Smart cities rely on sensor systems to collect data that support urban management. However, the high cost and frequent malfunctions of traffic sensors in certain areas lead to sparse data, which limits the performance of downstream tasks. This paper tackles these limitations through inductive spatio-temporal extrapolation, which forecasts time-series data for locations without sensors by leveraging data from surrounding sensor-equipped areas. We introduce a novel Spatio-Temporal Prompt (STP) framework to address two primary challenges: spatial uncertainty and temporal dynamics. Spatial uncertainty arises from the inherent unpredictability of unseen locations during inference, while temporal dynamics refer to the evolving and complex correlations among nodes over time. Our STP leverages self-supervised training with randomly selected prompt nodes to effectively handle spatial uncertainty. Additionally, we employ a temporal prompt pool to capture dynamic temporal relationships. Extensive experiments on three real-world datasets demonstrate that STP significantly outperforms existing state-of-the-art models, showcasing its effectiveness in dealing with sparse sensor data .