Understanding the structure-property relations of materials, and optimizing chemical synthesis or device manufacturing processes requires integrating multimodal datasets from both theory and experiments that often encompass spatial and temporal dependence. The explicit spatiotemporal characteristics may be exploited in model-building for data integration. Historically, spatial and spatiotemporal models were largely developed in the contexts of geoinformatics, biostatistics, and quantitative ecology, many of which remain underappreciated by the materials science community. In these models, the temporal and spatial subsystems are typically considered in 1D and 2D/3D, respectively. Spatiotemporal models describe their subsystems jointly to capture the interactions through covariance functions or dynamical processes derived from physical knowledge . They are structured and interpretable and are considerably more tractable than first-principles methods.