Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (5): 1073-1082.doi: 10.12082/dqxxkx.2020.190413

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Spatiotemporal Variability of Urban Management Events based on the Bayesian Spatiotemporal Model

DONG Wenqian1, DONG Liang2,3, XIANG Lin1,*(), TAO Haijun1, ZHAO Chuanhu4, QU Hanbing2,3   

  1. 1. College of Information Engineering, China Jiliang University, Hangzhou 310018, China
    2. Beijing Academy of Science and Technology, Beijing 100089, China
    3. Beijing Institute of New Technology Applications, Beijing 100094, China
    4. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
  • Received:2019-07-30 Revised:2019-11-20 Online:2020-05-25 Published:2020-07-25
  • Contact: XIANG Lin
  • Supported by:
    National Natural Science Foundation of China(NSF91746207);National Key Research and Development Program of China(2018YFF0301000);National Key Research and Development Program of China(2018YFC0809700);National Key Research and Development Program of China(2018YFC0704800)


Urban management events are regional and periodic. The spatiotemporal laws and potential impact factors implied in urban management events are vital for improving urban management. However, research on the temporal and spatial changes of urban management events and influencing factors are rare. In this paper, by using the Bayesian space-time model, we modeled and analyzed the temporal and spatial evolution characteristics of three types of city management events-street order, urban environment, and publicity advertising-in the P district of H city, Northwest China, and explored the impact of urban management events as well as the underlying impact factors. We found that: (1) There were spatial differences in the relative risk distribution of the three types of urban management events. The street order type was concentrated in the residential and commercial areas of the city, while the urban environment type was concentrated in the residential areas of the city. The advertising type was mainly concentrated in the commercial areas of the city. The spatial risk posterior probability estimate indicated that the above two regions are hotspots of urban management events. (2) The relative risks of urban management events were more prominent on Tuesdays, Fridays, and Saturdays, but there was no obvious monotony in general trends. Meanwhile, the hourly trends had irregular fluctuation, everyday from 8 to 10 and from 14 to 15, it was a period of the high incidence of urban management events, and its relative risk was much higher than other periods. (3) For different built environments, the potential impacts of these factors were quite different. The relative risk of urban management events was significantly associated with restaurants, transportation, and living services, all positively correlated. (4) The relative risk of urban management events presented obvious spatial and temporal heterogeneity. and it is reasonable and necessary to consider the impact of spatial and temporal effects when analyzing urban management events data. Our findings are meaningful for relavant government departments to make effective policies to control and reduce the relative risk of urban management events, especially for the study area.

Key words: urban management events, Bayesian spatiotemporal model, structured effect, unstructured effect, relative risk, spatiotemporal variability, hot/cold spots, INLA