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

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0317659 - e0317659

Published: Jan. 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,

Language: Английский

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

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0317659 - e0317659

Published: Jan. 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,

Language: Английский

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