perceptual dimensions of hue, lightness, and saturationMOST MAP DESIGNERS WHO WORK WITH DIGITAL DATA create maps in color on their computer screens and other devices. But choosing from among the millions of colors that can be specified for display is a daunting task. If all you had to do was choose pretty colors, the task would be fairly straightforward; but I fear too many maps would end up blue, the most common favorite color.
Choosing map colors goes beyond considering what colors might be related to the mapped topic, such as blue for bodies of water and brown for pollution data. Because people’s opinions about which colors they like and which colors best represent a topic often conflict, this chapter takes an analytical approach to choosing map colors.
GIS and graphics software offer a variety of color systems for specifying color on maps, such as HSV (hue-saturation-value), CMYK (cyan, magenta, yellow, and black), and RGB (red, green, and blue). Why learn more than one system? HSV makes partial use of perceptual dimensions, so it can be used to adjust the appearance of map colors. CMYK is the language of graphic arts, so if you are printing your maps and adjusting colors for print, you need to be conversant in this system. RGB is the main color system used for computer graphics, so designing for screen display means you will be using this method of color specification (or the hexadecimal version of RGB in base-16 coding).
You will probably choose only one color system for most of your work, because software can make conversions between them for you. Expert color communication, however, will require you to understand color specification in all of the systems to produce the color characteristics suited to each mapping challenge. To do this, you will need to understand the utility of three-dimensional perceptual color space and specify colors with specific hue, lightness, and saturation characteristics in CMYK and RGB systems.
Map designers who produce quality maps know how to work with color on their computers and consider the following:
perceptual dimensions of hue, lightness, and saturation
perceptual color systems and their relationship to HSV and color mixture cubes
how to mix color to create map symbols using CMYK and RGB
Maps mostly fall into two categories: reference maps and thematic maps. On reference maps, color hue is used to symbolize different kinds of features. A simple example is using blue and green to differentiate water from land areas, but maps often show features that people do not see on the ground and that have no direct association with particular hues. When mapping this sort of thematic data, hue has an analytical purpose. You will rarely choose colors that are associated with land surface characteristics when designing a thematic map. For example, public land may be mapped in yellow and private land in brown. Areas of a state that lost population could be shown in orange, and areas that gained population could be shown in purple. The blue/green, yellow/brown, and orange/purple examples seem to simply be pairs of “colors,” so why use this extra word “hue?”
When using color as a symbol, each color is a combination of three perceptual dimensions: hue, lightness, and saturation. Hue is the most familiar and, therefore, most easily understood dimension. Lightness is the most important dimension for representing quantitative data. Saturation has the most subtle use in map symbology, but uncontrolled saturation differences can damage a map’s effectiveness. Color is three dimensional. You can split any color into its hue, lightness, and saturation dimensions. By mixing red-green-blue or cyan-magenta-yellow primaries, you can produce and adjust colors to have the perceptual attributes you want to see on your maps. The overall goal is to envision the map colors you want and then be able to create them with any of the color mixing tools offered by GIS and graphics software, such as RGB and CMYK.
Hue is the perceptual dimension of color that we associate with color names, such as red and yellow. Dominant wavelength is a similar measure used in physics. The rainbow, or spectrum, places saturated hues in wavelength order from long-wavelength reds to short-wavelength blues: red, orange, yellow, green, blue (figure 7.1). Purples and magentas are not colors of the rainbow. They result from mixing red and blue light from opposite ends of the spectrum.
Figure 7.1 Set of ten hues shown in spectral order.
White sunlight contains a full range of visible wavelengths, but our television and computer screens get by with a reduced set of primaries—red, green, and blue (RGB) light—which are mixed to form all the other hues. Cyan, magenta, and yellow (CMY) pigments are used to mix colors for printing and painting. Mixing lights is called additive mixture (RGB are the additive primaries), and mixing pigments is called subtractive mixture (CMY are the subtractive primaries) (figures 7.2 and 7.3).
Figure 7.2 Red, green, and blue are the primary hues used to mix colors with light on computer screens.
