Interpreting regenerated post-industrial lands as green spaces: Comparing public perceptions of post-industrial landscapes using human factor design framework

Abstract

Urban green spaces shaped by the post-industrial landscape possess distinctive characteristics and aesthetic features due to the exceptional properties of industrial remains. Previous studies have indicated that various types of urban green spaces without industrial remains, such as urban parks, waterfronts, and streets, have a positive impact on the public's physiological and psychological well-being. However, the extent and mechanisms of the public's response to post-industrial landscapes remain unclear. To investigate mechanisms of public perceptions of and preferences for post-industrial landscape types and elements, we conducted quantitative analysis by coupling multimodal data and comparative or difference analyses among urban green spaces. We conducted a randomized controlled experiment with 48 participants who viewed images of different combinations of post-industrial landscapes in a distraction-free environment. After a pause, Positive-Negative perception and Preference questionnaires were completed. Eye tracking data were recorded during the experiment. Changes in electrodermal activity and heart rate variability were used to measure physiological stress. This study employed Pearson's correlation model to examine the relationships between objective data and subjective data. The ANOVA test was utilized to investigate the variability of objective and subjective data across different groups. Additionally, a multiple-stepwise regression model was employed to analyze the specific influencing factors and their respective degrees of influence. We found that (1) artificial landscape elements in post-industrial landscapes were more appealing to participants and heightened their sense of stress; natural landscape elements can alleviate stress and promote restoration. (2) The percentage of flowering shrub and tree elements was significantly positively correlated with preference, while the percentage of ground-covering plant elements was significantly negatively correlated with preference. (3) Industrial construction was significantly positively correlated with the sense of historical value while industrial construction and pavement elements negatively correlated with preference. However, pavement elements significantly mitigated the sense of insecurity, poor accessibility, and pollution risk. (4) Industrial construction and pavement distinguished the post-industrial landscape from other green spaces without industrial remains while maximizing historical value and curiosity helped increase positive effects. Although impact mechanisms of natural elements in other green spaces without industrial remains showed homogeneous similarities, the scene composed of industrial mixed with natural elements may lead to different perceptions. Our findings indicate that multimodal data coupled with comparative analysis of multiple green space types can help to fully identify mechanisms by which landscape types and elements influence public perceptions and preferences. This understanding can help increase public satisfaction with post-industrial landscapes and enable researchers to determine the sustainability of post-industrial sites using interrogative design.