The Mapping Clusters toolset

The Mapping Clusters toolset is probably the most well-known and commonly used toolset in the Spatial Statistics Tools toolbox, and for a good reason. The output from these tools is highly visual and beneficial in the analysis of clustering phenomena. There are many examples of clustering: housing, businesses, trees, crimes, and many others. The degree of this clustering is also important. The tools in the Mapping Clusters toolset don't just answer the question Is there clustering?, but they also take on the question of Where is the clustering?

Tools in the Mapping Clusters toolset are among the most commonly used in the Spatial Statistics Tools toolbox:

• Hot Spot Analysis: This tool is probably the most popular tool in the Spatial Statistics Tools toolbox, and given a set of weighted features, it will identify statistically hot and cold spots using the Getis-Ord Gi* statistics, as shown in the output of real estate sales activity in the following screenshot:

• Similarity Search: This tool is used to identify candidate features that are most similar or most dissimilar to one or more input features by the attributes of a feature. Dissimilarity searches can be equally as important as similarity searches. For example, a community development organization, in its attempts to attract new businesses, might show that their city is dissimilar to other competing cities when comparing crimes.
• Grouping Analysis: This tool groups features based on feature attributes, as well as optional spatial/temporal constraints. The output of this tool is the creation of distinct groups of data where the features that are part of the group are as similar as possible and between groups are as dissimilar as possible. An example is displayed in the following screenshot. The tool is capable of multivariate analysis and the output is a map and a report. The output map can have either contiguous groups or non-contiguous groups:

• Cluster and Outlier Analysis: The final tool in the Mapping Clusters toolset is the Cluster and Outlier Analysis tool. This tool, in addition to performing hot spot analysis, identifies outliers in your data. Outliers are extremely relevant to many types of analyses. The tool starts by separating features and neighborhoods from the study area. Each feature is examined against every other feature to see whether it is significantly different from the other features. Likewise, each neighborhood is examined in relationship to all other neighborhoods to see whether it is statistically different than other neighborhoods. An example of the output from the Cluster and Outlier Analysis tool is provided in the following screenshot: