The Find Outliers tool identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.

## Example

A police precinct wants to identify the areas in its precinct with consistently higher burglaries. The precinct uses the Find Outliers tool to identify the streets that are hot spots and outliers with high values. Police officers use the results to design prevention strategies, allocate their sparse resources, and initiate neighborhood watch programs.

## Usage notes

The Find Outliers tool includes configurations for input features, outlier settings, and the result layer.

### Input features

The Input features group includes the Input layer parameter, which is the point or polygon layer on which cluster and outlier analysis will be performed.

##### Note:

Web Mercator is not an appropriate projection for spatial analysis. If the spatial reference system of the input layer is WGS 1984 Web Mercator (Auxiliary Sphere) the data will be converted to a geographic coordinate system in order to use chordal distances in the analysis.

### Outlier settings

The Outlier settings group includes the following parameters:

- Variable type determines whether analysis is performed on the feature counts or values. The options are as follows:
- Field—Analysis will be applied to the values of the field specified by Analysis field.
- Point counts—Point features will be aggregated into polygons or cells and counted. Analysis will be applied to the aggregated point counts. This option is available when the input layer is point features.

- Aggregation shape type specifies the shape of the cells within which the point features will be aggregated. This parameter is available when Point counts is specified for Variable type. The following shape options are available:
- Fishnet cells—Point features will be aggregated within fishnet (square) cells.
- Hexagon cells—Point features will be aggregated within hexagon cells.
- Polygon layer—Point features will be aggregated within the polygon features specified by Aggregation polygon layer.

- Aggregation polygon layer specifies the layer that contains the polygon features within which the points will be aggregated. This parameter is available when Polygon layer is specified for Aggregation shape type
- Define where points are possible is the layer that will define the extent of the analysis. Points that fall outside of the bounds of the layer will not be included in the analysis. This parameter is available when either Fishnet cells or Hexagon cells is specified for Aggregation shape type.
- Analysis field specifies the field that will be analyzed to determine outliers. This parameter is available when Field is specified for Variable type.
- Divide by determines how to divide the values in the analysis field or the aggregated point counts. The options are as follows:
- Field—The field in the input layer that will be used to divide the analysis field values.
- Enrichment data—Enriches the features or aggregation shape with Esri population data then divides the analysis field values or the aggregated point counts by the population, if Esri Population is specified. The source of the Esri population data is Esri Demographics Global Coverage. This option uses GeoEnrichment services and will consume additional credits.

Aggregation shape type specifies the shape of the cells within which the point features will be aggregated. This parameter is available when Point counts is specified for Variable type

The options are as follows:- Fishnet cells—Point features will be aggregated within fishnet cells.
- Hexagon cells—Point features will be aggregated within hexagon cells.
- Polygon layer—Point features will be aggregated within the polygon features specified by Aggregation polygon layer.

- Optimization option specifies whether the number of permutations will be selected to optimize the performance of the tool (Speed), the precision of the pseudo p-value (Precision), or both (Balance). The features in a target feature's neighborhood will be permuted to evaluate the observed Local Moran's I value and to determine the likelihood of finding the observed spatial distribution around a target feature. A permutation will randomly rearrange the features in a target feature's neighborhood, and calculate a Local Moran's I value. Several permutations will result in a distribution of Local Moran's I values for a target feature. The pseudo p-value is then calculated by comparing the observed Local Moran's I value to the distribution of Local Moran's I values. The following optimization options are available:
- Speed—Runs 199 permutations to optimize the speed at which the tool runs. The smallest possible pseudo p-value is 0.005.
- Balance—Runs 499 permutations to optimize both speed and precision. The smallest possible pseudo p-value is 0.002.
- Precision—Runs 999 permutations to optimize the precision of the pseudo p-value. The smallest possible pseudo p-value is 0.001.

- Random number seed is an integer value that initiates a random number generator. The random number generator will be used to permute the features in each target feature's neighborhood before calculating a Local Moran's I value.
- Cell size is a numeric value that defines the length of a side of each cell.
- Cell size unit is the units that will be used for the cell size. Supported units are feet, miles, meters, and kilometers.
- Distance band is a numeric value that defines the distance from a target feature that will be included in a target feature's neighborhood. All of the features that fall within the distance band will be included in the target feature's neighborhood. The entire neighborhood will be used to determine whether the target feature is part of a cluster with high or low values and whether the feature is an outlier.
- Distance band unit is the units of the distance band. Supported units are feet, miles, meters, and kilometers.

### Result layer

The Result layer group includes the following parameters:

- Output name determines the name of the layer that is created and added to the map. The name must be unique. If a layer with the same name already exists in your organization, the tool will fail and you will be prompted to use a different name.
- Save in folder specifies the name of a folder in My Content where the result will be saved.

## Limitations

The following limitations apply to the tool:

- If Variable type is specified as Point counts, the following limitations apply:
- The input layer must contain at least 60 point features.
- At a minimum, 30 aggregation cells or polygons must contain at least one point feature.
- The point counts within the aggregation cells or polygons cannot be identical. There must be variation in the point counts between aggregation cells or polygons.

- If Variable type is specified as Analysis field, the following limitations apply:
- At a minimum, 30 features must contain non-null values in the specified analysis field.
- The values in the specified analysis field cannot be identical. There must be variation in the values.

- At a minimum, 30 points must fall within the bounding area specified by Define where points are possible.
- The cell size value cannot exceed the distance band.
- The availability of Esri population data will depend on the location of the input features.
- Esri population data is not available for the Divide by parameter when your organization has a custom GeoEnrichment service configured.

## Environments

Analysis environment settings are additional parameters that affect a tool's results. You can access the tool's analysis environment settings from the Environment settings parameter group.

This tool honors the following analysis environments:

- Output coordinate system
- Processing extent
##### Note:

The default processing extent in Map Viewer is Full extent. This default is different from Map Viewer Classic in which Use current map extent is enabled by default.

## Credits

This tool consumes credits.

Use Estimate credits to calculate the number of credits that will be required to run the tool. For more information, see Understand credits for spatial analysis.

## Outputs

The tool outputs a layer with the results of the cluster and outlier analysis. The layer includes fields for the count, cluster-outlier type, Local Moran's I value, p-value, z-score, number of neighbors, spatial lag, and z-transform of each feature. The cluster-outlier type field distinguishes between a statistically significant cluster of high values (HH), a cluster of low values (LL), a high value outlier surrounded by low values (HL), and a low value outlier surround by high values (LH). The Local Moran's I value indicates whether the feature and its neighborhood have similar (positive) or dissimilar (negative) values. Outliers will have a negative Local Moran's Index.

You can view additional details about the analysis on the output layer's item page. To access the layer's item page, click Analysis on the Settings toolbar. Click History, and find and click the successful tool run. The analysis details will open on the Results tab. Click the options button next to the output layer, and click Open item details.

## Licensing requirements

This tool requires the following licensing and configurations:

- Creator or GIS Professional user type
- Publisher, Facilitator, or Administrator role, or an equivalent custom role

The GeoEnrichment privilege is required to use Esri population data.

## Resources

Use the following resources to learn more:

- Optimized Outlier Analysis in ArcGIS Pro
- Cluster and Outlier Analysis (Anselin Local Moran's I) in ArcGIS Pro
- Find Outliers in ArcGIS REST API
- find_outliers in ArcGIS API for Python