The Mapping Clusters tools perform cluster analysis to identify the locations of statistically significant hot spots, cold spots, spatial outliers, and similar features or zones. The Mapping Clusters toolset is particularly useful when action is needed based on the location of one or more clusters. An example is 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?, Which features are most alike?, How can we group these features so each group is the most dissimilar?, and How can we group these features so each zone is homogenous?.
Creates spatially contiguous zones in your study area using a genetic growth algorithm based on criteria that you specify.
Given a set of weighted features, identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.
Finds clusters of point features within surrounding noise based on their spatial distribution. Time can also be incorporated to find space-time clusters.
Given a set of weighted features, identifies statistically significant hot spots and cold spots using the Getis-Ord Gi* statistic.
Finds natural clusters of features based solely on feature attribute values.
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.
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.
Identifies which candidate features are most similar or most dissimilar to one or more input features based on feature attributes.
Identifies spatial outliers in point features by calculating the local outlier factor (LOF) of each feature. Spatial outliers are features in locations that are abnormally isolated, and the LOF is a measurement that describes how isolated a location is from its local neighbors. A higher LOF value indicates higher isolation. The tool can also be used to produce a raster prediction surface that can be used to estimate if new features will be classified as outliers given the spatial distribution of the data.
Finds spatially contiguous clusters of features based on a set of feature attribute values and optional cluster size limits.
The Grouping Analysis tool was available in this toolset prior to ArcGIS Pro 2.2 but has been removed since the algorithms behind this tool have been enhanced. To simplify the new methods and features, two 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.