• Adrijana CAR - Josef STROBL - Robert VOGLER - Gerald GRIESEBNER (Eds.)

GI_Forum 2023, Volume 11, Issue 1

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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at

“GI_Forum” publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain. “GI_Forum” implements the policy of open access publication (CC-BY-ND-License) after a double-blind peer review process through a highly international team of established scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions.

The 2023-1 Issue comprises work of researchers from different disciplines, most of whom presented their work at the GI_Salzburg 2023 conference (https://gi-salzburg.org/en/). The articles address diverse collections of spatiotemporal data such as multispectral LIDAR, SAR or biology-related datasets, and advanced concepts, methods and tools applied for their analysis like OBIA or Google Earth Engine. Applications range from natural resources management, natural phenomena and hazards to green spaces, urban place perception, mobility, and infrastructure. The impact of ChatGPT on teaching and learning GIScience based on an anecdotal approach contributes to the overall discourse in the scientific and educational community respectively.

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GI_Forum 2023, Volume 11, Issue 1

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-9443-9
Online Edition



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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2,
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: bestellung.verlag@oeaw.ac.at
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A Comparative Study of Geocoder Performance on Unstructured Tweet Locations

    Helen Ngonidzashe Serere, Umut Nefta Kanilmaz, Sruthi Ketineni, Bernd Resch

GI_Forum 2023, Volume 11, Issue 1, pp. 110-117, 2023/06/27

doi: 10.1553/giscience2023_01_s110

doi: 10.1553/giscience2023_01_s110


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doi:10.1553/giscience2023_01_s110



doi:10.1553/giscience2023_01_s110

Abstract

Geocoding is a process of converting human-readable addresses into latitude and longitude points. Whilst most geocoders tend to perform well on structured addresses, their performance drops significantly in the presence of unstructured addresses, such as locations written in informal language. In this paper, we make an extensive comparison of geocoder performance on unstructured location mentions within tweets. Using nine geocoders and a worldwide English-language Twitter dataset, we compare the geocoders’ recall, precision, consensus and bias values. As in previous similar studies, Google Maps showed the highest overall performance. However, with the exception of Google Maps, we found that geocoders which use open data have higher performance than those which do not. The open-data geocoders showed the least per-continent bias and the highest consensus with Google Maps. These results suggest the possibility of improving geocoder performance on unstructured locations by extending or enhancing the quality of openly available datasets.

Keywords: commercial geocoders, natural language, Twitter, open data, spaCy