Figure 7.3 Cyan, magenta, and yellow are the primary hues used to mix colors with ink.
The hue circle in figure 7.4 is constructed using these two sets of primaries: RGB and CMY. Notice that magenta lies between red and blue—the ends of the spectrum.
You may be thinking, “But I learned that red, yellow, and blue were the primary colors when I mixed paints as a kid.” Please set aside your school days red-yellow-blue approach to color. It may confuse you in this modern era of on-screen displays and color laser printing.
The red-yellow-blue (RYB) color set, however, is akin to the perceptual concept of unique hues. The unique hues that do not look like mixtures of other hues are red, green, yellow, and blue. You may find yourself gravitating toward these unique hues (RGYB) as you develop color-codings because they are clearly different from each other. Red-green and yellow-blue are also the opponent hues on which our eye-brain color vision system is based. In addition to red, green, yellow, and blue, the other basic colors named in all fully developed languages are pink, purple, orange, brown, gray, white, and black.
Figure 7.4 A hue circle showing RGB and CMY primaries in spectral order.
You may notice that the more unfamiliar colors mentioned, magenta and cyan, are not in the list of common color names. You have probably encountered both of these hues when working with color desktop printers and color photocopiers or if you have communicated with publishers. Magenta is a pinkish red hue (a red with no yellow), and cyan is a greenish blue (a blue with no red). The purer these primaries, the more varied the hues that can be mixed with CMY.
Figure 7.5 This hue circle shows unique hues in opponent positions across the color circle from each other—red across from green and yellow across from blue. Notice that this hue circle differs from the previous example built from primaries. There is no single correct hue circle, but each shows hues in spectral order.
The hues shown previously in the hue circles vary in lightness. Figure 7.6 shows the effect of holding lightness constant and changing only hue.
Four light hues are shown on the map (figure 7.7) to differentiate census tracts. Hues are used on this map in the simplest way to distinguish one area from another. The hues are not used as symbols. That is, they do not indicate characteristics of the tracts, such as number of people or different languages spoken at home.
For figure 7.7, any area can be any of the colors used as long as two areas with the same color do not share a boundary. Only four hues are needed to solve this type of map coloring problem (called the four color theorem in mathematics), but this strategy has limited application in cartography because color is not being used to represent attributes of the areas.
Figure 7.6 Five hues shown at the same lightness.
A second map of census tracts (figure 7.8) uses hue as a symbol to show the most common non-English languages spoken in the area around Ithaca, New York. For example, Spanish/Creole is purple, Chinese and other Asian languages are blues, and two greens are used for the related German and West Germanic language categories. There are eight languages total on the map, so this is not a “four-color” problem. Each group needs to be represented by a distinct hue and many of these fill adjacent polygons.
Differences in hue indicate different kinds of features. For example, hue is used differently on the two maps in figures 7.7 and 7.8. It differentiates areas in the first example and indicates area attributes in the second.
Figure 7.7 Four hues are used to differentiate census tracts in central New York State. Mapped at 1:1,100,000.
Figure 7.8 Eight colors with five distinct hues are used to identify the majority non-English languages spoken in each tract in central New York State. Mapped at 1:1,100,000.
Data source: US Census Bureau. Maps by P. Limpisathian, Department of Geography, The Pennsylvania State University (Penn State Geography).
Variation in lightness is frequently used to represent a ranking within mapped data. Light colors are usually associated with low data values and dark colors with high data values. For example, shallow water may be represented by light blue and deep water by dark blue. Areas of high crime may be represented by dark green and low crime by light yellow.
The map in figure 7.9 includes a hue change (yellow to green) as well as a lightness change (light to dark), but it is the lightness change that most clearly communicates the low-to-high sequence in the data.
Figure 7.9 On this map of crime density a hue transition, from yellow to green, is used to augment color differences, but lightness carries the main message.
Changing lightness only, while maintaining a constant hue, is shown in figure 7.10. Areas with high vacant lot density are represented by dark purple which grades to light purple for low density. Compared to the previous map, this approach may have less contrast between adjacent colors because the scheme has not been augmented with variations in hue.
