Author
Listed:
- Xuefei Wang
(College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China)
- Baoyao Zhu
(College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China)
- Zhiqi Chen
(College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China)
- Dawei Ma
(School of Management, Guangzhou University, Guangzhou 510091, China)
- Chuanhao Sun
(College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China)
- Mo Wang
(College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Architectural Design and Research Institute, Guangzhou University, Guangzhou 510091, China)
- Xing Jiang
(College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China)
Abstract
As economic growth and societal shifts reshape urban environments, cultural and creative industrial parks are emerging as vital contributors to sustainable urban development. The design of these landscapes plays a pivotal role in enhancing user satisfaction, increasing spatial attractiveness, and promoting eco-friendly urban practices. This study examines visitor landscape perception preferences in the Textile and Garment Cultural and Creative Industrial Park, located in Haizhu District, Guangzhou, through a novel methodology combining user-generated content (UGC), deep learning models, outdoor electrodermal activity (EDA) measurements, and questionnaire surveys. The UGC-based landscape recognition model achieved an accuracy of 86.8% and was validated against user preferences captured through questionnaires. Results demonstrate that visitors prefer areas featuring cultural landmarks and natural elements, while spaces dominated by human activity and transportation infrastructure are less favored. Key landscape elements, such as signage, thematic sculptures, brand logos, and trees, were identified as highly preferred features within the park. While EDA experiments revealed significant variations in physiological responses across different spatial settings, no strong correlation was observed between EDA indicators and subjective questionnaire scores. This integrative approach enables a comprehensive, objective assessment of landscape perception, providing a data-driven, user-centered framework for improving landscape design in cultural and creative industrial parks.
Suggested Citation
Xuefei Wang & Baoyao Zhu & Zhiqi Chen & Dawei Ma & Chuanhao Sun & Mo Wang & Xing Jiang, 2024.
"Landscape Perception in Cultural and Creative Industrial Parks: Integrating User-Generated Content (UGC) and Electrodermal Activity Insights,"
Sustainability, MDPI, vol. 16(21), pages 1-13, October.
Handle:
RePEc:gam:jsusta:v:16:y:2024:i:21:p:9228-:d:1505615
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9228-:d:1505615. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.