ArcGIS Pro

A Workflow for Creating Discrete Voxels

Note: In ArcGIS Pro 3.1 and later, it is recommended to use the Nearest Neighbor 3D tool to accomplish the workflow described in this blog.

First available in ArcGIS Pro 2.6, a voxel layer represents multidimensional spatial and temporal information in a 3D volumetric visualization. Voxel layers are based on data stored in a netCDF file.  In ArcGIS Pro, the netCDF file can be created from a geostatistical layer or a space-time cube. A typical workflow is to take points with attributes representing a continuous variable (for example, temperature), perform 3D kriging using the Empirical Bayesian Kriging tool, and convert the output geostatistical layer to a netCDF file using the GA Layer 3D To NetCDF tool. This netCDF file can be added to a scene in ArcGIS Pro as a multidimensional voxel layer.

subsurface samples of soil type
Subsurface samples of soil type.

But what if the attribute of your points represents discrete categories (for example, soil type) rather than a continuous variable like temperature? 3D kriging expects the input data to have a continuous attribute for interpolation, but it can be configured to perform nearest-neighbor interpolation. In nearest-neighbor interpolation, the value at a new location (a location where you don’t have a sample point) is assigned the value of the nearest input point in 3D space. This allows you to visualize and explore the categories of the points as a full voxel cube in 3D.

Perform Nearest-Neighbor Interpolation

This blog will provide the configurations to perform nearest-neighbor interpolation, but it will not explain the parameters or what they mean. To learn about 3D interpolation and all parameters of Empirical Bayesian Kriging 3D, consider completing this lesson: Interpolate 3D oxygen measurements in Monterey Bay.

Screen capture of geoprocessing tool dialog.
Parameter settings to approximate nearest-neighbor interpolation.

In the example below, we’ll use soil type recorded at various depths in boreholes located just west of St. Louis, Missouri. The data were downloaded and processed from the ArcGIS Code Sharing site and contributed by Jennifer E. Carrell, Illinois State Geological Survey, University of Illinois at Urbana-Champaign. The 3D points contain a subset of the soil types (formations) in this region: clay, loam, sand, and shale.

1. Convert soil type categories to a number (integer) field.

2. Configure settings to perform nearest-neighbor interpolation. Some parameters are required, but most are to avoid as many unnecessary kriging calculations as possible.

3. Add the output netCDF file from the previous step to a local scene as a voxel layer.

Screen capture of layer properties.
Layer properties.

Discrete Voxel Layer in a Scene

The voxel layer now displays in the local scene and aligns with the input points. Each individual voxel within the voxel layer contains the value of the soil type of its closest neighbor in 3D. You can explore voxel layers in many ways, including advanced transparency, sectioning, isovolumes, and animations. Learn more about the capabilities and options of voxel layers.

Full discrete voxels.
Discrete voxels of soil type.

Considerations and Limitations

  1. Nearest-neighbor (NN) interpolation should be considered a display option rather than an interpolation method. Unlike other interpolation methods that mathematically estimate continuous values at new locations, NN interpolation is a simple assignment algorithm.
  2. The quality of the visualization heavily depends on your sampling plan. It is best to have samples evenly distributed throughout your study area in 3D.
  3. This technique is most appropriate as an initial visualization and may not work well in poorly stratified or highly interfingered formations.

Thank to Eric Krause (Spatial Statistics Team) for configuring the parameters for nearest-neighbor interpolation.

About the author

Kevin Butler is a Product Engineer on Esri’s Analysis and Geoprocessing Team working as a liaison to the science community. He holds a Ph.D. in Geography from Kent State University. Over the past decade he has worked on strategic projects, partnering with customers and other members of the science community to assist in the development of large ecological information products such as the ecological land units, ecological marine units and ecological coastal units. His research interests include a thematic focus on spatial statistical analytical workflows, a methodological focus on spatial clustering techniques and a geographic focus on Puerto Rico and midwestern cities.

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