Figure 7.10 Density of vacant lots shown by lightness change only.
Data sources: City of Baltimore, data.baltimorecity.gov; ©OpenStreetMap contributors. Maps by A. Dennis, Penn State Geography.
Lightness is a relative measure (figure 7.11). It describes how much light appears to reflect (or emit) from an object compared to what looks white in the scene. Its relative character makes it a different measure from related terms like brightness, luminance, and intensity. Another word for lightness is “value,” but that term becomes confusing in quantitative work if you are also describing data values.
All of the colors in the lightness sequence in figure 7.12 share the same orange hue. Notice that brown, at the end of the sequence, is actually just a dark and desaturated orange. This final perceptual dimension of color, saturation, is the most conceptually difficult to understand.
Figure 7.11 Four blues that vary in lightness from light to dark.
Figure 7.12 A series of seven colors of medium saturation that change gradually in lightness.
Saturation is a measure of the vividness of a color, and there are a host of terms related to saturation with slightly varied definitions, such as chroma, colorfulness, purity, and intensity (which is confusingly used for both lightness and saturation). Some of these terms are from perception and the scientific realm of psycho-physics, and some describe the physics of light. Still others, like shades, tints, and tones, are from the language of art. A tint mixes white into a color, a tone mixes in gray, and a shade mixes in black. As any hue is desaturated, it becomes more neutral—grayish or pale. Achromatic colors, such as white, gray, and black, have no saturation and no hue.
Only a few noticeably different steps will be available when you vary only the saturation (and not the hue or lightness) of a symbol (figures 7.13). Including lightness change with saturation change improves contrast (figure 7.14). Using saturation as the primary difference among symbols is difficult. Figures 7.15 and 7.16 show elevation data with saturation steps (yellows) and with lightness steps (grays). Notice how poorly the landforms are presented by varying saturation alone.
Figure 7.13 A set of dark purples ranging from near gray to more vivid purple that demonstrate a saturation range while retaining the same hue and same lightness.
Figure 7.14 A set of colors with decreasing lightness, ranging from gray through desaturated cyans to the saturated hue.
Figure 7.15 Saturated yellows represent higher elevations, and desaturated grayish colors represent the lowest elevations in the Horn of Africa region. These colors have constant hue and lightness. Saturation alone is not an effective symbol variable.
Figure 7.16 A series of grays from light to dark represent the range of elevations for the Horn of Africa. Lightness alone represents these elevation ranges well.
Data source: US Geological Survey (USGS).
You can use saturation changes to reinforce lightness changes to help distinguish map symbols. For example, you may choose colors that range from light-desaturated to dark-saturated. This strategy is similar to using hue changes to augment lightness differences, as seen in the yellow-green map showing crime density in figure 7.9. Saturation can also be used with variations in hue and lightness to add emphasis to categories with small polygons.
Notice the difference in the relationship between lightness and saturation in the two examples in figures 7.17 and 7.18. Both would be useful in mapmaking because the perceptual dimensions are used systematically.
Figure 7.17 A series of four colors with the same hue, decreasing in lightness and increasing in saturation toward the right. They range from light-desaturated red to dark-saturated red.
Figure 7.18 A series of four colors with both lightness and saturation decreasing toward the right. This set ranges from light-saturated red to dark-desaturated red.
Saturation is the hardest of the three color dimensions to use in map design. Even if you do not use it explicitly, ignoring saturation can produce a confusing map. Individual colors that are accidentally more vivid than others will stand out strongly from other symbols for no apparent reason (figure 7.19). These lapses will put inappropriate emphasis on map categories that are not more important than others. Be particularly careful about choosing colors for large map areas; large areas of high-saturation colors will dominate the look of the map.
Figure 7.19 Two sets of five hues, all with the same lightness. The first set all have the same saturation. In the second set, yellow has a much higher saturation than the other hues. If these colors were used on a map, the data represented with yellow should be highlighted for a reason. It should be either an important category or a tiny area that would otherwise be lost.
