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Georeferencing Non-Gazetteered Place Names Using Biological Specimen Records

Authors Aneesha Fernando , Surangika Ranathunga , Kristin Stock , Raj Prasanna , Christopher B. Jones

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Document Identifiers

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • Computing methodologies → Natural language processing
  • Mathematics of computing → Probabilistic inference problems
Keywords
  • Non-gazetteered Place Names
  • Gazetteers
  • Toponyms
  • Georeferencing
  • Spatial Relation Modelling
  • Large Language Models

Metrics

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.

Cite As Get BibTex

Aneesha Fernando, Surangika Ranathunga, Kristin Stock, Raj Prasanna, and Christopher B. Jones. Georeferencing Non-Gazetteered Place Names Using Biological Specimen Records. In 17th International Conference on Spatial Information Theory (COSIT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 393, pp. 11:1-11:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.COSIT.2026.11

Author Details

Aneesha Fernando
  • School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
Surangika Ranathunga
  • School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
Kristin Stock
  • School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
Raj Prasanna
  • Joint Centre for Disaster Research, Massey University, Wellington, New Zealand
Christopher B. Jones
  • School of Computer Science and Informatics, Cardiff University, UK

Funding

This work was supported by the Ministry of Business Innovation and Employment Smart Ideas Fund (grant number MAUX2104), New Zealand.

Supplementary Materials

References

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