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.
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.
arcpy.stats.ClustersOutliersRendered(Input_Feature_Class, Input_Field, Output_Layer_File, Output_Feature_Class)
The feature class for which cluster analysis will be performed.
The field to be evaluated.
The output layer file to store rendering information.
The output feature class to receive the results field, z-score, p-value, and cluster type designation.
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()
- Basic: Yes
- Standard: Yes
- Advanced: Yes