Julia — Data Kartta

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

The magic: poly accepts arbitrary polygons and maps a continuous color scale in real time. With GLMakie , you can orbit, zoom, and slice through temporal data at 60 FPS. Cartography’s oldest trap is projection distortion. Julia’s Proj4.jl (bindings to PROJ) gives you full control. julia data kartta

using GeoArrays, ArchGDAL ga = GeoArray("landsat_band4.tif") roi = ga[100:200, 100:200] Apply a filter (e.g., NDVI calculation) ndvi = (ga.band4 - ga.band3) / (ga.band4 + ga.band3) Write back with preserved georeferencing GeoArrays.write("ndvi_map.tif", ndvi) using GLMakie, Random Random

The best map is the one you build yourself. So fire up the REPL, ]add Makie GeoJSON CSV Proj4 , and start tracing the true shape of your data. Have you built a Julia geospatial workflow? Share your maps or gotchas in the comments. Julia’s Proj4