The advantages of the system dynamics that handle dynamic variables have been verified in diverse fields for a long period of time. However, as there are many restrictions in incorporating spatial variables into the model, it has had a limit in handling feedback between time and space. In this study, in order to improve such a limit, spatial variables were handled as individual variables in system dynamics and efforts were made to find a way for the dynamic change process of such variables to mutually link with GIS. Accordingly, a time and space integrated model was built so that the variables of GIS drawn using the spatial analysis could be combined again and analyzed in GIS after going through dynamic modeling inside system dynamics.
The model built was used to carry out a simulation on the inundation effect on Haeundae-gu of Busan Metropolitan City resulting from the sea level rise scenario of IPCC and storm surge, which is the worst case. Through this, the flooded area and population by unit of time until 2100 were predicted. Also, the result and significance of each alternative was reviewed improving the model by establishing alternative scenarios of protection, accommodation, and retreat as plans of reaction to rise of sea level.
The dynamic change in the variables by time flow could be looked into by enabling spatial variables to be handled in system dynamics using the time and space integrated model built in this study, and the spatial characteristics could also be examined by combining this with the GIS environment again. The advantage that temporal flow can be reflected has a great significance in when the process up to the target point in time can be looked into rather than when the result at the target point in time can be simply calculated. That is, the combination of system dynamics and GIS has advantages of how the diverse variables change until the target year can be traced and, accordingly, not only the result but also the process of spatial change can be examined by calculating the value of change process at each set point in time.
From the viewpoint of system dynamics, the interaction of spatial and aspatial variables could be reflected by handling within the model the spatial variables which used to be difficult to handle in the past, and visualization at each set point in time and target point in time and its potential as basic data for other spatial analyses could be exhibited by interlinking it with GIS. Moreover, the idea that experiments with diverse policies are flexibly carried out by modifying the variables inside the model can also be said to be an advantage of system dynamics; that effect could be visually confirmed by interlinking it with GIS.
The model built in this study has been simplified and generalized putting the focus on integration of system dynamics and GIS and, if diverse variables and also their dynamics are incorporated in the future, it will become a foothold for enhancement of modeling accuracy and prediction of the effect of policy enforcement. Also, the combination of the advantages of system dynamics that dynamic changes in the variables with time can be checked, and appropriate alternatives that can be groped for through improvement of the model with GIS as a tool which supports decision-making through spatial analyses and visualization, is expected to exercise a synergy effect in diverse fields.
In particular, it is presumed to be a foothold for solving the problems which are becoming increasingly difficult to predict due to increase in uncertainty and complexity, and in which not only the process of temporal change, but also the aspect of spatial change is importantly handled, that is to say, by gathering the opinions of diverse experts and establishing the model structure also in the interdisciplinary fields such as decline in growth of cities, traffic problems, environmental problems, and the effect of climate change.