Interpolate From Spatiotemporal Points (Image Analyst)

Available with Image Analyst license.

Summary

Interpolates temporal point data into a multidimensional raster.

Usage

• The points are grouped by time using the Temporal Aggregation parameter.

• All points within a temporal period are used for interpolation to create a slice in the output multidimensional raster.

• The result of this tool is a multidimensional raster that contains the variable interpolated across time and space.

• The tool supports the following interpolation methods:

  • IDW—The output is calculated from the average of points weighted by their inverse distance to the power of 2. The search radius is 20 times the output cell size.
  • Triangulation—The output is calculated using a linear interpolation from a triangulated irregular network constructed from the input points.
  • Mean—The output is calculated using the average of input points.
  • Median—The output is calculated using the median of input points.
  • Natural neighbor—The output is calculated using a natural neighbor interpolation from a triangulated irregular network constructed from the input points.
  • Nearest neighbor—The output is calculated using the nearest point from the center of the output cell.
  • Quadratic—The output is calculated using a quadratic function:

    

    Where x,y are the location of the measurement's points, t is the time of measurements. The function is fit to all measurements within an output cell and the coefficients are estimated using an iterative least squares approach. The output value for each cell is estimated from the quadratic function given the center coordinate of the output cell and dates.

Parameters

Dialog

Label	Explanation	Data Type
Input Temporal Dataset	The input point layer, trajectory layer, or trajectory dataset.	Trajectory Layer; Feature Layer; Mosaic Dataset; Mosaic Layer
Variable Field	A field containing variable values.	String
Time Field	A field containing time values.	String
Temporal Aggregation (Optional)	Specifies the temporal aggregation of the output multidimensional raster. The interpolation algorithm uses all available data within these time periods to calculate the output slice. Daily—The data values will be aggregated into daily time steps. This is the default. Weekly—The data values will be aggregated into weekly time steps. Monthly—The data values will be aggregated into monthly time steps. Quarterly—The data values will be aggregated into quarterly time steps. Yearly—The data values will be aggregated into yearly time steps.	String
Cellsize (Optional)	The output cell size. By default, the cell size will be the shorter of the width or the height of the input point feature extent, divided by 250.	Double
Interpolation Method	Specifies the interpolation method that will be used. IDW—Inverse distance weighted interpolation will be used. Triangulation—Triangulation interpolation will be used. Mean—Mean interpolation will be used. Median— Median interpolation will be used. Natural Neighbor—Natural neighbor interpolation will be used. Nearest Neighbor—Nearest neighbor interpolation will be used. Quadratic—Quadratic interpolation will be used.	String

Return Value

Label	Explanation	Data Type
Output Raster	The output multidimensional raster dataset.	Raster Dataset

Python

InterpolateFromSpatiotemporalPoints(in_dataset, variable_field, time_field, {temporal_aggregation}, {cell_size}, interpolation_method)

Name	Explanation	Data Type
in_dataset	The input point layer, trajectory layer, or trajectory dataset.	Trajectory Layer; Feature Layer; Mosaic Dataset; Mosaic Layer
variable_field	A field containing variable values.	String
time_field	A field containing time values.	String
temporal_aggregation (Optional)	Specifies the temporal aggregation of the output multidimensional raster. The interpolation algorithm uses all available data within these time periods to calculate the output slice. DAILY—The data values will be aggregated into daily time steps. This is the default. WEEKLY—The data values will be aggregated into weekly time steps. MONTHLY—The data values will be aggregated into monthly time steps. QUARTERLY—The data values will be aggregated into quarterly time steps. YEARLY—The data values will be aggregated into yearly time steps.	String
cell_size (Optional)	The output cell size. By default, the cell size will be the shorter of the width or the height of the input point feature extent, divided by 250.	Double
interpolation_method	Specifies the interpolation method that will be used. IDW—Inverse distance weighted interpolation will be used. TRIANGULATION—Triangulation interpolation will be used. MEAN—Mean interpolation will be used. MEDIAN— Median interpolation will be used. NATURAL_NEIGHBOR—Natural neighbor interpolation will be used. NEAREST_NEIGHBOR—Nearest neighbor interpolation will be used. QUADRATIC—Quadratic interpolation will be used.	String

Return Value

Name	Explanation	Data Type
out_raster	The output multidimensional raster dataset.	Raster Dataset

Code sample

InterpolateFromSpatioTemporalPoints example 1 (stand-alone script)

This example interpolates elevation point data into a multidimensional raster.

# Import system modules
import arcpy
from arcpy import *

#Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")

# Set local variables
in_dataset = "icesat_trajectory"
out_raster = r"C:\temp\icesat_surface.crf"
variable_field = "elevation"
time_field = "Time"
temporal_aggregation = "Daily"
cell_size = 5000
interpolation_method = "Quadratic"

# Execute
interpolation_output = arcpy.ia.InterpolateFromSpatiotemporalPoints(in_dataset, out_raster, variable_field,
		       time_field, temporal_aggregation, cell_size, interpolation_method)

Environments

Cell Size, Current Workspace, Extent, Geographic Transformations, Mask, Output Coordinate System, Parallel Processing Factor, Snap Raster