{"title":"Assessment of visual quality and social perception of cultural landscapes: application to Anyi traditional villages, China","authors":"Ning Kang, Chunqing Liu","doi":"10.1186/s40494-024-01326-x","DOIUrl":null,"url":null,"abstract":"<p>The assessment of landscape visual quality (LVQ) holds significant importance in the preservation and advancement of traditional villages. One challenge in measuring human perception lies in establishing a connection between public preferences and landscape characteristics. This study conducted an analysis of social media data from Anyi traditional villages in China to address this issue and identified eight human perceptions: naturalness, ancientness, colorfulness, variety, uniqueness, ingenuity, vividness, and pleasantness. A total of thirty characteristic indicators with potential explanations for LVQ were determined by research group through field investigations. A questionnaire survey was developed to assess human’s preferences using 82 traditional village photos, and scores for the eight perceptions were obtained. The logistic regression was employed to establish distinct perception models, with perceptions serving as the dependent variables and characteristic indicators as the independent variables. Nomograms were subsequently utilized to visualize regression results and display the correlation between these two factors. The findings suggest that nomograms facilitate intuitive determination of the weights assigned to characteristic indicators in perceptual models, as well as their influence on LVQ. This work provides a reference for decision-making related to the adaptive protection and development of traditional villages, thereby helping to enhance the competitiveness of tourist destinations.</p>","PeriodicalId":13109,"journal":{"name":"Heritage Science","volume":"61 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Heritage Science","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1186/s40494-024-01326-x","RegionNum":1,"RegionCategory":"艺术学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The assessment of landscape visual quality (LVQ) holds significant importance in the preservation and advancement of traditional villages. One challenge in measuring human perception lies in establishing a connection between public preferences and landscape characteristics. This study conducted an analysis of social media data from Anyi traditional villages in China to address this issue and identified eight human perceptions: naturalness, ancientness, colorfulness, variety, uniqueness, ingenuity, vividness, and pleasantness. A total of thirty characteristic indicators with potential explanations for LVQ were determined by research group through field investigations. A questionnaire survey was developed to assess human’s preferences using 82 traditional village photos, and scores for the eight perceptions were obtained. The logistic regression was employed to establish distinct perception models, with perceptions serving as the dependent variables and characteristic indicators as the independent variables. Nomograms were subsequently utilized to visualize regression results and display the correlation between these two factors. The findings suggest that nomograms facilitate intuitive determination of the weights assigned to characteristic indicators in perceptual models, as well as their influence on LVQ. This work provides a reference for decision-making related to the adaptive protection and development of traditional villages, thereby helping to enhance the competitiveness of tourist destinations.
期刊介绍:
Heritage Science is an open access journal publishing original peer-reviewed research covering:
Understanding of the manufacturing processes, provenances, and environmental contexts of material types, objects, and buildings, of cultural significance including their historical significance.
Understanding and prediction of physico-chemical and biological degradation processes of cultural artefacts, including climate change, and predictive heritage studies.
Development and application of analytical and imaging methods or equipments for non-invasive, non-destructive or portable analysis of artwork and objects of cultural significance to identify component materials, degradation products and deterioration markers.
Development and application of invasive and destructive methods for understanding the provenance of objects of cultural significance.
Development and critical assessment of treatment materials and methods for artwork and objects of cultural significance.
Development and application of statistical methods and algorithms for data analysis to further understanding of culturally significant objects.
Publication of reference and corpus datasets as supplementary information to the statistical and analytical studies above.
Description of novel technologies that can assist in the understanding of cultural heritage.