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On This Page:  • Nominal Data   • Ordinal Data  • Continuous Quantitative Data

On this page we discuss and give examples of several distinct ways that color can be used to label data. We also point out some pitfalls and suggest solutions.

Color is a powerful tool for labeling graphic elements and has been extensively used in scientific and engineering data visualization. Labeling applications can be broken down by the type of data involved, which determines the constraints on choice of labeling colors:

Nominal Data This link will take you back to the top of the page

Nominal data only fall into distinct classes. They have no ordinal or quantitative structure. With nominal data the color label only indicates membership in a non-quantitative class (e.g., labeling the lines of a graph).

The particular colors chosen need only to be discriminable from each other and identifiable from the legend.

Use of color to distinguish the different data curves in a graph.

Example: Line Graphics. In simple line graphics color has long been used to distinguish among lines. If the conditions represented by the lines differ on only one dimension, other line characteristics (e.g., type, stroke width) can be used as a separate, correlated coding that is useful to users with anomalous color vision. If the conditions vary on multiple dimensions, color and other line characteristics can be used independently to represent overlapping groupings.

Potential Problems: Failure of discrimination or identification. The usual causes are trying to label too many classes (six to ten is usually the maximum--four or five is easier to achieve), too small symbols or stroke widths, too similar colors, and poor luminance contrast between symbols and backgrounds.

Potential Solutions: Use fewer colors (i.e., code some of the distinctions with other graphic dimensions--for example, symbol shape), increase stroke widths on symbols and lines, use maximally separated colors, and choose symbol and line colors that have moderate luminance-contrast with the backgrounds.

More about discrimination and identification.

Ordinal Data This link will take you back to the top of the page

Ordinal data lie in classes that can be arranged in an ordered sequence on the basis of some ordinal relationship, such as "greater than/less than". They can be further subdivided into monopolar (increasing or decreasing) and bipolar (both increasing and decreasing from zero or neutral).

With ordinal data the labeling colors of the graphic elements must be not only discriminable and identifiable, but also visually ordered. The color assignments have to express the sequential relationships among the graphic elements. This can be achieved with a hue sequence, a saturation sequence, a lightness sequence, or some combination of the dimensions. Monopolar hue sequences can be obtained by mixtures of varying amounts of two non-opponent hues, i.e., some pair other than red/green or yellow/blue. Saturation and lightness are naturally visually ordered. Combinations of saturation and lightness work well (see example below). For bipolar ordinal labeling a combination of saturation and lightness in two hues works well.

Example: Map Labels. Color is often used to associate one of several quantities or attributes with an area on a graphic. Such "choropleth" maps are used widely to display such variables as economic or environmental distributions.

Potential Problems: In addition to failure of discriminability or identifiability, the colors may fail to form a visual sequence.

Potential Solutions: Use saturation and lightness rather than hue.

For more about color in maps, see the ColorBrewer color selection tool.

Section of a US map in which five different colors are applied to the states to indicate their relative mortality rates
Larger Image New Window.

Continuous Quantitative Data This link will take you back to the top of the page

Continuous quantitative data have not only sequential order but metric spacing as well. There are two kinds, interval-scale data and ratio-scale data. A difference of one unit on an interval scale is the same size over the whole scale. This is also true of a ratio scale, with the added constraint that there is a true zero on the scale. A quantity which has twice the scale value of another is twice as large in magnitude.

Color labeling of interval-scale and ratio-scale data has all of the constraints of the above classes and a few more. The visual relationships among the colors are intended to express the quantitative relationship among the elements. For interval-scale data elements this means that two pairs of data that differ by the same amount should be labeled with two pairs of colors that have the same visual difference, e.g., the same brightness difference. A ratio-scale data element that is twice the magnitude of a second data element should be labeled with a color with twice the visual magnitude of the label for the second, e.g., twice the brightness.

In the best designed quantitative cases the relationships among the colors express the quantitative relationships well enough to reveal "the big picture"-- trends, groupings, and other structure -- without the user having to laboriously recover each value from the scale. This generally requires some art and judgment. The quantitative relationships between the visual responses to the labeling colors are not often readily calculated due to dependence on the viewing context. A skillful mapping will approximately preserve values and rates of change without introducing patterns in the colors that are not in the data.

Section of a US weather map in which various levels of an atmospheric variable are shown in shades of cyan and magenta color
                          Larger Image New Window.

Potential Problems: In addition to failure of discriminability or identifiability, the visual spacing of the coding colors may not conform to the spacing of the coded quantities.

Examples: Visualization.
Data values that vary over a 2D or 3D space are often color-coded in scientific graphics, using a technique known as "pseudocoloring". In this plot, positive values of a bipolar variable are plotted in various lightnesses of cyan and negative values in magenta. Topographic maps are also frequently labeled in this way. In the NOAA aviation map below, terrain elevations are coded by a smooth gradient of colors. Higher elevations are in darker browns, lower elevations are in greens.

Section of a NOAA aviation map in which various levels of surface elevation are shown in shades of brown, tan and green, along with navigation symbols and ground feature labels
                        Larger Image New Window.

Potential Solutions: Under some circumstances one can approximate the metric spacing of the data by selecting colors from CIE (nominally) uniform color spaces, e.g., L*u*v*. Colors can be viewed in the L*u*v* space with the color tool of this website.

More about color metrics .
More about color labeling in maps.

Related Topics:
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Color Discrimination and Identification

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