gpkg - Utilities for the OGC ‘GeoPackage’ Format

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High-level wrapper functions to build Open Geospatial Consortium (OGC) ‘GeoPackage’ files. GDAL utilities for read and write of spatial data (vector and gridded) are provided via the {terra} package. Additional ‘GeoPackage’ and ‘SQLite’ specific functions manipulate attributes and tabular data via the {RSQLite} package.

gpkg hexsticker

Installation

Install the latest release from CRAN:

install.packages("gpkg")

The development package can be installed from GitHub with {remotes}

if (!requireNamespace("remotes")) 
  install.packages("remotes")
remotes::install_github("brownag/gpkg")

Background

What is a GeoPackage?

GeoPackage is an open, standards-based, platform-independent, portable, self-describing, compact format for transferring geospatial information. The GeoPackage Encoding Standard describes a set of conventions for storing the following within an SQLite database:

Create a Geopackage

gpkg_write() can handle a variety of different input types. Here we start by adding two DEM (GeoTIFF) files.

library(gpkg)
library(terra)
#> terra 1.7.55

dem <- system.file("extdata", "dem.tif", package = "gpkg")
stopifnot(nchar(dem) > 0)
gpkg_tmp <- tempfile(fileext = ".gpkg")

if (file.exists(gpkg_tmp))
  file.remove(gpkg_tmp)

# write a gpkg with two DEMs in it
gpkg_write(
  dem,
  destfile = gpkg_tmp,
  RASTER_TABLE = "DEM1",
  FIELD_NAME = "Elevation"
)

gpkg_write(
  dem,
  destfile = gpkg_tmp,
  append = TRUE,
  RASTER_TABLE = "DEM2",
  FIELD_NAME = "Elevation",
  NoData = -9999
)

Insert Vector Layers

We can also write vector data to GeoPackage. Here we use gpkg_write() to add a bounding box polygon layer derived from extent of "DEM1".

# add bounding polygon vector layer via named list
r <- gpkg_tables(geopackage(gpkg_tmp))[['DEM1']]
v <- terra::as.polygons(r, ext = TRUE)
gpkg_write(list(bbox = v), destfile = gpkg_tmp, append = TRUE)

Insert Attribute Table

Similarly, data.frame-like objects (non-spatial “attributes”) can be written to GeoPackage.

z <- data.frame(a = 1:10, b = LETTERS[1:10])
gpkg_write(list(myattr = z), destfile = gpkg_tmp, append = TRUE)

Read a GeoPackage

geopackage() is a constructor that can create a simple container for working with geopackages from several types of inputs. Often you will have a character file path to a GeoPackage (.gpkg) file.

g <- geopackage(gpkg_tmp, connect = TRUE)
g
#> <geopackage>
#> --------------------------------------------------------------------------------
#> # of Tables: 20
#>  
#>  DEM1, DEM2, bbox, gpkg_2d_gridded_coverage_ancillary,
#>  gpkg_2d_gridded_tile_ancillary, gpkg_contents, gpkg_extensions,
#>  gpkg_geometry_columns, gpkg_metadata, gpkg_metadata_reference,
#>  gpkg_ogr_contents, gpkg_spatial_ref_sys, gpkg_tile_matrix,
#>  gpkg_tile_matrix_set, myattr, rtree_bbox_geom, rtree_bbox_geom_node,
#>  rtree_bbox_geom_parent, rtree_bbox_geom_rowid, sqlite_sequence
#> --------------------------------------------------------------------------------
#> <SQLiteConnection>
#>   Path: /tmp/RtmpQZ3zKV/file304b339abcc96.gpkg
#>   Extensions: TRUE
class(g)
#> [1] "geopackage"

Other times you may have a list of tables and layers you want to be in a GeoPackage that does not exist yet.

g2 <- geopackage(list(dem = r, bbox = v))
g2
#> <geopackage>
#> --------------------------------------------------------------------------------
#> # of Tables: 18
#>  
#>  bbox, dem, gpkg_2d_gridded_coverage_ancillary,
#>  gpkg_2d_gridded_tile_ancillary, gpkg_contents, gpkg_extensions,
#>  gpkg_geometry_columns, gpkg_metadata, gpkg_metadata_reference,
#>  gpkg_ogr_contents, gpkg_spatial_ref_sys, gpkg_tile_matrix,
#>  gpkg_tile_matrix_set, rtree_bbox_geom, rtree_bbox_geom_node,
#>  rtree_bbox_geom_parent, rtree_bbox_geom_rowid, sqlite_sequence
#> --------------------------------------------------------------------------------
#> <SQLiteConnection>
#>   Path: /tmp/RtmpQZ3zKV/Rgpkg304b329e168a3.gpkg
#>   Extensions: TRUE
class(g2)
#> [1] "geopackage"

Note that a temporary GeoPackage (/tmp/RtmpQZ3zKV/Rgpkg304b329e168a3.gpkg) is automatically created when using the geopackage(<list>) constructor.

