HBW Destination Choice Model

Production & Attraction Pairs by Model

dc = {
  const modT = transpose(dist_sum_mod);
  const obsT = transpose(dist_sum_obs);

  return modT.map(m => {
    const o = obsT.find(d =>
      d.p_DistLrg === m.p_DistLrg &&
      d.a_DistLrg === m.a_DistLrg &&
      d.Source === m.Source
    );

    if (!o || o.total_trips === 0) return null;

    return {
      p_DistLrg: m.p_DistLrg,
      a_DistLrg: m.a_DistLrg,
      Source: m.Source,
      tmeMod: m.total_trips,
      tmeObs: o.total_trips,
      tmeErrorPct: (m.total_trips - o.total_trips) / o.total_trips
    };
  }).filter(d => d !== null);
}

// Source Selection
viewof vSource = Inputs.select(
  Array.from(new Set(dc.map(d => d.Source))).sort(),
  { label: "Model Source:" }
)

// Filtered data based on selection
dc_filtered = dc.filter(d => d.Source === vSource)

// Shared Max for the Scatter Plot axes
chartMax = d3.max(dc_filtered, d => Math.max(d.tmeObs, d.tmeMod))

// CHART 1a: Model vs Observed Trips
Plot.plot({
  grid: true,
  width: 460,
  height: 380,
  marginRight: 50,
  caption: html`<h4>1a. Model vs Observed Trips</h4>`,
  x: { label: "Observed Trips", domain: [0, chartMax] },
  y: { label: "Modeled Trips", domain: [0, chartMax] },
  marks: [
    Plot.link([0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4], {
      x1: 0, y1: 0,
      x2: (k) => (k <= 1 ? chartMax : chartMax / k),
      y2: (k) => (k <= 1 ? chartMax * k : chartMax),
      strokeOpacity: (k) => k === 1 ? 1 : 0.2,
      stroke: "gray",
      strokeWidth: (k) => k === 1 ? 2 : 1
    }),
    Plot.text([0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4], {
      x: (k) => (k <= 1 ? chartMax : chartMax / k),
      y: (k) => (k <= 1 ? chartMax * k : chartMax),
      text: (k) => k === 1 ? "Equal" : d3.format("+.0%")(k - 1),
      textAnchor: "start", dx: 6, fill: "gray", fontSize: 10
    }),
    Plot.dot(dc_filtered, {
      x: "tmeObs", 
      y: "tmeMod", 
      r: 3,
      fill: "rgb(80, 116, 230)", 
      fillOpacity: 0.5,
      tip: true,
      title: (d) => 
        `District: ${d.p_DistLrg} to ${d.a_DistLrg}\n` +
        `Observed: ${d3.format(",.0f")(d.tmeObs)}\n` +
        `Modeled: ${d3.format(",.0f")(d.tmeMod)}`
    }),
    Plot.linearRegressionY(dc_filtered, {
      x: "tmeObs", y: "tmeMod",
      stroke: "rgb(80, 116, 230)", strokeDasharray: "4 4"
    })
  ]
})

// CHART 1b: Percent Error
Plot.plot({
  grid: true,
  width: 460,
  height: 380,
  caption: html`<h4>1b. Model vs Observed Percent Error</h4>`,
  x: { label: "Observed Trips", domain: [0, chartMax] },
  y: { label: "Percent Error", domain: [-2, 2], tickFormat: d3.format(".0%") },
  marks: [
    Plot.ruleY([0], { stroke: "#000", strokeWidth: 1.5 }),
    Plot.dot(dc_filtered, {
      x: "tmeObs", 
      y: "tmeErrorPct", 
      r: 3, 
      fill: "rgb(80, 116, 230)", 
      fillOpacity: 0.5,
      tip: true,
      title: (d) => 
        `District: ${d.p_DistLrg} to ${d.a_DistLrg}\n` +
        `Observed: ${d3.format(",.0f")(d.tmeObs)}\n` +
        `Error: ${d3.format("+.1%")(d.tmeErrorPct)}`
    }),
    Plot.line([[0, 1.0], [500, 1.0], [500, 0.5], [1000, 0.5], [1000, 0.25], [chartMax, 0.25]], 
      { stroke: "gray", strokeWidth: 1.5, strokeDasharray: "2 2", curve: "step-after" }),
    Plot.line([[0, -1.0], [500, -1.0], [500, -0.5], [1000, -0.5], [1000, -0.25], [chartMax, -0.25]], 
      { stroke: "gray", strokeWidth: 1.5, strokeDasharray: "2 2", curve: "step-after" })
  ]
})

