Navigating New Grids: Evaluating DGGS Configurations for Marine Spatial Analysis

Accurate and efficient spatial analysis is crucial for the mapping and sustainable management of marine environments, where large-scale and diverse datasets present significant analytical challenges. Traditional latitude-longitude methods, while widely used, often encounter limitations in data integration and handling distortion caused by Earth’s curvature. Discrete Global Grid Systems (DGGS) have emerged as a promising solution, offering a hierarchical, global, and equal-area framework for geospatial analysis. Despite their potential, the performance in marine spatial analysis remains underexplored.

This study evaluates the impact and suitability of DGGS-based spatial analysis by comparing its performance with the traditional latitude-longitude approaches. Using marine datasets representing point and raster data formats, the workflow begins with quantization, converting the data into DGGS cells.The implementation utilizes open-source Python tools from the Pangeo ecosystem, including xarray-xdggrid, to enable seamless integration and efficient analysis of large geospatial datasets. Three DGGS configurations – ISEA7H, HEALPIX, and ISEA3H are compared alongside traditional latitude-longitude grid for computational efficiency (processing time and memory usage) and their ability to preserve spatial patterns. Spatial analysis methods include density estimation, nearest neighbor evaluation, and clustering for point data, as well as zonal statistics, spatial autocorrelation, and resampling for raster data.

To further illustrate the application of DGGS-based methods, the study includes a case study on estuary characterization. This characterization relies on spatial analysis methods, integrating physical oceanographic parameters from Delft3D-FM, biogeochemical and optical data products, and in-situ point measurements from the Copernicus Marine Environment Monitoring Service (CMEMS). Representing these diverse datasets within the DGGS framework highlights its ability to manage varying data types and scales, offering insights into estuarine environments and demonstrating its scalability for addressing complex marine spatial challenges.

Results indicate that DGGS frameworks deliver comparable computational performance while offering consistent spatial representation. Configuration-specific trade-offs influence their effectiveness, emphasizing the importance of aligning DGGS configurations with specific analytical tasks and applications. Findings suggest that DGGS-based methods offer a promising alternative to traditional analysis techniques, providing greater flexibility in adapting to datasets, scale, and resolution. This contributes to more efficient mapping, sustainable marine environmental management, and advancing geospatial applications through open-source tools from the Pangeo ecosystem.