Spatiotemporal modeling is a research field that combines spatial and temporal analysis to understand and predict changes in a system over both space and time. It involves the development of statistical and mathematical models that can analyze and integrate information from multiple sources, such as satellite images, sensor networks, and weather data, to predict patterns and trends in the spatial and temporal dimensions. Applications of spatiotemporal modeling can be found in various fields, including environmental science, epidemiology, transportation planning, and urban development. Spatiotemporal models can range from simple linear regression models to complex machine learning algorithms that use sophisticated techniques like deep learning and neural networks. The goal of spatiotemporal modeling is to provide accurate and reliable predictions that can be used to inform decision-making in a wide range of contexts.