Production & Attraction Pairs by Auto Ownership & Income

dc_2 = {
  const modT2 = transpose(dist_sum_mod2);
  const obsT2 = transpose(dist_sum_obs2);

  return modT2.map(m => {
    const o = obsT2.find(d =>
      d.p_DistLrg === m.p_DistLrg &&
      d.a_DistLrg === m.a_DistLrg &&
      d.veh_inc   === m.veh_inc
    );

    if (!o || o.total_trips === 0) return null;

    // Parse "HBW_0veh_lo" → veh="0veh", inc="lo"
    const parts = m.veh_inc.replace("HBW_", "").split("_");
    const veh   = parts[0];   // "0veh" | "1veh" | "2veh"
    const inc   = parts[1];   // "lo"   | "hi"

    return {
      p_DistLrg: m.p_DistLrg,
      a_DistLrg: m.a_DistLrg,
      veh_inc:   m.veh_inc,
      veh,
      inc,
      Source:    m.Source,
      tmeMod:    m.total_trips,
      tmeObs:    o.total_trips,
      tmeErrorPct: (m.total_trips - o.total_trips) / o.total_trips
    };
  }).filter(d => d !== null);
}

// Stable sorted facet dimension lists
// Explicit order: Gravity first (left), Destination Choice second (right)
source_vals_2 = ["Gravity", "Destination Choice"]
veh_vals      = ["0veh", "1veh", "2veh"]

viewof vInc_2 = Inputs.select(["lo", "hi"],
  { label: "Household Income Level:", value: "lo",
    format: d => d === "lo" ? "Low-Income" : "Not Low-Income" })

dc_2_filtered = dc_2.filter(d => d.inc === vInc_2)

// Axis max from income-filtered data
chartMax_2 = d3.max(dc_2_filtered, d => Math.max(d.tmeObs, d.tmeMod)) * 1.05

// 2-D FACETED SCATTER: columns = Source, rows = auto count
Plot.plot({
  grid: true,
  width: 900,
  height: 300 * veh_vals.length + 60,
  marginRight: 80,
  marginLeft: 110,
  marginTop: 50,
  marginBottom: 50,
  style: { fontSize: "15px" },
  fx: { label: "Model Source", domain: source_vals_2, padding: 0.1 },
  fy: { label: "Households", domain: veh_vals, tickFormat: d => d.replace("veh", "-vehicle").replace("2-vehicle", "2+-vehicle"), padding: 0.1 },
  x: { label: "Observed Trips →", domain: [0, chartMax_2], labelOffset: 40 },
  y: { label: "↑ Modeled Trips",  domain: [0, chartMax_2], labelOffset: 55 },
  caption: html`<b>Modeled vs. Observed — ${vInc_2 === "lo" ? "Low-Income" : "Not Low-Income"}</b> &nbsp;|  columns: Model &nbsp;|  rows: Auto Ownership Category`,
  marks: [
    // 45-degree reference line in every panel
    Plot.link(
      source_vals_2.flatMap(s => veh_vals.map(v => ({ s, v }))),
      {
        x1: 0, y1: 0,
        x2: chartMax_2, y2: chartMax_2,
        fx: "s", fy: "v",
        stroke: "gray", strokeOpacity: 0.7, strokeWidth: 1.5
      }
    ),
    // Data points
    Plot.dot(dc_2_filtered, {
      x: "tmeObs",
      y: "tmeMod",
      fx: "Source",
      fy: "veh",
      r: 3,
      fill: "rgb(80, 116, 230)",
      fillOpacity: 0.5,
      tip: true,
      title: d =>
        `District: ${d.p_DistLrg} → ${d.a_DistLrg}\n` +
        `Source: ${d.Source}  |  Auto Ownership: ${d.veh}  |  Income: ${d.inc}\n` +
        `Observed: ${d3.format(",.0f")(d.tmeObs)}\n` +
        `Modeled:  ${d3.format(",.0f")(d.tmeMod)}`
    }),
    // Forced-through-origin regression line per panel
    Plot.link(
      source_vals_2.flatMap(s => veh_vals.map(v => {
        const subset = dc_2_filtered.filter(d => d.Source === s && d.veh === v);
        const denom  = d3.sum(subset, d => d.tmeObs ** 2);
        const slope  = denom === 0 ? 0 : d3.sum(subset, d => d.tmeObs * d.tmeMod) / denom;
        return { s, v, slope };
      })),
      {
        x1: 0, y1: 0,
        x2: chartMax_2,
        y2: d => d.slope * chartMax_2,
        fx: "s", fy: "v",
        stroke: "rgb(80, 116, 230)",
        strokeDasharray: "4 4",
        strokeWidth: 2
      }
    )
  ]
})