Cluster and Outlier Analysis (Anselin Local Moran's I) (Spatial Statistics)


Given a set of weighted features, identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.

Learn more about how Cluster and Outlier Analysis (Anselin Local Moran's I) works


Cluster and Outlier Analysis illustration



ClustersOutliers_stats (Input_Feature_Class, Input_Field, Output_Feature_Class, Conceptualization_of_Spatial_Relationships, Distance_Method, Standardization, Distance_Band_or_Threshold_Distance, {Weights_Matrix_File})
ParameterExplanationData Type

The feature class for which cluster/outlier analysis will be performed.

Feature Layer

The numeric field to be evaluated.


The output feature class to receive the results fields.

Feature Class

Specifies how spatial relationships among features are conceptualized.

  • INVERSE_DISTANCENearby neighboring features have a larger influence on the computations for a target feature than features that are far away.
  • INVERSE_DISTANCE_SQUAREDSame as INVERSE_DISTANCE except that the slope is sharper so influence drops off more quickly and only a target feature's closest neighbors will exert substantial influence on computations for that feature.
  • FIXED_DISTANCE_BANDEach feature is analyzed within the context of neighboring features. Neighboring features inside the specified critical distance receive a weight of 1, and exert influence on computations for the target feature. Neighboring features outside the critical distance receive a weight of zero and have no influence on a target feature's computations.
  • ZONE_OF_INDIFFERENCEFeatures within the specified critical distance of a target feature receive a weight of 1 and influence computations for that feature. Once the critical distance is exceeded, weights (and the influence a neighboring feature has on target feature computations) diminish with distance.
  • POLYGON_CONTIGUITY_(FIRST_ORDER)Only neighboring polygon features that share a boundary will influence computations for the target polygon feature. (Requires an ArcInfo license)
  • GET_SPATIAL_WEIGHTS_FROM_FILESpatial relationships are defined in a spatial weights file. The path to the spatial weights file is specified in the Weights Matrix File parameter.

Specifies how distances are calculated from each feature to neighboring features.

  • EUCLIDEAN_DISTANCEThe straight-line distance between two points (as the crow flies)
  • MANHATTAN_DISTANCEThe distance between two points measured along axes at right angles (city block); calculated by summing the (absolute) difference between the x- and y-coordinates

Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme.

  • NONENo standardization of spatial weights is applied.
  • ROWSpatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features).

Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance, while those inside the distance threshold are equally considered. The value entered should match that of the output coordinate system.

For the Inverse Distance conceptualizations of spatial relationships, a value of 0 indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value is computed and applied. This default value is the Euclidean distance that ensures every feature has at least one neighbor.

This parameter has no effect when Polygon Contiguity or Get Spatial Weights From File spatial conceptualizations are selected.


The path to a file containing spatial weights that define spatial relationships among features.


Code Sample

ClusterandOutlierAnalysis Example (Python Window)

The following Python Window script demonstrates how to use the ClusterandOutlierAnalysis tool.

import arcpy
arcpy.env.workspace = "c:/data/911calls"
arcpy.ClustersOutliers_stats("911Count.shp", "ICOUNT","911ClusterOutlier.shp","GET_SPATIAL_WEIGHTS_FROM_FILE","EUCLIDEAN_DISTANCE", "NONE","#", "euclidean6Neighs.swm")
ClusterandOutlierAnalysis Example (stand-alone Python script)

The following stand-alone Python script demonstrates how to use the ClusterandOutlierAnalysis tool.

# Analyze the spatial distribution of 911 calls in a metropolitan area
# using the Cluster-Outlier Analysis Tool (Anselin's Local Moran's I)

# Import system modules
import arcpy

# Set geoprocessor object property to overwrite outputs if they already exist = True

# Local variables...
workspace = r"C:\Data\911Calls"

    # Set the current workspace (to avoid having to specify the full path to the feature classes each time)
    arcpy.env.workspace = workspace

    # Copy the input feature class and integrate the points to snap
    # together at 500 feet
    # Process: Copy Features and Integrate
    cf = arcpy.CopyFeatures_management("911Calls.shp", "911Copied.shp",
                         "#", 0, 0, 0)

    integrate = arcpy.Integrate_management("911Copied.shp #", "500 Feet")

    # Use Collect Events to count the number of calls at each location
    # Process: Collect Events
    ce = arcpy.CollectEvents_stats("911Copied.shp", "911Count.shp", "Count", "#")

    # Add a unique ID field to the count feature class
    # Process: Add Field and Calculate Field
    af = arcpy.AddField_management("911Count.shp", "MyID", "LONG", "#", "#", "#", "#",
                     "NON_NULLABLE", "NON_REQUIRED", "#",
    cf = arcpy.CalculateField_management("911Count.shp", "MyID", "[FID]", "VB")

    # Create Spatial Weights Matrix for Calculations
    # Process: Generate Spatial Weights Matrix... 
    swm = arcpy.GenerateSpatialWeightsMatrix_stats("911Count.shp", "MYID",
                        "#", "#", "#", 6) 

    # Cluster/Outlier Analysis of 911 Calls
    # Process: Local Moran's I
    clusters = arcpy.ClustersOutliers_stats("911Count.shp", "ICOUNT", 
                        "EUCLIDEAN_DISTANCE", "NONE",
                        "#", "euclidean6Neighs.swm")

    # If an error occurred when running the tool, print out the error message.
    print arcpy.GetMessages()


Output Coordinate System

Feature geometry is projected to the Output Coordinate System prior to analysis, so values entered for the Distance Band or Threshold Distance parameter should match those specified in the Output Coordinate System. All mathematical computations are based on the spatial reference of the Output Coordinate System.

Related Topics

Licensing Information

ArcView: Yes
ArcEditor: Yes
ArcInfo: Yes