Urban Built Environment as a Predictor for Coronary Heart Disease—A Cross-Sectional Study Based on Machine Learning DOI Creative Commons
Dan Jiang, Fei Guo, Ziteng Zhang

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(12), P. 4024 - 4024

Published: Dec. 18, 2024

The relationship between coronary heart disease (CHD) and complex urban built environments remains a subject of considerable uncertainty. development predictive models via machine learning to explore the underlying mechanisms this association, as well formulation intervention policies planning strategies, has emerged pivotal area research. A cross-sectional dataset hospital admissions for CHD over course year from in Dalian City, China, was assembled matched with multi-source environment data residential addresses. This study evaluates five models, including decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), multi-layer perceptron (MLP), support vector (SVM), compares them multiple linear regression models. results show that DT, RF, XGBoost exhibit superior capabilities, all R2 values exceeding 0.70. DT model performed best, an value 0.818, best performance based on metrics such MAE MSE. Additionally, using explainable AI techniques, reveals contribution different factors identifies significant influencing cold regions, ranked age, Digital Elevation Model (DEM), house price (HP), sky view factor (SVF), interaction factors. Stratified analyses by age gender variations groups: those under 60 years old, Road Density is most influential factor; 61–70 group, top 71–80 81 building height leading males, GDP females, factor. explores feasibility predicting risk regions provides comprehensive methodology workflow cardiovascular refined neighborhood-level factors, offering scientific construction sustainable healthy cities.

Language: Английский

Thermal hazards in urban spaces: A review of climate-resilient planning and design to reduce the heat stress DOI
Aman Gupta, Bhaskar De,

Sutapa Das

et al.

Urban Climate, Journal Year: 2025, Volume and Issue: 59, P. 102296 - 102296

Published: Jan. 25, 2025

Language: Английский

Citations

5
Assessing the Winter Indoor Environment with Different Comfort Metrics in Self-Built Houses of Hot-Humid Areas: Does Undercooling Matter for the Elderly? DOI

Jialiang Guo,

Dawei Xia, Lei Zhang

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 263, P. 111871 - 111871

Published: July 25, 2024

Language: Английский

Citations

9
Analysis of parameters for spray-local exhaust ventilation (SLEV) to minimize high-temperature smoke pollutants and reduce energy consumption DOI

Shengnan Guo,

Yanqiu Huang, Yi Wang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 107, P. 105464 - 105464

Published: April 21, 2024

Language: Английский

Citations

7
Framework of street grid-based urban heat vulnerability assessment: Integrating entropy weight method and BPNN model DOI
Fei Guo,

Ruwei Zheng,

Jun Zhao

et al.

Urban Climate, Journal Year: 2024, Volume and Issue: 56, P. 102067 - 102067

Published: July 1, 2024

Language: Английский

Citations

7
Urban heat health risk inequality and its drivers based on Local Climate Zones: A case study of Qingdao, China DOI
Fei Guo,

Gao-Ming Fan,

Jun Zhao

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112827 - 112827

Published: March 1, 2025

Language: Английский

Citations

1
Assessing the potential for green roof retrofitting: A systematic review of methods, indicators and data sources DOI
Jing Dong, Chunli Li, Ruonan Guo

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106261 - 106261

Published: Feb. 1, 2025

Language: Английский

Citations

0
Gender Dynamics and the Role of Women in Refugee Communities in Pakistan: A Case Study of Afghan Refugee Camps DOI Creative Commons

A. H. Ismail,

Weihong Wang, Muhammad Arif

et al.

Published: April 1, 2025

Language: Английский

Citations

0
Unlocking Urban Green Spaces: Retrofitting Potential Green Roofs to Enhance Bird Connectivity and Comprehensive Ecological Benefits in High-Density Areas DOI
Qinghua Xu, Xidong Ma, Zhifan Ding

et al.

Urban forestry & urban greening, Journal Year: 2025, Volume and Issue: unknown, P. 128817 - 128817

Published: April 1, 2025

Language: Английский

Citations

0
A Systematic Review of Methodological Advances in Urban Heatwave Risk Assessment: Integrating Multi-Source Data and Hybrid Weighting Methods DOI Open Access

Chang Xu,

Ruihan Wei,

Hui Tong

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(8), P. 3747 - 3747

Published: April 21, 2025

As climate change intensifies, urban populations face growing threats from frequent and severe heatwaves, underscoring the urgent need for advanced risk assessment frameworks to inform adaptation strategies. This systematic review synthesizes methodological innovations in heatwave (2007–2024), analyzing 259 studies through bibliometric analysis (CiteSpace 6.4.R1) multi-criteria evaluation. We propose hazard–exposure–vulnerability–adaptability (HEVA) framework, an extension of Crichton’s triangle that integrates dynamic adaptability metrics supports high-resolution spatial assessment. Our reveals three key gaps: (1) Inconsistent indicator selection across studies; (2) limited microclimatic variations; (3) sparse integration IoT- or satellite-based monitoring. The study offers practical solutions enhancing accuracy, including refined weighting methodologies techniques. conclude by proposing a research agenda prioritizes interdisciplinary approaches—bridging planning, science, public health—while advocating policy tools address inequities heat exposure. These insights advance development more precise, actionable systems support climate-resilient development.

Language: Английский

Citations

0
How does high temperature weather affect tourists’ nature landscape perception and emotions? A machine learning analysis of Wuyishan City, China DOI Creative Commons

Cuicui Ye,

Zhengyan Chen,

Zheng Ding

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0323566 - e0323566

Published: May 15, 2025

Natural landscapes are crucial resources for enhancing visitor experiences in ecotourism destinations. Previous research indicates that high temperatures may impact tourists’ perception of and emotions. Still, the potential value natural landscape regulating emotions under high-temperature conditions remains unclear. In this study, we employed machine learning models such as LSTM-CNN, Hrnet, XGBoost, combined with hotspot analysis SHAP methods, to compare reveal impacts elements on different temperature conditions. The results indicate: (1) Emotion prediction spatial a significant increase proportion negative conditions, reaching 30.1%, emotion hotspots concentrated downtown area, whereas, non-high accounted 14.1%, more uniform distribution. (2) Under four most influential factors were Color complexity (0.73), Visual entropy (0.71), Greenness (0.68), Aquatic rate (0.6). contrast, (0.6), Openness (0.56), (0.55), (0.55). (3) Compared enhanced positive effects environmental emotions, (0.94), (0.84), Enclosure (0.71) showing stable impacts. Additionally, aquatic had emotional regulation effect (contribution 1.05), effectively improving overall experience. This study provides data foundation optimizing destinations, integrating advantages various proposing framework collection, comparison, evaluation It thoroughly explores enhance sustainable planning recommendations conservation ecosystems ecotourism.

Language: Английский

Citations

0