How High/Low Clustering: Getis-Ord General G (Spatial Statistics) works

The High/Low Clustering tool measures how concentrated the high or low values are for a given study area. As an example, you can use this statistic to monitor the number of emergency room visits and look for unexpected spikes which might indicate an outbreak of a local or regional health problem.


Computations for the General G Statistic

View additional General G statistic computations.

The p-value is a numerical approximation of the area under the curve for a known distribution, limited by the test statistic. See What is a Z score? What is a p-value?.


The High/Low Clustering tool is an inferential statistic, which means that the results of the analysis are interpreted within the context of a null hypothesis. The null hypothesis for the General G statistic states "there is no spatial clustering of the values". When the absolute value of the Z score is large and the p-value is very small, the null hypothsis can be rejected (see What is a Z score? What is a p-value?). If the null hypothesis is rejected, then the sign of the Z score becomes important. If the Z score value is positive, it means that high values cluster together in the study area. If the Z Score value is negative, it means that low values cluster together.

Potential applications


This tool calculates the High/Low General G value (observed and expected) and the associated Z score and p-value for a given input feature class. These values are written to the message section of the command line window, and the observed General G, Z score, and p-value are passed as derived output.

Additional Resources:

The following books and journal articles have further information about this tool.

Getis, Arthur, and J.K. Ord. "The Analysis of Spatial Association by Use of Distance Statistics." Geographical Analysis 24, no. 3. 1992

Mitchell, Andy. The ESRI Guide to GIS Analysis, Volume 2. ESRI Press, 2005.