Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Atmosphere, Год журнала: 2024, Номер 15(6), С. 671 - 671

Опубликована: Май 31, 2024

With the ongoing advancement of globalization significantly impacting ecological environment, continuous rise in Land Surface Temperature (LST) is increasingly jeopardizing human production and living conditions. This study aims to investigate seasonal variations LST its driving factors using mathematical models. Taking Wuhan Urban Agglomeration (WHUA) as a case study, it explores characteristics employs Principal Component Analysis (PCA) categorize factors. Additionally, compares traditional models with machine-learning select optimal model for this investigation. The main conclusions are follows. (1) WHUA’s exhibits significant differences among seasons demonstrates distinct spatial-clustering different seasons. (2) Compared geographic spatial models, Extreme Gradient Boosting (XGBoost) shows better explanatory power investigating effects LST. (3) Human Activity (HA) dominates influence throughout year positive correlation LST; Physical Geography (PG) negative Climate Weather (CW) show similar variation PG, peaking transition; Landscape Pattern (LP) weak LST, winter while being relatively inconspicuous summer transition. Finally, through comparative analysis multiple constructs framework exploring features aiming provide references guidance development WHUA regions.

Язык: Английский

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

ISPRS International Journal of Geo-Information, Год журнала: 2025, Номер 14(3), С. 131 - 131

Опубликована: Март 20, 2025

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

Язык: Английский

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Liberation from location ties: A descriptive systematic review of shifts in location perception during and after the COVID-19 pandemic

Transportation Research Interdisciplinary Perspectives, Год журнала: 2025, Номер 31, С. 101395 - 101395

Опубликована: Март 22, 2025

Язык: Английский

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City laboratory: Embracing new data, new elements, and new pathways to invent new cities

Environment and Planning B Urban Analytics and City Science, Год журнала: 2024, Номер 51(5), С. 1068 - 1072

Опубликована: Май 19, 2024

Язык: Английский

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Assessing the space-use efficiency of French cities by coupling city volumes with mobile data traffic

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106292 - 106292

Опубликована: Март 1, 2025

Язык: Английский

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Towards typology-based management of urban commuting carbon emission characteristics: Identification of commuting behavior and carbon emission accounting based on individual spatiotemporal big data

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106327 - 106327

Опубликована: Март 1, 2025

Язык: Английский

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Changes in visitor behaviour across COVID-19 pandemic: Unveiling urban visitation dynamics and non-linear relationships with the built environment using mobile big data

Habitat International, Год журнала: 2024, Номер 154, С. 103216 - 103216

Опубликована: Ноя. 5, 2024

Язык: Английский

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Exploring Temporal and Spatial Patterns and Nonlinear Driving Mechanism of Park Perceptions: A Multi-Source Big Data Study

Sustainable Cities and Society, Год журнала: 2024, Номер unknown, С. 106083 - 106083

Опубликована: Дек. 1, 2024

Язык: Английский

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