Complex Systems Analysis using Space-Time Information Systems and Model Transition Sensitivity Analysis

G. M. Jacquez, G. AvRuskin, E. Do, H. Durbeck, D. A. Greiling, P. Goovaerts,  A. Kaufmann, and B. Rommel 
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Real-world systems are  dynamic,  complex  and  geographic,  yet many  mathematical  modeling tools  do  not evaluate  sensitivity of  results to  underlying  assumptions, and GIS do  not adequately represent time.  This presentation describes two new approaches: Space-Time  Information Systems (STIS), and Model Transition Sensitivity Analysis (MTSA). Current GIS are based on spatial data models that inadequately characterize  the temporal dimension needed for effective representation of complex systems. They do not deal readily with space- time georeferencing nor  space-time queries, and  are best suited to “snapshots” of static systems. These deficiencies prompted many geographers to call for a “higher-dimensional GIS” (a STIS) to better  represent space-time dynamics. When formulating models of complex systems, critical choices are made regarding model type and complexity. Model type is the mathematical approach employed, for example, a deterministic model versus a stochastic model.  Model complexity is determined by the amount of abstraction and simplification employed during model construction. A growing body of work demonstrates that choice of model type and complexity has substantial impacts on simulation results and on model-based decisions. This  paper  briefly describes STIS and MTSA approaches that allow researchers to more effectively represent complex systems and to evaluate the sensitivity of their results to underlying assumptions.

Keywords: space time information system; model transition sensitivity analysis; complex systems

In: McRoberts, R. et al. (eds).  Proceedings of the joint meeting of The 6th International Symposium On Spatial Accuracy Assessment In Natural Resources and Environmental Sciences and The 15th Annual Conference of The International Environmetrics Society, June 28 – July 1 2004, Portland, Maine, USA.

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