Optimized Outlier Analysis Tool, Mapping Clusters Toolset, Spatial Statistics ArcToolbox
summary
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.
Usage
This tool identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots) as well as high and low outliers within your dataset. It automatically aggregates incident data, identifies an appropriate scale of analysis, and corrects for both multiple testing and spatial dependence. This tool interrogates your data in order to determine settings that will produce optimal cluster and outlier analysis results. If you want full control over these settings, use the Cluster and Outlier Analysis tool instead.
Note:
Incident data are points representing events (crime, traffic accidents) or objects (trees, stores) where your focus is on presence or absence rather than a measured attribute associated with each point.
The computerized settings used to produce optimal cluster and outlier analysis results are reported in the Results window. The associated workflows and algorithms are explained in How Optimized Outlier Analysis works.
This tool creates a new Output Feature Class with a Local Moran's I index (LMiIndex), z-score, pseudo p-value and cluster/outlier type (COType) for each feature in the Input Feature Class. It also includes a field (NNeighbors) with the number of neighbors each feature included in its calculations.
The COType field identifies statistically significant high and low clusters (HH and LL) as well as high and low outliers (HL and LH), corrected for multiple testing and spatial dependence using the False Discovery Rate (FDR) correction method.
The z-scores and p-values are measures of statistical significance that tell you whether or not to reject the null hypothesis, feature by feature. In effect, they indicate whether the apparent similarity (a spatial clustering of either high or low values) or dissimilarity (a spatial outlier) is more pronounced than one would expect in a random distribution. The z-score and p-values in the Output Feature Class do not reflect any kind of FDR (False Discovery Rate) corrections. For more information on z-scores and p-values, see What is a z-score? What is a p-value?
A high positive z-score for a feature indicates that the surrounding features have similar values (either high values or low values). The COType field in the Output Feature Class will be HH for a statistically significant cluster of high values and LL for a statistically significant cluster of low values.
A low negative z-score (for example, less than -3.96) for a feature indicates a statistically significant spatial data outlier. The COType field in the Output Feature Class will indicate if the feature has a high value and is surrounded by features with low values (HL) or if the feature has a low value and is surrounded by features with high values (LH).
The COType field will always indicate statistically significant clusters and outliers based on a False Discovery Rate corrected 95 percent confidence level. Only statistically significant features have values for the COType field.
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Explanation of the tool:
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