Tile County Choropleth — Streets Tile

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

Section

Same data as the basic tile example, but with style: "open-street-map". Street context helps identify urban/rural patterns in the data.

alias Plotly.{Figure, Choroplethmap}

geojson_url = "https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json"
geojson = Req.get!(geojson_url).body |> Jason.decode!()

ca_geojson = Map.update!(geojson, "features", fn features ->
  Enum.filter(features, fn f -> String.starts_with?(f["id"], "06") end)
end)

data_url = "https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv"
body = Req.get!(data_url).body

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

data =
  rows
  |> Enum.map(fn row ->
    values = String.split(row, ",")
    Enum.zip(keys, values) |> Map.new()
  end)
  |> Enum.filter(fn r ->
    r["fips"] != nil and r["fips"] != "" and String.starts_with?(String.pad_leading(r["fips"], 5, "0"), "06")
  end)

fips = Enum.map(data, fn r -> String.pad_leading(r["fips"], 5, "0") end)
unemp = Enum.map(data, fn r ->
  case Float.parse(r["unemp"]) do
    {f, _} -> f
    :error -> 0.0
  end
end)

Figure.new()
|> Figure.add_trace(
  Choroplethmap.new(
    geojson: ca_geojson,
    locations: fips,
    z: unemp,
    featureidkey: "id",
    colorscale: "YlOrRd",
    zmin: 0,
    zmax: 12,
    colorbar: %{title: %{text: "Unemployment %"}},
    marker: %{opacity: 0.7}
  )
)
|> Figure.update_layout(
  title: "California County Unemployment — Streets Tile",
  map: %{
    style: "open-street-map",
    center: %{lat: 37.5, lon: -120.0},
    zoom: 5
  }
)
|> Plotly.show()