We propose a spatial-temporal inference model, which uses a large amount of spatialtemporal data in the city to help governmentsenterprises predict future long-short-term important urban indicator values, such as traffic flow, human mobility, pollution level, number of criminal caseseven commercial profitability. The SIM model exploits IoT to integrate multiple real-time geospatial big data, including population, the flow of people, geographical data, Traffic,real-time sensor values. SIM can make effective predictionsprovide explainability for making decisions.