Predict Using Trend Raster

inputMultidimensionalRaster

(Required)

The Portal Item ID, Image Service URL, cloud multidimensional raster dataset, or shared multidimensional raster dataset. It must be a trend raster from the Generate Trend Raster tool.

Syntax: JSON object describes the input multidimensional trend raster.

Example

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inputMultidimensionalRaster={"itemId": <portal item id>}
inputMultidimensionalRaster={“url”: <image service url>}
inputMultidimensionalRaster={“uri”: <cloud raster uri or shared data path>}

At least one type of input needs to be provided in the JSON object. If multiple inputs are given, the itemid takes the priority.

outputName

(Required)

Output hosted image service properties. If the hosted image service is already created, the portal item ID or service URL can be given to the service tool. The output path of the multidimensional raster dataset generated in the raster store will be used to update the existing service definition. The service tool can also generate new hosted image service with the given service properties. The output hosted image service is stored in a raster store and shared on either the Raster Analysis Image Server or Image Hosting Image Server depending on the Enterprise configuration.

Syntax: JSON object describes the output multidimensional raster.

Example

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outputName={"itemId": <portal item id>}
outputName={“url”: <image service url>}
outputName={“uri”: <cloud raster uri or shared data path>}
or
outputName={"serviceProperties": {"name": "predicted_water_temp", "capabilities": "image, metadata"}}

At least one type of input needs to be provided in the JSON object. If multiple inputs are given, the itemid takes the priority.

variables

The variable or variables that will be predicted in the analysis. If no variables are specified, all variables will be used.

Syntax: Either a string representing the variables, with multiple variables separated by comma, or a list containing the variables.

Example

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variables=["ceiling","cover"]

dimensionDefinition

Specifies the method used to provide prediction dimension values.

• BY_VALUE —The prediction will be calculated for a single dimension value or a list of dimension values defined by the dimensionValues parameter. This is the default. For example, you want to predict yearly precipitation for the years 2050, 2100, and 2150.
• BY_INTERVAL —The prediction will be calculated for an interval of the dimension defined by a start and an end value. For example, you want to predict yearly precipitation for every year between 2050 and 2150.

Syntax: A string of one of the keywords.

Example

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dimensionDefinition="BY_VALUE"

dimensionValues

The dimension value or values to be used in the prediction.

The format of the time, depth, and height values must match the format of the dimension values used to generate the trend raster. If the trend raster was generated for the StdTime dimension, the format should be YYYY-MM-DDTHH:MM:SS, for example, 2050-01-01T00:00:00.

This parameter is required when the dimensionDefinition parameter is set to BY_VALUE .

Syntax: A list of dimension values.

Example

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dimensionValues=["2050-01-01T00:00:00","2100-01-01T00:00:00"]

start

The start date, height, or depth of the dimension interval to be used in the prediction. The format of the time, depth, and height values must match the format of the dimension values used to generate the trend raster. If the trend raster was generated for the StdTime dimension, the format should be YYYY-MM-DDTHH:MM:SS .

Syntax: A string representing the start value.

Example

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start="2150-01-01T00:00:00"

end

The end date, height, or depth of the dimension interval to be used in the prediction. The format of the time, depth, and height values must match the format of the dimension values used to generate the trend raster. If the trend raster was generated for the StdTime dimension, the format should be YYYY-MM-DDTHH:MM:SS .

Syntax: A string representing the end value.

Example

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end="2050-01-01T00:00:00"

intervalValue

The number of steps between two dimension values to be included in the prediction. The default value is 1. For example, to predict temperature values every five years, use a value of 5.

Syntax: A string representing the interval value.

Example

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intervalValue="5"

intervalUnit

Specifies the unit that will be used for the value interval. This parameter only applies when the dimension of analysis is a time dimension.

• HOURS —The prediction will be calculated for each hour in the range of time described by the start, end, and interval_value parameters.
• DAYS —The prediction will be calculated for each day in the range of time described by the start, end, and interval_value parameters.
• WEEKS —The prediction will be calculated for each week in the range of time described by the start, end, and interval_value parameters.
• MONTHS —The prediction will be calculated for each month in the range of time described by the start, end, and interval_value parameters.
• YEARS —The prediction will be calculated for each year in the range of time described by the start, end, and interval_value parameters.

Syntax: A string of one of the keywords.

Example

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intervalUnit="YEARS"

context

Contains additional settings that affect task execution. This task has the following settings:

• Cell Size (cellSize)—The output raster will have the resolution specified by cell size.

• Extent (extent)—A bounding box that defines the analysis area.

• Mask (mask )—Only cells that fall within the analysis mask will be considered in the operation.

• Output Spatial Reference (outSR)—The output raster will be projected into the output spatial reference.

• Parallel Processing Factor (parallelProcessingFactor )—The specified number or percentage of processes will be used for the analysis.

• Resampling Method (resamplingMethod )—Specifies the resampling method that will be used when creating the raster dataset for download.

  • NEAREST—The value of the closest cell will be used to assign a value to the output cell when resampling. This is the default.
  • BILINEAR—The new value of a cell will be based on a weighted distance average of the four nearest input cell centers.
  • CUBIC—The new value of a cell will be based on fitting a smooth curve through the 16 nearest input cell centers.

• Snap Raster (snapRaster)—The output raster will have its cells aligned with the specified snap raster.

f

The response format. The default response format is html.

Values: html | json