Georeferencing non-gazetteered place names using biological specimen records

Fernando, Aneesha, Ranathunga, Surangika, Stock, Kristin, Prasanna, Raj and Jones, Christopher B. ORCID: https://orcid.org/0000-0001-6847-7575 2026. Georeferencing non-gazetteered place names using biological specimen records. Presented at: 17th Conference on Spatial Information Theory, York, UK, 22-25 September 2026. Published in: Timpf, Sabine, Filomena, Gabriele, Kapaj, Armand, Zhu, Rui, Giudice, Nicholas A. and Manley, Ed eds. Proceedings of COSIT 2026. Leibniz International Proceedings in Informatics , vol.393 Schloss Dagstuhl – Leibniz-Zentrum für Informatik, pp. 11:1-11:22. 10.4230/LIPIcs.COSIT.2026.11

PDF - Published Version Available under License Creative Commons Attribution. Download (1MB)

Abstract

Biological specimen records collected by natural history institutions constitute a rich source of temporal geographic knowledge, capturing biodiversity information about regional landscapes as they were recorded at different times. Using digitised data from the Allan Herbarium (New Zealand), this study identifies place names in these specimen locality descriptions that are absent from current gazetteers; we refer to these as non-gazetteer place names (NGPs). These place names are typically historical, vernacular, or colloquial and were used as landmarks to describe a specimen’s location at the time of collection. We then investigate the problem of georeferencing the NGPs using only the limited information available in the specimen records. To resolve this, we leverage repeated occurrences of the same place name across specimen records with different specimen locations and spatial relation terms, extracting and inverting these relations to derive constraints on NGP locations. This approach is instantiated within deterministic, probabilistic, and LLM-based methods, enabling a comparative analysis of their strengths and limitations for text-based spatial inference. On a pseudo-NGP benchmark, probabilistic inference achieves the highest accuracy (median error 1.43 km; A@1 km 36%), while the LLM yields competitive but less precise estimates (median error 1.80 km; A@1 km 31%), indicating that, despite advances in LLMs, traditional modelling remains advantageous when high spatial precision is required.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Publisher:	Schloss Dagstuhl – Leibniz-Zentrum für Informatik
ISBN:	9783959774383
Date of First Compliant Deposit:	5 October 2026
Last Modified:	05 Oct 2026 13:15
URI:	https://orca.cardiff.ac.uk/id/eprint/189997

Actions (repository staff only)

Edit Item