SAS/SPECTRAVIEW Software User's Guide

# Introduction

 Understanding the Volume Grid

Examples of data that would result in a complete grid is an air quality survey that includes a full grid of sample data from an entire area, scientific numerical models, medical images, or complete financial models like a mortgage table.

To have an idea of how much data is required for a complete grid, think of it like a three-dimensional spreadsheet where multiple sheets extend along the Z axis and where each cell on each sheet represents the values for one observation. Suppose the variables ROW represents X, COLUMN represents Y, and SHEET represents Z. The values ROW=2, COLUMN=2, and SHEET=1, which is one observation, would be located in the spreadsheet as shown in Three-Dimensional Spreadsheet.

For a complete column 2, you would need these observations:

```ROW   COLUMN   SHEET
1     2        1
2     2        1
3     2        1
4     2        1
5     2        1```

For a complete sheet 1, you would need observations for all five columns:

```ROW   COLUMN   SHEET
1     1        1
2     1        1
3     1        1
4     1        1
5     1        1
1     2        1
2     2        1
3     2        1
4     2        1
5     2        1
1     3        1
2     3        1
3     3        1
4     3        1
5     3        1
1     4        1
2     4        1
3     4        1
4     4        1
5     4        1
1     5        1
2     5        1
3     5        1
4     5        1
5     5        1```

Finally, to complete the entire grid, you would need all those observations for sheet 2 and for sheet 3.

For example, consider the following eight observations, which contain three unique values for each axis:

```OBS   X   Y   Z   Response
1     1   1   1   111
2     2   1   1   211
3     3   1   3   313
4     3   2   1   321
5     3   3   1   331
6     2   2   1   221
7     2   2   2   222
8     2   3   2   232```
The software would generate and plot 27 data points (3x3x3) -- 8 actual data points representing the observations and 19 filler points as shown in 3x3x3 Volume Grid. The first volume grid shows the actual data points; the second volume grid shows the actual data points and the filler points.

The larger the number of unique values for an axis, the larger the resulting number of data points. For example, consider the following eight observations, which contain 7 unique values for the X axis, and three unique values for the Y and Z axes.

```OBS   X   Y   Z   Response
1     1   4   5   145
2     3   2   3   323
3     2   2   3   223
4     4   6   5   465
5     6   4   3   643
6     7   2   1   721
7     5   2   5   525
8     1   6   1   161```
The software would generate and plot 63 data points (7x3x3) - .8 actual data points representing the observations and 55 filler points as shown in 7x3x3 Volume Grid. The first volume grid shows the actual data points; the second volume grid shows the actual data points and the filler points.

Unlike for locations having at least one value for x,y,z coordinate, the software does not replace non-existent x,y,z coordinates with filler points. Instead, the volume grid displays a visual gap indicating an area within the volume grid where no data is available. The actual data points appear to be non-uniformly distributed because of the gap in the data. Consider the following data, which contains three unique values for the axis variables:

```OBS   X   Y   Z   Response
1     1   4   5   145
2     1   2   3   123
3     2   2   3   223
4     7   6   5   765
5     2   4   3   243
6     1   2   1   121
7     7   2   5   725
8     2   6   1   261```
When the actual data values are plotted and the volume grid is completed, the actual data points are not uniformly distributed, resulting in a volume grid that appears to have gaps. The software would generate and plot 27 data points (3x3x3) - 8 actual data points representing the observations and 19 filler points as shown in Sparse Data Volume Grid. The first volume grid shows the actual data points; the second volume grid shows the actual data points, the filler points, and visual gaps:

Note that when loading character data, gaps will not occur. The software assigns sequential numerical values to the character values, resulting in uniformly distributed data points.

Missing values, by default, have no color. If you want missing values to display in an image, you must use the color palette to assign a color as explained in Assigning Color to Missing Values.

If your data represents an incomplete grid or sparse data, the software may create many filler points. However, if your data represents a complete grid, displaying missing values lets you see holes, which may indicate a possible failure of the measuring equipment.