Colors on the parcel classes map in figure 7.20 might be seen as an unsuitable mix of saturation levels. The magenta and cyan are more saturated than other colors. They are also darker than other colors. But consider the map—the highly saturated colors are used for single small areas. The map works because saturation makes these small features visible.
Figure 7.20 Parcel classes for Glens Falls, New York, show telecommunication parcels in vivid magenta and police and fire department parcels in vivid cyan. Saturation is used appropriately to emphasize these small areas on the map. Data source: Warren County, New York State. Map by P. Limpisathian, Penn State Geography.
The perceptual dimensions of color can be used to construct three-dimensional color spaces. Selecting colors from a true perceptual space would be the ideal tool for designing color schemes for maps, but unfortunately, the ability to match your own mental image of a complete three-dimensional perceptual color space is not readily available in GIS or graphics software. The HSV color system that some offer is a poor approximation.
Different systems for specifying and representing color use different dimensions. RGB and CMY both have three dimensions, but they mix color in different media (light versus pigments). Along with these two systems, ArcGIS also lets you mix color using HSV. Similarly, hue-saturation-luminance (HSL) color mixing is offered by Windows operating systems, and other perceptual color systems include HCL (hue-chroma-luminance), HSB (hue-saturation-brightness), HVC (hue-value-chroma), Ljg (lightness-yellowness-greenness), Luv (luminance, u-axis, v-axis), Lch (lightness-chroma-hue), and IHS (intensity-hue-saturation). All have three dimensions that can be used to conceptualize three-dimensional spaces, which are useful for structuring the way we think about and use color.
Some of the most rigorously designed color systems from color science are CIELAB (a system with L,a,b dimensions defined by the Commission Internationale de l’Eclairage), Munsell (HVC, see figures 7.21 and 7.22), and the Optical Society of America Uniform Color Scales (OSA-UCS Ljg). All of these systems arrange hues in spectral order around a central vertical axis of lightness. Saturation or a similar measure increases outward from the lightness axis.
Each of these systems aims to be at least partly perceptually scaled, such that equal distances in color space produce equal color difference perceptions. For example, if a red and a purple-red are ten units apart, two blues that are also ten units apart should appear equally different. These systems are complemented by CIECAM02 and other color appearance models for calculation of color interactions, such as simultaneous contrast, and color differences.
Figure 7.21 The Munsell color space is a color order system that is perceptually scaled. Its perceptual dimensions are hue (arranged radially around the value axis), value (a vertical lightness dimension), and chroma (increasing outward from the value axis).
Figure 7.22 Filling the volume of Munsell color space produces an oddly shaped, asymmetrical solid. This lumpy shape happens because high-saturation (high-chroma) yellows are light (the upper part of the color solid bulges toward yellow) and high-chroma purple blues are dark (the lower part of the solid bulges toward purple and blue).
A vertical slice through the yellow and purplish blue hues of the Munsell color solid is shown in figure 7.23. The colors increase in saturation (chroma) away from the central axis of neutral grays, which is shown between the two vertical black lines. Inherent differences in lightness between the two hues cause the shape of the purplish blue “hue leaf” on the left to be quite different from that of yellow on the right. A useful set of map colors (figure 7.24) can be selected using a systematic path through perceptual color space (figure 7.25).
Figure 7.23 A slice through the Munsell color solid along purplish blue and yellow hues. Notice the different shapes of each hue leaf when colors are ordered perceptually.
Figure 7.24 A color scheme selected from the arc across the pair of hue leaves shown in figure 7.25.
Computer science offers some poorer cousins to these perceptual spaces that are simpler to include in software interfaces. Two common ones, HSV and HSB, cast themselves as perceptual systems because they make use of the hue, brightness/value, and saturation terminology. But they are simple mathematical transformations of RGB without perceptual scaling. They can be useful for color selection, but you should be critical of them as you use them.
