Space-time GIS emerged in the early 1990s to incorporate temporal information and analytical functions so that GIS technology could handle both spatial and temporal data. To do so, GIS technology has to embrace spatial and temporal data throughout the processes of conceptualization, representation, computation, and visualization. Conceptualization captures ontological constructs and how they manifest themselves and relate to each other in space and time meaningfully with respect to the geographic domain of interest. Representation formalizes the conceptualized ontological constructs based on their characteristics, behaviors, and relationships to organize spatial and temporal data effectively in accordance with the geographic domain. Computation operates on digital representations of the ontological constructs to measure spatial and temporal quantities, analyze patterns, model relationships, simulate possible scenarios, and make predictions in space and time. Finally, visualization creates visual means to inspect space-time data and analytical findings throughout GIS processing. Visual analytics, furthermore, utilizes an interactive visual interface to facilitate analytical reasoning, and hence engages visualization in computation. Advances in teal-time or near real-time geospatial data acquisition as well as data streaming and machine learning methods have significantly accelerated the development of space-time GIS since 2010.
A Hidden Climate Indices Modeling Framework for Multi‐Variable Space‐Time Data
