### Calculations

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?.

### Interpretation

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

- Comparing the spatial pattern of different types of retail within a city to see which types cluster with competition to take advantage of comparison shopping (automobile dealerships, for example) and which types repel competition (fitness centers/gyms, for example).
- Summarizing the level at which spatial phenomena cluster in order to examine changes at different times or in different locations. For example, it is known that cities and their populations cluster. Using High/Low Clustering analysis, you can compare the level of clustering for western versus eastern cities (urban morphology) or the level of population clustering within a single city over time (analysis of urban growth and density).