Available with Image Analyst license.
- URL:https://<rasteranalysistools-url>/MultidimensionalPrincipalComponents
- Version Introduced:11.1
Description
The MultidimensionalPrincipalComponents task transforms multidimensional rasters into their principal components (either dimension reduction or spatial reduction components), loadings, and eigenvalues.
License:
You must license ArcGIS Server as an ArcGIS Image Server to use this resource.Request parameters
Parameter | Details |
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inputMultidimensionalRaster (Required) | The input multidimensional raster. The input raster can be the portal folder ID, portal item ID, image service URL, cloud multidimensional raster dataset, or shared multidimensional raster dataset. The dataset can be of type Cloud Raster Format (CRF) or a multidimensional mosaic dataset. 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. Syntax: A JSON object describes the input raster.
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mode (Required) | Specifies the method that will be used to perform principal component analysis.
Note:When DIMENSION_REDUCTION is used, the output principal component is a raster, and the output loading is a table. When SPATIAL_REDUCTION is used the output principal component is a table, and the output loading is a raster. Syntax: A String value representing the mode. Example
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dimension (Required) | The dimension name used to process the principal components. Syntax: A string representing the dimension name. Example
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outputPrincipalComponentsName (Required) | The name of the output raster or table. When the mode parameter is specified as DIMENSION_REDUCTION, the output will be a multiband raster 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. The input can be the portal item ID, image service URL, or service properties of the raster. When the mode parameter is specified as SPATIAL_REDUCTION, the output is a table containing a set of time series data representing the principal components. Syntax: A JSON object that describes the name of the output principal components raster or table. Example
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outputLoadingsName (Required) | The name of the output raster or table. When the mode parameter is specified as DIMENSION_REDUCTION, the output will be a table 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. You can specify the name, or you can create an empty service using Portal Admin Sharing API and use the return JSON object as input to this parameter. When the mode parameter is specified as SPATIAL_REDUCTION, the output is a raster in which pixel values are the weights contributing to the principal components. Pixels with larger values are more corelated 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. Syntax: A JSON object that describes the name of the output loadings raster or table. Example
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outputEigenValuesTableName | The output eigenvalues table. Eigenvalues indicate the variance percentage of each component. They help define the number of principal components that are needed to represent the dataset. You can specify the name, or you can create an empty service using Portal Admin Sharing API and use the return JSON object as input to this parameter. Syntax: A String value representing the variable. Example
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variable | The variable of the input multidimensional raster used in computation. If the input raster is multidimensional and no variable is specified, only the first variable will be analyzed, by default. Syntax: A string representing the variable. Example
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numberOfPrincipalComponents | The number of principal components to compute, typically fewer than the number of input rasters or image bands. This parameter also takes the form of percentage (%). For example, 90% means the number of components that can explain 90 percent of variance in the data will be computed. Syntax: A String value representing the variable. Example
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context (Optional) | Contains additional settings that affect task processing. This parameter has the following settings:
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f | The response format. The default response format is html. Values: html | json |
Response
When you submit a request, the task assigns a unique job ID for the transaction.
Syntax:{ "jobId": "<unique job identifier>", "jobStatus": "<job status>" }
After the initial request is submitted, you can use the jobId to periodically check the status of the job and messages, as described in Check job status. Once the job has successfully completed, use the jobId to retrieve the results. To track the status, you can make a request of the following form:
To track the status of the job:
http://<analysis url>/MultidimensionalPrincipalComponents/jobs/<jobId>
Access results
When the status of the job request is esriJobSucceeded, you can access the results of the analysis by making a request of the following form:
To access the output principal components:
http://<raster analysis url>/MultidimensionalPrincipalComponents/jobs/<jobId>/results/outputPrincipalComponentsName?token=<your token>&f=json
To access the output loadings table:
http://<raster analysis url>/MultidimensionalPrincipalComponents/jobs/<jobId>/results/outputLoadingsName?token=<your token>&f=json
To access the output eigenvalues table:
http://<raster analysis url>/MultidimensionalPrincipalComponents/jobs/<jobId>/results/outputEigenValuesName?token=<your token>&f=json