An overview of the Mapping Clusters toolset

The Mapping Clusters tools perform cluster analysis to identify the locations of statistically significant hot spots, cold spots, spatial outliers, and similar features. The Mapping Clusters toolset is particularly useful when action is needed based on the location of one or more clusters. An example would be the assignment of additional police officers to deal with a cluster of burglaries. Pinpointing the location of spatial clusters is also important when looking for potential causes of clustering; where a disease outbreak occurs can often provide clues about what might be causing it. Unlike the methods in the Analyzing Patterns toolset, which answer the question, "Is there spatial clustering?" with Yes or No, the Mapping Clusters tools allow visualization of the cluster locations and extent. These tools answer the questions, "Where are the clusters (hot spots/cold spots)?" , "Where are the spatial outliers?", and "Which features are most alike?".

Mapping clusters tools

Tool	Description
Cluster and Outlier Analysis	Given a set of weighted features, identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.
Grouping Analysis	Groups features based on feature attributes and optional spatial or temporal constraints.
Hot Spot Analysis	Given a set of weighted features, identifies statistically significant hot spots and cold spots using the Getis-Ord Gi* statistic.
Optimized Hot Spot Analysis	Given incident points or weighted features (points or polygons), creates a map of statistically significant hot and cold spots using the Getis-Ord Gi* statistic. It evaluates the characteristics of the input feature class to produce optimal results.
Optimized Outlier Analysis	Given incident points or weighted features (points or polygons), creates a map of statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic. It evaluates the characteristics of the input feature class to produce optimal results.
Similarity Search	Identifies which candidate features are most similar or most dissimilar to one or more input features based on feature attributes.