# An overview of the Analyzing Patterns toolset

Identifying geographic patterns is important for understanding how geographic phenomena behave.

Although you can get a sense of the overall pattern of features and their associated values by mapping them, calculating a statistic quantifies the pattern. This makes it easier to compare patterns for different distributions or different time periods. Often the tools in the Analyzing Patterns toolset are a starting point for more in-depth analyses. Using the Spatial Autocorrelation tool to identify distances where the processes promoting spatial clustering are most pronounced, for example, might help you select an appropriate distance (scale of analysis) to use for investigating hot spots (Hot Spot Analysis).

The tools in the Analyzing Patterns toolset are inferential statistics; they start with the null hypothesis that your features, or the values associated with your features, exhibit a spatially random pattern. They then compute a p-value representing the probability that the null hypothesis is correct (that the observed pattern is simply one of many possible versions of complete spatial randomness). Calculating a probability may be important if you need to have a high level of confidence in a particular decision. If there are public safety or legal implications associated with your decision, for example, you may need to justify your decision using statistical evidence.

The Analyzing Patterns tools provide statistics that quantify broad spatial patterns. These tools answer questions such as, "Are the features in the dataset, or the values associated with the features in the dataset, spatially clustered?". The following table lists the tools available and provides a brief description of each.

Tool

Description

Calculates a nearest neighbor index based on the average distance from each feature to its nearest neighboring feature. Results are accessible from the Results window.

Measures the degree of clustering for either high values or low values using the Getis-Ord General G statistic. Results are accessible from the Results window.

Measures spatial autocorrelation based on feature locations and attribute values using the Global Moran's I statistic. Results are accessible from the Results window.

Determines whether features, or the values associated with features, exhibit statistically significant clustering or dispersion over a range of distances.

Analyzing patterns tools

3/7/2012