Discrete Global Grid Systems as scalable geospatial frameworks for characterizing coastal environments

Area of Science:

• Environmental Science
• Geospatial Analysis
• Data Science

Background:

• Coastal water quality data accessibility has improved, but processing tools remain limited.
• Existing geospatial frameworks are often adapted from open-water or watershed models, posing challenges for land-sea interface analysis.

Purpose of the Study:

• To explore the utility of hexagon-based Discrete Global Grid Systems (DGGS) for coastal geospatial data processing.
• To compare two DGGS implementations, dggridR and H3, for their effectiveness in aggregating, integrating, and connecting land-sea data flows.

Main Methods:

• Two DGGS implementations, dggridR and H3, were evaluated in a coastal setting.
• Frameworks were compared on data aggregation, cross-framework integration, and land-sea connectivity.
• Point, line, and grid data were aggregated to H3 units for modeling and visualization.

Main Results:

• dggridR offered simplicity and flexibility in matching scales and using smaller units.
• H3 demonstrated superior performance in neighbor identification and efficient scale transitions.
• H3 successfully aggregated diverse data types and facilitated coastal data modeling and visualization.

Conclusions:

• Hexagon-based DGGS, particularly H3, show significant promise for advancing coastal data management and analysis.
• H3 provides a performant and versatile solution for integrating and visualizing complex coastal datasets across the land-sea interface.