Does the built environment of settlements affect our sentiments? A multi-level and non-linear analysis of Xiamen, China, using social media data

Abstract

IntroductionHumans spend most of their time in settlements, and the built environment of settlements may affect the residents' sentiments. Research in this field is interdisciplinary, integrating urban planning and public health. However, it has been limited by the difficulty of quantifying subjective sentiments and the small sample size.MethodsThis study uses 147,613 Weibo text check-ins in Xiamen from 2017 to quantify residents' sentiments in 1,096 neighborhoods in the city. A multilevel regression model and gradient boosting decision tree (GBDT) model are used to investigate the multilevel and nonlinear effects of the built environment of neighborhoods and subdistricts on residents' sentiments.ResultsThe results show the following: (1) The multilevel regression model indicates that at the neighborhood level, a high land value, low plot ratio, low population density, and neighborhoods close to water are more likely to improve the residents' sentiments. At the subdistrict level, more green space and commercial land, less industry, higher building density and road density, and a smaller migrant population are more likely to promote positive sentiments. Approximately 19% of the total variance in the sentiments occurred among subdistricts. (2) The proportion of green space and commercial land, and the density of buildings and roads are linearly correlated with residents' sentiments. The land value is a basic need and exhibits a nonlinear correlation with sentiments. The plot ratio, population density, and the proportions of industrial land and the migrant population are advanced needs and are nonlinearly correlated with sentiments.DiscussionThe quantitative analysis of sentiments enables setting a threshold of the influence of the built environment on residents' sentiments in neighborhoods and surrounding areas. Our results provide data support for urban planning and implementing targeted measures to improve the living environment of residents.

The Relationship Between Living Density and Mental Health: Moderating Role of Internet Use

“…Previous studies have suggested a negative correlation between living density and residents' mental health, and the perceived living environment plays a role in residents' mental health [2][3][4]10,11,15]. Although the correlation between objective living space and mental health was not significant in this study, there was a negative correlation between subjective living space and mental health.…”

“…Research has shown that people have basic requirements for physical space [2] and that living in crowded and unfavorable urban environments can significantly impact people's mental health [1,3]. Those who reside in areas with high population density and poor living conditions tend to experience more negative sentiments compared to those who live in less crowded and more favorable environments [4]. This phenomenon is not limited to urban areas alone, as rural residents can be affected as well.…”

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“…Spatiotemporal analysis of public emotions by using LBSN data has become a popular research topic in urban research and has yielded notable achievements across various aspects. Examples include the relationship between the built environment and public emotions ( Fan et al, 2023 ), the effect of public green space on public emotions ( Chen S. et al, 2022 ), and the opinions of people on public transportation through Twitter data ( Das and Zubaidi, 2023 ). In addition, previous studies used traditional global regression models, such as ordinary least squares (OLS) to explore the relationship between the built environment and public emotions.…”

Examining the impact of neighborhood environment factors on residents’ emotions during COVID-19 lockdown and reopening: A Wuhan study on mediation and moderation

“…However, these methods rely too much on surveys, interviews, and questionnaires and have disadvantages such as being time-consuming, having a limited sample size, being too subjective, and being difficult to replicate (Rahnema et al, 2019). Questionnaires and interviews may not accurately reflect a subject's psychological state due to potential biases introduced by the questions and questionnaire design (Fan et al, 2022).…”