Aspects of Data in Visualization: Overview
Characterization of data
- "data" = information that can be represented in computer readable
- data model = conceptual view of data
- data model data format (physical view of data)
- choose expressive/effective visualization technique
- avoid "mental road blocks"
Overview of selected data characteristics
- nominal, ordinal, quantitative
- point, scalar, vector
- "continuous" data
- topology/structure for non-continuous data
- data reliability
- valid range of data
- time descriptors
Nominal, ordinal, quantitative
- Nominal data
- members of certain class, e.g. [Georgia, Florida, North Carolina, Delaware], or [Maple,
- 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]
Point, Scalar, Vector
Syntactical categories, additionally characterized by dimensions
- 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
- 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
- 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
Topology/structure of non-continuous data
- Types of topology/structure, e.g.
- sequential (text)
- single points and connectors
- Examples and corresponding expressive visualizations
- molecules (ball-and-stick model)
- data bases (cone tree; perspective wall)
Other data characteristics
- 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, email@example.com