319:
215:
472:
198:
importance. Studies have shown that humans are better at judging relative differences in linear distance (e.g. one road being twice as thick as another) than relative differences in area (e.g., one circle having twice the area of another). Such estimations are the most accurate from squares. Area differences of circles are generally underestimated, but there is a large variation between people in ability to estimate two-dimensional size. Correctly estimating relative volume has proven even more difficult.
280:
131:, citing neither Bertin nor Robinson but saying that it was a "traditional categorization," suggesting its widespread nature by that point. Several terms were proposed for this set of categories, including Bertin's "retinal variables" (used to distinguish them from his two spatial location variables), as well as "Graphic Variables," "Symbol Dimensions," and "Primary Graphic Elements," before eventually settling on "Visual Variables," as used almost universally (in English) today.
186:
223:
conveyed. Some aspects of shape are inherent to the phenomenon and may not be easily manipulable, especially in line and region symbols, such as the shape of a road or a country. However, shape can still play a role in line and region symbols, such as a region filled with tree symbols or an arrowhead on a line. Also, the shape of a feature may be purposefully distorted by
366:
438:. This was a core part of Bertin's model, who distinguished these "imposition variables" from the other "retinal variables." This has largely been dropped from most subsequent lists by cartographers, since location in a map is predetermined by geography. However, it is crucial for representing information in
613:
are those with sufficiently strong variation so that the reader can isolate one value from all the others (e.g., "where are all the blue points?" amid points of various hues). All of the dissociative variables are also selective, plus
Pattern Spacing and Hue (and Pattern Arrangement post-1967). These
596:
have variations that can be mentally subdued so they are easily grouped together, with none naturally standing out from the others so they do not contribute much to visual hierarchy. Bertin included Shape, Orientation, Color (Hue), and Grain (Pattern spacing) in this list; Of the post-1967 variables,
620:
show a clear linear order between different values. Bertin listed Size, Value, and Grain as ordered; later ordered variables would include Height, Saturation, Transparency, Crispness, and
Resolution. This ordering makes them useful for representing Ordinal and Interval data. Hue and Orientation are
528:
a symbol or feature as a way of generalizing and obscuring it, usually for communicating some form of uncertainty about the feature. This was also first introduced in that context by MacEachren, but is not commonly used, and has rarely been mentioned since. By extension, this can also refer to the
462:
In a regular arrangement, the direction in which the sub-symbols are arrayed. Bertin considered this just the area version of the primary variable of orientation, but
Morrison included it as a separate variable, likely because the orientation of the individual sub-symbols may be different than the
388:
in this context generally refers to an aggregate symbol composed of recurring sub-symbols. This can include areas (such as a forest filled with small tree point symbols) and line symbols (such as a railroad with recurring cross-hatches). These sub-symbols can themselves be created by any or all of
336:
of a color is its purity or intensity, created by the variety of light composing it; a single wavelength of light is of the highest saturation, while white, black, or gray has no saturation (being an even mixture of all visible wavelengths). Of the three psychological aspects of color, this is the
817:
from pale yellow to dark green, using both hue and value. Alternatively, different visual variables may be used to represent different properties; for example, symbols for cities may be differentiated by size to indicate population, and by shape to indicate provincial and national capitals. Some
222:
A shape is a simple design that is used to symbolize an attribute on a map. Shape is most commonly attached to point features in maps. Some shapes are simple in nature and thus are more abstract, while other shapes are more pictorial and are easy for the reader to comprehend what is trying to be
143:
or any other, and was primarily a practical summation of patterns he found in practice. The "truth" of the visual variables concept was largely established by its widespread and long-lasting acceptance. Thirty years later, MacEachren connected the scientific support for this and other aspects of
417:
The orderliness of the location of the sub-symbols in the pattern, generally either regularly spaced in rows and columns (often indicating a human construction, such as an orchard), or randomly spaced (often indicating a natural distribution). This variable first appears in
Morrison's 1974 list
197:
The size of a symbol is how much space it occupies. This commonly refers to the area of point symbols, and the thickness of line symbols. Size differences are relatively easy to recognize, making it a useful variable to convey information, such as a quantitative amount of something, or relative
491:
These corollary terms refer to the degree to which a symbol blends with other symbols at the same location, giving the illusion of the symbol in front being translucent. A fairly recent addition, the control of opacity has become common in digital cartography. While it is rarely used to convey
