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@Nowosad I've been looking at reading compressed vector files, such as this:
GB_election_2024_sim.gpkg.zip
In terra
:
> xx <- terra::vect("GB_election_2024_sim.gpkg.zip")
> xx
class : SpatVector
geometry : polygons
dimensions : 632, 19 (geometries, attributes)
extent : 5512.998, 655970.4, 5342.9, 1220287 (xmin, xmax, ymin, ymax)
source : GB_election_2024_sim.gpkg.zip (GB_election_2024_sim)
coord. ref. : OSGB36 / British National Grid (EPSG:27700)
names : Constituency Name Area_Code Area_Description
type : <chr> <chr> <chr> <chr>
values : Aberafan Maesteg Aberafan Maest~ WMC Westminster Co~
Aberdeen North Aberdeen North~ WMC Westminster Co~
Aberdeen South Aberdeen South~ WMC Westminster Co~
File_Name Feature_Serial_Number Collection_Serial_Number
<chr> <int> <int>
ABERAFAN_MAEST~ 1 1
ABERDEEN_NORTH~ 472 472
ABERDEEN_SOUTH~ 473 473
Global_Polygon_ID Admin_Unit_ID Census_Code (and 9 more)
<int> <int> <chr>
146759 187003 W07000081
147374 187618 S14000060
147375 187619 S14000061
In sf
:
> sim <- st_read("GB_election_2024_sim.gpkg.zip")
Reading layer `GB_election_2024_sim' from data source
`/home/rsb/topics/packages/github-r-spatial/LICD/GB_election_2024_sim.gpkg.zip'
using driver `GPKG'
Simple feature collection with 632 features and 19 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 5512.998 ymin: 5342.9 xmax: 655970.4 ymax: 1220287
Projected CRS: OSGB36 / British National Grid
> all.equal(st_geometry(sim), st_geometry(st_cast(st_as_sf(xx), "MULTIPOLYGON")))
[1] TRUE
> all.equal(st_drop_geometry(sim), st_drop_geometry(st_as_sf(xx)))
[1] TRUE
Could we compress the GPKG in spData
see Nowosad/spData#62 ? Or are too many using GDAL < 3.7 ? This actually also works from GDAL >= 3.1 for ESRI Shapefiles too.
agila5
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