Spatiotemporal statistics (STS) in a modern knowledge synthesis context accounts for important issues of real-world system modelling and prediction, including core scientific theories, site-specific information, datasets with varying physical meanings, critical reasoning modes, and ~Shuman agent-natural system~T interactions.  The underlying methodological postulate is that everyday truth is bigger than technical proof, i.e. knowing how to prove something and effectively process the relevant numerical data do not necessarily imply that one understands the deeper meaning of what one has proved and that possesses an adequate interpretation of the data that one has processed. These considerations are particularly significant in the case of interdisciplinary real-world systems that vary in a composite space-time domain under conditions of multi-sourced uncertainty. Accordingly, modern STS seeks to develop a rigorous mathematical framework that is able to integrate ~Struth~T with ~Sproof~T in the living experience sense considered above.