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    Author(s): Jeff Jenness; J. Judson Wynne
    Date: 2005
    Source: Open-File Report OF 2005-1363. Flagstaff, AZ: U.S. Geological Survey, Southwest Biological Science Center. 86 p
    Publication Series: Miscellaneous Publication
    PDF: View PDF  (5.6 MB)


    In the field of spatially explicit modeling, well-developed accuracy assessment methodologies are often poorly applied. Deriving model accuracy metrics have been possible for decades, but these calculations were made by hand or with the use of a spreadsheet application. Accuracy assessments may be useful for: (1) ascertaining the quality of a model; (2) improving model quality by identifying and correcting sources of error; (3) facilitating a comparison of various algorithms, techniques, model developers and interpreters; and, (4) determining the utility of the data product in a decision-making context. When decisions are made with models of unknown or poorly-assessed accuracy, resource managers run the risk of making wrong decisions or drawing erroneous conclusions. Untested predictive surface maps should be viewed as untested hypotheses and, by extension, poorly tested predictive models are poorly tested hypotheses. Often, if any accuracy measure is provided at all, only the overall model accuracy is reported. However, numerous accuracy metrics are available which can describe model accuracy and performance. Because issues concerning data quality and model accuracy in landscape analyses have received little attention in the management literature, we found it useful to develop a systematic and robust procedure for assessing the accuracy of spatially explicit models. We created an ArcView 3.x extension that provides end users with a packaged approach for accuracy assessment, using Cohen's Kappa statistic as well as several other metrics including overall accuracy, overall misclassification rate, model specificity and sensitivity, omission and commission errors, and positive and negative predictive power. Collectively, these metrics may be used for gauging model performance. When multiple models are available, these metrics offer end users the ability to quantitatively compare and identify the "best" model within a multi-criteria model selection process.

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    Jenness, Jeff; Wynne, J. Judson. 2005. Cohen''s Kappa and classification table metrics 2.0: An ArcView 3.x extension for accuracy assessment of spatially explicit models. Open-File Report OF 2005-1363. Flagstaff, AZ: U.S. Geological Survey, Southwest Biological Science Center. 86 p


    spatially explicit modeling, model accuracy metrics, accuracy metrics, ArcView, Cohen's Kappa statistic

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