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 littering. Littering in urban areas negatively affects their appearance, is harmful to the environment and increases pollution. It is a typical urban problem looming large upon Beijing and other megacities striving for liveability and harmony in economy, society and environment. This study analyzed the amount and spatial distribution of urban litter generation in Beijing based on the Kernel Density Estimation method and Anselin’s Local Moran I method. We analyzed multiple factors affecting littering in urban areas based on the random forest machine learning method. The results show that the density distribution of litter presents a typical core edge diffusion spatial distribution pattern. High clusters of litter were found in most regions of Dongcheng District and central regions of Haidian District. We have verified that littering in urban areas is mostly affected by population, POIs (interest points), road networks, and the management of the city environment. Among these, permanent population, level of road cleaning, the presence of branch roads and commercial places are the four most important influencing factors. This study is of great significance to the prevention and treatment of littering in urban areas and can help city managers better address this problem.