Geospatial Data Extrapolation Using Data Mining Techniques and Cellular Automata

This paper describes a geospatial knowledge discovery model of historical maps data set with relative geographic referenced. The knowledge about spatiotemporal dynamic is represented by the transition rules of cellular automata model. Set of transition rules obtained by applying three data mining techniques on large amount of data grid. First, multiple linear regression analysis applied on each subsequent pair of N data grid to obtained (N-1) rules. Second, by applying clustering analysis, then they extracted into a small number of rules, which is represented all of the rules, and they associated with the first data grid of the related pair. Finally, the selected rules used in determining the next value of the given data using classification analysis. Selection of the rule applied to the data based on the distance between the data and the associated data grid of the selected rule. The model had been evaluated on ordinal data type from fire danger rating and nominal data from land use and land cover status. Model accuration measured and visualized by comparing actual data and the simulated data. The accuration ranges between 80% - 95% in the first case and 90,5% - 95,2% in the second. In the first case, by the segmentation of the model, the performance can be improved significantly, especially for von Neumann scheme.