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Using SAS/SPECTRAVIEW Software

Data Set Requirements

To use SAS/SPECTRAVIEW, your data must be stored in a SAS data set, which consists of variables and observations. A variable is a column in the data set, such as quantities or characteristics being measured, that has attributes such as a name and a type (character or numeric). An observation is the horizontal component of the data set, such as collections of values associated with a single entity; each observation contains one value for each variable in the data set.

The SAS data set that you use must have at least four variables:

• three variables, which can be character or numeric, to be the axis variables, that is, the X variable, Y variable, and Z variable. Each axis variable must contain at least two unique values. For best results, the axis variables should contain discrete data, which consists of distinct values (noncontinuous) containing natural gaps, like patient IDs and years.

• a fourth variable, which must be numeric, to be the response variable. The response variable must contain at least two unique values. For best results, the response variable should contain measured or modeled response values (like sales or population) that are related in some way to the axis values.

You can specify an optional fifth variable, which can be character or numeric, as a BY variable. A BY variable allows you to animate an image so that you can see how response values change according to some grouping, like over time.

How the Software Displays Data

When you load a SAS data set into SAS/SPECTRAVIEW, the software does the following:

1. The software reads each observation from the data set and creates a three-dimensional volume grid by plotting the values for the axis variables, which creates the x,y,z coordinates. That is, for each observation, the following occurs:
   • The value for the X variable is plotted on the X axis, which is the horizontal axis.

   • The value for the Y variable is plotted on the Y axis, which is the vertical axis.

   • The value for the Z variable is plotted on the Z axis, which is the depth axis.

   The volume grid is actually an invisible network of lines that intersect in three-dimensional space. Each intersection of an x,y,z coordinate is a location in three-dimensional space, referred to as a data point. The shape and size of the volume grid is determined by the number of unique X, Y, and Z values and is displayed with a bounding box, which is a set of lines that outline the three-dimensional volume grid.

2. The software divides the values for the response variable (the response values) into ranges, then color codes them using default ranges and preset colors. According to the response value (and its associated response value range color), the software then maps an appropriate color to each data point that has an associated response value. Note that you can customize the ranges and colors as explained in Setting Response Value Colors for Images.

Spatial Data

The variables that you specify for the axes frequently (but not always) represent dimensions of spatial data. For example, in a spatial diagram like the following cubic volume, the x,y,z coordinate 6,5,5 represents a location that is 6 ticks along the X axis, 5 ticks along the Y axis, and 5 ticks along the Z axis (counting from an origination point shown as 0 in this figure):

Coordinate 6,5,5 in Three-Dimensional Space

To illustrate the relationship among the axis values and the response values, consider the following spatial data example:

To determine the age of someone sitting in a specific seat in a stadium, you need to know the section, the row, and the seat number to locate that person. The three values are x,y,z coordinates that identify a specific location in space, which in this case is a stadium. Once located, the person can be asked his age; that number becomes the fourth value...the response value. You could collect the same information for everyone in the stadium. That is, you could attach an age response value to each location identified by section, row, and seat.

If you created a data set of the seating information and loaded it into SAS/SPECTRAVIEW, the following would occur:

• The resulting data points would form a volume grid displayed within a bounding box.

• The data points would represent the seat holders.

• The response value for each data point would be the age of the occupant.

You could then explore the data visually and determine, for example, whether age groupings occur in various locations in the stadium. You could also display any empty seats, which have no response value at that location.

Locations in three-dimensional space are similar to stadium seat locations. For example, if you want to test the amount of sulphur in the air at various locations, you would need three coordinates similar to section, row, and seat. These might be 20 km east, 10 km north, and 200 meters up. The coordinates describe a specific location in space where a sulphur sample can be taken and recorded. When you display the data, a color is mapped to each response value, representing ranges of values, for example, values between 0.0 and 0.5 could be red, values between 0.51 and 1.0 could be yellow, and so on.

