Exploring the influencing factors of noise complaints in New York City based on an interpretable machine learning model
文献类型：期刊论文
| 作者 | Song, Liuyi2,3; Zhang, An1,2; Kwan, Mei-Po3,4 |
| 刊名 | ENVIRONMENTAL IMPACT ASSESSMENT REVIEW
 |
| 出版日期 | 2026 |
| 卷号 | 116页码:108100 |
| 关键词 | Noise complaints Interpretable machine learning Demographic characteristics Spatial inequality |
| ISSN号 | 0195-9255 |
| DOI | 10.1016/j.eiar.2025.108100 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Noise pollution significantly impacts public health and overall well-being, making research and control of noise issues critically important. Noise complaints serve as a self-reported source of official data, contributing to a more comprehensive and accurate understanding of noise problems and their effects on urban environments. Therefore, this study collected 5.57 million noise complaint records from New York City, as well as data on traffic noise features, demographic characteristics, socio-economic characteristics, urban functional categories, and urban built environments. Employing the XGBoost model and the SHAP interpretation model, this study systematically investigates the complex influence mechanisms between various noise complaints and these factors in New York City, focusing on the non-linear relationships among them. The results indicated that the XGBoost model performs best in fitting the noise complaint density in New York City. Instead of traffic noise levels, demographic characteristics, and building density are found to be the most important factors influencing complaint density, and complaint density does not always increase linearly with increasing population density and building density. Furthermore, ethnic minority populations in New York City may experience inequalities in their housing environments and noise complaints. While reducing residential unit density can lower noise complaint density, this effect follows a non-linear pattern. The findings of this research contribute to a deeper understanding of the causes of urban noise problems and provide important theoretical and empirical support for the development of effective noise pollution control measures. |
| URL标识 | 查看原文 |
| WOS关键词 | ROAD-TRAFFIC NOISE ; AIR-POLLUTION ; ENVIRONMENTAL JUSTICE ; EXPOSURE ; ANNOYANCE ; INEQUALITY ; GREEN ; PERCEPTION ; COMMUNITY ; IMPACT |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001542143600001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215619]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhang, An; Kwan, Mei-Po |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, D-100190 Beijing, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 3.Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Fok Ying Tung Remote Sensing Sci Bldg, Hong Kong, Peoples R China; 4.Chinese Univ Hong Kong, Dept Geog & Resource Management, Wong Foo Yuan Bldg, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Liuyi,Zhang, An,Kwan, Mei-Po. Exploring the influencing factors of noise complaints in New York City based on an interpretable machine learning model[J]. ENVIRONMENTAL IMPACT ASSESSMENT REVIEW,2026,116:108100. |
| APA | Song, Liuyi,Zhang, An,&Kwan, Mei-Po.(2026).Exploring the influencing factors of noise complaints in New York City based on an interpretable machine learning model.ENVIRONMENTAL IMPACT ASSESSMENT REVIEW,116,108100. |
| MLA | Song, Liuyi,et al."Exploring the influencing factors of noise complaints in New York City based on an interpretable machine learning model".ENVIRONMENTAL IMPACT ASSESSMENT REVIEW 116(2026):108100. |
入库方式： OAI收割
来源：地理科学与资源研究所
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