Julia Data Kartta May 2026

Unlike Python’s pyproj which incurs Python-C round-trip overhead, Proj4.jl transforms millions of coordinates in a tight loop without leaving native speed. Sometimes your data isn’t vector polygons but satellite imagery or climate model outputs. Enter GeoArrays.jl —a spatial array with embedded geotransform and CRS.

using GLMakie, Random Random.seed!(42) lats = 60.17 .+ randn(10_000_000) * 0.01 lons = 24.94 .+ randn(10_000_000) * 0.01 julia data kartta

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") using GLMakie, Random Random

But here’s the cartographic insight: . Julia’s missing union type forces you to be explicit. No silent NaN propagation. You must decide: impute, drop, or mark. You must decide: impute, drop, or mark

fig, ax, plt = poly(poly_coords, color = df.gdp_per_capita, colormap = :viridis, axis = (; aspect = DataAspect()))