167:
The earliest lists commonly suggested six variables: location size, shape, value, hue, orientation, and grain (pattern spacing). To this list, several additions have been suggested, with a few entering the canonical lists found in textbooks, while other suggestions have largely been dropped in
356:
Orientation refers to the direction labels and symbols are facing on a map (occasionally called "direction" or "angle"). Although it is not used as often as many of the other visual variables, it can be useful for communicating information about the real-world orientation of features. Common
604:
have variations that are harder to ignore, because some values stand out much more than others; therefore, they play a strong role in the visual hierarchy. Size and Value are the original members of this group, 3-D Height, Color
Saturation, Transparency, Crispness, and Resolution are also
581:(e.g., size: Large-small ~ more-less). These modes make each variable better for representing certain kinds of information, and serving certain purposes, than others. Specifically, Bertin introduces four properties of these variables, which tie them directly to their role in the
138:
was the first systematic and theoretical treatment, and his overall approach to graphical symbolization is still in use today with only minor modifications. Despite the title of Bertin's work, it actually contained little reference to the scientific knowledge in the field of
369:
Texture (dot density) representing disease incidence (a ratio or count-level variable), which gives the appearance of density. Also, saturation (color vs. gray) is used to create a visual hierarchy, and value (gray vs. white) establishes a figure-ground contrast for
812:
Map symbols commonly employ multiple visual variables simultaneously. This can be used to reinforce the depiction of a single property; for example, a capital city having a symbol that is larger and a different shape than other cities, or a color progression on a
95:(EPHE) in Paris, where he created maps and graphics for faculty from various disciplines using a wide variety of data. Seeing recurring patterns, he created a system for symbolizing qualitative and quantitative information, apparently inspired by the sciences of
283:
Population density (a ratio-level variable) represented as color value, with an intuitive correspondence (i.e., dark looks like more people). Value also establishes figure-ground (color vs. white). Hue does not carry information here, but serves an aesthetic
271:, and others. Maps often use hue to differentiate categories of nominal variables, such as land cover types or geologic layers. Hue is also often used for its psychological connotations, such as red implying heat or danger and blue implying cold or water.
627:
have values that can be directly measured, and are thus best for representing quantitative properties, especially Ratio-level. Bertin included only size in this variable, although some would argue that value is quantitative, if less easily measured than
201:
Because geographical features have an actual size on the Earth, this cannot always be controlled, and sometimes works against the wishes of a cartographer; for example, it can be difficult to make a world map in which Russia does not stand out. In a
43:
Bertin identified a basic set of these variables and provided guidance for their usage; the concept and the set of variables has since been expanded, especially in cartography, where it has become a core principle of education and practice.
176:
Starting with
Robinson and Bertin, a core set of visual variables has become largely canonical, appearing in cartography and information visualization textbooks, and built into most design software in some form.
818:
visual variable can be combined harmoniously to make a map clearer and more informative, while other combinations tend to add more confusion than usefulness. For example, early experiments with using
568:: Location, Loudness, Pitch, Register, Timbre, Duration, Rate of Change, Order (sequential), Attack/Decay. To date, sound has been rarely used to encode information in maps and information displays.
88:, which quickly became the dominant textbook on the subject, discussed size, shape, color, and pattern as the qualities of map symbols that establish contrast and represent geographic information.
614:
are generally better for representing precise information than non-selective variables (Orientation and Shape, although even they can be made selective if they are made distinct enough).
35:, is an aspect of a graphical object that can visually differentiate it from other objects, and can be controlled during the design process. The concept was first systematized by
463:
angle in which they are arranged. In recent years, it has rarely been included, likely due to the overall decrease in the use of fill patterns in the era of digital cartography.
348:
Bertin mentions saturation in his discussion of "color" (hue), but did not include it as a distinct variable. However, it has been included in almost all lists since the 1970s
426:
A number of additional variables have been suggested at times. Some are recent technology-driven proposals, while others are earlier entries that have fallen out of favor.
549:
Following on the widespread usefulness of Bertin's variables, cartographers have proposed analogous sets of controllable variables for media beyond static paper maps:
492:
specific information, it is effective for reducing contrast or to retain underlying information. Despite its widespread use, it is rarely mentioned in textbooks.
