Spatiotemporal prediction plays a critical role in numerous real-world applications such as urban planning, transportation optimization, disaster response, and pandemic control. In recent years, researchers have made significant progress by developing advanced deep learning models for spatiotemporal prediction. However, most existing models are deterministic, i.e., predicting only the expected mean values without quantifying uncertainty, leading to potentially unreliable and inaccurate outcomes. While recent studies have introduced probabilistic models to quantify uncertainty, they typically focus on a single phenomenon (e.g., taxi, bike, crime, or traffic crashes), thereby neglecting the inherent correlations among heterogeneous urban phenomena. To address the research gap, we propose a novel Graph Neural Network with Uncertainty Quantification, termed UQGNN for multivariate spatiotemporal prediction. UQGNN introduces two key innovations: (i) an Interaction-aware Spatiotemporal Embedding Module that integrates a multivariate diffusion graph convolutional network and an interaction-aware temporal convolutional network to effectively capture complex spatial and temporal interaction patterns, and (ii) a multivariate probabilistic prediction module designed to estimate both expected mean values and associated uncertainties. Extensive experiments on four real-world multivariate spatiotemporal datasets from Shenzhen, New York City, and Chicago demonstrate that UQGNN consistently outperforms state-of-the-art baselines in both prediction accuracy and uncertainty quantification. For example, on the Shenzhen dataset, UQGNN achieves a 5% improvement in both prediction accuracy and uncertainty quantification.

Understanding the workings and behaviors of a city requires knowledge of the different processes that allow people and other biological organisms to live and thrive, as well as understanding of their interrelationships--many of which are complicated and have yet to be deeply explored. "Cities are immensely complex, with many facets and interactions within them," said Pete Beckman, a computer scientist at the U.S. Department of Energy's (DOE) Argonne National Laboratory. "For instance, weather influences human movement; air quality affects long-term health; and availability to transportation helps determine opportunities ranging from employment to social interaction. What we need is a new generation of methods and tools that can help us find relationships hidden within the growing volume and diversity of data that are being collected about cities." Central to these methods is machine learning--the increasingly potent process by which computers train to make predictions or determinations from large quantities of data. Machine learning has revolutionized many parts of our lives, from the game of chess to facial recognition systems, and it is now coming to our cities.