Abstract: Objectives: Accurate crime hotspot detection technology plays an important role in decision-making support for urban public security management and police force deployment. However, due to the contingency and concealment of urban crime, criminal behavior is usually unable to be observed directly and accurately, resulting in the uncertainty of recorded crime information in time and space. Most of the existing hot spot detection methods are based on the assumption of complete data. When applied to real crime data sets, it is easy to produce unreliable hot spot detection results. Methods: Based on this, this paper proposes a crime hot spot detection method under data uncertainty. In terms of time, based on the idea of probability expression and Aoristic analysis, the time sequence law of crime is modeled; Spatially, based on rational choice theory and daily activity theory, geographic weighted regression is used to correct the incomplete prior probability. Finally, the crime hot spots are extracted by Expectation-Maximization (EM) algorithm to draw a credible hot spot distribution map. Results: The experimental verification is based on the policing alert data of a prefecture level city in China. The results show that the method in this paper can detect stable and reasonable hotspot results in the face of criminal events with different uncertainty strengths. Conclusions: The hot spots detected by this method sufficiently eliminate the impact of the uncertainty of criminal events, fit with the daily activity theory in criminology, and can provide accurate decision support for police prevention and control deployment.