You also may have a DBIConnection to a GeoPackage database already opened that you want to use. In any case (character, list, SQLiteConnection) there is an S3 method to facilitate creating the basic geopackage class provided by {gpkg}. All other methods are designed to be able to work smoothly with geopackage class input.

Inspect Contents of GeoPackage

We can list the table names in a GeoPackage with gpkg_list_tables() and fetch pointers (SpatRaster, SpatVectorProxy, and lazy data.frame) to the data in them with gpkg_table(). We can check the status of the internal geopackage class SQLiteConnection with gpkg_is_connected() and disconnect it with gpkg_disconnect().

# enumerate tables
gpkg_list_tables(g)
#>  [1] "DEM1"                               "DEM2"                              
#>  [3] "bbox"                               "gpkg_2d_gridded_coverage_ancillary"
#>  [5] "gpkg_2d_gridded_tile_ancillary"     "gpkg_contents"                     
#>  [7] "gpkg_extensions"                    "gpkg_geometry_columns"             
#>  [9] "gpkg_metadata"                      "gpkg_metadata_reference"           
#> [11] "gpkg_ogr_contents"                  "gpkg_spatial_ref_sys"              
#> [13] "gpkg_tile_matrix"                   "gpkg_tile_matrix_set"              
#> [15] "myattr"                             "rtree_bbox_geom"                   
#> [17] "rtree_bbox_geom_node"               "rtree_bbox_geom_parent"            
#> [19] "rtree_bbox_geom_rowid"              "sqlite_sequence"

# inspect tables
gpkg_tables(g)
#> $DEM1
#> class       : SpatRaster 
#> dimensions  : 30, 31, 1  (nrow, ncol, nlyr)
#> resolution  : 0.008333333, 0.008333333  (x, y)
#> extent      : 6.008333, 6.266667, 49.69167, 49.94167  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326) 
#> source      : file304b339abcc96.gpkg:DEM1 
#> varname     : file304b339abcc96 
#> name        : DEM1 
#> min value   :  195 
#> max value   :  500 
#> 
#> $DEM2
#> class       : SpatRaster 
#> dimensions  : 30, 31, 1  (nrow, ncol, nlyr)
#> resolution  : 0.008333333, 0.008333333  (x, y)
#> extent      : 6.008333, 6.266667, 49.69167, 49.94167  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326) 
#> source      : file304b339abcc96.gpkg:DEM2 
#> varname     : file304b339abcc96 
#> name        : DEM2 
#> min value   :  195 
#> max value   :  500 
#> 
#> $myattr
#> # Source:   table<myattr> [10 x 2]
#> # Database: sqlite 3.41.2 [/tmp/RtmpQZ3zKV/file304b339abcc96.gpkg]
#>        a b    
#>    <int> <chr>
#>  1     1 A    
#>  2     2 B    
#>  3     3 C    
#>  4     4 D    
#>  5     5 E    
#>  6     6 F    
#>  7     7 G    
#>  8     8 H    
#>  9     9 I    
#> 10    10 J    
#> 
#> $bbox
#>  class       : SpatVectorProxy
#>  geometry    : polygons 
#>  dimensions  : 1, 0  (geometries, attributes)
#>  extent      : 6.008333, 6.266667, 49.69167, 49.94167  (xmin, xmax, ymin, ymax)
#>  source      : file304b339abcc96.gpkg (bbox)
#>  layer       : bbox 
#>  coord. ref. : lon/lat WGS 84 (EPSG:4326)

# inspect a specific table
gpkg_table(g, "myattr", collect = TRUE)
#>     a b
#> 1   1 A
#> 2   2 B
#> 3   3 C
#> 4   4 D
#> 5   5 E
#> 6   6 F
#> 7   7 G
#> 8   8 H
#> 9   9 I
#> 10 10 J

Note that the collect = TRUE forces data be loaded into R memory for vector and attribute data; this is the difference in result object class of SpatVectorProxy/SpatVector and tbl_SQLiteConnection/data.frame for vector and attribute data, respectively.

gpkg_collect() is a helper method to call gpkg_table(..., collect = TRUE) for in-memory loading of specific tables.

gpkg_collect(g, "DEM1")
#>   id zoom_level tile_column tile_row     tile_data
#> 1  1          0           0        0 blob[3.98 kB]

Note that with grid data returned from gpkg_collect() you get a table result with the tile contents in a blob column of a data.frame instead of SpatRaster object.

The inverse function of gpkg_collect() is gpkg_tbl() which always returns a tbl_SQLiteConnection.

gpkg_tbl(g, "gpkg_contents")
#> # Source:   table<gpkg_contents> [4 x 10]
#> # Database: sqlite 3.41.2 [/tmp/RtmpQZ3zKV/file304b339abcc96.gpkg]
#>   table_name data_type   identifier description last_change   min_x min_y  max_x
#>   <chr>      <chr>       <chr>      <chr>       <chr>         <dbl> <dbl>  <dbl>
#> 1 DEM1       2d-gridded… DEM1       ""          2023-11-19…    6.01  49.7   6.27
#> 2 DEM2       2d-gridded… DEM2       ""          2023-11-19…    6.01  49.7   6.27
#> 3 bbox       features    bbox       ""          2023-11-19…    6.01  49.7   6.27
#> 4 myattr     attributes  myattr     ""          2023-11-19… -180    -90   180   
#> # ℹ 2 more variables: max_y <dbl>, srs_id <int>

More on how to use this type of result next.

Lazy Data Access

There are several other methods that can be used for working with tabular data in a GeoPackage in a “lazy” fashion.

Method 1: gpkg_table_pragma()

gpkg_table_pragma() is a low-frills data.frame result containing important table information, but not values. The PRAGMA table_info() is stored as a nested data.frame table_info. This representation has no dependencies beyond {RSQLite} and is efficient for inspection of table structure and attributes, though it is less useful for data analysis.

head(gpkg_table_pragma(g))
#>                                      dsn table_name nrow table_info.cid
#> 1 /tmp/RtmpQZ3zKV/file304b339abcc96.gpkg       DEM1    1              0
#> 2 /tmp/RtmpQZ3zKV/file304b339abcc96.gpkg       DEM1    1              1
#> 3 /tmp/RtmpQZ3zKV/file304b339abcc96.gpkg       DEM1    1              2
#> 4 /tmp/RtmpQZ3zKV/file304b339abcc96.gpkg       DEM1    1              3
#> 5 /tmp/RtmpQZ3zKV/file304b339abcc96.gpkg       DEM1    1              4
#> 6 /tmp/RtmpQZ3zKV/file304b339abcc96.gpkg       DEM2    1              0
#>   table_info.name table_info.type table_info.notnull table_info.dflt_value
#> 1              id         INTEGER                  0                  <NA>
#> 2      zoom_level         INTEGER                  1                  <NA>
#> 3     tile_column         INTEGER                  1                  <NA>
#> 4        tile_row         INTEGER                  1                  <NA>
#> 5       tile_data            BLOB                  1                  <NA>
#> 6              id         INTEGER                  0                  <NA>
#>   table_info.pk
#> 1             1
#> 2             0
#> 3             0
#> 4             0
#> 5             0
#> 6             1

Method 2: gpkg_vect() and gpkg_query()

gpkg_vect() is a wrapper around terra::vect() you can use to create ‘terra’ SpatVector objects from the tables found in a GeoPackage.

gpkg_vect(g, 'bbox')
#>  class       : SpatVector 
#>  geometry    : polygons 
#>  dimensions  : 1, 0  (geometries, attributes)
#>  extent      : 6.008333, 6.266667, 49.69167, 49.94167  (xmin, xmax, ymin, ymax)
#>  source      : file304b339abcc96.gpkg (bbox)
#>  coord. ref. : lon/lat WGS 84 (EPSG:4326)

The table of interest need not have a geometry column, but this method does not work on GeoPackage that contain only gridded data, and some layer in the GeoPackage must have some geometry.

gpkg_vect(g, 'gpkg_ogr_contents')
#>  class       : SpatVector 
#>  geometry    : none 
#>  dimensions  : 0, 2  (geometries, attributes)
#>  extent      : 0, 0, 0, 0  (xmin, xmax, ymin, ymax)
#>  source      : file304b339abcc96.gpkg (SELECT)
#>  coord. ref. :  
#>  names       : table_name feature_count
#>  type        :      <chr>         <int>

The SpatVectorProxy is used for “lazy” references to of vector and attribute contents of a GeoPackage; this object for vector data is analogous to the SpatRaster for gridded data. The ‘terra’ package provides “GDAL plumbing” for filter and query utilities.

gpkg_query() by default uses the ‘RSQLite’ driver, but the richer capabilities of OGR data sources can be harnessed with SQLite SQL dialect. These additional features can be utilized with the ogr=TRUE argument to gpkg_query(), or gpkg_ogr_query() for short. This assumes that GDAL is built with support for SQLite (and ideally also with support for Spatialite).