298:
refers to how light or dark an object appears. Value effectively connotes "more" and "less," an ordinal measure; this makes it a very useful form of symbology in
310:; elements that contrast most with the value of the background tend to stand out most (e.g., black on a white sheet of paper, white on a black computer screen).
556:: Duration, Order/sequence, Rate of change, Display time, Frequency of change, Synchronization (of multiple series). Many of these have entered mainstream use.
597:
Pattern
Orientation and Arrangement are also associative, while Color Saturation is a possibility. These are well-suited for representing nominal variables.
577:
According to Bertin, each of the visual variables suggests its own mode of perception and interpretation, which MacEachren ties to the cognitive theory of
446:. Even in cartography, position becomes a variable when labeling and laying out the non-map elements on the page. It is also relevant when representing
322:
The synergy of
Saturation (color vs. gray), value (dark vs. light), and position (centrality) to strongly establish figure-ground and visual hierarchy
153:
60:
to represent non-visual information since the 17th
Century, and information visualization blossomed in the 19th Century, highlighted by the work of
562:: Vibration, Flutter, Pressure, Temperature, Resistance, Friction, Location, Height/Elevation, and analogues of most of the core visual variables.
168:
cartography. With the rise of multimedia as a cartographic tool, analogous sets of non-visual communication variables have also been presented.
92:
1193:. ACM International Conference Proceeding Series; Vol. 142, Proceedings of the Asia-Pacific symposium on Information visualisation - Volume 24
401:
was translated as "texture" in the 1983 English edition, and appeared frequently as such in subsequent lists, but others have suggested that
1121:
DiBiase, D., MacEachren, A. M., Krygier, J. B., & Reeves, C. (1992). Animation and the role of map design in scientific visualization.
802:
338:
801:
Each of these variables may be employed to convey information, to provide contrast between different features and layers, to establish
318:
1219:
240:
1038:
1005:
541:
On three-dimensional perspective maps, it is common to extrude shapes in the z direction, so that height represents a property.
997:
134:
Bertin has largely been given credit for the system of visual variables; even though he was not the first to mention the idea,
1190:
1092:
621:
ordered; not in a typical "more-less" metaphor, but in a cyclical order. Thus they can be used to represent cyclical data.
486:
84:
published an early version of his list of visual variables: shape, value, and "sparkling" (grain). Robinson, in his 1960
951:
Morrison, Joel, A Theoretical
Framework for Cartographic Generalization With Emphasis on the Process of Symbolization,
530:
435:
224:
128:
632:
Bertin's classification is rarely mentioned as such, but the resultant applicability preferences form a core part of
218:
National Park standard point symbols, using shape to represent different types of facilities, a nominal variable.
120:
111:. Despite having a background in cartography, and deriving many of his ideas by evaluating maps, he intended for
442:
and other data visualizations; for example, position is the main method of visualizing quantitative values in a
231:, although this distortion is rarely used to convey information, only to reduce emphasis on shape and location.
214:
471:
1214:
1147:
Griffin, A. L. (2001). Feeling it out: the use of haptic visualization for exploratory geographic analysis.
529:
general level of detail in a symbol, which is used more often than pixelation, especially in the context of
80:
discussed the role of size, shape, and color in establishing contrast in maps. At the same time in France,
876:
375:
65:
916:
Palsky, Gilles (2019) Jacques Bertin, from classical training to systematic thinking of graphic signs,
279:
868:
1209:
721:
647:
641:
586:
68:. However, the direct study of this abstract use of graphical appearance began with the emergence of
1191:"An empirical evaluation of Chernoff faces, star glyphs, and spatial visualizations for binary data"
24:
500:
This is the degree to which a symbol is drawn with crisp or fuzzy edges. Briefly mentioned in the
781:
762:
702:
157:
104:
77:
32:
1022:
475:
Transparency and fuzziness is used effectively here to indicate overlapping sovereignty claims.
107:(it is sometimes hard to tell because his early works rarely cite any sources), culminating in
871:, in D. Richardson, N. Castree, M.F. Goodchild, A. Kobayashki, W. Liu, and R.A. Marston, eds.
447:
185:
454:
is an abstract visualization of a property, not the location of a real-world linear feature.
806:
637:
582:
480:
342:
337:
least effective at conveying specific information, but it is very effective at establishing
307:
61:
505:
849:, English Edition, Translation by William J. Berg, University of Wisconsin Press, 1983.)
