<b>Shaping Well-Being in Urban Environment: Interactions of Motivation, Engagement and Perception, a case study of Suzhou City, China (questionnaire data)</b>

This dataset was collected through a face-to-face questionnaire survey conducted across 16 representative urban sites in Suzhou, China, targeting both residents and visitors. It includes demographic information (e.g., gender, age), motivations for site visits, engagement (nature-related activities, cultural-related activities), environmental characteristics (natural, cultural, and other urban characteristics), and self-reported well-being across five dimensions: physical, emotional, cognitive, social, and spiritual. The survey also captures lengh of stay, and satisfaction with site experiences, along with open-ended feedback on improvements. The data provide insights into how individual motivations, perceptions of urban environments, and engagement collectively shape self-reported well-being outcomes.<br>This dataset includes associated R scripts (<code>3.1.R</code>, <code>correlation.R</code>, <code>open-feedback.R</code>, <code>SEM.R</code>) used for data cleaning and statistical analysis. These scripts are provided to ensure reproducibility of results.File: 3.1.R<b>Description:</b> We conducted correlation between satisfaction and self-reported well-being across three types of urban environments and got mean scores of physical, emotional, cognitive, social, and spiritual well-being across three types of urban environments, along with differences in mean scores and 95% confidence intervals.File: correlation.R<b>Description:</b> We conducted correlation analyses to examine how self-reported well-being relate to social demographic factors across environments.File: open-feedback.R<b>Description:</b> We analyzed participants’ open-ended responses regarding suggestions for improving environmental characteristics to better support well-being.File: SEM.R<b>Description:</b> To identify the specific drivers of well-being, we first employed piecewise structural equation modeling to examine the pathways linking motivations, environmental characteristics, engagement, and self-reported well-being outcomes across different urban environments.<br>