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February 9, 2026Transactions in GIS0 citations

The KnowWhereGraph : A Large‐Scale Geo‐Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo‐Enrichment

RZRui ZhuCSCogan ShimizuSSShirly Stephen

Key Points

  • The aim is to create a comprehensive knowledge graph to overcome challenges in geospatial data integration and accessibility.
  • Developed a large-scale geospatial knowledge graph framework named KnowWhereGraph.
  • Incorporated schemas related to human and environmental systems.
  • Implemented design principles based on space, place, and time to enhance data interconnectedness.
  • Demonstrated multiple use cases showing practical applications of the knowledge graph.
  • Established a preintegrated, AI-ready data warehouse.
  • Facilitated effective data consolidation across various domains.
  • Enabled decision-makers to discover insights from complex datasets through advanced tools.

Abstract

ABSTRACT Global challenges such as food supply chain disruptions, public health crises, and natural hazard responses require access to and integration of diverse datasets, many of which are geospatial. Over the past few years, a growing number of (geo)portals have been developed to address this need. However, most existing (geo)portals are stacked by separated or sparsely connected data “silos” impeding effective data consolidation. A new way of sharing and reusing geospatial data is therefore urgently needed. In this work, we introduce KnowWhereGraph, a knowledge graph‐based data integration, enrichment, and synthesis framework that not only includes schemas and data related to human and environmental systems but also provides a suite of supporting tools for accessing this information. The KnowWhereGraph aims to address the challenge of data integration by building a large‐scale, cross‐domain, preintegrated, FAIR‐principles‐based, and AI‐ready data warehouse rooted in knowledge graphs. We highlight the design principles of KnowWhereGraph, emphasizing the roles of space, place, and time in bridging various data “silos.” Additionally, we demonstrate multiple use cases where the proposed geospatial knowledge graph and its associated tools empower decision‐makers to uncover insights that are often hidden within complex and poorly interoperable datasets.

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Cite This Study

Zhu et al. (2026) studied this question.

synapsesocial.com/papers/69897a86f0ec2af6756e8bfchttps://doi.org/10.1111/tgis.70184
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