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Kappa Stats

NAME: Cohen’s Kappa and Classification Table Metrics 2.1a:
           An ArcView 3x Extension for Accuracy Assessment of Spatially-Explicit Models 
   (Click name to download)

Download PDF Version of Manual:  Kappa Manual (2.8 mb)

Review USGS Open File Report OF 2005-1363 (Very similar to Manual):

View and Download Kappa Poster

View Online PDF Manual:

Aka: kappa_stats.avx

Last modified: December 10, 2007

TOPICS: Kappa, classification, accuracy, sensitivity, specificity, omission, commission, user accuracy, producer accuracy, P-value, sample size, model, statistics, distributions, t, F, logistic, normal, skewness, kurtosis, binomial, probability, critical, Poisson, chi-square


Jeff Jenness
     Wildlife Biologist, GIS Analyst
     Jenness Enterprises
     3020 N. Schevene Blvd.
     Flagstaff, AZ 86004 USA
     Tel (928) 607-4638
J. Judson Wynne
     Wildlife Ecologist
     USGS - Southwest Biological Science Center
     Colorado Plateau Research Station
     2255 North Gemini Drive
     Flagstaff, AZ 86011 USA
     Fax (928) 556-7500
     Tel (928) 556-7172


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.

This extension offers several functions to aid in accuracy assessment, as well as several general statistical tools.  Please review the manual for a full discussion of all functions, but in summary they are:

bullet  Kappa Analysis: The Kappa statistic is used to measure the agreement between predicted and observed categorizations of a dataset while correcting for agreement that occurs by chance. This statistic is especially useful in landscape ecology and wildlife habitat relationship (WHR) modeling for measuring the predictive accuracy of classification grids.
bullet  Compare Kappa Analyses: This tool allows you to compare the kappa statistics between different analyses, perhaps comparing different observers, predictive algorithms or dates of remote sensing imagery.
bullet   Sample Size: This tool provides a means to estimate the sample size required to achieve a confidence level and precision for statistical analysis.
bullet   Summary Statistics: From any numeric field in a table, this function will calculate the Mean, Standard Error of the Mean, Confidence Intervals, Minimum, 1st Quartile, Median, 3rd Quartile, Maximum, Variance, Standard Deviation, Average Absolute Deviation, Skewness (normal and Fisher’s G1), Kurtosis (normal and Fisher’s G2), Number of Records, Number of Null Values, and Total Sum.
bullet  Probability Calculators: This function will allow you to calculate the probability, cumulative probability and inverse probability (i.e. given a cumulative probability, calculate the corresponding critical value) of a wide range of statistical distributions, including the Beta, Binomial, Cauchy, Chi-Square, Exponential, F, Logistic, LogNormal, Normal, Poisson, Student’s T and Weibull distributions. This function is available as a general calculator that remains open until you are finished with it, or as a Table tool that performs the calculations on all selected records in a table.

Acknowledgments: The tools in this extension are based primarily on functions described in “Assessing the Accuracy of Remotely Sensed Data: Principles and Practices” by Russell G. Congalton and Kass Green (Congalton and Green 1999).

Special thanks to Dr. William Block, USDA FS, Rocky Mountain Research Station for use of the retrospective dataset used in the case study. Wynne (2003) used these data as the basis of WHR model development for his M.S. thesis research. Also, special thanks to Mr. Rudy King and Ms. Kristen Covert-Bratland, also of the Rocky Mountain Research Station, for assistance in statistical questions.

Certain tools (esp. the Field Statistics and the histogram) were originally developed by the author for the University of Arizona’s Saguaro project (see and are included with their permission. The authors thank Mr. Larry Kendall of the University of Arizona, and Mr. Scott Walker of Northern Arizona University, for their help in developing those tools and their willingness to share them.

Special thanks also to Dr. John Prather and Dr. Russell Congalton for reviewing early drafts of this manual, and to Dr. Congalton for suggesting the correction for locational uncertainty.

The Statistical Probability tools are almost identical to those in the author’s Statistics and Probability Tools extension (see and are included because they enhance and complement the statistical functions. The manual for that extension has also been incorporated into this manual.

REQUIRES: ArcView 3.x; Spatial Analyst

This extension also requires the file "avdlog.dll" be present in the ArcView/BIN32 directory (or $AVBIN/avdlog.dll) and the Dialog Designer extension be located in your ArcView/ext32 directory, which they usually are if you're running AV 3.1 or better. The Dialog Designer doesn't have to be loaded; it just has to be available. If you are running AV 3.0a, you can download the appropriate files for free from ESRI at:

REVISIONS:  Version 2.1 (May 23, 2006) includes minor code revisions to avoid problems with Chinese versions of Windows, as well as adds menu items to search the Kappa scripts for errors.

Version 2.1a (December 10, 2007): Minor update to allow extension to work with PointZ, PointM, PolygonZ and PolyonM shapefiles.

Recommended Citation Format:  For those who wish to cite this extension, the authors recommend something similar to:

Jenness, J. and J. J. Wynne. 2006. Kappa analysis (kappa_stats.avx) extension for ArcView 3.x. Jenness Enterprises. Available at:

Please let us know if you cite this extension in a publication ( or, so we may update the citation list accordingly.

General Instructions:

  1. Begin by placing the "kappa_stats.avx" file into the ArcView extensions directory (../../Av_gis30/Arcview/ext32/).
  2. After starting ArcView, load the extension by clicking on File --> Extensions… , scrolling down through the list of available extensions, and then clicking on the checkbox next to the extension called "Kappa Analysis."
  3. This extension will add five buttons to your View button bar:
  4. This extension will also add two buttons to your Table button bar:
  5. For detailed instructions, view on-line PDF version of Kappa Analysis Manual         
  6. The manual has also been published through the USGS as an Open File Report (OF 2005-1363).

Enjoy! Please contact the authors if you have problems or find bugs.

Jeff Jenness
Wildlife Biologist
USFS Rocky Mountain Research Station
2500 S. Pine Knoll Drive
Flagstaff, AZ  86001  USA

Jenness Enterprises
3020 N. Schevene Blvd.
Flagstaff, AZ  86004   USA
Fax (928) 556-2130
Tel (928) 556-2012
Tel (928) 607-4638
J. Judson Wynne
United States Geologic Survey
Southwest Biological Science Center
Colorado Plateau Research Station
2255 N. Gemini Drive
Flagstaff, AZ  86011   USA
Fax (928) 556-7500
Tel (928) 556-7172

Please visit Jenness Enterprises ArcView Extensions site for more ArcView Extensions and other software by the author.  We also offer customized ArcView-based GIS consultation services to help you meet your specific data analysis and application development needs.