Color and color schemes

Introduction

Now that you have learned the basics about visual variables, let us find out more about color, one of the most important and most used graphic variables. Good color use depends on understanding the way humans perceive color and associate it with their experience and culture. It requires an eye for aesthetically pleasing combinations and knowledge of the use of color variables for different data levels.

1 Basics

In the previous module you learned about the various graphic variables map makers can use to represent information cartographically. One of those variables was color with its components hue, saturation, and value. When used appropriately, color is an effective means of communication; otherwise, it can be an effective means of mis-communication. Three categories provide guidance on color principles when creating thematic maps: visual variables, cultural associations, and aesthetics.

2 Color Associations

Colors have strong connotations and interactions. Be aware of these associations and use them wisely! Color associations are based on both direct experience and cultural norms. Visual associations in nature are reinforced through repeated contact with the landscape: vegetation, water, and soil. Contact with products and structures in the human landscape also develops color associations. For example, most everyone associates green with plants, but only some associate the postal service with yellow. Visual and tactile experience combine for the association hot=red, cold=blue.

The cultural base for color associations comes from metaphor and from conventions. Common metaphors come from daily life, for example green=OK, red=danger. Conventions are often only applicable to a certain field. For example, in geography, relief maps show higher elevations in red and lower ones in green. In politics, the Social Democratic Party is red and the Green Party is green.

2.1 Hue vs. value

We already discussed the fact that hue is inherently tied to value. Using hue to denote ordinal data is problematic. Darker (lower) values are associated with higher rank, and as certain hues are darker than others, for example red is darker than yellow and blue is darker than green, these are perceived to denote higher rank. The problem comes from the fact that even when hues are sorted from highest value to lowest, we see not only the value difference, but the hue variation as well. The map is much clearer when displayed in gray scale than when hue variations are used.

Hue and grayscale

Hue and grayscale

3 Color Aesthetics

Before going further, read: Slocum et al. (2004). Thematic Cartography and Geographic Visualization. 2nd ed. Prentice Hall. – Section 10.1 (pp. 181-2) and Sections 13.3.7-13.3.8 (pp. 257-258).

Aesthetics play a large role in map design. To some extent, what is pleasing to the eye depends on culture and experience, but there are some rules. Generally, larger regions should be displayed in lighter values and smaller ones in darker ones. Point symbols and lines should be shown in darker colors. Balance hues as well as color values by using colors that contrast well; complementary colors work well.

Keep in mind that there is a small part of the population with color vision impairment. Comprised of mostly men, this group makes up 4-8% of the population in Europe and the United States and around 2% of the population in arctic and equatorial regions. There are two groups of color-impaired people, anomalous trichromats and dichromats, divided according to how many colors can be recognized and combined. The anomalous trichromats use three, the dichromats two. Distinguishing between red and green hues presents the most problems.
For a list of color pairs acceptable for color-impaired users see Table 13.2 in Slocum.

4 Color Models

Before going further, read: Slocum et al. (2004). Thematic Cartography and Geographic Visualization. 2nd ed. Prentice Hall. – Section 10.3 (pp. 192-197).

4.1 RGB

For the creation of different colors, there are three major color models. The most common is the RGB model, in which colors are created by additive combination of the three primary colors red, green and blue. It can be visualized using a color cube. The three primary colors compose the main coordinate axes of the cube.

RGB cube

RGB cube

The proportion of each primary color can range from 0 (not present) to 1 (fully saturated). Within this interval [0,1] all values are theoretically possible. Each point (color vector) in the color cube is described by a precise color combination (r, g, b).

Practically, the number of colors is limited by the perfomance of a computer’s graphic components. In the case of true color, the real number interval [0,1] is displayed as whole numbers [0,255] per primary color. RGB (255, 255, 255) codes for white. Gray tones are found along the diagonal from white to black. Note: black and white are not colors. They represent the value component of color. Completely desaturated colors appear as gray tones.

4.2 CMY

The complement to the RGB model is the CMY model, based on the subtractive primary colors cyan, magenta and yellow. In the next diagram the additive and subtractive mixture of colors are shown. While the RGB model applies, for example, to computer displays with cathode rays, the CYM model is used for print. Both models are visualized with the color cube presented in the preceding section. To emphasize color variations while ignoring value differences, the color cube can be viewed from one corner as a hexagon (see figure right, below).

