# Calculate Composite Index

The Calculate Composite Index tool combines multiple numeric variables to create a single index.

## Example

Example scenarios for using this tool include the following:

• Members of an environmental protection department want to create an air quality index to inform public policy and the public about pollution. They collect data from monitoring stations corresponding to criteria pollutants. An analyst can then run the Calculate Composite Index tool to combine the individual pollutant indicators into a single air quality index.
• A jurisdiction wants to apply for an infrastructure grant. To qualify for the grant, it must prove that the resources will go to underserved communities. It can create an index that combines infrastructure and demographic variables to identify the most underserved areas.

## Usage notes

The Calculate Composite Index tool includes configurations for inputs, index settings, and the result layer.

### Inputs

The Inputs group includes the following parameters:

• Input features or table is the table or features containing the variables that will be combined into the index.

For feature inputs, a count of features is displayed below the layer name. The count includes all features in the layer, except features that have been removed using a filter. Environment settings, such as Processing extent, are not reflected in the feature count.

• Input variables are the variables that will be combined to create the index. Provide at least two variables. For each variable, specify the following:
• Field is the numeric field from the Input features or table value containing the variable. Any records in the field with missing values will not be included in the analysis.
• Reverse direction specifies whether the values of the variable will be reversed. When checked, the feature or record that originally had the highest value will have the lowest value, and vice versa. Values will be reversed after scaling. To create an index, variables must be on a compatible scale; reversing some variables may be required to ensure that the meaning of low and high values in each variable is consistent.

• Weight is the relative influence of the variable on the index. Each weight has a default value of 1, so each variable has equal contribution. Increase or decrease the weight to reflect the relative importance of the variable. For example, if a variable is twice as important as another, use a weight value of 2.

### Index settings

The Index settings group includes the following parameters:

• Method to scale and combine variables contains the methods that will be used to scale the input variables and combine the scaled variables to create the index. Scaling is a type of preprocessing that ensures the variables are on a compatible scale before they are combined. These scaled variables are then combined to create a single index value. The following options are available:
• Combine scaled values (Mean of scaled values) creates the index by scaling the input variables between 0 and 1 (minimum-maximum scaling) and calculating the mean of the scaled values. This method is useful for creating an index that is easy to interpret. The shape of the distribution and outliers in the input variables will impact the index.
• Combine ranks (Mean of percentiles) creates the index by scaling the ranks of the input variables between 0 and 1 (scaling by percentile) and calculating the mean of the scaled ranks. This option is useful when the rankings of the variable values are more important than the differences between values. The shape of the distribution and outliers in the input variables will not impact the index.
• Combine raw values (Mean of raw values) creates the index by calculating the mean of the raw input variables. This option is useful when variables are already on a compatible scale.
• Compound scaled values (Geometric mean of scaled values) creates the index by scaling the input variables between 0 and 1 (minimum-maximum scaling) and calculating the geometric mean of the scaled values. High values will not cancel low values, so this option is useful for creating an index in which higher index values will occur only when there are high values in multiple variables.
• Compound ranks (Geometric mean of percentiles) creates the index by scaling the ranks of the input variables between 0 and 1 (scaling by percentile) and calculating the geometric mean of the scaled ranks. This option is useful when the rankings of the variable values are more important than the differences between values and when high variable values should not cancel out low variable values.
• Compound raw values (Geometric mean of raw values) creates the index by calculating the geometric mean of the raw input variables. This option is useful when variables are already on a compatible scale and when high variable values should not cancel out low variable values.
• Highlight extremes (Count of values above 90th percentile) creates the index by counting the number of input variables with values greater than or equal to the 90th percentile. This method is useful for identifying locations that may be considered the most extreme or the most in need.

• Reverse index values specifies whether the output index values will be reversed in direction. When checked, high index values will be treated as low index values and vice versa. Reversing is applied after combining the scaled variables.

• Index minimum and maximum values are the minimum and maximum of the output index values. Specifying a minimum and maximum value will apply minimum-maximum scaling to the combined variables.

### Result layer

The Result layer group includes the following parameters:

• Output name specifies 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.

## 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.

## Limitations

Creating an appropriate composite index depends on careful consideration of the question the index is trying to answer, variable choice, and the methods applied.

## Outputs

The tool output includes a layer with the index results. The layer includes fields containing the input variables after preprocessing (reversing and scaling), the raw index prior to reversing and minimum-maximum scaling, the index value, the index rank, and the index percentile. It also includes fields with the index value reclassified into quantile classes, equal interval classes, and standard deviation classes. Apply styles and configure charts with these fields to explore the spatial patterns and distributions of the results.

To view additional details about the analysis, 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. The Results tab includes additional details about the analysis. You can also view the additional details on the layer's item page. Click the options button next to the output layer and click View details.

## Licensing requirements

This tool requires the following user type and configurations:

• Creator, Professional, or Professional Plus user type
• Publisher, Facilitator, or Administrator role, or an equivalent custom role