Abstract: Facing the worldwide rapid spreading of COVID-19 pandemic, we need to understand its diffusion in the urban environments with heterogeneous population distribution and mobility. However, challenges exist in the choice of proper spatial resolution, integration of mobility data into epidemic modelling, as well as incorporation of unique characteristics of COVID-19.To address these challenges, we build a data-driven epidemic simulator with COVID-19 specific features, which incorporates real-world mobility data capturing the heterogeneity in urban environments. Based on the simulator, we conduct two series of experiments to: (1) estimate the efficacy of different mobility control policies on intervening the epidemic; and (2) study how the heterogeneity of urban mobility affect the spreading process. Extensive results not only highlight the effectiveness of fine-grained targeted mobility control policies, but also uncover different levels of impact of population density and mobility strength on the spreading process. With such capability and demonstrations, our open simulator contributes to a better understanding of the complex spreading process and smarter policies to prevent another pandemic.