Geography Space-Time GIS
May Yuan
  • LAST REVIEWED: 27 February 2019
  • LAST MODIFIED: 27 February 2019
  • DOI: 10.1093/obo/9780199874002-0142


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.

General Overviews

Historically, Geographic Information Systems (GIS) technology was built for spatial data digitized from maps or acquired from airborne sensors to support natural resource management (e.g., Canadian GIS); spatial data encoding, mapping, and planning (e.g., United States Census Bureau); spatial data infrastructure (e.g., United States National Spatial Data Infrastructure); and many other geospatial applications. However, maps and imagery, by definition, portray geographic information in static, 2D media. While a map is often time-stamped to indicate its publication date, it consists of no information regarding the time when the mapped data were collected. As an extreme example, the map of Cassini published in 1815 (the first geometrical map for the entire kingdom of France) consists of data from surveys between 1756 and 1789. What was mapped would be no longer representative of what was there when the map was published. Since the initial development of space-time GIS in the early 1990s, much progress has been made in all fronts of conceptualization, representation, computation, and visualization. While temporal data were difficult to acquire in the past, increasing feeds of geospatial data in real time or near real time from Global Positioning Systems (GPS), geosensor networks, and location-aware devices, and social media sources have been accelerating advances in space-time GIS. Significant leaps in hardware and software technologies, especially in the 2000s, have enabled GIS implementations of space-time conceptual models, such as Hägerstrand’s time geography, that were once considered merely theoretical. Space-time GIS, taking advantage of cyberinfrastructure and cloud computing facilities, can now be implemented on supercomputers, web services, and mobile platforms. New representation frameworks, computational methods, and visualization techniques transcend GIS from maps and map analysis to “datafication” and modeling of geographic dynamics. Space-time GIS has a rich literature and continues to grow rapidly with multidisciplinary contributions. While multiple pathways exist by which we can examine the development of space-time GIS, this article traces its key developments over time and highlights some notable academic publications. The selected references are by no means comprehensive, and the overview, like all reviews of multidisciplinary research, is constrained by the author’s perspectives. As a multidisciplinary field, space-time GIS research has attracted researchers from geography, computer science, environmental science, cognitive psychology, urban and regional science, ecology, criminology, epidemiology, landscape architecture, and many other related disciplines. Such an intellectual diversity has fostered exciting advances in conceptualization, representation, computation, and visualization of dynamics in features, patterns, and processes over space and time.

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