How Urban Street Spatial Composition Affects Land Surface Temperature in Areas with Different Population Densities: A Case Study of Zhengzhou, China DOI Open Access
Mengze Fu, Kangjia Ban,

Li Jin

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(22), P. 9938 - 9938

Published: Nov. 14, 2024

The arrangement and design of urban streets have a profound impact on the thermal conditions within cities, including mitigation excessive street land surface temperatures (LSTs). However, previous research has mainly addressed linear relationships between physical spatial elements LST. There been limited exploration potential nonlinear influence population density variations. This study explores multi-dimensional composition indicators obtained from street-view imagery applies generalized additive models (GAMs) geographically weighted regression (GWR) to evaluate indicators’ LST in areas with various densities. results indicate following: (1) six indicators—green space index (GSI), tree canopy (TCI), sky open (SOI), enclosure (SEI), road width (RWI), walking (SWI)—all significant effects summer daytime (2) Among all categories, GSI negatively affects Moreover, TCI’s shifts negative positive as its value increases. SOI SWI positively affect categories. SEI’s effect changes total high-population (HP) it remains low-population (LP) category. RWI category, LP HP (3) ranking is > SEI TCI RWI, being most factor. These findings provide key insights for mitigating LSTs through interventions, contributing sustainable development.

Language: Английский

Thermal comfort in sight: Thermal affordance and its visual assessment for sustainable streetscape design DOI
S. Yang, Adrian Chong, Pengyuan Liu

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112569 - 112569

Published: Jan. 1, 2025

Language: Английский

Citations

1
Unveiling Differential Impacts of Multidimensional Urban Morphology on Heat Island Effect Across Local Climate Zones: Interpretable CatBoost-SHAP Machine Learning Model DOI
Qiqi Liu,

Hang Tian,

Yunfei Wu

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112574 - 112574

Published: Jan. 1, 2025

Language: Английский

Citations

0
Comprehensive Comparative Analysis and Innovative Exploration of Green View Index Calculation Methods DOI Creative Commons

D.C. Yin,

Terumitsu HIRATA

Land, Journal Year: 2025, Volume and Issue: 14(2), P. 289 - 289

Published: Jan. 30, 2025

Despite the widespread use of street view imagery for Green View Index (GVI) analyses, variations in sampling methodologies across studies and potential impact these differences on results, including associated errors, remain largely unexplored. This study aims to investigate effectiveness various GVI calculation methods, with a focus analyzing point selection coverage angles results. Through systematic review extensive relevant literature, we synthesized six predominant methods: four-quadrant method, six-quadrant eighteen-quadrant panoramic fisheye method pedestrian method. We further evaluated strengths weaknesses each approach, along their applicability different research domains. In addition, address limitations existing methods specific contexts, developed novel technique based three 120° images experimentally validated its feasibility accuracy. The results demonstrate method’s high reliability, making it valuable tool acquiring images. Our findings that choice significantly influences calculations, underscoring necessity researchers select optimal approach context. To mitigate errors arising from initial angles, this introduces concept, “Green Circle”, which enhances precision calculations through meticulous segmentation observational particularly complex urban environments.

Language: Английский

Citations

0
Optimizing Roadside Vegetation Using Deep Reinforcement Learning to Improve Thermal Environment DOI
Li Bin, Changxiu Cheng

Urban forestry & urban greening, Journal Year: 2025, Volume and Issue: unknown, P. 128729 - 128729

Published: Feb. 1, 2025

Language: Английский

Citations

0
ZenSVI: An open-source software for the integrated acquisition, processing and analysis of street view imagery towards scalable urban science DOI
Koichi Ito, Yihan Zhu, Mahmoud Abdelrahman

et al.

Computers Environment and Urban Systems, Journal Year: 2025, Volume and Issue: 119, P. 102283 - 102283

Published: March 20, 2025

Language: Английский

Citations

0
Impacts of street tree canopy coverage on pedestrians' dynamic thermal perception and walking willingness DOI

Yijuan Sang,

Yanjun Hu, Xiao Qin

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106196 - 106196

Published: Feb. 1, 2025

Language: Английский

Citations

0
The Mechanism of Street Spatial Form on Thermal Comfort from Urban Morphology and Human-Centered Perspectives: A Study Based on Multi-Source Data DOI Creative Commons
Fei Guo, Mengqi Luo, Chenxi Zhang

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(10), P. 3253 - 3253

Published: Oct. 14, 2024

The influence of street spatial form on thermal comfort from urban morphology and human-centered perspectives has been underexplored. This study, utilizing multi-source data focusing central districts, establishes a refined index system for prediction model based extreme gradient boosting (XGBoost) Shapley additive explanations (SHAP). results reveal the following: (1) Thermal levels display heterogeneity, with areas discomfort concentrated in commercial zones plaza spaces. (2) Compared to perspective, indicators correlate strongly comfort. (3) key factors influencing comfort, descending order importance, are distance green blue infrastructure (GBI), tree visibility factor (TVF), aspect ratio (H/W), orientation, functional diversity indices, sky view factor. All but TVF negatively correlates (4) In local analyses, primary affecting vary across streets different heat-risk levels. high streets, is mainly influenced by GBI, H/W, whereas low vegetation-related dominate. These findings provide new methodological approach optimizing environments both human perspectives, offering theoretical insights creating more comfortable cities.

Language: Английский

Citations

2
No “true” greenery: Deciphering the bias of satellite and street view imagery in urban greenery measurement DOI
Yingjing Huang, Rohit Priyadarshi Sanatani, Chang Liu

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: unknown, P. 112395 - 112395

Published: Dec. 1, 2024

Language: Английский

Citations

2
Heat exposure assessment and comfort path recommendations for leisure jogging based on street view imagery and GPS trajectories DOI
Wei Yang, Guangyu Zhang, Yong Liu

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106099 - 106099

Published: Dec. 1, 2024

Language: Английский

Citations

1
How Urban Street Spatial Composition Affects Land Surface Temperature in Areas with Different Population Densities: A Case Study of Zhengzhou, China DOI Open Access
Mengze Fu, Kangjia Ban,

Li Jin

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(22), P. 9938 - 9938

Published: Nov. 14, 2024

The arrangement and design of urban streets have a profound impact on the thermal conditions within cities, including mitigation excessive street land surface temperatures (LSTs). However, previous research has mainly addressed linear relationships between physical spatial elements LST. There been limited exploration potential nonlinear influence population density variations. This study explores multi-dimensional composition indicators obtained from street-view imagery applies generalized additive models (GAMs) geographically weighted regression (GWR) to evaluate indicators’ LST in areas with various densities. results indicate following: (1) six indicators—green space index (GSI), tree canopy (TCI), sky open (SOI), enclosure (SEI), road width (RWI), walking (SWI)—all significant effects summer daytime (2) Among all categories, GSI negatively affects Moreover, TCI’s shifts negative positive as its value increases. SOI SWI positively affect categories. SEI’s effect changes total high-population (HP) it remains low-population (LP) category. RWI category, LP HP (3) ranking is > SEI TCI RWI, being most factor. These findings provide key insights for mitigating LSTs through interventions, contributing sustainable development.

Language: Английский

Citations

1