A native flatgeobuf implementation in Julia. Flatgeobuf is a cloud optimized geospatial vector format.
] add FlatGeobufimport FlatGeobuf; const FGB=FlatGeobuf
fgb = FGB.read_file("countries.fgb")
FlatGeobuffer with 179 GeometryTypeMultiPolygon features.FlatGeobuf supports the Tables.jl interface.
using DataFrames
DataFrame(fgb)
179×3 DataFrame. Omitted printing of 1 columns
│ Row │ id │ name │
│ │ String │ String │
├─────┼────────┼─────────────────────────────────────┤
│ 1 │ ATA │ Antarctica │
│ 2 │ ATF │ French Southern and Antarctic Lands │
│ 3 │ NAM │ Namibia │
│ 4 │ BWA │ Botswana │
│ 5 │ SWZ │ Swaziland │
│ 6 │ ZAF │ South Africa │
│ 7 │ LSO │ Lesotho │
│ 8 │ MOZ │ Mozambique │
│ 9 │ MWI │ Malawi │
│ 10 │ ZMB │ Zambia │
│ 11 │ ZWE │ Zimbabwe │
│ 12 │ AGO │ Angola │
│ 13 │ COD │ Democratic Republic of the Congo │
│ 14 │ RWA │ Rwanda │FlatGeobuffers have a spatial index that we can query with a bounding box
bbox = [-92.73405699999999, 32.580974999999995, -92.73405699999999, 32.580974999999995]
filter!(fgb, bbox)
julia> DataFrame(fgb)
2×3 DataFrame. Omitted printing of 1 columns
│ Row │ id │ name │
│ │ String │ String │
├─────┼────────┼──────────────────────────┤
│ 1 │ MEX │ Mexico │
│ 2 │ USA │ United States of America │- GeoInterface support
- Write support
I've used https://github.com/rjkat/flatbuffers-julia to autogenerate the files found in src/schema, but had to adapt them. Good autogeneration from schema is broken as of now. Probably needs waiting on a 1.6/1.7 Julia release so JuliaLang/julia#32658 is merged, so an official flatbuffer implementation can be made over at google/flatbuffers#5088.