COLOUR CONTRASTS ANALYSIS FOR A BETTER LEGIBILITY OF GRAPHIC SIGNS FOR RISK MAPS

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1 COLOUR CONTRASTS ANALYSIS FOR A BETTER LEGIBILITY OF GRAPHIC SIGNS FOR RISK MAPS E. Chesneau A. Ruas O. Bonin Doctoral Student at the French National Mapping Agency (IGN, COGIT laboratory, 2-4 av. Pasteur, Saint- Mandé Cedex, France) and Marne-La-Vallée University, elisabeth.chesneau@ign.fr French National Mapping Agency (IGN, COGIT Laboratory), anne.ruas@ign.fr French National Mapping Agency (IGN, COGIT Laboratory), olivier.bonin@ign.fr There are lots of surimposed informations on risk maps. Thus, risk data are difficult to portray. Colour is generally used thanks to its selective, symbolic and aesthetic qualities. We believe that a best use of colour to risk maps could clarify their legibility. Therefore, we propose a model to automatically improve colour contrasts. Our model is based on Itten colour contrasts theory, a German painter of the Bauhaus of the twentieth century. In our model, each graphic sign analyses colour contrasts with its neighbours. Then, this analysis is validated at a more global level. If systematisms are detected, another graphic solution is proposed. We repeat the process until we obtain a more legible map. 1. INTRODUCTION Maps are essential for communication. Their increasing number is the consequence of the explosion of numerical tools. Thus, more and more various professionals produce and use maps for their own need. For example, to portray natural or technological risks, map is an essential negotiation, communication or information tool. At first, a risk map helps to take decisions for global or local developments. Then, it is used to control land use. Finally, it informs the population about risk on a territory. In any case, map should be legible to be understood. Nevertheless, risk data 1 are difficult to portray because they are locally dense. Moreover, information is surimposed. We believe a better use of colour can improve their legibility. Therefore, we suggest developing a model to automatically improve the colour contrasts based on Itten colour contrasts theory (Itten, 1985). Maps on natural risk are used to validate our model. Part 2 presents general guidelines to improve legibility of risk maps. Part 3 introduces some colour advantages in risk maps and the colour contrasts defined by Itten. Part 4 describes our model: at first, we define its objective and dynamics; secondly, we introduce its components (the data schema, the knowledge base); then, we detail the process of colour contrasts analysis and improvements; at least, we provide a simple demonstration of how our model works and some evolution perspectives. 2. SOME SOLUTIONS TO IMPROVE LEGIBILITY OF RISK MAPS In this part, we demonstrate that risk data are difficult to portray. Then, we propose some solutions to make more legible maps. These solutions are not substitutions of the graphic language, but they are rather evolution or enrichment possibilities of this language. To illustrate our remarks, we join an example of risk map (figure 1). It portrays natural hazards in a district of the French department Isère at 1: The legend is provided by a French public administration. 1 Risk data are information about hazard, vulnerability, element at risk or risk. - Hazard is the uncertainty about realisations of an accident; - Vulnerability corresponds to possible consequences of a phenomenon on threatened elements; - Element at risk is a natural or man-made element threatened by a hazard; Risk is the conjunction of latent hazard and vulnerable elements.

