PROGRESS IN GEOGRAPHY ›› 2020, Vol. 39 ›› Issue (8): 1356-1366.doi: 10.18306/dlkxjz.2020.08.010

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Spatial pattern of urban management cases based on Log Gaussian Cox Processes

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:2019-07-09 Revised:2019-11-04 Online:2020-08-28 Published:2020-10-28
  • Contact: XIANG Lin;
  • Supported by:
    National Key Research and Development Program of China(2018YFC0809700);National Key Research and Development Program of China(2018YFF0301000);National Key Research and Development Program of China(2018YFC0704800);National Natural Science Foundation of China(91746207);Innovation Team Project of Beijing Academy of Science and Technology(IG201801N)


Digital urban management systems have accumulated a large amount of historical data of urban management cases, but there is a general lack of research on the overall spatial pattern and cause of urban management cases. Therefore, it is necessary to fully explore the spatial distribution pattern and cause mechanism behind the incidents of urban management, which could provide decision support for the urban management departments to prevent and control the cases. Taking street order, urban environment, and publicity advertising urban management cases as an example and considering the points of interest (POI) data, this study used the Log Gaussian Cox Processes (LGCP) model to analyze the differences of spatial distribution and influencing factors between street order cases, urban environment cases, and publicity advertising cases in P district of H city, Northwest China. The study found that: 1) All the three types of urban management cases present obvious spatially aggregated distribution, and no spatial correlation is believed to exist beyond 924 meters. 2) The spatial features of the agglomeration space are different. The street order cases are close to the main trunk roads in the urban area, resembling a road network. The urban environment cases tend to cluster as blocks around the center of the district, while more scattered and dispersed in the peripheral areas of the district. The publicity advertising cases are in elongated distribution near the main traffic lines, but clustered as blocks in the commercial areas of the city. 3) Different types of POI in the study area have different impacts. Shopping services, health care, and residential areas show the most significant attractiveness, indicating that the flow and density of people in specific areas are the most important factors that affect the distribution of urban management cases, and increased flow and concentration of the crowd will sharply increase the number of urban management incidents. The results of this study include spatial hotspot identification and cause analysis, which can meet the urban management departments' needs.

Key words: urban management cases, Log Gaussian Cox Processes, spatial point pattern, integrated nested Laplace approximation (INLA), stochastic partial differential equations (SPDE)