Associations Between Environmental Factors and Perceived Density of Residents in High-Density Residential Built Environment in Mountainous Cities—A Case Study of Chongqing Central Urban Area, China

Lingqian Tan, Peiyao Hao () and Ningjing Liu
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Lingqian Tan: College of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Peiyao Hao: College of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Ningjing Liu: Green Space Institute of Landscape Architecture and Landscape Research Branch of China Academy of Urban Plannings Design, Beijing 100044, China

Land, 2025, vol. 14, issue 9, 1-30

Abstract: In high-density built environments, perceived density (PD)—shaped by physical, socio-cultural, and perceptual factors—often induces sensations of crowding, stress, and spatial oppression. Although green spaces are recognised for their stress-reducing effects, the influence of built-environment characteristics on public sentiment under stringent mobility restrictions remains inadequately explored. This study takes Chongqing, a representative mountainous metropolis in China, as a case to examine how natural and built environmental elements modulate emotional valence across varying PD levels. Using housing data ( n = 4865) and geotagged Weibo posts ( n = 120,319) collected during the 2022 lockdown, we constructed a PD-sensitive sentiment dictionary and applied Python’s Jieba package and natural language processing (NLP) techniques to analyse emotional scores related to PD. Spatial and bivariate autocorrelation analyses revealed clustered patterns of sentiment distribution and their association with physical density. Using entropy weighting, building density and floor area ratio were integrated to classify residential built environments (RBEs) into five tiers based on natural breaks. Key factors influencing positive sentiment across PD groups were identified through Pearson correlation heatmaps and OLS regression. Three main findings emerged: (1) Although higher-PD areas yielded a greater volume of positive sentiment expressions, they exhibited lower diversity and intensity compared to low-PD areas, suggesting inferior emotional quality; (2) Environmental and socio-cultural factors showed limited effects on sentiment in low-PD areas, whereas medium- and high-PD areas benefited from a significantly enhanced cumulative effect through the integration of socio-cultural amenities and transportation facilities—however, this positive correlation reversed at the highest level (RBE 5); (3) The model explained 20.3% of the variance in positive sentiment, with spatial autocorrelation effectively controlled. These findings offer nuanced insights into the nonlinear mechanisms linking urban form and emotional well-being in high-density mountainous settings, providing theoretical and practical guidance for emotion-sensitive urban planning.

Keywords: perceived density; social media data (SMD); residential built environment; natural language processing; spatial grid-based clustering; hierarchical modelling; mountainous city (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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