Spatiotemporal pattern mining attempts to discover unknown, potentially interesting and useful event sequences where events occur within a specific time interval and region. Previous works use partition-based or ill-defined representation of spatial objects which will miss some spatial properties in original spatiotemporal data. Moreover, the problem of non-transactional spatiotemporal database can not be resolved by traditional sequential pattern mining. In this paper, we propose an practical approach to retain the disappearance of spatial correlation which is caused by improper data representation, called Spatiotemporal Frequent Pattern Mining (abbreviated as STFPM), to discover frequent sequential spatiotemporal pattern. Finally, with a case study of crime pattern analysis, our experimental studies show that the proposed (STFPM) framework effectively discovers high-quality spatiotemporal patterns.