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A Homogeneous Approach to Reasoning Over Global Geographic Data

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Artificial Intelligence XLI (SGAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15446))

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Abstract

Much of the information that we use is geospatially referenced. The need for homogeneous representation of global geographic themes is recognised as critical for sustainable development goals. The richness of local geographic data created and maintained by individual countries vary widely, creating what is known as a geospatial digital divide. Attempts to bridge this divide include the adoption of Discrete Global Grid Systems that provide an abstract and uniform method of partitioning space on Earth. This paper considers how the local methods of partitioning space adopted in individual countries and provided as open data can be integrated with this global grid system. The paper proposes a novel ontology design pattern for representing the integration of both grid systems, and evaluates it against existing methods. It is shown how a uniform treatment of spatial semantics is used to represent geographic places across grid systems. This proposal is a step towards the effective utilisation of these grid systems in building global geographic information systems.

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Notes

  1. 1.

    https://osdatahub.os.uk/.

  2. 2.

    https://data.usgs.gov/datacatalog/data/.

  3. 3.

    https://salb.un.org/en.

  4. 4.

    https://gadm.org.

  5. 5.

    https://docs.ogc.org/as/15-104r5/15-104r5.html#7.

References

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Authors and Affiliations

  1. School of Computer Science Informatics, Cardiff University, Wales, UK

    Alia I. Abdelmoty & Abdurauf Satoti

Authors
  1. Alia I. Abdelmoty
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  2. Abdurauf Satoti
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Correspondence to Alia I. Abdelmoty .

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Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. DFKI Niedersachsen, Oldenburg, Germany

    Frederic Stahl

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Abdelmoty, A.I., Satoti, A. (2025). A Homogeneous Approach to Reasoning Over Global Geographic Data. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XLI. SGAI 2024. Lecture Notes in Computer Science(), vol 15446. Springer, Cham. https://doi.org/10.1007/978-3-031-77915-2_20

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  • DOI: https://doi.org/10.1007/978-3-031-77915-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77914-5

  • Online ISBN: 978-3-031-77915-2

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