2 Figure 1. Map of natural hazards in a district of Isère (French Department) In figure 1, hazard areas are surimposed on to a topographic background 2 which portrays elements at risk. Particularly for background, the map is not very readable because of the density of surimposed information. To improve the map legibility, we could: - create many maps or synthetise information: We could create a collection of maps, each map portraying one hazard type and the elements of the background. We could also aggregate data to obtain a synthesis map. But, in this case, informations are less detailed which can be prejudicial. - improve the legend: We could use graphic variables in a best way. In the sixties, Bertin established syntactics for the graphic language in his book Sémiologie Graphique (Bertin, 1960). Yet, these syntactics are not always applied on maps. For example, lots of maps use the hue of colour to portray ordinal ranges whereas the value would be more appropriate. We find lots of maps created by syntactics but not legible. Colour is often used to portray risk data. It is a good choice since colour is the most multi-dimensionnal graphic variable but it is also the most complex. Therefore, correctly used, colour can improve the map legibility. So, it seems relevant to focalise our research on colour contrasts between signs of a map. The second part of the paper explains some colour advantages for risk maps and the colour contrasts defined by Itten. 3. COLOUR AND COLOUR CONTRASTS BY ITTEN Risk maps are often in colour. Our model has to improve colour contrasts. Colour is usually regarded as having three dimensions: hue is the «colour dimension associated with different dominant wavelengths»; value is the «sensation of lightness or darkness invoked by a colour relative to standard black and white areas» and saturation is the «perceived amount of white in a hue relative to its brightness» (Robinson et al., 1995). At first, we analyse the use of colour for risk maps. Then, the colour contrasts theory of Itten is introduced Colour in risk maps 2 A topographic background is the portrayal of natural and man-made elements localized on the surface of earth.

3 The high number of colours in numerical tools explains the large number of colour maps. Moreover, colour has many qualities and advantages. At first, colour is selective. It can be used to portray lots of data on a same map without decreasing map understanding. In figure 1, colour portrays four hazard types (landslide, flowing on slope, flood, torrent), each with three different intensities (low, medium, high). Moreover, nominal data can be portrayed by the hue of colour and ordinal data by its value. In figure 1, hazard types are differentiated by hue whereas their intensities by value. Then, colour can easily be combined to other graphic variables. For example, transparency is often used with colour to see the background below the figure of the map. Next, colour symbolism facilitates the graphic signs interpretation. In figure 1, cold colours (blue, green) are referring to water hazards whereas warm colours (red, orange) to ground hazards. Yet, colour symbolism could vary because of different cultures or educations between percipients 3. Thus, multiple interpretations could appear for a same symbolisation. Colour incites also percipients to feel emotions. For example, the colour red, seen at first by our eyes, stimulates percipient attention. Red is often used to warn people of a danger. Finally, colour has a large aesthetic power. «Colour is Life, a world without colour seems dead» (Itten, 1985). Therefore, colour facilitates memorisation of a cartographic message Some legend points about colour contrasts Leonardo da Vinci (sixteenth century) is one of the first painter to write on colour theory. In particular, he studied contrast effects between light and dark. From the nineteenth century, a more physiological approach of colour appears. In his book «Zur Farbenlehre», Goethe ( ) states that colours are not the simple result of light decomposition. Eyes see colours according to the fundamental contrast of light and darkness. Goethe introduces also the effects of the simultaneous contrast and those of the successive contrast. Finally, he proposes light values between pure colours to reach harmony, i.e. the spirit exaltation (Goethe, 1990). The chemist E. Chevreul ( ) is inspired by Goethe s ideas when he defines an objective law about the simultaneous contrast of colours. According to him, eyes, spontaneously attracted by a colour, search immediately its complementary (Roque, 1997). During the nineteenth century and in the first part of the twentieth century, artists are inspired by scientific works. Founded in 1919 by Gropius at Weimar, the Bauhaus school offers lecture in architecture, sculpture and painting. Among teachers of this school, Kandinsky and Itten work on colour contrasts. For Kandinsky, the warm-cold contrast exists between yellow, a corporal colour, and blue, a spiritual colour. The light-dark contrast freezes the movement. The third contrast, between red and green, extinguishes the spirituality of yellow and blue. Finally, the fourth contrast is realized between orange and purple (Kandinsky, 1910). According to Itten, «we talk contrast between two colour effects to compare when we can establish sensible differences or intervals». In 1961, he introduces seven contrasts of colour. Other authors have generally proposed these contrasts but he is the first to define them explicitly. There are the contrast of hue, the light-dark contrast, the warm-cold contrast, the complementary contrast, the simultaneous contrast, the contrast of quality and the contrast of quantity. In the second part of the twentieth century, there are fewer researches on colour contrasts. Cartographers specify possibilities of colour use in maps. In particular, Anglo-Saxon cartographers analyse and propose colour palettes to portray data in choropleth maps (Brewer, 2003 and Mersey, 1990). To develop our model, we have been inspired by Itten colour contrasts. However, our model, in its architecture and its principles, could be adapted to other types of contrasts The colour contrasts defined by Itten 3 A map percipient is «one who obtains information about the milieu by looking at a map» (Robinson and Petchenik, 1976).

