LoiczView was used for the final modeling of Mexico’s potential vegetation.  LoiczView was chosen because of its utility and novelty in clustering high-dimensional data sets with the capability of color-coded similarity analysis, two or three-dimensional viewing of clusters, choice of distance measures, and optimal cluster algorithms.  Thirty-eight classifications were performed, each differing in either variable composition, number of clusters, distance measure, size of grid, and/or grid resolution.  After each classification, the resulting cluster map was compared with the previous cluster map and Rzedowski’s Potential Vegetation Distribution.  Progressive refinements were made with respect to variable selection and number of clusters produced.  Rzedowski’s Aquatic/ Subaquatic vegetation class was excluded from classification because this vegetation type, identified primarily as small bodies of water, such as lakes, is difficult if not impossible to predict with the environmental data sets used in the study.
 

    Accuracy Assessment: An accuracy assessment was performed to analyze the similarity or correspondence between the classified grids and Rzedowski’s Potential Vegetation map.  Several remote sensing techniques and statistics were used to assess accuracy including error matrices, producer’s accuracy, overall accuracy and the kappa statistic.
 
 
 

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