For example, we use built-in functions such as ST_MinX() to calculate summaries for "bbox" table, geometry column "geom". In this case we expect the calculated quantities to match the coordinates/boundaries of the bounding box:

res <- gpkg_ogr_query(g, "SELECT 
                           ST_MinX(geom) AS xmin,
                           ST_MinY(geom) AS ymin, 
                           ST_MaxX(geom) AS xmax, 
                           ST_MaxY(geom) AS ymax 
                          FROM bbox")
as.data.frame(res)
#>       xmin     ymin     xmax     ymax
#> 1 6.008333 49.69167 6.266667 49.94167

Method 3: gpkg_rast()

Using gpkg_rast() you can quickly access references to all tile/gridded datasets in a GeoPackage.

For example:

gpkg_rast(g)
#> class       : SpatRaster 
#> dimensions  : 30, 31, 2  (nrow, ncol, nlyr)
#> resolution  : 0.008333333, 0.008333333  (x, y)
#> extent      : 6.008333, 6.266667, 49.69167, 49.94167  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326) 
#> sources     : file304b339abcc96.gpkg:DEM1  
#>               file304b339abcc96.gpkg:DEM2  
#> varnames    : file304b339abcc96 
#>               file304b339abcc96 
#> names       : DEM1, DEM2 
#> min values  :  195,  195 
#> max values  :  500,  500

Method 4: gpkg_table()

With the gpkg_table() method you access a specific table (by name) and get a “lazy” tibble object referencing that table.

This is achieved via {dplyr} and the {dbplyr} database connection to the GeoPackage via the {RSQLite} driver. The resulting object’s data can be used in more complex analyses by using other {dbplyr}/{tidyverse} functions.

For example, we inspect the contents of the gpkg_contents table that contains critical information on the data contained in a GeoPackage.

gpkg_table(g, "gpkg_contents")
#> # Source:   table<gpkg_contents> [4 x 10]
#> # Database: sqlite 3.41.2 [/tmp/RtmpQZ3zKV/file304b339abcc96.gpkg]
#>   table_name data_type   identifier description last_change   min_x min_y  max_x
#>   <chr>      <chr>       <chr>      <chr>       <chr>         <dbl> <dbl>  <dbl>
#> 1 DEM1       2d-gridded… DEM1       ""          2023-11-19…    6.01  49.7   6.27
#> 2 DEM2       2d-gridded… DEM2       ""          2023-11-19…    6.01  49.7   6.27
#> 3 bbox       features    bbox       ""          2023-11-19…    6.01  49.7   6.27
#> 4 myattr     attributes  myattr     ""          2023-11-19… -180    -90   180   
#> # ℹ 2 more variables: max_y <dbl>, srs_id <int>

As a more complicated example we access the gpkg_2d_gridded_tile_ancillary table, and perform some data processing.

We dplyr::select() mean and std_dev columns from the dplyr::filter()ed rows where tpudt_name == "DEM2". Finally we materialize a tibble with dplyr::collect():

library(dplyr, warn.conflicts = FALSE)

gpkg_table(g, "gpkg_2d_gridded_tile_ancillary") %>% 
  filter(tpudt_name == "DEM2") %>% 
  select(mean, std_dev) %>% 
  collect()
#> # A tibble: 1 × 2
#>    mean std_dev
#>   <dbl>   <dbl>
#> 1  324.    58.5

Managing Connections

Several helper methods are available for checking GeoPackage SQLiteConnection status, as well as connecting and disconnecting an existing geopackage object (g).

# still connected
gpkg_is_connected(g)
#> [1] TRUE

# disconnect geopackage
gpkg_disconnect(g)

# reconnect
gpkg_connect(g)
#> <geopackage>
#> --------------------------------------------------------------------------------
#> # of Tables: 21
#>  
#>  DEM1, DEM2, bbox, dummy_feature, gpkg_2d_gridded_coverage_ancillary,
#>  gpkg_2d_gridded_tile_ancillary, gpkg_contents, gpkg_extensions,
#>  gpkg_geometry_columns, gpkg_metadata, gpkg_metadata_reference,
#>  gpkg_ogr_contents, gpkg_spatial_ref_sys, gpkg_tile_matrix,
#>  gpkg_tile_matrix_set, myattr, rtree_bbox_geom, rtree_bbox_geom_node,
#>  rtree_bbox_geom_parent, rtree_bbox_geom_rowid, sqlite_sequence
#> --------------------------------------------------------------------------------
#> <SQLiteConnection>
#>   Path: /tmp/RtmpQZ3zKV/file304b339abcc96.gpkg
#>   Extensions: TRUE

# disconnect
gpkg_disconnect(g)