**"data"**= information that can be represented in computer readable format**data model**= conceptual view of data**data model** data format (physical view of data)- choose expressive/effective visualization technique
- avoid "mental road blocks"

[BRO92], [GAL94]

- nominal, ordinal, quantitative
- point, scalar, vector
- "continuous" data
- topology/structure for non-continuous data
- data reliability
- valid range of data
- time descriptors

- Nominal data
- members of certain class, e.g. [Georgia, Florida, North Carolina, Delaware], or [Maple, Birch, Oak]
- effective visual attributes: color - hue, symbol

- Ordinal data
- related by order, e.g. [low, medium, high], or [tiny, small, medium, large]
- effective visual attributes: brightness, size, (color - hue)

- Quantitative data
- carry precise numerical value, e.g. [2.3, 4.56, 0.8, 2.5E-35]
- effective visual attributes: position, length, (color - hue)

**Priorities of Visual Attribute for Various Data Types (Excerpt) [MAC86]**

Quantitative |
Ordinal |
Nominal |

Position | Position | Position |

Length | Density | Hue |

Angle | Saturation | Density |

Slope | Hue | Saturation |

Area | Length | Shape |

Density | Angle | Length |

Saturation | Slope | Angle |

Hue | Area | Slope |

Shape | Shape | Area |

Syntactical categories, additionally characterized by dimensions

- Point
- each data element is considered as a position in n-dimensional space.
- example: measurements of leaves: [length, width, tree type, age], e.g.

[2.3, 1.2, B, 1], [4.3, 2.2, B, 3], [1.5, 1.5, M, 1], [3.0, 2.9, M, 3], .... - expressive visualizations: scatter plots, glyphs

- Scalars
- each data element has a numeric expression
- example: topography of terrain, expressed as 2-d field containing elevations

- Scalar arrays
- often "discrete samples of continuous functions"
- usually 1 (linear), 2 (image) , or 3 (volumetric) dimensional data sets; samples in equidistant or non-equidistant steps.
- expressive visualizations: line graph, shaded surface, volume viewing

- Vectors
- each data element is considered as a straight directed line with a certain length (magnitude) in n-dimensional space.
- example: Direction of particle flow in channel.
- expressive visualizations: arrows, stream lines, particle tracks

"Continuous" data can be represented by (samples of) function:

- yi = fi (X), where X = (x1 , x2 , x3 , ..., xn ); i=[1,....,m]
- x .... independent variables; e.g space, time, spectral ("dimensions")
- y .... dependent variables ("parameters")
- x, y large ... multidimensional, multiparameter, multivariate data

**=>** regular/irregular format

**Expressive visualizations** of functions: similar to scalar, quantitative, ordinal

**Interpolation methods:** must be meaningful in problem space

**Computation time** for visualization techniques faster on regular grids

Note: for valid data must avoid aliasing artifacts

- Types of topology/structure, e.g.
- sequential (text)
- hierarchical
- relational
- single points and connectors

- Examples and corresponding expressive visualizations
- molecules (ball-and-stick model)
- data bases (cone tree; perspective wall)

- Data reliability: Missing data or unreliable data
- expressive visualizations: error bars; indicate borders between real/missing data
- careful with interpolation

- Valid range of data: min / max / mean / median
- Time descriptors
- Various meanings of time: simulation time, simulated/actual time frame,computation time, recording and playback time, user's time frame
- "time models" to support time conversions necessary to synchronize

Aspects of Data

HyperVis Table of Contents

*Last modified on February 11, 1999, G. Scott Owen, owen@siggraph.org*