ArcMap

Cluster/Outlier Analysis with Rendering

Résumé

Given a set of weighted features, identifies hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic. It then applies cold-to-hot rendering to the z-score results.

Illustration

Cluster and Outlier Analysis illustration

Utilisation

  • The Cluster/Outlier Analysis with Rendering tool combines the Clusters and Outlier Analysis and ZScore Rendering tools in a model. The Output Layer File is automatically added to the TOC with hot/cold rendering applied to feature z-scores.

  • Begining with ArcGIS 9.3, output from the Clusters and Outlier Analysis is automatically added to the TOC with default rendering applied to the COTYPE field, showing statistically significant hot spots, cold spots and spatial outliers.

Syntaxe

arcpy.stats.ClustersOutliersRendered(Input_Feature_Class, Input_Field, Output_Layer_File, Output_Feature_Class)
ParamètreExplicationType de données
Input_Feature_Class

The feature class for which cluster analysis will be performed.

Feature Layer
Input_Field

The field to be evaluated.

Field
Output_Layer_File

The output layer file to store rendering information.

Layer File
Output_Feature_Class

The output feature class to receive the results field, z-score, p-value, and cluster type designation.

Feature Class

Exemple de code

Cluster and Outlier Analysis with Rendering Example (Python Window)

The following Python Window script demonstrates how to use the Cluster and Outlier Analysis with Rendering tool.

import arcpy
arcpy.env.workspace = "c:/data/911calls"
arcpy.ClustersOutliersRendered_stats("911Count.shp", "ICOUNT","911ClusterOutlier_rendered.lyr", "911ClusterOutlier.shp")
Cluster and Outlier Analysis with Rendering Example (Stand-alone Python script).

The following stand-alone Python script demonstrates how to use the Cluster and Outlier Analysis with Rendering tool.

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

# Import system modules
import arcpy

# Set geoprocessor object property to overwrite outputs if they already exist
arcpy.gp.OverwriteOutput = True

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

try:
    # 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", "#")

    # Cluster/Outlier Analysis of 911 Calls
    # Process: Local Moran's I
    clusters = arcpy.ClustersOutliersRendered_stats("911Count.shp", "ICOUNT", 
                        "911ClusterOutlier_rendered.lyr", "911ClusterOutlier.shp")

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

Environnements

Informations de licence

  • Basic: Oui
  • Standard: Oui
  • Advanced: Oui

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