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Using Information Theory To Extract Patterns From Categorical Raster Data, David Percy
Using Information Theory To Extract Patterns From Categorical Raster Data, David Percy
Systems Science Faculty Publications and Presentations
Information theory -- Reconstructability Analysis (RA) implemented in the Occam software -- was used to extract patterns from National Land Cover Data. The aim was to predict temporal change in evergreen forests from time-lagged and spatially adjacent states. The NLCD satellite data were preprocessed with Python and submitted to Occam for analysis, and Occam output was also explored with R-studio. The effectiveness of RA methodology for the analysis of this type of categorical space-time grid data was demonstrated.