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

Shirly Stephen, Mitchell Faulk, Krzysztof Janowicz, Colby Fisher, Thomas Thelen, Rui Zhu, Pascal Hitzler, Cogan Shimizu, Kitty Currier, Mark Schildhauer, Dean Rehberger, Zhangyu Wang, Antrea Christou

TL;DR

This work demonstrates the potential of DGGS frameworks, particularly S2, for building scalable GeoKGs, and outlines the implementation of S2 within KnowWhereGraph, emphasizing its role in topologically enriching and semantically compressing data.

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.

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

TL;DR

This work demonstrates the potential of DGGS frameworks, particularly S2, for building scalable GeoKGs, and outlines the implementation of S2 within KnowWhereGraph, emphasizing its role in topologically enriching and semantically compressing data.

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.

Paper Structure

This paper contains 21 sections, 18 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Theoretical Background and Related Research
  3. Geospatial Knowledge Graphs
  4. The KnowWhereGraph
  5. Discrete Global Grid Systems
  6. The S2 Discrete Global Grid
  7. Methodology
  8. Conceptual Schema for the S2 Geometry in KnowWhereGraph
  9. Challenges
  10. Technical Strategy for Data Quantization in KnowWhereGraph
  11. Implementing Data Quantization on S2 in KnowWhereGraph
  12. Topological Enrichment of Vector Data
  13. Grid-Based Data Discretization
  14. Vector Data to S2 Cell Discretization
  15. Raster Data to S2 Cell Discretization
  16. ...and 6 more sections

Figures (18)

  • Figure 1: GeoSPARQL's core concepts are shown in blue boxes, and Simple Features geometry classes in beige boxes. White-headed arrows represent $\texttt{rdfs}$:$\texttt{subClass}$ relations, while filled arrows denote object or data properties
  • Figure 2: Schema diagram that denotes the core concepts/themes in KWG (explicit geographic features denoted using yellow boxes and geographically themed observations denoted using the purple box) and their extension of SSN/SOSA, GeoSPARQL, and OWL-Time ontologies (denotes using blue boxes). The SSN/SOSA pattern denotes the generic graph to query KWG observations. White-headed arrows denote $\texttt{rdfs}$:$\texttt{subClass}$ relationships.
  • Figure 3: Congruence (i.e., parent-child containment relationships), and aggregation or decomposition resolutions (e.g., squares can be aggregated in groups of four to form coarser resolution objects) in DGGS with varying polyhedron shapes.
  • Figure 4: (a) Hierarchical tessellation of the Earth using the S2 Geometry. (b) The S2Cell hierarchy projecting the Earth onto six "base cells". These illustrations are adopted from the S2 Library documentation veach2017s2.
  • Figure 5: Schema diagram illustrating the extension of GeoSPARQL concepts to model S2 data.
  • ...and 13 more figures

Theorems & Definitions (3)

  • Definition 2.1
  • Definition 2.2
  • Definition 2.3