ABSTRACT
Dynamic simulation is defined as creating time-dependent models for physical systems (Fishwick, 2007). Various approaches to the dynamic simulation of earth surface systems have been developed and most of these are based on geographical information systems (GIS). For instance, in the 1970s, spatial interaction modeling was proposed to test ideas concerning the development and evolution of urban spatial structures (Harris and Wilson, 1978). A cellular model for residential site selection behavior was designed as an experiment into dynamic modeling using GIS (Gimblett, 1989). A spatial modeling workstation was developed to apply parallel processors to spatial ecosystem modeling by linking GIS with a general dynamic simulation system (Costanza and Maxwell, 1991). A spatial dynamic simulation and assessment system was described for the evaluation and simulation of environmental and ecological processes (Bali and Gimblett, 1992). A spatio-temporal interpolation method for GIS temporal modeling for urban expansion processes was presented to model missing information on changes that take place between consecutive snapshots (Dragicevic and Marceau, 2000). A dynamic information architecture system was used to build a suite of models for the purpose of assessing the ecological impacts of military land use and land management practices (Sydelko et al., 2001). The current status of real-time hydrological models was assessed for flood nowcasting and hazard mitigation (Al-Sabhan et al., 2003). A Web-based geographic information system that could delineate watersheds in real time was developed to support hydrologic model operation on the internet (Choi and Engel, 2003). Cellular automata were applied to representations of the future evolution of cities (Barredo et al., 2003). A real-time GIS-driven surveillance pilot system was studied to enhance West Nile virus dead bird surveillance in Canada (Shuai et al., 2006). A generic dynamic crop model was developed to meet the requirements of a variety of agricultural stakeholders on estimates of yield, pest-related losses, soil carbon, nitrogen dynamics, and emissions of various greenhouse
gases (Aggarwal et al., 2006). Dynamic simulation was conducted by linking an erosion model with a GIS and then developing the resulting spatial information into visualizations of the evolving coastal environment (Brown et al., 2006). A promising first step in the effort to develop tangible geospatial modeling environments allowed users to interact with 3D landscape data by coupling a tangible physical model with GIS (Mitasova et al., 2006). A dynamic modeling approach using cellular automata was also explored to assess the regional distribution patterns of rock-glaciers (Frauenfelder et al., 2008). A real-time automatic interpolation system was presented to better understand natural variability and, improve our ability to detect radiological accidents (Hiemstra et al., 2009). An integrated software system was designed for the dynamic simulation of fires following an earthquake (Zhao, 2010). As demonstrated above, it is likely that GIS use will extend beyond mapping, toward a richer use of its dynamic simulation capabilities.