Figure 7.25 A systematic path through a perceptual space yields a color scheme that would be useful for mapping thematic data. An arc is sketched through blues that get lighter and more desaturated and then shifts to yellows that increase in saturation and get darker.
The HSV color system is a symmetrical cone-shaped color space (figures 7.26 and 7.27). It is handy to have access to approximate perceptual specifications for creating map colors, but true color perception does not produce a symmetrical color space. Therefore, the symmetry of HSV produces flaws in its perceptual characteristics that you need to work around.
Figure 7.26 HSV axes are arranged much like other perceptually organized color order systems: with saturation increasing horizontally from a vertical lightness scale outward toward a hue circle.
Figure 7.27 The HSV color solid is a regular cone. Compare this symmetrical shape with the irregular form of the perceptually ordered Munsell solid (figure 7.22).
All saturated hues are on the upper and outer edge of the HSV cone, regardless of their intrinsic differences in lightness. This symmetry means that value specifications between hues are not comparable. For example, a saturated yellow and saturated blue are designated as the same “value” but have a large difference in perceived lightness (figures 7.28 and 7.29).
Figure 7.28 A yellow with 100-percent value (V) and 75-percent saturation (S) is shown in the ArcGIS Color Selector window. Notice the hue circle that runs through the hue (H) slider. Hue is measured in degrees around the color circle. Red is at the 0° position.
Figure 7.29 This blue has the same value and saturation settings as the yellow in figure 7.28 (the color patch in the lower left of the window shows the color being mixed), but it is much darker in appearance.
If you want to use the HSV system to select a set of different hues that have similar values, you need to do it by eye rather than using the V units. This is because the top of the HSV cone is flattened, with white pressed down to the same lightness as the pure hues. The flat top causes all saturated hues to have the same value. For example, at high value, low-to-high saturation intervals progress from white to green (figure 7.30). Green is darker than white, so lightness and saturation have been combined, making both difficult to control. HSV confusion between lightness and saturation lessens at lower values (figures 7.31 and 7.32).
Figure 7.30 For this set of three colors, hue is constant, value is maximized at 100 percent, and saturations are set at 0, 25, and 50 percent, respectively. Notice that the lightness of these three colors varies, showing how lightness and saturation are confounded in HSV. Zero saturation combined with high value settings yields white.
Figure 7.31 HSV does a better job of maintaining lightness at lower value settings. The three colors here each share the same hue, have a value of 50 percent, and saturations of 0, 50, and 100 percent, respectively. At lower value settings, zero saturation yields a more accurate gray. The middle color was set in the Color Selector window shown in figure 7.32.
The symmetry of the HSV system makes it difficult to control the lightness of colors in a systematic manner when using multiple hues or when adjusting saturation. Develop your ability to see the three perceptual components of colors, and then use this knowledge to work around the flaws of HSV. In situations where much tweaking is required to achieve the desired effect, the system offers little benefit over grappling with raw specifications in RGB or CMY.
Figure 7.32 At lower values, lightness is held more constant within the saturation dimension. Notice the improved consistency along the saturation (S) slider in the Color Selector window.
Although we want to control the perceptual dimensions of color, we often must work with color mixtures of CMY and RGB because they specify the amount of pigment or light that produces map colors. The color spaces for mixing primaries are not perceptually scaled like Munsell or CIELAB. Instead, mixtures of the three primaries of either system form a regular 3D cube. The color cubes are like three-dimensional graphs with three axes (figure 7.33).
The CMY cube has a cyan (C) axis, a magenta (M) axis, and a yellow (Y) axis. White is at the origin of these axes (0 percent of each primary). Full amounts of each primary mix to black (indicated by K for blacK) at the opposite corner of the cube from white. Look to each corner to see what colors (secondary hues) are made by mixing pairs of primaries. Cyan and magenta mix to blue. Magenta and yellow mix to red. Yellow and cyan mix to green.
Figure 7.33 CMY color mixtures produce a cube-shaped space. K is used as the abbreviation for blacK, because B is used for blue.
