GeoTiles.jl

GeoTiles provides tooling to handle table compatible data that is separated into geographic tiles to support global-scale workflows.

Global scale processing of large datasets often requires implementing some form of geographic tiling schema to facilitate efficient data access and processing. GeoTiles.jl provides tooling to help create and manage such tiling schemes.

GeoTiles.jl relies on a defined file-prefix naming convention to avoid needing a separate file catalogue or database. GeoTiles.jl current only supports reading of Arrow files but it would be trivial to modify for other DataFrame supported file formats.

Example

load packages

julia> using GeoTiles;
julia> using DataFrames;
julia> using Extents;

Define a global 2-degree GeoTile grid

julia> geotiles = GeoTiles.define(2.)
16200×3 DataFrame
   Row │ id                        extent                             geometry   ⋯
       │ String                    Extent…                            Polygon…   ⋯
───────┼──────────────────────────────────────────────────────────────────────────
     1 │ lat[-90-88]lon[-180-178]  Extent{(:X, :Y), Tuple{Tuple{Flo…  Polygon{fa ⋯
     2 │ lat[-90-88]lon[-178-176]  Extent{(:X, :Y), Tuple{Tuple{Flo…  Polygon{fa
     3 │ lat[-90-88]lon[-176-174]  Extent{(:X, :Y), Tuple{Tuple{Flo…  Polygon{fa
   ⋮   │            ⋮                              ⋮                             ⋱
 16198 │ lat[+88+90]lon[+174+176]  Extent{(:X, :Y), Tuple{Tuple{Flo…  Polygon{fa
 16199 │ lat[+88+90]lon[+176+178]  Extent{(:X, :Y), Tuple{Tuple{Flo…  Polygon{fa ⋯
 16200 │ lat[+88+90]lon[+178+180]  Extent{(:X, :Y), Tuple{Tuple{Flo…  Polygon{fa
                                                   1 column and 16194 rows omitted

Make a DataFrame with columns of latitude and longitude and some data

julia> df = DataFrame(latitude = [70.2, 71.4, 80.], longitude = [50.1, 50.5, 30.], data = [4.5, 8.1, 20.])
3×3 DataFrame
 Row │ latitude  longitude  data    
     │ Float64   Float64    Float64 
─────┼──────────────────────────────
   1 │     70.2       50.1      4.5
   2 │     71.4       50.5      8.1
   3 │     80.0       30.0     20.0

Group data into geotiles

julia> gts = GeoTiles.group(df, geotiles)
2-element Vector{DataFrame}:
 2×3 DataFrame
 Row │ latitude  longitude  data    
     │ Float64   Float64    Float64 
─────┼──────────────────────────────
   1 │     70.2       50.1      4.5
   2 │     71.4       50.5      8.1
 1×3 DataFrame
 Row │ latitude  longitude  data    
     │ Float64   Float64    Float64 
─────┼──────────────────────────────
   1 │     80.0       30.0     20.0

Check that returned dataframes are geotile compliant to be compliant the dataframe must have latitude & longitude columns and contain geotile_id in metadata

julia> GeoTiles.isgeotile.(gts)
2-element BitVector:
 1
 1

Save geotiles to disk

julia> folder = "/Users/gardnera/Downloads/Test";
julia> suffix = "_test.arrow";
julia> GeoTiles.save.(Ref(folder), Ref(suffix), gts)
2-element Vector{String}:
 "/Users/gardnera/Downloads/Test/lat[+70+72]lon[+050+052]_test.arrow"
 "/Users/gardnera/Downloads/Test/lat[+78+80]lon[+028+030]_test.arrow"

Load only geotiles that intersect extent

julia> extent = Extent(Lat=(70.,71.), Lon=(50.,51.));
julia> GeoTiles.readall(folder; extent=extent)
1-element Vector{DataFrame}:
 2×3 DataFrame
 Row │ latitude  longitude  data    
     │ Float64   Float64    Float64 
─────┼──────────────────────────────
   1 │     70.2       50.1      4.5
   2 │     71.4       50.5      8.1

alex-s-gardner/GeoTiles.jl

GeoTiles.jl

Example