Bivariate maps

The package has functions to deal with bivariate maps.

using SpeciesDistributionToolkit
const SDT = SpeciesDistributionToolkit
import Statistics
using CairoMakie

A note about legends

Makie does not currently make it easy (possible?) to define custom legends from recipes. For this reason, the legends for bivariate plots are provided as plots. Note that Makie recipes also (as far as we can tell) do not set default axis properties, so getting the legends is a little more awkward than we'd like.

Getting data

pol = getpolygon(PolygonData(OpenStreetMap, Places); place = "Corsica");
spatialextent = SDT.boundingbox(pol; padding = 0.1)

(left = 8.434716606140137, right = 9.660012817382812, bottom = 41.23319091796875, top = 43.127706146240236)

We will get a series of layers here for illustrations:

provider = RasterData(CHELSA2, BioClim)
L = SDMLayer{Float32}[
    SDMLayer(
        provider;
        layer = i,
        spatialextent...,
    ) for i in layers(provider)
]

mask!(L, pol)

19-element Vector{SDMLayer{Float32}}:
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)
 🗺️  A 229 × 148 layer (13685 Float32 cells)

Bivariate

Bivariate maps encode two dimensions of information in a single palette. They take two layers as arguments.

bivariate(L[1], L[12]; axis = (aspect = DataAspect(),))

The number of bins for each dimension can be changed.

bivariate(L[1], L[12]; xbins = 25, ybins = 25, axis = (aspect = DataAspect(),))

The extension comes with a handful of robust color choices, which need to be called and then splatted.

bivariate(L[1], L[12]; StevensRedBlue()..., axis = (aspect = DataAspect(),))

bivariate(L[1], L[12]; StevensYellowPurple()..., axis = (aspect = DataAspect(),))

bivariate(L[1], L[12]; StevensBluePurple()..., axis = (aspect = DataAspect(),))

bivariate(L[1], L[12]; StevensBlueGreen()..., axis = (aspect = DataAspect(),))

bivariate(L[1], L[12]; ArcMapOrangeBlue()..., axis = (aspect = DataAspect(),))

The legend of a bivariate colormap is simply a heatmap.

f = Figure()
ax = Axis(f[1, 1]; aspect = DataAspect())
le = Axis(f[1, 2]; aspect = 1, xlabel = "Temperature", ylabel = "Precipitation")

bivariate!(ax, L[1], L[12]; ArcMapOrangeBlue()..., xbins = 3, ybins = 3)

bivariatelegend!(le, L[1], L[12];
    ArcMapOrangeBlue()...,
    xbins = 3,
    ybins = 3)

tightlimits!(ax)
hidespines!(ax)
hidedecorations!(ax)
tightlimits!(le)
scatter!(le, L[1], L[12]; color = :grey30, markersize = 1)

current_figure()

The color palette can be set for each axis separately, for example to use a diverging colormap. We get the z-scores of precipitation and temperature, and clamp them to the -1, 1 interval:

z(layer::SDMLayer) = (layer - Statistics.mean(layer)) / Statistics.std(layer)

z1 = clamp(z(L[1]), -1.5, 1.5)
z2 = clamp(z(L[12]), -1.5, 1.5)

🗺️  A 229 × 148 layer with 13685 Float32 cells
   Spatial Reference System: +proj=longlat +datum=WGS84 +no_defs

The two color maps use complementary colors, the same midpoints, and the resulting figure is, to use a technical term, an absolute abomination.

palettes = (
    xcolormap = [:gold, :seashell, :darkorchid2],
    ycolormap = [:skyblue, :seashell, :darkorange],
)

f = Figure()
ax = Axis(f[1, 1]; aspect = DataAspect())
le = Axis(
    f[1, 2];
    aspect = 1,
    xlabel = "Normalized temperature",
    ylabel = "Normalized precipitation",
)
bivariate!(
    ax,
    z1,
    z2;
    palettes...,
    xbins = 7,
    ybins = 7)

bivariatelegend!(le, z1, z2;
    palettes...,
    xbins = 7,
    ybins = 7)

tightlimits!(ax)
hidespines!(ax)
hidedecorations!(ax)
tightlimits!(le)

current_figure()