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.