Visual analytics (VA) is commonly used at two stages of the modeling process: either early on, to find patterns in the data which should then be captured by a model; or at the very end, to convey the final results of a simulation model. We posited that VA can play an important role at a more intermediate stage, to assist with calibrating and validating complex simulation models (Giabbanelli & Jackson 2015). We developed this idea further for two different types of model. First, for Fuzzy Cognitive Maps (FCM), we proposed a feedback loop such that the FCM could guide analysts in their visual exploration of the data, and in turn, they can use the newly identified patterns to improve the FCM (Pratt et al., 2013). Second, for Cellular Automata (CA), we developed a prototype that facilitates the visual generation of insight when many time steps are involved in the simulation (Giabbanelli et al., 2016). Our combinations of visual analytics with both FCM and CA has most recently been taken to the next stage, as we produced complete open-source environments and applied them to practical cases.
- Pratt, S.F., Giabbanelli, P.J., Mercier, J-S. (2013) Detecting unfolding crises with visual analytics and conceptual maps emerging phenomena and big data. In Proceedings of the 2013 IEEE International Conference on Intelligence and Security Informatics (ISI).
- Giabbanelli, P.J., Jackson, P.J. (2015) Using visual analytics to support the integration of expert knowledge in the design of medical models and simulations. Procedia Computer Science, 51, 755-764.
- Giabbanelli, P.J., Babu, G.J., Baniukiewicz, M. (2016) A Novel Visualization Environment to Support Modelers in Analyzing Data Generated by Cellular Automata. Proceedings of the International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management.