Julia Data Kartta [repack] May 2026

Colorbar(fig[1, 2], plt) fig

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. julia data kartta

In the golden age of Python’s pandas and R’s tidyverse, why would a data scientist reach for Julia? The answer lies not in syntax prettiness, but in a more fundamental cartographic principle: the map is not the territory, but a well-crafted map reveals hidden valleys, unseen ridges, and the true flow of information. Colorbar(fig[1, 2], plt) fig Because GeoArray behaves like

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

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]

using Proj4 wgs84 = Proj4.Proj("+proj=longlat +datum=WGS84") webmerc = Proj4.Proj("+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m") Transform a point x_merc, y_merc = Proj4.transform(wgs84, webmerc, -74.006, 40.7128) # NYC