Landscape evaluation and optimization of urban historic waterfront spaces

Historic waterfront spaces integrate cultural and hydrological elements, shaping urban memory and local identity. Existing studies often focus on single sites with fragmented, non-quantitative evaluations. This study develops a systematic framework to assess landscape quality and applies it to three historic waterfront sites in Fuzhou: Zhuzi Lane Historical District, Nangongyuan Historic and Cultural Preservation Zone, and Shangxiahang Historical District. Indicators were identified via literature review, expert consultation, and the Semantic Differential (SD) method. Questionnaire data were analyzed using factor analysis, followed by One-way analysis of variance (one-way ANOVA) and Revised Importance – Performance Analysis (Revised IPA). The findings reveal that: (a) the overall landscape quality is primarily influenced by artificial landscape and social and cultural factors, with mean differences (I – J) of 0.817 and 0.539 (p < 0.001); (b) significant differences were observed among the three historic waterfront spaces, with comprehensive scores ranked as follows: Shangxiahang (0.093) >Nangongyuan (0.065) >Zhuzi Lane (−0.157); (c) the Revised IPA identifies eight key landscape elements that require priority optimization. Building on these results, targeted optimization strategies are proposed. They include improvements to recreational facilities, enrichment of folk and cultural activities, and optimization of traffic accessibility, thereby providing transferable guidance for historic waterfront management.

Authors

Institutions

Publication Details

Journal
Journal of Asian Architecture and Building Engineering
Published
2026-04-02
DOI
https://doi.org/10.1080/13467581.2026.2652693
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Landscape evaluation and optimization of urban historic waterfront spaces

Honggang Chen, Junhan Liu, Xi Chen, Yang Zhao
Journal of Asian Architecture and Building Engineering
Flood Risk Assessment and Management
article

Landscape evaluation and optimization of urban historic waterfront spaces

Honggang Chen, Junhan Liu, Xi Chen, Yang Zhao
article en

Abstract

Historic waterfront spaces integrate cultural and hydrological elements, shaping urban memory and local identity. Existing studies often focus on single sites with fragmented, non-quantitative evaluations. This study develops a systematic framework to assess landscape quality and applies it to three historic waterfront sites in Fuzhou: Zhuzi Lane Historical District, Nangongyuan Historic and Cultural Preservation Zone, and Shangxiahang Historical District. Indicators were identified via literature review, expert consultation, and the Semantic Differential (SD) method. Questionnaire data were analyzed using factor analysis, followed by One-way analysis of variance (one-way ANOVA) and Revised Importance – Performance Analysis (Revised IPA). The findings reveal that: (a) the overall landscape quality is primarily influenced by artificial landscape and social and cultural factors, with mean differences (I – J) of 0.817 and 0.539 (p < 0.001); (b) significant differences were observed among the three historic waterfront spaces, with comprehensive scores ranked as follows: Shangxiahang (0.093) >Nangongyuan (0.065) >Zhuzi Lane (−0.157); (c) the Revised IPA identifies eight key landscape elements that require priority optimization. Building on these results, targeted optimization strategies are proposed. They include improvements to recreational facilities, enrichment of folk and cultural activities, and optimization of traffic accessibility, thereby providing transferable guidance for historic waterfront management.

Journal of Asian Architecture and Building Engineering
Huaqiao University (CN), Mahasarakham University (TH), Khon Kaen University (TH), Binzhou Technician College (CN)
Sustainable cities and communities
Openalex Percentile: Top 25%
Flood Risk Assessment and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.