Space and time are integral components of geographic information. There are many ways in which to conceptualize space and time in the geographic realm that stem from time geography research in the 1960s. Cartographers and geovisualization experts alike have grappled with how to represent spatiotemporal data visually. Four broad types of mapping techniques allow for a variety of representations of spatiotemporal data: (1) single static maps, (2) multiple static maps, (3) single dynamic maps, and (4) multiple dynamic maps. The advantages and limitations of these static and dynamic methods are discussed in this entry. For cartographers, identifying the audience and purpose, medium, available data, and available time to design the map are vital aspects to deciding between the different spatiotemporal mapping techniques. However, each of these different mapping techniques offers its own advantages and disadvantages to the cartographer and the map reader. This entry focuses on the mapping of time and spatiotemporal data, the types of time, current methods of mapping, and the advantages and limitations of representing spatiotemporal data.
Volumes in Geographic Information Science (GIScience) represent three-dimensional (3D) spatial phenomena that are often dynamic and have hard-to-determine boundaries. Studying their temporal evolution and dynamics is crucial for understanding and managing complex spatial systems such as atmospheric layers, aquatic systems, and subsurface structures. This chapter introduces a comprehensive framework for analyzing 3D volumes, including voxel-based models, Triangulated Irregular Networks (TIN), and boundary representation techniques. Additionally, it explores space-time volume representation, integrating temporal dynamics into 3D models to capture the evolution of phenomena over time through a graph-based spatiotemporal data framework. Practical applications in climate change detection and disaster management are highlighted, showcasing the potential of this framework to enhance predictive accuracy and support strategic decision-making.
Atmospheric science studies Earth’s atmosphere and its interactions with the land, oceans, and the Sun. It encompasses both weather and climate research—weather being short-term atmospheric conditions and climate representing long-term patterns. Climate change refers to persistent shifts in these patterns over decades, including temperature variations, precipitation changes, and shifts in extreme weather events. Because weather and climate vary across space and time, they are inherently geospatial. Geographic Information Systems (GIS) have become critical to atmospheric science, offering tools for data collection, analysis, modeling, visualization, and real-time monitoring. GIS enables scientists to understand atmospheric processes by integrating diverse datasets and visualizing complex phenomena in maps and models. Although early GIS applications in atmospheric science were limited by technical barriers, advances in GIScience—particularly in managing big, dynamic, and multidimensional data—have greatly expanded their capabilities. Maps, long used by meteorologists and climatologists, are fundamental to GIS, allowing researchers to visualize atmospheric variability across space. GIS tools also allow for detailed spatial and statistical analysis, helping scientists explore interactions among atmosphere, land, water, ecosystems, and society. These insights support informed decisions about risks from weather hazards and climate change impacts on infrastructure, ecosystems, and resources.