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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 and cold spots)?" , "Where are incidents most dense?", "Where are the spatial outliers?", and "Which features are most alike?".

ToolDescription

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.

Density-based Clustering

Finds clusters of point features within surrounding noise based on their spatial distribution.

Hot Spot Analysis

Given a set of weighted features, identifies statistically significant hot spots and cold spots using the Getis-Ord Gi* statistic.

Multivariate Clustering

Finds natural clusters of features based solely on feature attribute values.

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.

Spatially Constrained Multivariate Clustering

Finds spatially contiguous clusters of features based on a set of feature attribute values and optional cluster size limits.

Mapping clusters tools
Legacy:

The Grouping Analysis tool was available in this toolset prior to version 2.2 but has been removed since the algorithms behind this tool have been enhanced. To simplify the new methods and features, two new tools have been created to replace the Grouping Analysis tool. Use the Spatially Constrained Multivariate Clustering tool to create spatially contiguous groups. Use the Multivariate Clustering tool to create groups with no spatial constraints.

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