Geographic name resolution service: A tool for the standardization and indexing of world political division names, with applications to species distribution modeling.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2022
Historique:
received: 21 04 2022
accepted: 20 10 2022
entrez: 14 11 2022
pubmed: 15 11 2022
medline: 18 11 2022
Statut: epublish

Résumé

Massive biological databases of species occurrences, or georeferenced locations where a species has been observed, are essential inputs for modeling present and future species distributions. Location accuracy is often assessed by determining whether the observation geocoordinates fall within the boundaries of the declared political divisions. This otherwise simple validation is complicated by the difficulty of matching political division names to the correct geospatial object. Spelling errors, abbreviations, alternative codes, and synonyms in multiple languages present daunting name disambiguation challenges. The inability to resolve political division names reduces usable data, and analysis of erroneous observations can lead to flawed results. Here, we present the Geographic Name Resolution Service (GNRS), an application for correcting, standardizing, and indexing world political division names. The GNRS resolves political division names against a reference database that combines names and codes from GeoNames with geospatial object identifiers from the Global Administrative Areas Database (GADM). In a trial resolution of political division names extracted from >270 million species occurrences, only 1.9%, representing just 6% of occurrences, matched exactly to GADM political divisions in their original form. The GNRS was able to resolve, completely or in part, 92% of the remaining 378,568 political division names, or 86% of the full biodiversity occurrence dataset. In assessing geocoordinate accuracy for >239 million species occurrences, resolution of political divisions by the GNRS enabled the detection of an order of magnitude more errors and an order of magnitude more error-free occurrences. By providing a novel solution to a significant data quality impediment, the GNRS liberates a tremendous amount of biodiversity data for quantitative biodiversity research. The GNRS runs as a web service and is accessible via an API, an R package, and a web-based graphical user interface. Its modular architecture is easily integrated into existing data validation workflows.

Identifiants

pubmed: 36374834
doi: 10.1371/journal.pone.0268162
pii: PONE-D-22-11796
pmc: PMC9662723
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0268162

Informations de copyright

Copyright: © 2022 Boyle et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

PLoS One. 2018 Mar 6;13(3):e0193085
pubmed: 29509789
Trends Ecol Evol. 2010 Dec;25(12):686-91
pubmed: 20961649
BMJ Glob Health. 2020 Jan 26;5(1):e002232
pubmed: 32133183
Nature. 2021 Sep;597(7877):516-521
pubmed: 34471291
PeerJ. 2018 Oct 4;6:e5644
pubmed: 30310740
Philos Trans A Math Phys Eng Sci. 2010 Aug 28;368(1925):3875-89
pubmed: 20643682
Philos Trans R Soc Lond B Biol Sci. 2018 Nov 19;374(1763):
pubmed: 30455218
Ann GIS. 2020;26(1):1-12
pubmed: 32547679
PLoS One. 2014 Sep 23;9(9):e107510
pubmed: 25247892
Ecol Lett. 2005 Sep;8(9):993-1009
pubmed: 34517687
Ecol Evol. 2017 Jun 12;7(14):5467-5475
pubmed: 28770082
BMC Bioinformatics. 2013 Jan 16;14:16
pubmed: 23324024
PLoS One. 2012;7(1):e29715
pubmed: 22238640
Ecol Lett. 2020 Feb;23(2):316-325
pubmed: 31800170
Nature. 2022 Jul;607(7919):555-562
pubmed: 35483403
Sci Adv. 2019 Nov 27;5(11):eaaz0414
pubmed: 31807712

Auteurs

Bradley L Boyle (BL)

Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, United States of America.

Brian S Maitner (BS)

Eversource Energy Center and Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, United States of America.

George G C Barbosa (GGC)

Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, United States of America.

Rohith K Sajja (RK)

Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, United States of America.

Xiao Feng (X)

Department of Geography, Florida State University, Tallahassee, FL, United States of America.

Cory Merow (C)

Eversource Energy Center and Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, United States of America.

Erica A Newman (EA)

School of Natural Resources & the Environment, University of Arizona, Tucson, AZ, United States of America.

Daniel S Park (DS)

Department of Biological Sciences, Purdue University, West Lafayette, IN, United States of America.
Purdue Center for Plant Biology, Purdue University, West Lafayette, IN, United States of America.

Patrick R Roehrdanz (PR)

The Moore Center for Science, Conservation International, Arlington, VA, United States of America.

Brian J Enquist (BJ)

Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, United States of America.
The Santa Fe Institute, USA, Santa Fe, NM, United States of America.

Articles similaires

Lakes Salinity Archaea Bacteria Microbiota
Rivers Turkey Biodiversity Environmental Monitoring Animals
Humans Recurrence Male Female Middle Aged

Insect diversity estimation in polarimetric lidar.

Dolores Bernenko, Meng Li, Hampus Månefjord et al.
1.00
Animals Biodiversity Insecta Algorithms Cluster Analysis

Classifications MeSH