Lost Works of Jacques Bertin on Typography

It’s been 50 years since Jacques Bertin’s Sémiologie Graphique was published. Bertin looms large in history of both data visualization and cartography. Before we had textbooks on data visualization by Munzner (Visualization Analysis and Design), Ware (Information Visualization: Perception for Design) or even Spence (Information Visualization: Design for Interaction), Bertin provided the theoretical foundation that much of visualization relies on today. Sémiologie Graphique structures the design space of visualization with the now familiar concepts of marks (point, line, area); visual attributes (only six in his version); type of perception (quantitative, ordered, categoric and associative); and layout. Beyond these, Bertin also considered many other aspects such as spatial separation (to form small multiples) ordifferent use cases of visualization including communication, analysis, and inventory.

However, only one aspect of Bertin’s work never made the translation from the French original to English: typography! Strangely, out of 450 pages+ only 4 pages on typography were not translated. In these 4 pages Bertin discusses the importance of the literal information represented by text. He notes that text is often the only encoding commonly accessible to both the textual/verbal system of encoding information; and the visualization system of encoding data.

Furthermore, Bertin points out that text is not selective. In other words, text is not preattentive, meaning that patterns do not automatically pop-out. If I ask you to find the word “six” occurring in the first paragraph, you need to linearly scan through the text – the benefits of visualization do not occur. Typographers would agree: they put significant effort in making text appear uniform with no visual anomalies to standout, carefully tweaking letterforms and kerning pairs to achieve this effect.

However, typographers also understand the need to make some words visually pop-out from surrounding text and therefore provide forms of emphasis such as italics and bold. Bertin also points out that these forms of emphasis are available and discusses them in the context of the technology of his time: pencil, pen, professional printers (which would have used phototypesetting in the late 60’s), and dry-transfer lettering (e.g. Letraset). And he nicely itemizes attributes of typography, available on page 415 of the French edition of Sémiologie Graphique:

BertinType2

Bertin’s font attributes included letter forms, font family, font width, spacing, size, weight, case, and slope (italic).

So, why was Bertin wildly successful, but his commentary on typography so minimal that it was dropped from the English translation? Good question!

One answer is that even though Bertin indicates the potential of text to indicate data beyond literal text; he says the incremental addition of text only helps low-level elementary reading, not the higher level of visual perception of patterns. However, Bertin is writing in the late 1960’s: 15-20 years before Edward Tufte popularizes the notion that a visualization can be read at many different levels depending on the task, which Tufte calls micro/macro reading (Tufte: Envisioning Information).

 

Another answer is that even though Bertin acknowledges typographic attributes such as individual letter forms, typeface, width, spacing, size, weight, case and italic – he doesn’t provide any examples of the use of these attributes. On the otherhand, he provides hundreds if not thousands of examples of the other six visual attributes (size, orientation, hue, brightness, texture, shape), making sure that his core concepts are well explained and illustrated.  A parallel can be seen in open-source visualization libraries: there were many different open-source visualization alternatives in the early 2010’s.  However, Mike Bostock not only provided a well organized library with D3.js, he also provided a lot of compelling examples of visualizations implemented in D3 with source code. Mike made it far easier to adapt and extend D3’s model by starting with examples rather than requiring the extra effort to learn some other library and then figuring out how to create those examples.

There are other possible reasons, but the unfortunately reality is that Bertin’s typographic insights were side-stepped and never exposed to the English language research community. Bertin also wrote a follow-on article (Classification typographique : Voulez-vous jouer avec mon A doi : 10.3406/colan.1980.1369) specifically on the visual attributes of type in 1980 – but again, no examples and no translation (it does provide a better organization of the typographic attributes).