4 name definition Opposition between colours. All pure colours can create this contrast. More colours are far from the primary colours, worst the contrast is. hue pure colours close colours If black or white lines separate colours, the contrast is enhanced. light dark Opposition between two light and dark colours. The maximum of contrast is founded between white and black. purple with different white and intensities black Opposition between two colours, one primary and the other stemming from the mixture between the two other primary colours. complementary cyan and orange yellow and purple magenta and green Opposition between a cold colour and a warm colour. A relief effect appears: the warm colour advances on the cold colour. warm cold The two poles of the warm cold contrast are blue-green and red-orange. Opposition between a pure and luminous colour and its own grey colour. quality cyan yellow magenta Comparison between colours in a whole. quantity Itten defined harmonious proportions of quantity between pure colours according to their light values (proposed by Goethe). Quantitative harmonious circle of primary and secondary colours simultaneous Interaction of a colour on another. Eyes, for a given colour, search simultaneously its complementary. grey is radiant in orange, complementary colour of cyan orange appears intense because the eyes require this orange to be radiant in orange Figure 2. The Colour Contrasts of Itten 4. A MODEL TO AUTOMATICALLY IMPROVE COLOUR CONTRASTS

5 Risk maps are dense in surimposed informations. We propose building a model to improve map legibility. Our model analyses colours of the graphic signs and modifies them according to the quality of contrasts. To do this, our model is composed of a data schema and of algorithms that use colour contrasts rules. At first, we introduce the dynamics of our model. Then, we detail its components, i.e. the data schema and the contrasts rules (knowledge base). We describe the process of contrasts analysis and improvement. Finally, we conclude with an example of how our model works and by some perspectives of evolution Dynamics of our model Figure 3. Principle of working of our model To conceive our model, we integrate the data schema, the colour contrasts knowledge base and algorithms used for the dynamics (convergence) (1a). Moreover, we add colours, symbols and standard legends proposed to the user to portray his data without creating his own legends (1b). Then, the user integrates the risk data and the topographic background (2). He portrays his data (3). When the map is portrayed, our model automatically analyses the colour contrasts (4). If it detects inappropriate contrasts, it proposes an improved solution (5) to have a more legible map (6). The process is recursive: a new colour contrasts analysis of the improved solution (7) is realised until a satisfying final legend is obtained (8). Figure 4. Possible scenario of colour contrasts improvement in our model 4.2. Components of our model

6 Data schema The data schema is composed of three classes (and specific sub-classes): - a group of geographic objects classes for risk and topographic informations; - a group of portrayal objects classes describing legend elements; - a group of cartographic objects classes for portrayed geographic objects. geographic object 1 1 cartographic * 0, 1 object is portrayed by is refering to Figure 5.The three classes of the data schema portrayal object (caisson) Among geographic objects classes, each area to portray is linked to phenomena (flood or avalanche). Each area to portray is composed of geographic objects, either risk (hazard, vulnerability, element at risk) or contextual. Figure 6. Geographic objects classes A cartographic object portrays a geographic object. Thus, in the cartographic object class, we find copies of geographic objects geometries. We portray and we analyse contrasts on cartographic objects. Figure 7. Cartographic objects classes If we compare with classical GIS representations, in order to facilitate the analysis, we decompose each classical object into two different objects, one geographic and one cartographic. In portrayal objects classes, we have the map legend. It is composed of legend themes (for example, a legend theme is created for flood hazards, another for avalanche hazards or for elements at risk). Each legend theme is linked to