206:
the size of features is purposefully distorted to represent a variable other than area.
814:
397:
The amount of white space between the sub-symbols in the pattern. Bertin's French term
303:
116:
81:
36:
28:
1203:
819:
345:, with bright colors generally standing out more than muted tones or shades of gray.
124:
1064:
1009:
636:, including the power of Size, Value, Saturation, and Resolution for establishing a
578:
451:
327:
299:
259:
is the visual perceptual property corresponding in humans to the categories called
161:
100:
1049:
741:
443:
228:
69:
796:
633:
525:
508:
in 1992 as a tool for representing locational uncertainty; he first called it
389:
the above visual variables, but a few variables apply to the overall pattern:
39:, a French cartographer and graphic designer, and published in his 1967 book,
1152:
1180:. A. M. MacEachren and D. R. F. Taylor (Eds.). Oxford: Pergamon, pp. 149–166
289:
203:
190:
149:
140:
96:
357:
examples include wind direction and the direction in which a spring flows.
1165:
Proceedings, 15th Conference of the International Cartographic Association
1108:
Slocum, Terry A., Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard,
365:
434:
The absolute location of the symbol in the design, specified as (x,y)
76:(1952), often considered the genesis of American cartographic theory,
1163:
Vasconcellos, R. (1992) Knowing the Amazon through tactual graphics.
822:
on maps have been criticized as difficult to interpret correctly.
470:
439:
380:
Although terminology for this aspect still varies somewhat today,
364:
317:
278:
227:, especially when creating schematic representations such as many
213:
193:
representing population (a ratio or count-level property) by size.
184:
57:
72:
as an academic research discipline in the mid-20th Century. In
1176:
Krygier, J. B. (1994). Sound and geographic visualization. In
251:
123:. Soon the idea was gaining international acceptance; in 1974
53:
843:
Sémiologie Graphique. Les diagrammes, les réseaux, les cartes
585:
and to their ability to represent data in each of Steven's
516:, which has been the most common term in subsequent lists.
1136:
Some truth with maps: A primer on symbolization and design
504:
textbook in 1978, the concept was more fully developed by
985:
How Maps Work: Representation, Visualization, and Design
1189:
Michael D. Lee, Rachel E. Reilly, Marcus E. Butavicius
715:
Color value, Color saturation, Transparency, Crispness
1075:
Robinson, Arthur, Randall D. Sale, Joel L. Morrison,
845:. With Marc Barbut . Paris : Gauthier-Villars.
920:, 46:2, 189-193, DOI: 10.1080/15230406.2018.1523026
127:presented a very similar system in the context of
1112:, 3rd Edition, Pearson-Prentice Hall, 2009, p.84.
1087:
1085:
1065:https://www.e-education.psu.edu/geog486/node/1864
979:
977:
1123:Cartography and geographic information systems
947:
945:
943:
941:
939:
918:Cartography and Geographic Information Science
809:, or add to the aesthetic appeal of the map.
8:
963:
961:
873:The International Encyclopedia of Geography
863:
861:
859:
857:
855:
1110:Thematic Cartography and Geovisualization
164:, and 40 years of cartographic research.
154:Semiotic theory of Charles Sanders Peirce
998:"Graduated and Proportional Symbol Maps"
912:
910:
837:
835:
718:Size, Height, Color Hue, Pattern Spacing
645:
16:Graphic techniques used in visual design
1002:GEOG 486: Cartography and Visualization
831:
1138:. Association of American Geographers.
524:This is the technique of purposefully
953:International Yearbook of Cartography
640:, and the following ties to Steven's
52:Graphic techniques have been used in
7:
738:Size, Color saturation, Opacity, Hue
1178:Visualization in Modern Cartography
1054:Westfaelische Wilhelms Universitaet
904:University of Wisconsin Press, 1952
877:doi:10.1002/9781118786352.wbieg0761
969:Cartographic Design and Production
450:; for example, the location of an
314:Color: saturation/chroma/intensity
241:Color coding in data visualization
14:
1093:Visualizing Uncertain Information
1006:The Pennsylvania State University
732:Amount of quantitative difference
91:Bertin was a cartographer at the
692:Degree of qualitative difference
681:Pattern Arrangement, Orientation
306:. Value contributes strongly to
148:, bringing together research in
93:École pratique des hautes études
891:. New York: The Guilford Press.
