fig, ax, plt = poly(poly_coords, color = df.gdp_per_capita, colormap = :viridis, axis = (; aspect = DataAspect()))
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using DataFrames, CSV df = CSV.read("earthquakes.csv", DataFrame) julia data kartta
Makie is not a wrapper around C/C++ plotting libraries. It’s written entirely in Julia, uses GPU-accelerated rendering (via GLMakie or CairoMakie for publication), and supports interactive 3D scenes. using GLMakie, GeoJSON, ArchGDAL Load a GeoJSON of European regions geojson = GeoJSON.read("europe_regions.geojson") Assume df has columns: :region_name, :gdp_per_capita poly_coords = [feature.geometry for feature in geojson] fig, ax, plt = poly(poly_coords, color = df
fig = Figure() ax1 = Axis(fig[1,1], title="Population Density") ax2 = Axis(fig[1,2], title="Seismic Risk") linkxaxes!(ax1, ax2) linkyaxes!(ax1, ax2) Add scale bar (manual) lines!(ax1, [0, 100], [ymin, ymin], color=:black, linewidth=3) text!(ax1, 50, ymin-5, text="100 km") No rasterio boilerplate
Because GeoArray behaves like a regular Julia Array , all your linear algebra, FFTs, and statistical functions work out of the box. No rasterio boilerplate. A map without a legend is a puzzle. Makie’s composable Axis system (borrowing ideas from Grammar of Graphics) allows you to build legends as first-class objects.