Julia Data Kartta |work| -

using DataFrames, CSV df = CSV.read("earthquakes.csv", DataFrame)

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. julia data kartta

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 DataFrames, CSV df = CSV

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 Enter GeoArrays