The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of Tourists

The analysis of the spatial location of tourists is essential for effective tourism management. This study explores the potential effects of large language models (LLMs) on urban travel planning. Despite growing academic interest in LLMs, empirical research on their specific impact on urban tourist...

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Autor: Paül i Agustí, Daniel
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Recursos:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/468312
Acesso em linha:https://doi.org/10.3390/urbansci9070268
https://hdl.handle.net/10459.1/468312
Access Level:acceso abierto
Palavra-chave:Spatial analyst
Image gaps
Tourist destinations
ChatGPT
Instagram
Barcelona
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spelling The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of TouristsPaül i Agustí, DanielSpatial analystImage gapsTourist destinationsChatGPTInstagramBarcelonaThe analysis of the spatial location of tourists is essential for effective tourism management. This study explores the potential effects of large language models (LLMs) on urban travel planning. Despite growing academic interest in LLMs, empirical research on their specific impact on urban tourist locations remains limited, even though these models may significantly affect tourist behavior and spatial dynamics. This article compares the location of heritage sites in the city of Barcelona that are traditionally visited by tourists (as identified through Instagram) with those recommended by ChatGPT. The results show that ChatGPT tends to recommend a much smaller and more spatially concentrated number of tourist attractions than those shared on Instagram. The findings indicate that ChatGPT reinforces mainstream representations of cities by prioritizing well-known landmarks, potentially overlooking emerging or local attractions. This simplification can lead to tourist overcrowding and the marginalization of less-visited areas. Likewise, it may entail new needs for the management of urban spaces. Urban planners and tourism managers may need to intervene to redistribute tourist flows in a context where various models of tourist behavior will coexist.This research was funded by the Departament de Recerca i Universitats de la Generalitat de Catalunya, grant number 2021 SGR 01369 and the Agencia Española de Investigación, grant number PID2021-123063NB-I00.MDPI2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/urbansci9070268https://hdl.handle.net/10459.1/468312reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123063NB-I00Reproducció del document publicat a https://doi.org/10.3390/urbansci9070268Urban Science, 2025, vol. 9, num. 7, a268cc-by (c) Daniel Paül i Agustí, 2025Attribution 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/4683122026-06-24T12:42:17Z
dc.title.none.fl_str_mv The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of Tourists
title The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of Tourists
spellingShingle The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of Tourists
Paül i Agustí, Daniel
Spatial analyst
Image gaps
Tourist destinations
ChatGPT
Instagram
Barcelona
title_short The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of Tourists
title_full The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of Tourists
title_fullStr The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of Tourists
title_full_unstemmed The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of Tourists
title_sort The Concentrated City: Effects of AI-Generated Travel Advice on the Spatial Distribution of Tourists
dc.creator.none.fl_str_mv Paül i Agustí, Daniel
author Paül i Agustí, Daniel
author_facet Paül i Agustí, Daniel
author_role author
dc.subject.none.fl_str_mv Spatial analyst
Image gaps
Tourist destinations
ChatGPT
Instagram
Barcelona
topic Spatial analyst
Image gaps
Tourist destinations
ChatGPT
Instagram
Barcelona
description The analysis of the spatial location of tourists is essential for effective tourism management. This study explores the potential effects of large language models (LLMs) on urban travel planning. Despite growing academic interest in LLMs, empirical research on their specific impact on urban tourist locations remains limited, even though these models may significantly affect tourist behavior and spatial dynamics. This article compares the location of heritage sites in the city of Barcelona that are traditionally visited by tourists (as identified through Instagram) with those recommended by ChatGPT. The results show that ChatGPT tends to recommend a much smaller and more spatially concentrated number of tourist attractions than those shared on Instagram. The findings indicate that ChatGPT reinforces mainstream representations of cities by prioritizing well-known landmarks, potentially overlooking emerging or local attractions. This simplification can lead to tourist overcrowding and the marginalization of less-visited areas. Likewise, it may entail new needs for the management of urban spaces. Urban planners and tourism managers may need to intervene to redistribute tourist flows in a context where various models of tourist behavior will coexist.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/urbansci9070268
https://hdl.handle.net/10459.1/468312
url https://doi.org/10.3390/urbansci9070268
https://hdl.handle.net/10459.1/468312
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123063NB-I00
Reproducció del document publicat a https://doi.org/10.3390/urbansci9070268
Urban Science, 2025, vol. 9, num. 7, a268
dc.rights.none.fl_str_mv cc-by (c) Daniel Paül i Agustí, 2025
Attribution 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Daniel Paül i Agustí, 2025
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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