Notice that all of the subtractive primaries are light colors, their combinations in pairs mix to darker colors, and finally all three make the darkest color of all, black (figure 7.34). Did you also notice that the secondary hues mixed from the subtractive primaries are the familiar RGB set?
Figure 7.34 The slices through a color cube shown here are built from mixtures of six steps in each primary. The cube is pulled open along the magenta axis to demonstrate the volume of colors it encloses.
The RGB color cube looks exactly the same as the CMY cube, but the color mixing relationships are reversed (figure 7.35). The origin of the RGB axes is black (no light). Each pair of primaries mixes to the lighter CMY secondary colors, and full amounts of all three mix to white. Understanding that colors get lighter as amounts of the additive primaries are increased is a key to learning how to mix colors with RGB.
These color cubes that describe color mixing are more closely related to perceptual color space than expected. For example, lightness steps are arranged diagonally through the cube (figure 7.36). You can tip the cube, with the sequence from white through gray to black arranged vertically, for a rough approximation of perceptual color space (compare figures 7.36 and 7.37).
Figure 7.35 RGB mixtures also form a cube. The similarity between the CMY and RGB cubes is emphasized here by retaining the orientation of the CMY cube (figure 7.33).
Figure 7.36 A lightness axis runs from white to black diagonally through the color cube.
Figure 7.37 Tip the color mixture cube on its point to see the similarity between this space and the perceptual spaces discussed earlier. The lightness axis runs vertically through the cube in this orientation.
In addition, the lighter subtractive primaries are positioned above the darker, additive primaries. The hues are arranged around the cube in spectral order, like a hue circle. Each of these characteristics is consistent with the organization of perceptual color spaces (figures 7.38 and 7.39), but RGB and CMY specifications describe primary mixtures rather than perceptual characteristics. You will see the links between perceptual dimensions and color mixing in the guidelines for mixing color in the next section.
In computing environments, you use one of two sets of primary colors to mix other colors. On screen, you mix tiny dots of red, green, and blue light. On paper, you mix tiny dots of cyan, magenta, and yellow inks. These systems work in opposite ways.
Figure 7.38 A generic structure for perceptual dimensions of color includes a vertical axis for lightness, a horizontal axis for saturation, and a circular arrangement of hues in spectral order. This structure is shared by most color-order systems.
Figure 7.39 The generic structure for perceptual dimensions fits the tipped color-mixture cube reasonably well.
Because printing inks lack the purity of light, there are discrepancies in the range of colors that can be mixed in the two systems. You cannot mix all of the vivid colors of RGB light emitted from your computer screen using CMY printing inks on the page. Software programs can translate between CMY and RGB, though the results are not always as expected. You can visualize the difference by picturing the RGB cube as being a little bigger than the CMY cube, even though they look generally the same.
The relationship between the RGB and CMY color cubes can be further simplified. Looking down on the color cube from above the white corner, notice that the hues at the corners of the cube are arranged in the order of the colors of the spectrum (figure 7.40). This arrangement is a lot like the hue circle.
Figure 7.40 A bird’s-eye view of the CMY cube reveals that the primary and secondary hues are arranged in the same spectral order as the hue circle.
We can simplify this order of hues to a hue circle with six primary and secondary hues: red, yellow, green, cyan, blue, magenta, and back to red (figure 7.41). Practice sketching this simple hue circle; it will help you remember how to mix hues for both color mixture systems.
Figure 7.41 To remember how to mix hues, sketch a simple hue circle and mark the two sets of primaries (RGB and CMY).
If you are mixing CMY inks, then CMY are the primary colors and RGB are the secondaries. Notice that the red secondary color falls between yellow and magenta primaries on the circle; equal amounts of Y and M mix to R. More Y than M mixes a yellow red (orange) (figure 7.42).
Figure 7.42 Red is between the yellow and magenta ink primaries on the hue circle. Equal amounts of magenta and yellow mix red. Reducing the amount of magenta moves the color mixture closer to yellow through oranges.
Green falls between Y and C primaries on the circle, so equal amounts of each mix green. More C than Y mixes a bluish green (figure 7.43). Mix hues by choosing proportions of adjacent primaries.
