Spatiotemporal databases are common in applications dealing with objects that change over time and space. Instead of analyzing each piece of data separately, it's more helpful to group them together and find overall patterns. A new model has been developed to simplify how we calculate these groupings and apply functions to them. This model helps us compare different techniques for analyzing these groupings and suggests areas for future research.