Knowledge graphs have become a universal data representation and integration mechanism. They recently gained interest in the spatial area. Spatial-Temporal Knowledge Graphs (STKGs) in particular have been created to integrate diverse sets of spatial data and model the relationships of spatial entities. Public knowledge graphs, such as KnowWhereGraph and WorldKG, have received a high traction in the domain of STKGs. In this paper, we compare three STKGs using downstream tasks within the Spatio-Temporal domain, and also discuss the underlying modeling decisions. We conduct an evaluation on a wildfire dataset and a housing dataset, comparing different embedding methodologies for the different knowledge graphs. We show that modeling paradigms in STKGs as well as algorithmic choices can have an impact on the downstream performance, and discuss challenges in both areas.