This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
Bridging Built Environment Attributes and Perceived City Images: Exploring Dual Influences on Resident Satisfaction in Revitalizing Post-Industrial Neighborhoods
by
Xian Ji
Xian Ji 1,2,
Kai Li
Kai Li 1,*,
Chang Liu
Chang Liu 1 and
Furui Shang
Furui Shang 1
1
Jangho Architecture College, Northeastern University, Shenyang 110169, China
2
Liaoning Provincial Key Laboratory of Urban and Architectural Digital Technology, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7272; https://doi.org/10.3390/su16177272 (registering DOI)
Submission received: 23 June 2024
/
Revised: 14 August 2024
/
Accepted: 22 August 2024
/
Published: 23 August 2024
Abstract
The deterioration of physical spaces and changes in the social environment have led to significant challenges and low life satisfaction among residents in post-industrial neighborhoods. While resident satisfaction is closely linked to the built environment, physical attributes alone do not directly influence human feelings. The perception and processing of urban environments, or city images, play a critical mediating role. Previous studies have often explored the impact of either city image perception or physical space attributes on resident satisfaction separately, lacking an integrated approach. This study addresses this gap by examining the interplay between subjective perceptions and objective environmental attributes. Unlike previous studies that use the whole neighborhood area for human perception, our study uses the actual activity ranges of residents to represent the living environment. Utilizing data from Shenyang, China, and employing image semantic segmentation technology and multiple regression methods, we analyze how subjective city image factors influence resident satisfaction and how objective urban spatial indicators affect these perceptions. We integrate these aspects to rank objective spatial indicators by their impact on resident satisfaction. The results demonstrate that all city image factors significantly and positively influence resident satisfaction, with the overall impression of the area’s appearance having the greatest impact (β = 0.362). Certain objective spatial indicators also significantly affect subjective city image perceptions. For instance, traffic lights are negatively correlated with the perception of greenery (β = −0.079), while grass is positively correlated (β = 0.626). Key factors affecting resident satisfaction include pedestrian flow, traffic flow, open spaces, sky openness, and green space levels. This study provides essential insights for urban planners and policymakers, helping prioritize sustainable updates in post-industrial neighborhoods. By guiding targeted revitalization strategies, this research contributes to improving the quality of life and advancing sustainable urban development.
Share and Cite
MDPI and ACS Style
Ji, X.; Li, K.; Liu, C.; Shang, F.
Bridging Built Environment Attributes and Perceived City Images: Exploring Dual Influences on Resident Satisfaction in Revitalizing Post-Industrial Neighborhoods. Sustainability 2024, 16, 7272.
https://doi.org/10.3390/su16177272
AMA Style
Ji X, Li K, Liu C, Shang F.
Bridging Built Environment Attributes and Perceived City Images: Exploring Dual Influences on Resident Satisfaction in Revitalizing Post-Industrial Neighborhoods. Sustainability. 2024; 16(17):7272.
https://doi.org/10.3390/su16177272
Chicago/Turabian Style
Ji, Xian, Kai Li, Chang Liu, and Furui Shang.
2024. "Bridging Built Environment Attributes and Perceived City Images: Exploring Dual Influences on Resident Satisfaction in Revitalizing Post-Industrial Neighborhoods" Sustainability 16, no. 17: 7272.
https://doi.org/10.3390/su16177272
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article Access Statistics
For more information on the journal statistics, click
here.
Multiple requests from the same IP address are counted as one view.