07-09, 13:55–14:25 (US/Pacific), Room 318
Over the past few years, Discrete Global Grid Systems (DGGS) that subdivide the earth into (roughly) equally sized faces have seen increased popularity. However, their in-memory representation is different from traditional projection-based data, which is either comprises of evenly shaped rectangular grid (aka raster) or discrete geometries (aka vector), and thus requires specialized tooling. In particular, this includes libraries that can work on the numeric cell ids defined by the specific DGGS.
xdggs is a library that provides a unified interface for xarray that allows working with and visualizing a variety of DGGS-indexed data sets.
Traditionally, data in the geosciences have been sampled on a projection-based grid. This allowed for very simple and intuitive grids represented as two dimensions in memory, with the most popular being grids based on equirectangular projections (i.e. simple longitude / latitude grids).
However, this simplicity comes with a number of downsides:
- The cells are geometrically distorted, with the distortion growing with the distance from the projection center
- There are discontinuities along the edges of the projection space (for cylindrical projections e.g. dateline, poles)
- Data may be oversampled in some parts of the grid (e.g. in polar regions for cylindrical projections)
This is usually less relevant for small local grids, but becomes more important as the size of the area of interest grows or when combining multiple local grids.
DGGS aim to resolve this by equally and recursively subdividing the earth (approximated as a sphere, which can be extended to an ellipsoid) using flat surfaces such as triangles, rectangles, or hexagons, forming a hierarchy or tree of cells.
Since these cells are unique, each cell can be assigned a numeric ID. This ID can be used to efficiently traverse the hierarchy, allowing for operations like up-/downsampling, neighbours search, and alignment / co-location. Working with DGGS cells also allows avoiding the issues caused by discontinuities like the dateline. In addition, the cells are addressed by unique indexing systems that typically follow a space-filling curve. This cell ID serves as a 1-D index in Xarray. Not all of these grid systems or libraries currently implement such a DGG reference system (DGGRS) that allows for seamless traversal or neighbourhood operations.
It is important to note, though, that the choice of the concrete DGGS still requires a careful tradeoff: while they are better than planar projections, DGGS still cannot preserve area, shape/angles and distances at the same time. In particular, some DGGS are specialized on preserving shapes and distances and thus are best used for navigation, while others preserve areas and thus are better for geophysical applications. The most well-known examples for navigational DGGS include H3 and S2, while examples for area-preserving DGGS are HEALPix and various ISEA grids like ISEA7H or ISEA4T.
The way DGGS are designed means that new tooling and algorithms are required. Additionally, the geospatial location and refinement level represented by the cell IDs require specialized libraries, which all have a unique API.
xdggs is a library that extends xarray^1 to provide a unified interface for interacting with various DGGS based on the cell IDs. It implements basic operations like computing the cell centers and cell boundaries from cell ids, refinement level (cf. resolution), aligning datasets on the same grid, selecting cells using geographic coordinates as well as interactive visualization using libraries, such as lonboard^2.
Project links:
- github: https://github.com/xarray-contrib/xdggs
- docs: https://xdggs.readthedocs.io
- earlier presentation: https://discourse.pangeo.io/t/pangeo-showcase-xdggs-using-discrete-global-grid-systems-with-xarray/4728
Tina Todaka is a research engineer at IFREMER, working in the UMR-LOPS (Laboratoire d’Océanographie Physique et Spatiale). She leads the IAOCEA project, focusing on the hybridization of model, satellite, and in-situ data for oceanography using scattering transform techniques. She also leads the Pangeo-Fish project, a software package leveraging the Pangeo environment to help biologists efficiently compute fish tracks from biologging in-situ data and Earth science datasets.
Her research interests include optimizing scientific computing workflows in oceanography, from high-performance and cloud-based computing to their practical applications in policy decision-making. She actively contributes to open-source geospatial science, developing scalable and reproducible tools for large-scale oceanographic data analysis.
Senior research engineer at CNRS, specialize in data analysis across astrophysics and oceanography. With extensive expertise in AI, scattering transform, and statistical modeling, I contribute to cutting-edge projects such as Planck’s SRoll algorithm and the development of the FOSCAT library on PyPi. I currently lead projects integrating deep learning and dimensionality reduction techniques for Earth and space science applications, including sea ice analysis, galaxy mapping and turbulence modeling.
Alex is an Associate Professor in Geoinformatics and a Distributed Spatial Systems Researcher with many years of experience in geospatial data management and web- and cloud-based geoprocessing with a particular focus on land use, soils, hydrology, hydrogeology and water quality data. His interests include DIscrete Global Grid Systems (DGGS), OGC standards and web-services for environmental and geo-scientific data sharing, modelling workflows and interactive geo-scientific visualisation. He is also the European co-chair of the OGC DGGS working group.