CMY additive vs. subtractive

CMY additive vs. subtractive

CMY colour space

CMY color space on a hexagon

One major problem with both models is that changes in the color coordinates are not proportional to visual change. The visual representation of 125,0,0, for example, is not mid-way between 0,0,0 and 250,0,0.

4.3 HSV (IHS) model

Alternatively you can use the HSV (hue, saturation, value) model, also known as the IHS (intensity, hue, saturation) model. It is intuitive as it allows the user to directly manipulate the three visual components of color. As such, it is especially good for cartographic design. Along with the RGB model, it is used in computer programs like PowerPoint. CommonGIS uses the HSV model. For each value in the HSV model, there is a corresponding value in the RGB model. As with the RGB and CMY model, selecting a value midway between two colors does not result in a color that appears to be between them.

HSV/IHS color model visualization

HSV/IHS color model visualization

5 Color Schemes For Coropleth Maps

Before going further, read: Slocum et al. (2004). Thematic Cartography and Geographic Visualization. 2nd ed. Prentice Hall. – Sections 13.3.1-2 (pp. 253-255), 13.3.4-5 (pp. 256-257), and 13.3.9-10 (p. 258).

Based on the investigations of Mersey and Eastman, a deciding criterium for the selection of a color scheme is the intention behind the map. Brewer suggests that the kind of data play an important role. Depending on the visualization purpose and scale level, unsorted color combinations or highly sorted color sequences are appropriate. Differences are made visible by changing the hue or the intensity or a combination of hue and intensity changes.

5.1 Hue scheme or qualitative scheme

Hue variations can be used for nominally scaled variables, as for example land use. Different colors are used for different attribute types (forest, meadow, etc.). There should be no major intensity difference between the objects, as this would create an impression of differences in importance. Different intensity values should only be selected when they aid in differentiating between colors or when there is a particularly high number of attribute types.

Example map: Hue scheme for nominally scaled variables

Example map: Hue scheme for nominally scaled variables

5.2 Binary scheme

A binary scheme is used for nominal variables that can be broken into two classes (for example, population density >2500 inhabitants/km2 or

color_07

Example map: Binary scheme

5.3 Sequential scheme

In a sequential scheme, data classes are sorted according to attribute values. Data sequences should be displayed with adequate changes in the intensity of a color (a lightness scheme). Lower attribute values=light, higher attribute values=dark. The visual contrast of intensity levels in a lightness scheme can be increased when the saturation is varied as well. If this is done, do not overemphasize unimportant categories through too much saturation. Low saturation for light tones, higher saturation mid-tones and again low saturation for darker categories. Saturation levels should not be used alone in a sequential scheme, since the intensity levels will be lost.
Generally, no more than five colors should be used. Exception: if the data require it, the entire color spectrum can be used.

No hue, sequential intensity. Graytone maps are simple to reproduce and provide a clear hierarchy.

Example map: Graytone, no hue

Example map: Graytone, no hue

One hue, sequential intensity. Adding color does not necessarily increase the readability of a map, but many users find color more appealing than graytones. Using intensity changes with one hue allows reproduction in black and white, even from a map that is initally in color.

Example map: One hue with changing intensity values

Example map: One hue with changing intensity values

Hue transititon, sequential intensity. Adding a hue transition to the sequence allows for more classes, as users can differentiate intensity differences for each hue.

Example map: Hue transitions & intensity

Example map: Hue transitions & intensity

Two hues, diverging intensity. A diverging scheme puts emphasis on the middle class of data. In a five-class quantiles classification, this contains the median.

Example map:

Example map: Two hues, diverging intensity

6 Discussion

Maps are a graphical means of communication, which rely on clarity and intuitiveness to be understood. Being aware of the “vocabulary” or cartographic language will help you to make good maps. Color is a very powerful graphic variable, and it can make or break a map. Based on the theory of visual variables, you should use hue for qualities and value for order. Be aware of color associations and consider them when selecting a scheme for a map.


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