Evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024.
Museum architecture is essential for preserving cultural heritage. Understanding the spatio-temporal evolution of visitor preferences, image perceptions, and driving factors is vital for promoting cultural development. However, traditional methods such as questionnaires and interviews face challenge...
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| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327112 |
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| author | Yuandi Jiang Kalyna Pashkevych Shibo Bi |
| author_facet | Yuandi Jiang Kalyna Pashkevych Shibo Bi |
| author_sort | Yuandi Jiang |
| collection | DOAJ |
| description | Museum architecture is essential for preserving cultural heritage. Understanding the spatio-temporal evolution of visitor preferences, image perceptions, and driving factors is vital for promoting cultural development. However, traditional methods such as questionnaires and interviews face challenges in elucidating how exhibition layouts, environmental facilities, and service quality affect visitor experience and satisfaction. In this study, 30 museums in 6 categories were selected as samples, and over 64,000 public online reviews from Dianping and Ctrip were selected as data sets. Kernel density and standard deviational ellipse methods revealed the spatio-temporal evolution of museum space preferences (2016-2024). TF-IDF and LDA algorithms identified key image perception themes. Visitor satisfaction was then evaluated with SnowNLP sentiment analysis to examine the dynamic correlation between the perception themes and satisfaction. The findings showed: 1) Museum visitors were highly concentrated in eastern coastal regions, with spatial distribution evolving from single-core to multi-core clusters, gradually expanding into central areas (e.g., Henan, Hubei, Shaanxi). 2) Museum image perception has shifted from object-centered to more human-centered experiences, with significant differences across the various categories. 3) Over 75% of visitors reported positive experiences, with ethnography museums showing the highest satisfaction in 2024 (Pro = 0.922), whereas history museums consistently had the lowest. 4) Satisfaction drivers were dynamic, with 85.26% of perception themes significantly correlated with satisfaction (p < 0.01), with rich collections, distinctive features, immersive experiences, and diverse visitation forms identified as the primary contributors to positive visitor experiences. Based on the comprehensive perspective of typology and spatio-temporal dynamic evolution, this study not only provides empirical support for museum space optimization, but also provides new ideas and strategies for functional research and methodological insights of public spaces. |
| format | Article |
| id | doaj-art-3fdfb54f4e664b7dbbb1817fa13d916d |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3fdfb54f4e664b7dbbb1817fa13d916d2025-08-20T03:13:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032711210.1371/journal.pone.0327112Evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024.Yuandi JiangKalyna PashkevychShibo BiMuseum architecture is essential for preserving cultural heritage. Understanding the spatio-temporal evolution of visitor preferences, image perceptions, and driving factors is vital for promoting cultural development. However, traditional methods such as questionnaires and interviews face challenges in elucidating how exhibition layouts, environmental facilities, and service quality affect visitor experience and satisfaction. In this study, 30 museums in 6 categories were selected as samples, and over 64,000 public online reviews from Dianping and Ctrip were selected as data sets. Kernel density and standard deviational ellipse methods revealed the spatio-temporal evolution of museum space preferences (2016-2024). TF-IDF and LDA algorithms identified key image perception themes. Visitor satisfaction was then evaluated with SnowNLP sentiment analysis to examine the dynamic correlation between the perception themes and satisfaction. The findings showed: 1) Museum visitors were highly concentrated in eastern coastal regions, with spatial distribution evolving from single-core to multi-core clusters, gradually expanding into central areas (e.g., Henan, Hubei, Shaanxi). 2) Museum image perception has shifted from object-centered to more human-centered experiences, with significant differences across the various categories. 3) Over 75% of visitors reported positive experiences, with ethnography museums showing the highest satisfaction in 2024 (Pro = 0.922), whereas history museums consistently had the lowest. 4) Satisfaction drivers were dynamic, with 85.26% of perception themes significantly correlated with satisfaction (p < 0.01), with rich collections, distinctive features, immersive experiences, and diverse visitation forms identified as the primary contributors to positive visitor experiences. Based on the comprehensive perspective of typology and spatio-temporal dynamic evolution, this study not only provides empirical support for museum space optimization, but also provides new ideas and strategies for functional research and methodological insights of public spaces.https://doi.org/10.1371/journal.pone.0327112 |
| spellingShingle | Yuandi Jiang Kalyna Pashkevych Shibo Bi Evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024. PLoS ONE |
| title | Evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024. |
| title_full | Evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024. |
| title_fullStr | Evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024. |
| title_full_unstemmed | Evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024. |
| title_short | Evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024. |
| title_sort | evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024 |
| url | https://doi.org/10.1371/journal.pone.0327112 |
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