US Airports Map

Mix.install([
  {:plotly_ex, "~> 0.3"},
  {:kino, "~> 0.18"},
  {:req, "~> 0.5"}
])

Section

Airport data is fetched from a public CSV via Req.get!. Parse by splitting on newlines and commas, then plot with Scattergeo.

alias Plotly.{Figure, Scattergeo}

# Fetch US airport data
url = "https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv"
body = Req.get!(url).body

[header | rows] = String.split(body, "\n", trim: true)
keys = String.split(header, ",") |> Enum.map(&String.trim/1)

airports =
  rows
  |> Enum.map(fn row ->
    values = String.split(row, ",")
    Enum.zip(keys, values) |> Map.new()
  end)
  |> Enum.filter(fn a ->
    match?({_, _}, Float.parse(a["lat"] || "")) and
    match?({_, _}, Float.parse(a["long"] || ""))
  end)

lat = Enum.map(airports, &(elem(Float.parse(&1["lat"]), 0)))
lon = Enum.map(airports, &(elem(Float.parse(&1["long"]), 0)))
text = Enum.map(airports, fn a -> "#{a["airport"]}, #{a["state"]}: #{a["cnt"]} flights" end)

Figure.new()
|> Figure.add_trace(
  Scattergeo.new(
    lat: lat,
    lon: lon,
    mode: "markers",
    text: text,
    hoverinfo: "text",
    marker: %{size: 3, color: "blue", opacity: 0.5}
  )
)
|> Figure.update_layout(
  title: "US Airports — February 2011 Traffic",
  geo: %{
    scope: "usa",
    showland: true,
    landcolor: "#EAEAAE",
    showlakes: true,
    lakecolor: "#BDDDE4"
  }
)
|> Plotly.show()