Distance Calculation

Distance calculation to the nearest coastline from core locations

As the initial data analysis will be performed on the ICES-DOME sites located within European coastlines, the distances to the nearest coastline were calculated.

The distance data is available in the dist_to_coast column of the Site table.

Methods

Coastline data were derived from the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) database. High-resolution shoreline polygons were extracted for the region covering European countries. The coastline geometry was cleaned and filtered to ensure topological validity before calculation.

To ensure accurate measurements at high latitudes, where standard global projections (like Mercator) distort distances, all spatial data were projected to the ETRS89-extended Lambert Azimuthal Equal Area projection (EPSG:3035). This Coordinate Reference System is the standard for pan-European statistical mapping and minimizes distortion across the region. The shortest Euclidean distance from each sampling site to the nearest European coastline geometry was then calculated in meters.

The calculation was performed using the sf library in R.

Sediment Site Locations with Distances to Coast

db = {
  const StratumSQLite = await window._stratumSQLite;
  return StratumSQLite.open(window._dbPath, {
    sqlJsPath: window._sqljsBase,
    cacheKey:  "pilot-ices-dome@v0.1.5",
  });
}

df_loc = db.query(`
  SELECT DISTINCT
    p1.project_id,
    s2.site_id,
    s2.latitude,
    s2.longitude,
    CASE
      WHEN s2.dist_to_coast <= 10  THEN '0 to 10km'
      WHEN s2.dist_to_coast <= 30  THEN '10 to 30km'
      WHEN s2.dist_to_coast <= 100 THEN '30 to 100km'
      ELSE '≥100km'
    END AS distance
  FROM sample s1
  JOIN project p1 ON p1.project_id = s1.project_id
  JOIN site s2 ON s2.site_id = s1.site_id
`)

// --- World boundaries (Natural Earth via CDN) ---
world_topo = fetch("https://cdn.jsdelivr.net/npm/world-atlas@2/countries-50m.json")
  .then(r => r.json())

world_geojson = topojson.feature(world_topo, world_topo.objects.countries)

dist_counts = db.query(`
  SELECT distance AS Distance,
         COUNT(*) AS '# Sampling Sites'
  FROM (
      SELECT DISTINCT
        p1.project_id,
        s2.site_id,
        s2.latitude,
        s2.longitude,
        CASE
          WHEN s2.dist_to_coast <= 10  THEN '0 to 10km'
          WHEN s2.dist_to_coast <= 30  THEN '10 to 30km'
          WHEN s2.dist_to_coast <= 100 THEN '30 to 100km'
          ELSE '≥100km'
        END AS distance
      FROM sample s1
      JOIN project p1 ON p1.project_id = s1.project_id
      JOIN site s2 ON s2.site_id = s1.site_id
  ) t
  GROUP BY distance
`)

Plot.plot({
  width: 620,
  height: 540,
  marginRight: 120,
  caption: "Sediment Cores — LAEA Europe Projection",
  // Pan-European + Svalbard bounding box: 25°W–35°E, 33°N–82°N
  projection: ({width, height}) => {
    const lonMin = -25, lonMax = 35
    const latMin =  33, latMax = 82
    const mercY     = lat => Math.log(Math.tan(Math.PI / 4 + lat * Math.PI / 360))
    const yTop      = mercY(latMax), yBot = mercY(latMin)
    const yRange    = yTop - yBot
    const xRange    = lonMax - lonMin   // degrees
    // Fit the tighter of width/height to avoid overflow
    const scaleH    = (height - 20) / yRange
    const scaleW    = (width  - 20) / (xRange * Math.PI / 180)
    const scale     = Math.min(scaleH, scaleW)
    const yMid      = (yTop + yBot) / 2
    const latCenter = (Math.atan(Math.exp(yMid)) - Math.PI / 4) * 360 / Math.PI
    return d3.geoMercator()
      .center([(lonMin + lonMax) / 2, latCenter])
      .scale(scale)
      .translate([width / 2, height / 2])
  },
  color: {
    domain: ["0 to 10km", "10 to 30km", "30 to 100km", "≥100km"],
    range:  ["#30123b",   "#28bbec",    "#a2fc3c",    "#fb8022"],
    legend: true,
    label:  "Distance to Coastline"
  },
  marks: [
    // Ocean background clipped to the domain frame
    Plot.frame({ fill: "#d0e8f5" }),
    // Country polygons — clip: "frame" trims anything outside the domain bbox
    Plot.geo(world_geojson, { fill: "#d4d4d4", stroke: "#888", strokeWidth: 0.4, clip: "frame" }),
    // Graticule grid lines clipped to frame
    Plot.graticule({ stroke: "gray", strokeWidth: 0.4, strokeDasharray: "3,3", clip: "frame" }),
    // Frame border drawn on top
    Plot.frame({ stroke: "#555", strokeWidth: 0.8 }),
    // Site locations coloured by distance category
    Plot.dot(df_loc, {
      x: "longitude",
      y: "latitude",
      fill: "distance",
      r: 3.5,
      fillOpacity: 0.85,
      stroke: "#222",
      strokeWidth: 0.3,
      clip: "frame"
    })
  ]
})

Inputs.table(dist_counts, {
  columns: ["Distance", "# Sampling Sites"],
  layout: "fixed",
  width: { "Distance": "50%", "# Sampling Sites": "50%" }
})