Nowadays, social networks produce a huge amount of spatial and spatiotemporal data that provide interesting knowledge. This knowledge can be discovered by clustering algorithms and the result of that can be used for different applications. One of such applications is the geospatial event detection based on data from social networks. Many of such detection methods rely on clustering algorithms that should provide clusters with the high level of density in space and intensity in time. Meanwhile, traditional clustering methods are not always practical for spatial and spatiotemporal data because of the specific of such data. Therefore, in this paper, we present the density and intensity-based spatiotemporal clustering algorithm with fixed distance and time radius. This approach produces the clusters that have the density-based center in space and intensity-based center in time. In the paper, we provide the description of the method from the perspective of 2 aspects: spatial and temporal. We complete the paper with the full description of the algorithm methods and detailed explanation of the pseudo code.