vector2dggs

Python-based CLI tool to index raster files to DGGS in parallel, writing out to Parquet.

This is the vector equivalent of raster2dggs.

Currently only supports H3 DGGS, and probably has other limitations since it has been developed for a specific internal use case, though it is intended as a general-purpose abstraction. Contributions, suggestions, bug reports and strongly worded letters are all welcome.

Currently only supports polygons; but both coverages (strictly non-overlapping polygons), and sets of polygons that do/may overlap, are supported. Overlapping polygons are captured by ensuring that DGGS cell IDs may be non-unique (repeated) in the output.

Installation

pip install vector2dggs

Usage

vector2dggs h3 --help
Usage: vector2dggs h3 [OPTIONS] VECTOR_INPUT OUTPUT_DIRECTORY

  Ingest a vector dataset and index it to the H3 DGGS.

  VECTOR_INPUT is the path to input vector geospatial data. OUTPUT_DIRECTORY
  should be a directory, not a file or database table, as it will instead be
  the write location for an Apache Parquet data store.

Options:
  -v, --verbosity LVL             Either CRITICAL, ERROR, WARNING, INFO or
                                  DEBUG  [default: INFO]
  -r, --resolution [0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15]
                                  H3 resolution to index  [required]
  -pr, --parent_res [0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15]
                                  H3 Parent resolution for the output
                                  partition. Defaults to resolution - 6
  -id, --id_field TEXT            Field to use as an ID; defaults to a
                                  constructed single 0...n index on the
                                  original feature order.
  -k, --keep_attributes           Retain attributes in output. The default is
                                  to create an output that only includes H3
                                  cell ID and the ID given by the -id field
                                  (or the default index ID).
  -p, --partitions INTEGER        The number of partitions to create.
                                  Recommendation: at least as many partitions
                                  as there are available `--threads`.
                                  Partitions are processed in parallel once
                                  they have been formed.  [default: 50;
                                  required]
  -s, --spatial_sorting [hilbert|morton|geohash]
                                  Spatial sorting method when perfoming
                                  spatial partitioning.  [default: hilbert]
  -crs, --cut_crs INTEGER         Set the coordinate reference system (CRS)
                                  used for cutting large polygons (see `--cur-
                                  threshold`). Defaults to the same CRS as the
                                  input. Should be a valid EPSG code.
  -c, --cut_threshold INTEGER     Cutting up large polygons into smaller
                                  pieces based on a target length. Units are
                                  assumed to match the input CRS units unless
                                  the `--cut_crs` is also given, in which case
                                  units match the units of the supplied CRS.
                                  [default: 5000; required]
  -t, --threads INTEGER           Amount of threads used for operation
                                  [default: 7]
  -tbl, --table TEXT              Name of the table to read when using a
                                  spatial database connection as input
  -g, --geom_col TEXT             Column name to use when using a spatial
                                  database connection as input  [default:
                                  geom]
  -o, --overwrite
  --version                       Show the version and exit.
  --help                          Show this message and exit.

Example

Visualising output

Output is in the Apache Parquet format, a directory with one file per partition.

For a quick view of your output, you can read Apache Parquet with pandas, and then use h3-pandas and geopandas to convert this into a GeoPackage or GeoParquet for visualisation in a desktop GIS, such as QGIS. The Apache Parquet output is indexed by an ID column (which you can specify), so it should be ready for two intended use-cases:

• Joining attribute data from the original feature-level data onto computer DGGS cells.
• Joining other data to this output on the H3 cell ID. (The output has a column like h3_\d{2}, e.g. h3_09 or h3_12 according to the target resolution.)

Geoparquet output (hexagon boundaries):

>>> import pandas as pd
>>> import h3pandas
>>> g = pd.read_parquet('./output-data/nz-property-titles.12.parquet').h3.h3_to_geo_boundary()
>>> g
                  title_no                                           geometry
h3_12                                                                        
8cbb53a734553ff  NA94D/635  POLYGON ((174.28483 -35.69315, 174.28482 -35.6...
8cbb53a734467ff  NA94D/635  POLYGON ((174.28454 -35.69333, 174.28453 -35.6...
8cbb53a734445ff  NA94D/635  POLYGON ((174.28416 -35.69368, 174.28415 -35.6...
8cbb53a734551ff  NA94D/635  POLYGON ((174.28496 -35.69329, 174.28494 -35.6...
8cbb53a734463ff  NA94D/635  POLYGON ((174.28433 -35.69335, 174.28432 -35.6...
...                    ...                                                ...
8cbb53a548b2dff  NA62D/324  POLYGON ((174.30249 -35.69369, 174.30248 -35.6...
8cbb53a548b61ff  NA62D/324  POLYGON ((174.30232 -35.69402, 174.30231 -35.6...
8cbb53a548b11ff  NA57C/785  POLYGON ((174.30140 -35.69348, 174.30139 -35.6...
8cbb53a548b15ff  NA57C/785  POLYGON ((174.30161 -35.69346, 174.30160 -35.6...
8cbb53a548b17ff  NA57C/785  POLYGON ((174.30149 -35.69332, 174.30147 -35.6...

[52736 rows x 2 columns]
>>> g.to_parquet('./output-data/parcels.12.geo.parquet')

For development

In brief, to get started:

• Install Poetry
• Install GDAL
  • If you're on Windows, pip install gdal may be necessary before running the subsequent commands.
  • On Linux, install GDAL 3.6+ according to your platform-specific instructions, including development headers, i.e. libgdal-dev.
• Create the virtual environment with poetry init. This will install necessary dependencies.
• Subsequently, the virtual environment can be re-activated with poetry shell.

If you run poetry install, the CLI tool will be aliased so you can simply use vector2dggs rather than poetry run vector2dggs, which is the alternative if you do not poetry install.

Code formatting

Please run black . before committing.

Example commands

With a local GPKG:

vector2dggs h3 -v DEBUG -id title_no -r 12 -o ~/Downloads/nz-property-titles.gpkg ~/Downloads/nz-property-titles.parquet

With a PostgreSQL/PostGIS connection:

vector2dggs h3 -v DEBUG -id ogc_fid -r 9 -p 5 -t 4 --overwrite -tbl topo50_lake postgresql://user:password@host:port/db ./topo50_lake.parquet

Citation

@software{vector2dggs,
  title={{vector2dggs}},
  author={Ardo, James and Law, Richard},
  url={https://github.com/manaakiwhenua/vector2dggs},
  version={0.3.4},
  date={2023-04-20}
}

APA/Harvard

Ardo, J., & Law, R. (2023). vector2dggs (0.3.4) [Computer software]. https://github.com/manaakiwhenua/vector2dggs