JOURNAL ARTICLE
User Perception-Based Evaluation for Conservation Renewal in Historic Districts: A Case Study of Dongguan Street, Dalian, China.
Published In: Journal of Resources & Ecology, 2026, v. 17, n. 2. P. 671 1 of 3
Database: The Belt and Road Initiative Reference Source 2 of 3
Authored By: Xiao, Guo; Yifei, Ren; Yifeng, Fan 3 of 3
Abstract
The conservation renewal of historic districts is currently not effective due to insufficient public participation. Therefore, this study takes Dongguan Street in Dalian as an example and constructs a spatial environmental evaluation model for the historical district based on user perception. The Analytic Hierarchy Analysis (AHP) was used to determine the indicator weights, and a satisfaction survey was combined with the quadrant chart model method to analyze the discrepancies between the expert weights and user satisfaction. The results show that the overall satisfaction of the district reached a score of 80.43, but the key indicators such as participation and cultural experience showed characteristics of high weighting and low satisfaction. Accordingly, strategies such as the implantation of experiential businesses and cultural revitalization were proposed, and their effectiveness was verified through return visits. This study aims to provide multi-dimensional decision-making support for the conservation and renewal of historic districts, and to enhance the universality and accuracy of historic district design. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Resources & Ecology. 2026/03, Vol. 17, Issue 2, p671
- Document Type:Article
- Subject Area:Political Science
- Publication Date:2026
- ISSN:1674-764X
- DOI:10.5814/j.issn.1674-764x.2026.02.026
- Accession Number:193262650
- Copyright Statement:Copyright of Journal of Resources & Ecology is the property of Journal of Resources & Ecology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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