The structure of computational spatial analysis has mostly built on data where visualization of information takes priority over analysis. In these framings, spatial relationships cannot easily be encoded, hindering spatial analysis that emphasizes how interactions among spatial entities reflect mutual inter-relationships. This research explores how graph theoretic principles and associated mathematical tools can support spatiotemporal analysis by enabling assessment of spatial and temporal relationships in landscape monitoring.