Human-perceived temperature changes linked to local climate zones under extreme hot and cold weathers: A study in the North China Plain DOI
Xiang Li, Ming Luo, Jianfeng Li

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106201 - 106201

Опубликована: Фев. 1, 2025

Язык: Английский

Impact of block morphology on urban thermal environment with the consideration of spatial heterogeneity DOI
Chanjuan Wang,

Zongmao Li,

Yuan Su

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105622 - 105622

Опубликована: Июль 2, 2024

Язык: Английский

Процитировано

3
Comparative examinations of wind speed and energy extrapolation methods using remotely sensed data – A case study from Hungary DOI Creative Commons
István Lázár, István Hadnagy, Boglárka Bertalan-Balázs

и другие.

Energy Conversion and Management X, Год журнала: 2024, Номер unknown, С. 100760 - 100760

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

3
Exploring the impact of urban spatial morphology on land surface temperature: A case study in Linyi City, China DOI Creative Commons

Yongyu Feng,

Huimin Wang, Jing Wu

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0317659 - e0317659

Опубликована: Янв. 27, 2025

The increasing population density and impervious surface area have exacerbated the urban heat island effect, posing significant challenges to environments sustainable development. Urban spatial morphology is crucial in mitigating effect. This study investigated impact of on land temperature (LST) at township scale. We proposed a six-dimensional factor system describe morphology, comprising Atmospheric Quality, Remote Sensing Indicators, Terrain, Land Use/Land Cover, Building Scale, Socioeconomic Factors. Spatial autocorrelation regression methods were used analyze impact. To this end, township-scale data Linyi City from 2013 2022 collected. results showed that LST are significantly influenced by with strongest correlations found factors use types, landscape metrics, remote sensing indices. global Moran’s I value exceeds 0.7, indicating strong positive correlation. High-High LISA values distributed central western areas, Low-Low northern regions some scattered counties. Geographically Weighted Regression (GWR) model outperforms Error Model (SEM) Ordinary Least Squares (OLS) model, making it more suitable for exploring these relationships. findings aim provide valuable references town planning, resource allocation,

Язык: Английский

Процитировано

0
Leveraging machine learning to explore nonlinear associations between urban heat vulnerability and morbidity risk DOI
Jiaming Yang,

Zhaomin Tong,

Jiwei Xu

и другие.

Urban Climate, Год журнала: 2025, Номер 59, С. 102320 - 102320

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

0
Human-perceived temperature changes linked to local climate zones under extreme hot and cold weathers: A study in the North China Plain DOI
Xiang Li, Ming Luo, Jianfeng Li

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106201 - 106201

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0