Geographic Spatiotemporal Dynamic Model using Cellular Automata and Data Mining Techniques
Geospatial data and information availability has been increasing rapidly and has provided users with knowledge on entities change and movement in a system. Cellular Geography model applies Cellular Automata on Geographic data by defining transition rules to the data grid. This paper presents the techniques for extracting transition rule(s) from time series data grids, using multiple linear regression analysis. Clustering technique is applied to minimize the number of transition rules, which can be offered and chosen to change a new unknown grid. Each centroid of a cluster is associated with a transition rule and a grid of data. The chosen transition rule is associated with grid that has a minimum distance to the new data grid to be simulated. Validation of the model can be provided either quantitatively through an error measurement or qualitatively by visualizing the result of the simulation process. The visualization can also be more informative by adding the error information. Increasing number of cluster may give possibility to improve the simulation accuracy.
Keywords: Geocomputing, Predictive Spatiotemporal Data Mining, Cellular Automata, Continues Transition Rule, Fuzzy c Means Clustering
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