In Sémiologie Graphique, Bertin made 100 different visualizations of a dataset indicating  three major occupations across 90 departments in France. None use typographic attributes (although a few use simple plain labels). I decided to make one typographic example – here’s Bertin’s ternary plot (p. 115) where the bubbles have been replaced with text, sized in proportion to population, colored based on occupation proportions. You can choose to focus on the macro patterns (e.g. most districts have an agricultural bias, or most of the agricultural districts tend to have smaller populations); or you can choose to focus on the micro details, e.g. district P (Paris) has the largest population and no agriculture; or, district 32 is the district with the highest proportion of people employed in agriculture.  (If you want more non-Bertin examples, see many of my recent postings regarding typographic visualizations).

BertinTernaryPlot

Ternary plot, based on Bertin, using alphanumeric codes instead of dots.

According to Google Scholar, there are 6004 citations for Bertin’s Sémiologie Graphique (across the French, German and English editions); while there is only ONE citation for Classification typographique : Voulez-vous jouer avec mon A. Perhaps this short article and references will help Bertin’s ideas of typography get more recognition and citations in future visualization and cartography research work.

 

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Posted in Alphanumeric Chart, Data Visualization, Font Visualization, Text Visualization | Tagged , | Leave a comment

Patent Visualization and Litigation Ratios

Earlier this week, Scott Langevin and I were fortunate to speak at the Strata Big Data Conference in NYC. The topic was Text Analytics and New Visualization Techniques. It discussed some of the examples on this blog and my research; and additionally showed these techniques applied as a front-end to big data and text analytics in some large-scale real-world applications from Uncharted.

One example was an extension to a visualization of patents. Understanding patent activity is of interest, as patent activity is a leading indicator of new commercial opportunity and areas where new skills and expertise are required. Also, patent litigation is an indicator of areas with problems where people need to be more diligent in research and more careful in crafting patents.

At Uncharted , we created a visualization of all the patents granted since 1982 as a massive graph. All patent applications refer to earlier patents. From these references, we can build all the connections between patents into a massive graph. Then, we use a hierarchical graph layout technique so that patents that are highly interconnected are drawn close to each other (described here). The result is a visualization where each patent is a small transparent orange dot and links between them are thin transparent blue lines (Images courtesy Uncharted Software, used with permission).

PatentGraph

Graph of all patents since 1982.

The graph layout nicely clusters patents together into visible communities. The graph is labeled, by using two or three unique terms from the most heavily cited patent in each community.

As an interactive application, the viewer can zoom in to successively lower and lower levels to see sub-communities and sub-sub-communities. There are also additional features such as search, color-coding, trend analysis and so on. All these features are used to aid the viewer in the deep analysis of IP topics, growth areas, problem areas and so on. In this post, we’ll just look at one feature regarding litigation. In this next image, patents with litigation are colored with purple dots (labels turned off, so you can see all the dots).

PatentGraph_wLitigation.PNG

Communities of patents, with purple dots indicating patents with litigation.

Clearly, there are various communities that have significant patent litigation. But the ratio of litigated patents to uncontested patents in each community is not clearly distinguishable. While each individual patent is visible as a dot, what’s needed is some way to indicate summary metrics for each community.

Rather than adding extra visual elements that clutter the screen, we can re-use a scene element that already exists at the aggregate community level — in this case, the labels. Following the techniques discussed in this blog, we use the oblique angle of the text to indicate the litigation ratio: text with steep italics indicates communities that have high litigation, text with no italics have normal litigation, text with reverse italics have no or very low litigation.

PatentGraph_wLitigationRatio.PNG

Label oblique angle indicates ratio of patents under litigation in each community.

This is useful to know in advance if crafting a new patent related to a particular community: more care is likely required to create a new patent in a community that already has many disputes.

There are a half dozen other examples of text analytics and visualization in the full set of Strata slides, available here or at Strata.

 

 

 

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Variable Fonts vs. Parametric Fonts and Data Visualization

I’ve typically been using ready-made fonts to create font-based visualizations in this blog. However, sticking to ready-made fonts means that you get only one slope angle for italic, a few levels of weight, maybe a condensed version or maybe not. Instead, the ability to quantitatively adjust font parameters such as weight, width or slope angle sounds far more enticing for visualization.

The good news is that fonts can be manipulated by the font user, and that there’s two different technical ways to achieve this.

