# Visualization Techniques for Scalar Entities

## One - Dimensional Domain

The data is sampled from a function F(x1).

Line Graph: **E**^{S}_{1 }

Draw a line through a set of data points F(x1). A consideration is the interpolation
through the data points. It might be a straight line, or a spline curve, or it might fit
some other model.

Multiple Line Graphs: **E**^{mS}_{1 }

Display several plots on the same graph, using techniques to distinguish the different
data sets. These can include the following:

- line patterns (dot - dot, continuous, dash, etc.)
- line thickness
- line color

Bar Chart: **E**^{S}_{{1}}

Depicts values by length of bars drawn horizontally or vertically. Pie charts are
similar to bar charts.

Histogram: **E**^{S}_{[1]}

The data elements are placed into bins if they are in certain value ranges. Then the
histogram shows the number of elements in each bin by drawing bars of different lengths.
Click here for information on the IRIS Explorer Histogram module.

If the underlying model is known for the above types of plots, then this might also be
constructed If it is not known then a cubic spline interpolation can be used to connect
the points. Error in data points may be shown by using error bars or a similar method.
Usually the underlying method involves one or more parameters which can be varied so as to
more closely fit the experimental data. The parameters may be varied and then some measure
of closeness of fit is computed and the process iterated until the "best" set of
parameters is found. A good "closeness of fit" method is to minimize the squares
of the differences between the experimental and theoretical curves.

Two-Dimensional
Domain

Three-Dimensional
Domain

Visualization
Techniques for Data Display

HyperVis Table of
Contents

Last modified on April 05, 1999, G.
Scott Owen, owen@siggraph.org