7 caissons. Caissons are linked to portrayal signs (point, line, or area). Moreover, colour and form visual variables are objects. Figure 8. Portrayal objects classes Here again, in order to facilitate the reasoning on the graphics, we represent explicitly the basic elements of the legend as objects Colour contrasts knowledge base In the previous part, we note that Itten defined seven contrasts of colour: contrast of hue, light-dark contrast, warmcold contrast, complementary contrast, simultaneous contrast, contrast of quality and contrast of quantity. We use his theory to analyse contrasts of each map. Thus, the colour contrasts knowledge base refers to contrast marks, defined for each pair of colours in a reference matrix. In following, we first introduce the reference matrix and the colour contrasts knowledge base. The matrix of reference colours contains the colours on which reasoning will be made. We need a small number of colours to minimise the quantity of rules. Each colour of the legend will be compared to the colours of the reference matrix. We used Itten, Brewer and Mersey researches to choose colours in the reference matrix (Itten for pure colours of the chromatic circle (Itten, 1985); Brewer and Mersey for colour intensities (Brewer, 2003 and Mersey, 1990)). We obtain fourteen hues and the grey, each declined into seven intensities. Thus, 107 colours could portray hazards, risks or vulnerability of elements at risk in a map. The topographic background is often in grey in risk maps. We propose enhancing the background legibility using greyish colours (colours containing grey in their composition). Seven hues are defined, each having two added grey levels and declined into four intensities. Thus, 56 greyish colours could portray the elements of the background. Therefore, the reference matrix has 163 colours.

8 Figure 9. Colours of the reference matrix : the first circle is for the portrayal of risk data, the two others for the portrayal of background elements In our model, contrast marks between two colours of the reference matrix constitute the knowledge base of contrasts. Each contrast type has its own matrix for each pair of colours. Thus, we should have seven matrices of 163 lines and columns. A ë i mark is obtained by: - theoretical calculations according to Hue, Saturation and Luminosity codes of colours; - quantity proportions between pure colours defined by Itten according to their light values (Goethe); - pure colours of the chromatic circle; - practical tests to cartographers, graphic designers and non-professionals of colour. Different functions are built to compute a contrast value between each pair of colours. ë i qualifies, between 0 to 5, the contrast i between (C j, C k ) two colours of the reference matrix. If ë i is equal to 0, there is no contrast whereas if ë i is equal to 5, the contrast is maximal. ë i = Contrast i ª [1;7] (C j,c k ) j ª [1;163], k ª [1;163] ë ª N, ë ª [0;5] Figure 10. Computation formula of the contrast marks As a consequence, the colour contrasts knowledge base describes the contrast between coloured graphic signs in a map. But, we cannot know if their contrast is good or not. To interpret contrast, we propose adding some rules: - rules about the symbolism of colours (for example, flood hazard is portrayed in blue); - rules referring to the measurement scales of data (selective, associative, ordinal). Therefore, between two graphic signs, we are able to define a contrast quality. More details are provided below Analysis and improvement process To improve colour contrasts between graphic objects in a map, we have three analysis levels, which are local, intermediate and global. At the local level, there is a primary analysis. Each graphic object finds its neighbours. With each neighbour, by contrast type, the contrast mark is picked up in the appropriate matrix. And, by contrast type, an aggregated mark is computed between the graphic object and all its neighbours. Then, to know if contrast between two graphic objects is satisfying, there is an interpreted analysis. As an example, if two neighbouring objects belong to the same family (hydrography), they should have nearly no hue contrast but they should have a minimum contrast of value. On the contrary, if two neighbouring objects belong to two different families (flood, vegetation), they should have a minimum hue contrast. To detect systematisms in bad contrasts, an analysis at a higher level (intermediate level) is realised: the objects family. According to different convergence methods that we are testing, our model proposes another graphic solution. The process is repeated until we obtain a better legend. To know if a legend is better than another, we analyse colour contrasts at the map level. And, we define a global contrast mark called harmony. According to Kandinsky,