115:to be applied to all forms of
1:
1167:, Bournemouth, UK, pp.206-210
758:Transparency, Pattern Spacing
487:Transparency and translucency
724:(rich, middle class, poor)
531:cartographic generalization
225:Cartographic generalization
129:cartographic generalization
1236:
1134:MacEachren, A. M. (1994).
794:
484:
478:
373:
325:
287:
249:
238:
1220:Information visualization
1149:Cartographic Perspectives
1097:Cartographic Perspectives
1079:, 4th Edition, Wiley 1978
755:Size, Height, Color value
409:is a better translation.
121:information visualization
889:Principles of map design
791:Use in map symbolization
761:Population growth rate,
1077:Elements in Cartography
971:, London: Longman, 1973
931:Elements of Cartography
752:Proportional difference
573:Visualizing Information
294:As an aspect of color,
144:cartographic design in
86:Elements of Cartography
1153:DOI: 10.14714/CP39.636
1099:, 13 (Fall 1992), p.10
1039:"Cartographic Symbols"
803:figure-ground contrast
775:Color hue, orientation
625:Quantitative variables
602:Dissociative variables
476:
371:
323:
285:
275:Color: value/lightness
219:
194:
1091:MacEachren, Alan M.,
983:MacEachren, Alan M.,
887:Tyner, J. A. (2010).
847:Semiology of Graphics
684:Owner, Facility type
650:and Visual Variables
642:levels of measurement
594:Associative variables
587:levels of measurement
554:Dynamic/animated maps
474:
376:Texture (visual arts)
368:
321:
282:
217:
188:
172:Core visual variables
66:Charles Joseph Minard
41:SĂ©miologie Graphique.
955:, V.14 (1974), p.115
933:, Wiley, 1960, p.137
722:Socioeconomic status
545:Non-visual variables
467:Transparency/opacity
422:Additional Variables
136:SĂ©miologie Graphique
113:SĂ©miologie Graphique
109:SĂ©miologie Graphique
661:Preferred Variables
651:
611:Selective variables
560:Haptic (touch) maps
496:Crispness/fuzziness
458:Pattern Orientation
25:cartographic design
1050:"Visual Variables"
929:Robinson, Arthur,
900:Robinson, Arthur,
772:Angular difference
763:population density
703:Geologic formation
698:Shape, Arrangement
664:Marginal Variables
646:
477:
372:
324:
286:
220:
195:
158:Gestalt psychology
105:Gestalt psychology
78:Arthur H. Robinson
58:statistical charts
33:data visualization
1125:, 19(4), 201–214.
967:Keates, John S.,
902:The Look of Maps,
788:
787:
780:Day of the year,
675:Same or different
618:Ordered variables
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1008:. Archived from
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988:
987:, Guilford, 1995
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949:
934:
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869:Visual Variables
867:Roth, Robert E.
865:
850:
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807:visual hierarchy
678:Color Hue, Shape
652:
638:visual hierarchy
583:visual hierarchy
481:Opacity (optics)
343:visual hierarchy
308:Visual hierarchy
152:(especially the
74:The Look of Maps
62:William Playfair
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506:Alan MacEachren
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361:Pattern/Texture
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304:choropleth maps
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21:visual variable
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1027:GIS Dictionary
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1012:on 2017-07-13.
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146:How Maps Work
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125:Joel Morrison
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1210:Cartography
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742:Temperature
735:Color value
701:Languages,
658:Distinction
444:scatterplot
436:coordinates
413:Arrangement
403:granularity
352:Orientation
70:cartography
1204:Categories
826:References
797:Map symbol
526:pixelating
520:Resolution
334:saturation
246:Color: hue
695:Color Hue
667:Examples
514:crispness
290:Lightness
204:cartogram
191:cartogram
150:Semiotics
141:Semiotics
97:semiotics
769:Cyclical
729:Interval
502:Elements
430:Position
405:or just
284:purpose.
1023:"Shape"
744:, Year
709:Ordinal
672:Nominal
452:isoline
386:pattern
382:texture
370:Africa.
48:History
782:Aspect
537:Height
448:fields
440:charts
103:, and
31:, and
749:Ratio
712:Order
655:Level
628:size.
566:Sound
510:focus
407:grain
399:grain
296:value
265:green
235:Color
210:Shape
23:, in
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332:The
269:blue
181:Size
119:and
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261:red
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252:Hue
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