Figure 7.43 Green is between the cyan and yellow primaries on the hue circle. Equal amounts of cyan and yellow mix green, and more cyan than yellow moves the mixture around the circle to blue-green.
When mixing hues with RGB, the circle is used in the same way, but RGB are the primaries and CMY are the secondaries. Yellow falls between red and green, so equal amounts of R and G light mix to yellow (figure 7.44). Even though this may be an unfamiliar way to mix yellow, you can remember it if you can recall this simple hue circle (figure 7.41). Equal amounts of blue and red mix to magenta, and more B than R mixes a more bluish-purple hue.
Figure 7.44 The color circle works just as well for remembering how to mix hues with RGB. The most surprising mixture is yellow from red and green primaries.
CMY mixtures are specified with percentages of ink, which tells you how much of the page is covered by a thin film of ink broken into tiny dots. RGB mixtures are usually specified using numbers from 0 to 255 because that range balances detail and efficient computation. The emphasis in this chapter is on general amounts, more or less ink, and the perceptual results they produce, but you will see the different measurement scales reflected in examples.
The same hue circle helps you remember how to mix hues with either set of primaries, so all that remains is to adjust lightness and saturation. Lightness works in opposite ways in the two systems. Higher amounts of CMY in subtractive mixtures produce darker colors. For example, 100 percent M is darker than 20 percent M. In figure 7.45, a light green is mixed using low percentages of Y and C. Conversely, higher amounts of RGB in additive mixture produce lighter colors. In RGB, 255 R is lighter than 50 R, and dark colors are mixed by reducing amounts of the primaries. You can remember this by thinking about mixing lights—combining more and more light creates increasingly lighter colors.
Figure 7.45 Combinations of lighter (low percentage) cyan and yellow subtractive primaries mix to produce light greens.
Saturation is the one color dimension that you can control with a similar strategy in both systems. Big differences in primary amounts produce more saturated colors in both systems, while more equal amounts of the three primaries produce less saturated colors in both systems. For example, high M and no C or Y makes a saturated magenta in CMY mixing. A desaturated magenta is produced using similar amounts of CMY, but with M only a little higher than the other two, as shown in figure 7.46.
Figure 7.46 Magenta (M) is desaturated by adding smaller amounts of the other two subtractive primaries, cyan (C) and yellow (Y). Remember that this view is looking down on the perceptual dimensions structure, so saturation decreases toward the center of the circle. The color is lightened by reducing percentages of all the primaries.
A saturated magenta is produced with an RGB mixture of high R and B, no G. A big difference between one primary amount relative to the other two is the key to high saturation. A desaturated magenta is producing by mixing similar amounts of RGB, but with G (magenta’s complement) a little lower than the other two (figure 7.47).
Figure 7.47 Magenta (equal R and B) is desaturated by including a lower amount of the third primary, G. The color is darkened by reducing amounts of all of the primaries. Combining low numbers of the additive primaries, RGB, produces a darker color—the opposite result of combining low percentages of the subtractive primaries, CMY.
Although color systems are defined by only three dimensions, subtractive color mixture often uses four inks, rather than just the three subtractive primaries, cyan, magenta, and yellow. All hues—as well as black—could be mixed using only cyan, magenta, and yellow ink. But because black is such a common color in print, printers use black ink to clearly render black text and lines and to print grays and dark hues more accurately. In offset lithographic printing—the method used to print most professional color publications—printing with CMYK inks is referred to as four-color process printing. These four colors are also the pigments, whether powdered toner or liquid ink, used in most laser and inkjet printers.
CMYK percentages range from 0 percent (no ink) to 100 percent for full coverage of ink. A specification of 20 percent, for example, indicates that 20 percent of the colored area will be covered with tiny dots of solid ink and 80 percent of the white paper will show through, producing a light color.
