Unraveling Spatial Nonstationary and Nonlinear Dynamics in Life Satisfaction: Integrating Geospatial Analysis of Community Built Environment and Resident Perception via MGWR, GBDT, and XGBoost

• Published in: ISPRS international journal of geo-information Vol. 14; no. 3; p. 131
• Main Authors: Yang, Di, Lin, Qiujie, Li, Haoran, Chen, Jinliu, Ni, Hong, Li, Pengcheng, Hu, Ying, Wang, Haoqi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Built environment; Decision trees; Design; Educational resources; Environmental conditions; GBDT; Geospatial data; Green infrastructure; Heterogeneity; Housing conditions; Infrastructure; Integrated approach; life satisfaction; Machine learning; Mental health; MGWR; Nonlinear dynamics; nonlinear effects; Nonlinear systems; Nonlinearity; Patchiness; Perception; Perceptions; Regeneration (biological); Relative deprivation; residential perception; Social media; Spatial analysis; Spatial data; Spatial heterogeneity; spatial nonstationarity; Spatial planning; Sustainable development; Sustainable urban development; Thresholds; Urban development; Urban environments; Urban renewal; Urbanization; Well being; China
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi14030131

Rapid urbanization has accelerated the transformation of community dynamics, highlighting the critical need to understand the interplay between subjective perceptions and objective built environments in shaping life satisfaction for sustainable urban development. Existing studies predominantly focus on linear relationships between isolated factors, neglecting spatial heterogeneity and nonlinear dynamics, which limits the ability to address localized urban challenges. This study addresses these gaps by utilizing multi-scale geographically weighted regression (MGWR) to assess the spatial nonstationarity of subject perceptions and built environment factors while employing gradient-boosting decision trees (GBDT) to capture their nonlinear relationships and incorporating eXtreme Gradient Boosting (XGBoost) to improve predictive accuracy. Using geospatial data (POIs, social media data) and survey responses in Suzhou, China, the findings reveal that (1) proximity to business facilities (β = 0.41) and educational resources (β = 0.32) strongly correlate with satisfaction, while landscape quality shows contradictory effects between central (β = 0.12) and peripheral zones (β = −0.09). (2) XGBoost further quantifies predictive disparities: subjective factors like property service satisfaction (R2 = 0.64, MAPE = 3.72) outperform objective metrics (e.g., dining facilities, R2 = 0.36), yet objective housing prices demonstrate greater stability (MAPE = 3.11 vs. subjective MAPE = 6.89). (3) Nonlinear thresholds are identified for household income and green space coverage (>15%, saturation effects). These findings expose critical mismatches—residents prioritize localized services over citywide economic metrics, while objective amenities like healthcare accessibility (threshold = 1 km) require spatial recalibration. By bridging spatial nonstationarity (MGWR) and nonlinearity (XGBoost), this study advances a dual-path framework for adaptive urban governance, the community-level prioritization of high-impact subjective factors (e.g., service quality), and data-driven spatial planning informed by nonlinear thresholds (e.g., facility density). The results offer actionable pathways to align smart urban development with socio-spatial equity, emphasizing the need for hyperlocal, perception-sensitive regeneration strategies.