The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs

TL;DR

This paper explores how the S2 Hierarchical Discrete Global Grid system can serve as a foundational framework for efficient data representation, integration, and querying in large-scale geospatial knowledge graphs, addressing key challenges in geospatial AI.

Contribution

It demonstrates the implementation of Google's S2 DGGS within KnowWhereGraph, enhancing data integration, topological enrichment, and semantic compression for scalable geospatial knowledge graphs.

Findings

S2 enables efficient multi-source data processing.

Improves topological relation discovery via DGGS.

Facilitates scalable, cross-graph geospatial querying.

Abstract

Geospatial Knowledge Graphs (GeoKGs) have become integral to the growing field of Geospatial Artificial Intelligence. Initiatives like the U.S. National Science Foundation's Open Knowledge Network program aim to create an ecosystem of nation-scale, cross-disciplinary GeoKGs that provide AI-ready geospatial data aligned with FAIR principles. However, building this infrastructure presents key challenges, including 1) managing large volumes of data, 2) the computational complexity of discovering topological relations via SPARQL, and 3) conflating multi-scale raster and vector data. Discrete Global Grid Systems (DGGS) help tackle these issues by offering efficient data integration and representation strategies. The KnowWhereGraph utilizes Google's S2 Geometry -- a DGGS framework -- to enable efficient multi-source data processing, qualitative spatial querying, and cross-graph integration. This paper outlines the implementation of S2 within KnowWhereGraph, emphasizing its role in topologically enriching and semantically compressing data. Ultimately, this work demonstrates the potential of DGGS frameworks, particularly S2, for building scalable GeoKGs.