Exploring the Impact of Objective Characteristics and Subjective Perceptions of Street Environment on Cycling Preferences DOI
Haibin Xu,

Yiyi Jiang,

Tao Xue

et al.

Published: Jan. 1, 2024

Language: Английский

Nonlinear effects of multilevel factors on public transport commuting in China’s cities DOI Creative Commons
Xiaoxiao Liu, Zhengdong Huang, Wenliang Jian

et al.

Transportation Research Part D Transport and Environment, Journal Year: 2025, Volume and Issue: 143, P. 104724 - 104724

Published: April 9, 2025

Language: Английский

Citations

0
Uncovering spatial patterns of environmental influence on the paces of active leisure travel DOI
Chengbo Zhang, Xiao Yang, Jingxiong Huang

et al.

Cities, Journal Year: 2025, Volume and Issue: 162, P. 105971 - 105971

Published: April 10, 2025

Language: Английский

Citations

0
Nonlinear and interacting influence of 2D/3D factors on park cooling effect in China using Gradient Boosting Decision Tree and Shapely DOI
Yong Liu, Yankun Sun,

Yujia Ming

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 113066 - 113066

Published: April 1, 2025

Language: Английский

Citations

0
Towards better parking provision: Insights from parking lot utilization analysis of Hangzhou, China DOI
Wei Tang, Zheng Liang, Zhenyu Mei

et al.

Journal of Transport Geography, Journal Year: 2025, Volume and Issue: 126, P. 104247 - 104247

Published: April 23, 2025

Language: Английский

Citations

0
Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning DOI Creative Commons
Chen Cai, Jian Wang, Dong Li

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 4, 2024

Urban greening plays a crucial role in maintaining environmental sustainability and enhancing people's well-being. However, limited by the shortcomings of traditional methods, studying heterogeneity nonlinearity between factors green view index (GVI) still faces many challenges. To address concerns nonlinearity, spatial heterogeneity, interpretability, an interpretable machine learning framework incorporating Geographically Weighted Random Forest (GWRF) model SHapley Additive exPlanation (Shap) is proposed this paper. In paper, we combine multi-source big data, such as Baidu Street View data remote sensing images, utilize semantic segmentation models geographic processing techniques to study global local interpretation Beijing region with GVI key indicator. Our research results show that: (1) Within Sixth Ring Road Beijing, shows significant clustering phenomenon positive correlation linkage, at same time exhibits differences; (2) Among variables, increase coverage rate has most effect on GVI, while building density strong negative GVI; (3) The performance GWRF predicting excellent far exceeds that comparison models.; (4) Whether it rate, urban built environment or socioeconomic factors, their influence non-linear characteristics certain threshold effect. With help these influences explicit effects, quantitative analyses are provided, which can assist planners making more scientific rational decisions when allocating resources.

Language: Английский

Citations

3
Exploring nonlinear and interaction effects of urban campus built environments on exercise walking using crowdsourced data DOI Creative Commons
Bo Lü, Qingyun Liu, Hao Liu

et al.

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: Jan. 30, 2025

Introduction University campuses, with their abundant natural resources and sports facilities, are essential in promoting walking activities among students, faculty, nearby communities. However, the mechanisms through which campus environments influence remain insufficiently understood. This study examines universities Wuhan, China, using crowdsourced data machine learning methods to analyze nonlinear interactive effects of built on exercise walking. Methods utilized incorporated diverse characteristics construct a multidimensional variable system. By applying XGBoost algorithm SHAP (SHapley Additive exPlanations), an explainable framework was established evaluate importance various factors, explore relationships between variables activity, interaction these variables. Results The findings underscore significant impact several key including proportion land, proximity water bodies, Normalized Difference Vegetation Index NDVI, alongside notable six distinct area types. analysis revealed thresholds patterns that differ from other urban environments, some exhibiting fluctuated or U-shaped effects. Additionally, strong interactions were identified combinations, highlighting synergistic elements like green spaces, waterfront areas when strategically integrated. Conclusion research contributes understanding how affect activities, offering targeted recommendations for planning design. Recommendations include optimizing spatial configuration bodies maximize impacts activity. These insights can foster development inclusive, health-promoting, sustainable campuses.

Language: Английский

Citations

0
Unravelling the multiple effects of multilevel neighborhood characteristics on traffic crash risk from a spatiotemporal heterogeneity perspective DOI
Jian Liu,

K. Shen,

Xintao Liu

et al.

Travel Behaviour and Society, Journal Year: 2025, Volume and Issue: 40, P. 101044 - 101044

Published: April 26, 2025

Language: Английский

Citations

0
Comparing Differences in Jogging Support across Various Land Use Types in Urban Built-Up Areas Using User-Recommended Routes DOI Creative Commons
Tan Li, Jiayi Jiang, Meng Guo

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(3), P. 851 - 851

Published: March 21, 2024

Land use types other than specialized athletic fields provide a variety of jogging environments, addressing the shortage urban fitness facilities and promoting health as well sustainability. Currently, there is limited research comparing differences in support among various land types, which can assist decision-makers setting priorities targeted strategies for renewal, especially built-up areas with resources. Initially, spatial information, statistical data, recommendation reason text were extracted from recommended routes mobile apps categorized into six types. Subsequently, potential was measured through descriptive statistics, buffer area analysis, autocorrelation line density analysis. Environmental preferences gauged by analysis using jieba word segmentation grouped frequency calculation. Finally, measurement results different uses compared, including scale differences, differentiation, environmental perception, elements. The found that streets, residential areas, campuses, parks, greenways possess significant to jogging, particularly streets. These exhibit varying potentials attractions preferences. Targeted recommendations have been proposed renewal related fields.

Language: Английский

Citations

2
Association between outdoor jogging behavior and PM2.5 exposure: Evidence from massive GPS trajectory data in Beijing DOI

Wenbo Guo,

Jiawei He, Wei Yang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 947, P. 174759 - 174759

Published: July 14, 2024

Language: Английский

Citations

2
Comparing the impacts of built environment across different objective life neighborhoods on the out-of-home leisure activities of employed people using massive mobile phone data DOI

Qiangqiang Xiong,

Lijun Xing, Liye Wang

et al.

Applied Geography, Journal Year: 2024, Volume and Issue: 171, P. 103382 - 103382

Published: Aug. 17, 2024

Language: Английский

Citations

2