Following are detailed guidelines for subtractive color mixture (CMYK color can only be approximated for on-screen viewing of these example figures):
1. Set the hue using a single ink (C, M, or Y alone) or using proportions of two of these colored inks (figure 7.48).
Figure 7.48 20C 40Y (left) is approximately the same hue as 50C 100Y (right). There is half as much cyan as yellow in both color patches.
2. Set lightness using the overall magnitude of C, M, and K. Higher percentages produce darker colors (figure 7.49). Y will remain light regardless of its percentage, so it has minimal effect on lightness (figure 7.50).
Figure 7.49 15M 5K (left) is lighter than 30M 15K (right). Both magenta and black darken the color.
Figure 7.50 Both colors contain 50M. The patch on the right also has 30Y, which changes the hue but does not darken the color.
3. Set saturation by adding K (figure 7.51) or by adjusting the primary with the smallest magnitude (figure 7.52).
Figure 7.51 30C 30Y (left) is more saturated than 30C 30Y 15K (right). Black desaturates the color.
Figure 7.52 30C 30Y is also more saturated than 30C 15M 30Y (right). The color on the right is desaturated with magenta ink. It is a warmer desaturated color than the one made with black in figure 7.51.
4. Create systematic perceptual changes by making systematic percentage changes (figure 7.53).
Figure 7.53 Progressive magnitudes of 10, 30, 60, and 100 percent cyan (A) look more evenly spaced than a series of 10, 20, 70, and 100 percent cyan (B). Notice the large gap between 20 and 70 in the B series.
5. Equal percentage steps do not look like equal visual steps. Use bigger steps in higher percentages (figure 7.54).
Figure 7.54 A magnitude step of 5 to 20 percent black (A) looks more different than 80 to 95 percent black (B), even though they both have a 15 percent difference.
6. Do not use all four inks at once. Desaturate or darken with either K or the least percentage of CMY (figure 7.55).
Figure 7.55 Three inks (10C 40M 80Y on left) are easier to work with than all four inks (5C 40M 80Y 5K on right). Both colors look the same.
The second way of desaturating and darkening colors by using colored inks (CMY—figure 7.52) is important for publishing some maps because map colors are then easier to adjust and control on press. The press operator’s adjustments to ink flow for high-quality black type and lines will not overly darken and desaturate colors if they are not dependent on the black component.
Darkening and desaturating with black has advantages too. The hues in subtle color designs are more stable at the press and in varied media if they do not contain all three CMY inks. For example, a beige will not shift toward blue if black rather than cyan is the third ink that desaturates light orange to beige.
RGB color mixtures are usually specified with numbers that range from 0 to 255. The following are detailed guidelines for additive color mixture (RGB color can only be approximated with printing inks for these example book figures):
1. Set hue using one or two RGB primaries. When hues are created using two primaries, similar proportions produce similar hues (figure 7.56).
Figure 7.56 100R 50B (left) is approximately the same hue as 240R 120B (right). Both have half as much blue as red.
2. Set lightness using the overall magnitude of RGB numbers. Higher RGB numbers produce lighter colors (figure 7.57).
Figure 7.57 200B (left) is lighter than 100B (right). The lighter color has a higher amount of the blue primary in it.
3. Set saturation using the lowest RGB number (figure 7.58).
Figure 7.58 100R 200G 230B (left) is more saturated than 170R 200G 230B (right). Notice that different amounts of red, the lowest of the three primaries, produce the saturation difference.
4. Create systematic perceptual changes by making systematic RGB changes (figure 7.59).
Figure 7.59 The first series of four colors 100R, 150R, 200R, 255R (A) looks more evenly spaced than the second series 100R, 130R, 225R, 255R (B). Set B includes a large gap between 130 and 225.
5. Equal steps in RGB numbers do not look like equal visual steps. Use larger steps in the lower magnitudes to differentiate between dark colors (figure 7.60).
Figure 7.60 200G and 255G (left and right in A) look more different than 0G and 55G (B), though both pairs have differences of 55.
Red, green, and blue mix together in counterintuitive ways, but if you follow the guidelines in this section, you will be able to adjust RGB colors to produce the map symbols you want to see on screen.
