenario",
      strokeWidth: (d) => (d.tfr_scenario == 1.66 ? 3 : 0.5),
      tip: true,
      title: (d) =>
        d.tfr_scenario && d.tfr_scenario == 1.66
          ? `Pop: ${d3.format("0.2f")(d.population / 10 ** 9) + " B"}
Year: ${d.year}
TFR: ${d.tfr_scenario} (Where US is now)`
          : `Pop: ${d3.format("0.2f")(d.population / 10 ** 9) + " B"}
Year: ${d.year}
TFR: ${d.tfr_scenario}`
    }),
    Plot.dot(
      [
        { y: 2023, p: 8100000000, r: 1 },
        { y: 2085, p: 10400000000, r: 1 }
      ],
      {
        x: "y",
        y: "p",
        r: "r",
        fill: (d) => (d.y == 2023 ? "#393" : "#933"),
        stroke: (d) => (d.y == 2023 ? "#161" : "#611")
      }
    ),
    Plot.ruleX([-1000]),
    Plot.ruleY([0])
  ]
})

data = {
  let pop_history = await FileAttachment("population_history.csv").csv({
    typed: true
  });
  pop_history = pop_history.filter((o) => o.population).slice(9);
  pop_history.forEach(function (o) {
    o.tfr_scenario = "N/A";
  });
  let pop_projections = await FileAttachment("pop_projections.csv").csv({
    typed: true
  });
  pop_projections = d3
    .groups(pop_projections, (o) => o.tfr_scenario)
    .map((a) => a[1]);
  const data = [pop_history].concat(pop_projections).slice(0, 13).flat()
  return data;
}