Non-Spatial Data

Since the axis variables represent different dimensions of data, you can use SAS/SPECTRAVIEW to explore non-spatial data as well.

For example, the sample data set MORTGAGE (which contains mortgage payments for various numbers of years, interest rates, and loan amounts) can be represented several ways. The axis variables could be principal amount, percentage rate, and term of loan; it does not matter which variable you assign to X, which to Y, and which to Z. This presumes that the PAYMENT variable is the response you want to explore. Or you could assign the variables so that AMOUNT is the response to explore, with axes of term, rate, and affordable payment range.

Assume the following variables for SAS/SPECTRAVIEW:

RATE	as the X variable, which is the loan interest percentage rate.
AMOUNT	as the Y variable, which is the loan amount.
YEARS	as the Z variable, which is the number of years for the loan.
PAYMENT	as the response variable, which is the monthly payment amount.

SAS/SPECTRAVIEW reads the data and generates the horizontal X axis (representing the loan interest rate), the vertical Y axis (representing the loan amounts), and the depth Z axis (representing the number of years for the loan). Each resulting data point is applied a color representing a response value, which is a monthly payment amount.

MORTGAGE Data Set Displayed as a Point Cloud examines the data using a point cloud (which is one of the SAS/SPECTRAVIEW visualization techniques), showing the relationship of the monthly payments to percentage rate, loan amount, and length of the loan. This point cloud displays a subset of the data points, showing only the higher loan payments (response values). With this point cloud, you can determine that most of the higher loan payments are for a shorter number of years.

MORTGAGE Data Set Displayed as a Point Cloud

Summary of Software Tools

SAS/SPECTRAVIEW provides the following tools that you use to create and analyze images.

Visualization Techniques

Visualization techniques create images representing your data.

Cutting planes produce slices of data, either perpendicular to an axis or not perpendicular to any axis. In addition to the axis cutting planes, you can request two- and three-dimensional surface views (surfaces, charts, stacks, and plots) at a specific cutting plane's location.

Direct volume rendering creates a two-dimensional image of the entire volume of data points with transparency.

Isosurface produces a three-dimensional surface by connecting all the data points with one response value.

Point cloud displays response values with colored markers, showing individual data points.

Solid-volume image produces a colored, three-dimensional solid-block image of the data points, providing a surface view of the data at the volume's borders.

Customization Options

SAS/SPECTRAVIEW provides a variety of options that you can use with visualization techniques to aid in data exploration and analysis:

Probe analysis displays the response value for a specific data point in an image.

Bounding box displays a set of lines that outline the three-dimensional volume representing the data in the Volume window. By default, the bounding box is on, but you can turn it off.

Image annotation allows you to add the following to two- and three-dimensional images: axis labels that name the variables associated with the axes, major and minor tick marks along the axes, text, and a response legend (a visual key to the colored data points).

Image transformations let you view a three-dimensional image from any angle or in a different size. That is, you can rotate, move, and zoom an image.

Color palette lets you customize colors for the response value ranges, missing values, an isosurface, and image annotations (such as tick marks, axes labels, text, and the bounding box). You can also save and recall user-defined color palette files.

Processing Options

These options let you modify your view of the data:

BY variable	is a variable specification whose values define groups of observations, such as hour, month, or year. Specifying a BY variable allows you to animate an image so that you can see how response values change according to some grouping, like over time.
Categorizing data	groups numeric data to create discrete ranges for the X, Y, and Z axes, which results in reducing the number of data points created for the volume grid.
WHERE clause	allows you to specify a subset of data to be read into the software.
Data filtering	smooths or sharpens data to deemphasize or highlight variations in response values. The software provides several predefined filters, or you can create your own.
Data saving	lets you save all or part of the original data values to a SAS data set.
Image saving	lets you save a displayed image as a TIFF (Tagged Image File Format) file or a PostScript file.

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