Aggregate Multidimensional function

Available with Image Analyst license.

Available with Spatial Analyst license.

Overview

Creates a multidimensional raster layer by combining existing multidimensional raster variable data along a dimension.

Notes

Use the Dimension Definition parameter to first filter the input data you want to aggregate. For example, if you have 30 years of monthly data, but you only want to create an aggregated layer for the first 15 years, you can use the Dimension Definition parameter to specify the years to include in the analysis.

• Extract salinity data for the month of January over the 10-year period. Choose By Values, set Dimension to StdTime, and set Values to January.
• Slice salinity data over a depth range from 0 to 150 meters. Choose By Ranges, set Dimension to StdZ, and set Minimum Value to -150 and Maximum Value to 0.
• Extract salinity data for the first 10 days of every January over a 10-year period. Choose By Iteration, set Dimension to StdTime, set Start of first iteration and End of first iteration to the corresponding start and end of the iteration period, set Step to 1, and set Unit to Years.

Use the parameters in the Aggregation Definition group to choose the dimension to assess and the aggregation interval using a keyword, a value, or a range of values. For example, if you have 30 years of sea surface temperature data, collected daily and at every 5 meters depth up to 100 meters, you can use the different interval options for the following scenarios:

• Aggregate daily temperature data into monthly data in which the result is a multidimensional raster with 12 time slices, and each slice is the aggregate of each month across all the years. Choose Interval Keyword and set the keyword to Recurring Monthly.
• Aggregate daily temperature data into monthly data in which the result is a multidimensional raster with 360 slices, or 12 time slices per year (30 years x 12 months = 360 slices). Choose Interval Keyword and set the keyword to Monthly.
• Aggregate monthly temperature data into 4-month intervals. Choose Interval Value, set Value Interval to 4, and set Unit to Months.
• Aggregate temperature data from 0 to 25 meters, then from 25 to 50 meters, then from 50 to 100 meters. Choose Interval Ranges and specify minimum and maximum depths as 0 25; 25 50; 50 100.

Parameters

Parameter name	Description
Raster	The input multidimensional raster.
Variables	The variable or variables that will be aggregated along the given dimension.
Dimension Definition	Specifies the method to use to filter the input multidimensional data before performing the aggregation. All—The full range for each dimension will be used. This is the default. By Values—The dimension will be sliced using a dimension value or list of values. By Ranges—The dimension will be sliced using a range or a list of ranges. By Iteration—The dimension will be sliced over a specified interval size.
Values	The dimension values to use to filter the input multidimensional data for analysis. This parameter is required when the Dimension Definition parameter is set to By Values.
Ranges	The minimum and maximum dimension values to use to filter the input multidimensional data for analysis. This parameter is required when the Dimension Definition parameter is set to By Ranges.
Iteration definition parameters	The Iteration definition parameters allow you to define the dimension values to use to filter the input multidimensional data for analysis when the Dimension Definition parameter is set to By Iteration. Dimension—The dimension to use for filtering. Start of first iteration—The beginning of the first interval. This interval is used to iterate through the dataset. End of first iteration—The end of the first interval. This interval is used to iterate through the dataset. Step—The frequency with which the data will be sliced. Unit—The iteration unit.
Operation	Specifies the mathematical method that will be used to combine the aggregated slices in an interval. Majority—The pixel value that occurred most frequently will be calculated across all slices in the interval. Maximum—The maximum value of a pixel will be calculated across all slices in the interval. Mean—The mean of a pixel's values will be calculated across all slices in the interval. This is the default. Median—The median value of a pixel will be calculated across all slices in the interval. Minimum—The minimum value of a pixel will be calculated across all slices in the interval. Minority—The pixel value that occurred least frequently will be calculated across all slices in the interval. Range—The range of values for a pixel will be calculated across all slices in the interval. Standard Deviation—The standard deviation of a pixel's values will be calculated across all slices in the interval. Sum—The sum of a pixel's values will be calculated across all slices in the interval. Variety—The number of unique pixel values will be calculated across all slices in the interval. All options also have an equivalent with an Ignore NoData option. These will perform the mathematical operation on all valid pixels along the dimension and ignore any NoData pixels.
Dimension	The aggregation dimension. This is the dimension along which the variables will be aggregated.
Type	Specifies the dimension interval for which the data will be aggregated. All—The variable data will be aggregated across all slices. This is the default. Interval Keyword—The variable data will be aggregated using a commonly known interval. Interval Value—The variable data will be aggregated using a user-specified interval and unit. Interval Ranges—The variable data will be aggregated between specified pairs of values or dates.
Keyword Interval	Specifies the keyword interval that will be used when aggregating along the dimension. This parameter is required when the Type parameter is set to Interval Keyword. Hourly—The data values are aggregated into hourly time steps, and the result includes every hour in the time series. This is the default. Daily—The data values are aggregated into daily time steps, and the result includes every day in the time series. Weekly—The data values are aggregated into weekly time steps, and the result includes every week in the time series. Dekadly—The data values are aggregated into 3 periods of 10 days each. The last period can contain more or fewer than 10 days. The output includes 3 slices for each month. Pentadly—The data values are aggregated into 6 periods of 5 days each. The last period can contain more or fewer than 5 days. The output includes 6 slices for each month. Monthly—The data values are aggregated into monthly time steps, and the result includes every month in the time series. Quarterly—The data values are aggregated into quarterly time steps, and the result includes every quarter in the time series. Yearly—The data values are aggregated into yearly time steps, and the result includes every year in the time series. Recurring daily—The data values are aggregated into daily time steps, and the result includes one aggregated value per Julian day. The output includes, at most, 366 daily time slices. Recurring weekly—The data values are aggregated into weekly time steps, and the result includes one aggregated value per week. The output includes, at most, 53 weekly time slices. Recurring monthly—The data values are aggregated into monthly time steps, and the result includes one aggregated value per month. The output includes, at most, 12 monthly time slices. Recurring quarterly—The data values are aggregated into quarterly time steps, and the results include one aggregated value per quarter. The output includes, at most, 4 quarterly time slices.
Value Interval	The size of the interval used for the aggregation. This parameter is required when the Type parameter is set to Interval Value.
Ranges	Interval ranges specified in a table are used to aggregate groups of values. The minimum and maximum values specify the range to be included. This parameter is required when the Type parameter is set to Interval Ranges.