• Journal of Geo-information Science
  • Vol. 22, Issue 5, 1073 (2020)
Wenqian DONG1、1, Liang DONG2、2、3、3, Lin XIANG1、1、*, Haijun TAO1、1, Chuanhu ZHAO4、4, and Hanbing QU2、2、3、3
Author Affiliations
  • 1.中国计量大学信息工程学院,杭州 310018
  • 1College of Information Engineering, China Jiliang University, Hangzhou 310018, China
  • 2.北京市科学技术研究院,北京 100089
  • 2Beijing Academy of Science and Technology, Beijing 100089, China
  • 3.北京市新技术应用研究所,北京 100094
  • 3Beijing Institute of New Technology Applications, Beijing 100094, China
  • 4.河北工业大学人工智能与数据科学学院,天津 300401
  • 4School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
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    DOI: 10.12082/dqxxkx.2020.190413 Cite this Article
    Wenqian DONG, Liang DONG, Lin XIANG, Haijun TAO, Chuanhu ZHAO, Hanbing QU. Spatiotemporal Variability of Urban Management Events based on the Bayesian Spatiotemporal Model[J]. Journal of Geo-information Science, 2020, 22(5): 1073 Copy Citation Text show less

    Abstract

    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.
    Wenqian DONG, Liang DONG, Lin XIANG, Haijun TAO, Chuanhu ZHAO, Hanbing QU. Spatiotemporal Variability of Urban Management Events based on the Bayesian Spatiotemporal Model[J]. Journal of Geo-information Science, 2020, 22(5): 1073
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