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Analysis of urban environmental problems based on big data from the urban municipal supervision and management information system

Dong, Rencai, Li, Siyuan, Zhang, Yonglin, Zhang, Nana, Wang, Tao, Tan, Xinrui, Fu, Xiao
Ecological indicators 2018 v.94 pp. 52-69
environmental management, humans, information management, information systems, roads, urban areas, China
In China, urban municipal supervision and management information system (UMSMIS) is a new platform to implement all-time and all-round urban environmental management. The accumulated data in the operation of UMSMIS contain varieties of knowledge about the urban environment and human life. With the development of electronic navigation map, points of interest (POIs) are treated as an important data resource for the urban study. POIs contain not only location information but also social-economic information. They may be associated with the generation of urban environmental problems. To identify the spatial pattern of environmental problems and further explore the relationships between environmental problems and POIs, this study analyzed the spatial pattern and composition of points of environmental problems (POEPs) at three levels, including the global level, local level and road level, in Dongcheng District, Beijing, China. Then the study explored the relationships between POEPs and POIs at the three levels. The results showed that the spatial distribution of POEPs was statistically significant clustered (p<0.01) in Dongcheng District, Beijing. The major types of POEPs differed at the three levels and were consistent with the components of POIs only in some regions. At the road level, this study found that POEPs occurred more along the minor roads and the crossroads had the higher density of POEPs and POIs. Thus the minor roads and crossroads should be paid more attention for supervision. Although there was a significantly positive correlation between the density of POEPs and POIs at the global level, the relationships between POEPs and POIs remained complex at different regions. This research may provide methodologies and technical supports to identify spatial clusters of environmental problems, and further provide suggestions to optimize the allocation of urban management resources and improve the management efficiency.