Evidence map›Paper›PMID 41734232›Full record

ArticlePloS one2026

Mapping urban soundscape patterns using user-generated content: A place model approach to acoustic environment perception.

Haneul Lee, Youngchul Kim

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Haneul LeeKAIST Urban Design Lab, KAIST Smart City Research Center, Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.ORCID https://orcid.org/0000-0002-8836-8875
Youngchul KimKAIST Urban Design Lab, KAIST Smart City Research Center, Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.ORCID https://orcid.org/0000-0001-7945-028X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban acoustic environments significantly influence quality of life, prompting a paradigm shift from noise reduction to soundscape design that considers human perception. This study investigates urban soundscape perceptions using social media data to understand how acoustic environments reflect urban spatial characteristics. We collected Korean-language Twitter texts from 156 points of interest in Seoul's central and southeastern neighborhoods from October to November 2020. The collected texts were preprocessed through text cleaning and morphological analysis, then classified using an urban soundscape taxonomy based on Canter's place model that integrates physical setting, activity, and meaning dimensions. The results revealed that activity-related soundscapes (79.4%) dominated all target areas, and strong negative correlations between indoor, mechanical, and behavioral soundscapes suggest a spatial separation of acoustic zones by urban function. Especially, historical central districts (22.0%) showed a higher proportion of meaning-related soundscapes compared to modern commercial districts (13.4%). K-means clustering further identified three soundscape types: infrastructure-dominated, socially vibrant, and commercial indoor-focused zones. By combining social media analysis with spatial patterns, the study provides a scalable tool for urban planners to understand how citizens experience urban soundscapes, offering data-driven insights for contextualized urban design.

Indexed as

AcousticsAuditory PerceptionNoiseCitiesHumansSeoulSocial Media

Identifiers

PMID41734232
PMCPMC12931788

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.