In recent years, crowdsourcing, which utilizes the intelligence of crowds to solve problems, provides a novel data processing paradigm for traditional data management challenges and has become one of the hottest research topics. In particular, due to the rapid development of mobile Internet and sharing economy, crowdsourcing not only becomes a new approach for data collection, but is also integrated into all kinds of application scenarios especially spatiotemporal data management such as online-to-offline (O2O) applications, real-time traffic monitoring, and logistics management. In this paper, a survey is provided on existing research of spatiotemporal crowdsourcing. First of all, the concept and representative applications of spatiotemporal crowdsourcing is described, and its relationship with traditional crowdsourcing is explained. Then, the workflow of spatiotemporal crowdsourcing is illustrated. Furthermore, three core research problems and three categories of techniques of spatiotemporal crowdsourcing are discussed. Finally, the state-of-the-art studies of spatiotemporal crowdsourcing are summarized and promising future research directions for the research community are presented.