Spatial distribution and influencing factors of litter in urban areas based on machine learning – A case study of Beijing

•Random forest machine learning method was used to analyze urban littering in Beijing.•Different data sources, such as geospatial data, cell signaling data and survey data were used and aligned with littering information.•The permanent population and the road cleaning level had the highest impact on...

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Veröffentlicht in:	Waste management (Elmsford) 2022-04, Vol.142, p.88-100
Hauptverfasser:	Xiong, Nina, Yang, Xiuwen, Zhou, Fei, Wang, Jia, Yue, Depeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Anselin’s Local Moran I Beijing China Cities Environmental Pollution Influencing factor analysis Kernel Density Estimation Machine Learning Multi-source data Random forest model
Online-Zugang:	Volltext
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