In the past decade, crowdsourcing has become a prospective paradigm for commercial purposes, for it brings a lot of benefits such as low cost and high immediacy, particularly in location-based services (LBS). On the other side, there also exist many problems need to be solved in crowdsourcing. For example, the quality control for crowdsourcing systems has been identified as a significant challenge, which includes how to handle massive data more efficiently, how to discriminate poor quality content in workers’ submissions and so on. In this paper, we put forward an approach to control the crowdsourcing quality from spatial and temporal distribution. Our experiments have demonstrated the effectiveness and efficiency of the approach.