9 Harmony of colours refers to the principle of the entry in an efficient touch with the human spirit (Kandinsky, 1910). For him, harmony is essentially linked to the principle of contrast. This mark is useful to know if a proposed solution at a specific step is better than the previous solution. 5. CONCLUSION Our model is built on the Geographic Information System Lamps2 of the LaserScan society. On the one hand, the Lamps2 choice is explained by the use of this GIS at the Cogit Laboratory (French Mapping Agency). As a consequence, multiple utilitarian functions, which reinforce the analysis abilities of the software, have been developed. On the other hand, we can easily add extensions or new data structurations. Moreover, we can make complex spatial analysis on data. As part of this thesis, firstly, we conceived the data schema and the portrayal objects (whose the colours of the reference matrix). We also implemented coloured contrasts rules in our model. Now, we are working on the colour contrasts analysis and on the process of convergence. We have to test different ways to converge to a satisfying final legend. contrast mark of hue contrast mark of value contrast mark of hue contrast mark of value Figure 11. Example of automated computation of colour contrasts in hue and value with Lamps2 for two graphic objects included in squares of the same colour (marks between 0 and 5) Figure 11 illustrates the first results of contrasts analysis in our model. For each graphic object, our model computes its contrast marks of hue and value. Here, the light blue square has a bad contrast of hue with its neighbour but a good contrast of value. On the contrary, the red square has a good contrast of hue with its neighbour. 6. BIBLIOGRAPHY Bertin Jacques (1967) Sémiologie graphique: Les diagrammes, les réseaux, les cartes Ed. EHESS, 431p. Brewer, Cynthia A., Geoffrey W. Hatchard and Mark A. Harrower (2003) - ColorBrewer in Print: A Catalog of Color Schemes for Maps - in Cartography and Geographic Information Science 30 (1), pp Goethe Johann W. von (1990) - Traité des couleurs - trad. H. Bideau, Ed. Triades, 3 ème édition, 304 p. Edition originale (1810) - Die Farbenlehre. Itten Johannes (1985) Art de la couleur Ed. Dessain et Tolra, 155 p. Edition originale (1961) - Kunst Der Farbe. Kandinsky Wassily (1910) Du spirituel dans l art, et dans la peinture en particulier in Coll. Folio-Essais, Ed. Denoël, 214 p. Mersey Janet E. (1990) Colour and Thematic Map Design: The Role of Colour Scheme and Map Complexity in Choropleth Map Communication in Cartographica vol.27 n 3 Monograph 41, automne 1990, 157 p. Robinson Arthur H., Morrison J. L., Muehrcke Phillip C. and Kimerling A. J.(1995) Elements of Cartography 6 th Edition, John Wiley, 614 p. Robinson Arthur H., and Petchenik Barbara B. (1976) The Nature of Maps: Essays toward Understanding Maps and Mapping University of Chicago Press, 138 p. Roque Georges (1997) Art et Science de la couleur, Chevreul et les peintres, de Delacroix à l abstraction Ed. J. Chambon, 474 p. The author thanks the members of the French Equipment Departmental Direction of Isère for their natural risk data, used to validate our model.

10 7. BIOGRAPHY Elisabeth Chesneau is a doctoral student at the IGN (French Mapping Agency). She is supervised by Gilles Palsky (thesis director), Olivier Bonin (French Mapping Agency supervisor on risk) and Anne Ruas (French Mapping Agency supervisor on semiology). Her research is about the improvement of the cartographic portrayal of risk. In particular, she proposes an automated model improving colour contrasts for risk maps. 8. PUBLISHED ARTICLES Elisabeth Chesneau (2004) - Propositions pour une cartographie du risque - in Bulletin du Comité Français de Cartographie, n 181, septembre 2004, pp