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.
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
For cartography specifically, Julia’s is maturing fast: ArchGDAL, GeoArrays, and Proj4.jl allow you to reproject, rasterize, and transform coordinate systems at C speed with Julia’s expressiveness. 2. The Base Layers: DataFrames.jl and Typed Mapping Before you draw the map, you need the data model. Unlike pandas’s flexible-but-slow object-dtype columns, DataFrame in Julia is columnar and type-stable. The best map is the one you build yourself
fig, ax, plt = poly(poly_coords, color = df.gdp_per_capita, colormap = :viridis, axis = (; aspect = DataAspect())) Share your maps or gotchas in the comments
using DataFrames, CSV df = CSV.read("earthquakes.csv", DataFrame)
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.
using GLMakie, Random Random.seed!(42) lats = 60.17 .+ randn(10_000_000) * 0.01 lons = 24.94 .+ randn(10_000_000) * 0.01
For cartography specifically, Julia’s is maturing fast: ArchGDAL, GeoArrays, and Proj4.jl allow you to reproject, rasterize, and transform coordinate systems at C speed with Julia’s expressiveness. 2. The Base Layers: DataFrames.jl and Typed Mapping Before you draw the map, you need the data model. Unlike pandas’s flexible-but-slow object-dtype columns, DataFrame in Julia is columnar and type-stable.
fig, ax, plt = poly(poly_coords, color = df.gdp_per_capita, colormap = :viridis, axis = (; aspect = DataAspect()))
using DataFrames, CSV df = CSV.read("earthquakes.csv", DataFrame)