Assessing and optimizing cooling intensity of UGS via improved metrics: A study based on machine learning simulation model DOI
Jiongye Li, Rudi Stouffs

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112959 - 112959

Published: April 1, 2025

Language: Английский

Assessing urban population exposure risk to extreme heat: Patterns, trends, and implications for climate resilience in China (2000–2020) DOI

Chengcong Wang,

Zhibin Ren, Yüjie Guo

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 103, P. 105260 - 105260

Published: Feb. 7, 2024

Language: Английский

Citations

32
Spatially explicit assessment of the heat-related health risk in the Yangtze River Delta, China, using multisource remote sensing and socioeconomic data DOI
Hanyi Wu, Yongming Xu, Min Zhang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 104, P. 105300 - 105300

Published: Feb. 23, 2024

Language: Английский

Citations

18
Towards multi-scale and context-specific heat health risk assessment - A systematic review DOI
Jiaxing Ye, Feng Yang

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: 119, P. 106102 - 106102

Published: Jan. 5, 2025

Language: Английский

Citations

4
Assessing urban surface thermal environment and heat health risk in Chinese cities: A twenty-year study DOI
Chunxiao Zhang, Yang Yang, Le Yu

et al.

Urban Climate, Journal Year: 2025, Volume and Issue: 59, P. 102304 - 102304

Published: Jan. 27, 2025

Language: Английский

Citations

2
Assessing heat vulnerability in Philadelphia using geographically weighted principal component analysis (GWPCA): A geospatial big data-driven approach DOI Creative Commons
Ehsan Foroutan, Tao Hu, Fan Zhang

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 127, P. 103653 - 103653

Published: Jan. 16, 2024

The impact of climate change, specifically more intense heat waves, has increased concerns about vulnerability, particularly among high-risk populations. This research utilizes multi-source geospatial big data and employs Geographically Weighted Principal Component Analysis (GWPCA) as well Global (GPCA) to analyze vulnerability in Philadelphia. Using GPCA, four key components are identified Sensitivity, Adaptive Capacity, proxy for Exposure, respectively. subsequent GWPCA analysis reveals localized differences, showing distinct patterns across the city. Notably, Sensitivity factors prominent western southwestern regions, whereas Exposure is dominant central southern parts. study underscores significance considering spatial heterogeneity when assessing vulnerability. It also highlights potential capture subtle disparities within specific areas proposes targeted strategies reduce affected communities. Therefore, incorporation an advanced model enables a comprehensive understanding complex urban environments. progress crucial enhancing resilience adaptation evolving conditions.

Language: Английский

Citations

12
Assessing the impact of heat vulnerability on urban public spaces using a fuzzy-based unified computational technique DOI
Rajeev Kumar, Saswat Kishore Mishra

AI & Society, Journal Year: 2024, Volume and Issue: unknown

Published: April 2, 2024

Language: Английский

Citations

10
A multiscale examination of heat risk spatiotemporal dynamics in Chinese urban agglomerations: A hierarchical assessment method and planning framework DOI
Jinhui Ma,

D Liu

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: 117, P. 105198 - 105198

Published: Jan. 10, 2025

Language: Английский

Citations

1
Analyzing the typology and livability of 15-minute travel at metro stations in high-density cities: A case study of Singapore DOI
Xuan Zhang, Lei Wang, Yang Yang

et al.

Cities, Journal Year: 2025, Volume and Issue: 158, P. 105727 - 105727

Published: Jan. 17, 2025

Language: Английский

Citations

1
Simulating Land Surface Temperature Impacts of Proposed Land Use and Land Cover Plans Using an Integrated Deep Neural Network Approach DOI
Jiongye Li, Yingwei Yan, Rudi Stouffs

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115437 - 115437

Published: Feb. 1, 2025

Language: Английский

Citations

1
Spatial heterogeneity of driving factors for urban heat health risk in Chongqing, China: A new identification method and proposal of planning response framework DOI Creative Commons

Haijing Huang,

Jinhui Ma, Yufei Yang

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 153, P. 110449 - 110449

Published: June 7, 2023

With urban heat challenges increasingly severe, assessing health risk has become crucial for human settlements. Unfortunately, previous studies have not accurately identified the driving factors, posing a significant obstacle to translating assessment results into policymaking. Particularly, potential spatial heterogeneity of factors at indicator-level may exist. Therefore, this study developed systematic method examine in Chongqing. Based on heterogeneity, an integrated framework linking risk, and response strategies was proposed, supporting specific solutions different cities. The indicate that exhibit strong indicator-level. Even within same prevention zone functional areas, maximum differences number combination categories can reach four five, respectively. Moreover, relying solely obtained through traditional methods develop cooling measures is unreasonable. When these are consistent, there still, average, six combinations indicator-level, each includes average 2.9 indicators. higher level zone, more it contains. above moderate 3.9, than 1.1 2 found below levels. Overall, provides reference understanding offers approach assist policymakers formulating guided strategies.

Language: Английский

Citations

17