The Multidimensional Principal Components tool transforms the multidimensional imagery layers into a reduced number of components that account for the variance of the data, so that spatial and temporal patterns can be readily identified.
The output is a hosted imagery layer.
Examples
Use this tool to reduce the complexity and focus the analysis of 30 years of sea surface salinity data. The results of the tool can be used to focus further analysis on the raster bands that account for the most variance.
Usage notes
Multidimensional Principal Components includes configurations for the input layer, component settings, and result layers.
Input layer
The Input layer group includes the following parameters:
- Multidimensional imagery layeris the imagery layer that will be analyzed. If there are no imagery layers available to be selected in the tool, a multidimensional imagery layer must be added to the map. The tool processes data along one dimension, such as a time series raster or a data cube defined by a nontime dimension [X, Y, Z]. If an input variable includes multiple dimensions, such as depth and time, the first dimension value will be used by default.
- Dimension is the statistic that will be extracted. If the input raster is not a multidimensional raster, this parameter is not required.
- Variable is the variable that will be aggregated along the selected dimension. If no variable is specified, all variables with the selected dimension will be analyzed.
Component settings
The Component settings group includes the following parameters:
- Mode specifies the method that will be used to perform principal component analysis. The options are as follows:
- Dimension reduction—The input time series data will be treated as a set of images. Principal components that extract prevalent pattens over time will be computed. This is the default.
- Spatial reduction—The input time series data will be treated as a set of pixels. Principal components that extract prevalent pattens and locations over time will be computed as a set of one-dimensional arrays stored in a table.
Number of principal components defines the number of principal components that will be computed, usually fewer than the number of input rasters.
This parameter also takes the form of a percentage (%). For example, a value of 90% means the number of components that can explain 90 percent of variance in the data will be computed.
Result layers
The Result layers group includes the following parameters:
Output principal components is the name of the output data.
When the Mode parameter is set to Dimension Reduction, the output will be a multiband imagery layer with the components as bands. The first band is the first principal component with the largest eigenvalue, the second band has the principal component with the second largest eigenvalue, and so on.
When the Mode parameter is set to Spatial Reduction, the output is a table layer containing a set of time series data representing the principal components.Output loadings table is the name of the data contributing to the principal components.
When the Mode parameter is set to Dimension Reduction, the output will be a table layer containing the weights that each input raster contributed to the principal components. These weights define the correlations of the input data and the output principal components.
When the Mode parameter is set to Spatial Reduction, the output is an imagery layer in which pixel values are the weights contributing to the principal components. Pixels with larger values are more correlated to the principal components. This output may have a larger cell size than the input raster because a random reprojection is applied to reduce the computation complexity.- Output eigenvalues table is the name of the output eigenvalues table layer. Eigenvalues are values indicating the variance percentage of each component. Eigenvalues help you define the number of principal components that are needed to represent the dataset.
- Output layer type specifies the type of raster output that will be created. The output can be either a tiled imagery layer or a dynamic imagery layer.
- 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
- Geographic transformations
- Processing extent
Note:
The default processing extent is Full extent. This default is different from Map Viewer Classic in which Use current map extent is enabled by default.
- Snap raster
- Cell size
- Mask
- Resampling method
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
This tool includes the following outputs:
- One multidimensional imagery layer containing raster bands or one table layer of one-dimensional arrays that display dominant temporal patterns based on the Mode parameter value.
- One table layer showing the supporting data for the choice of the principal components called the loadings table.
- Optionally, one table layer showing eigenvalues indicating the variance percentage for each component.
Usage requirements
This tool requires the following user type and configurations:
- Professional or Professional Plus user type
- Publisher, Facilitator, or Administrator role, or an equivalent custom role with the Imagery Analysis privilege
Resources
Use the following resources to learn more:
- How Multidimensional Principal Components works
- Multidimensional Principal Components in ArcGIS REST API
- Multidimensional Principal Components in ArcGIS API for Python
- Multidimensional Principal Components in ArcGIS Pro