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Traditional methods of evaluating geographic models by statistical comparisons between observed and simulated variates are criticized. In particular, it is suggested that the correlation coefficient (r), its square and tests of their statistical significance are inadequate for such purposes. The root mean squared error (RMSE) and related measures as well as a new index of agreement (d) are alternatively presented as superior indices for making such comparisons. Arguments are made for increasing the number of digital algorithms and data plots being published.
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