Crowdsourcing has become an emerging data collection paradigm for smart city applications. A new category of crowdsourcing-based urban anomaly reporting systems have been developed to enable pervasive and real-time reporting of anomalies in cities (e.g., noise, illegal use of public facilities, urban infrastructure malfunctions). An interesting challenge in these applications is how to accurately predict an anomaly in a given region of the city before it happens. Prior works have made significant progress in anomaly detection. However, they can only detect anomalies after they happen, which may lead to significant information delay and lack of preparedness to handle the anomalies in an efficient way. In this paper, we develop a Crowdsourcing-based Urban Anomaly Prediction Scheme (CUAPS) to accurately predict the anomalies of a city by exploring both spatial and temporal information embedded in the crowdsourcing data. We evaluated the performance of our scheme and compared it to the state-of-the-art baselines using four real world datasets collected from 311 service in the city of New York. The results showed that our scheme can predict different categories of anomalies in a city more accurately than the baselines.