Parametric fonts are programatically defined fonts. Parameters, such as x-height, stroke width and letter width are numeric parameters that can be set and then a new font generated based on those parameters. One early example of parametric fonts is METAFONT by Don Knuth. He enthusiastically claims:

“Infinitely many alphabets can be generated by the programs in this book. All you have to do is define the 62 parameters and fire up the METAFONT system, then presto – out comes a new font of type!” – introduction to Computer Modern Typefaces, 1986.

Knuth has a lot of low level parameters, which affect only a few characters such as dot-size (for the dots on i and j), ess (for the stroke breadth on s), beak, apex correction and so on. Here’s a snapshot showing some of his parameters, which looks much like an “anatomy of type” illustration:

knuthSomeParameters

Some of the font parameters in Knuth’s METAFONT.

Prototypo.io is a modern incarnation of parametric fonts, with click and drag sliders, interactive previews and feedback when you go beyond the expected parameter ranges, all in a browser. The starting point is a ready-made font with manipulation of 30ish parameters, such as as x-height, width, thickness, slant, serif width, serif height, bracket curve and so on. Quickly you can create a low-x-height, heavyweight, tightly-spaced, flared bell-bottom font. However, shifting some parameters into the redzone results in a fun font that isn’t particularly legible (e.g. a, 5, 0 are filled in):

PrototypoBellBottom2.PNG

Manipulating Prototypo serif parameters into the red zone to create a heavy bell-bottomed font.

 

Quite a few of the font parameters are mutually exclusive. So they can be combined together to create combinations in a visualization to represent different data variables. Here’s one of Prototypo’s fonts where I’ve created 8 variants: a boxy, heavyweight, condensed, bulgy serifs, wide serifs, low x-height and default plain. All the pairwise combinations are shown below:

PrototypoPairwiseCombos.png

Pairwise combinations of eight different font variants.

The parameters have ranges of values, so instead of only binary settings (e.g. normal weight and heavyweight; normal x-height and low x-height; etc), a range of parameters could be used. Here’s a font with five different levels of x-height and five different levels of curviness (think of curviness as an ordering from angular to rounded to boxy):

PrototypoXheightVsCurviness

A font with 5 levels of weight and 5 levels of curvature.

One problem with parametric fonts and browsers today is that each parametric variant needs to be saved in a font-file: the 5 x 5 x-height by curivness font requires creating and saving 25 font variants, which is tedious; and then all these variants need to be loaded into the browser, which uses a lot of memory and can be slow. If we also want to add 5 weights, 5 oblique angles and 5 widths, we’d need to generate and save 5 x 5 x 5 x 5 x 5 = 3,125 font variants. This is problematic for visualizations that might want to encode 5 different data attributes each with 5 different levels into labels.

Variable Fonts, a new standard with OpenType 1.8, provides an alternative for font users to interact with font parameters. Variable fonts provide linear interpolation between defined extremes created by the type designer. Here’s a font with variations in width, weight and optical sizing (from John Hudson’s Variable Font blog post). Green letters are defined by the font designer, orange letters are interpolated from these.

VariableFonts.png

Variable font illustration indicating interpolated fonts (orange) from defined fonts (green).

Variable fonts are very new, so it’s exciting to watch the evolution including browser support coming out.

Parametric and Variable Fonts in Visualization

Parametric fonts and variable fonts are interesting for data visualization for a few reasons:

  • You can potentially access low-level parameters, such as x-height, that you can’t normally access.
  • You have quantitative access to each dimension in a font: you’re not limited to only a few weights or two widths.
  • And these parameters can be combined together in new ways.

So what? Here’s a quick snap of the introduction to Mr. Weston from Chapter 2 in Jane Austen’s Emma. Interesting uncommon words have a high x-height while boring frequent words have a low x-height and inbetween words have an inbetween x-height:

xHeight-EmmaWeston.PNG

Words with higher x-heights are less common words in English.

Not that this quick example is particularly readable, but seems reminiscent of some of the formatting in Ronell’s deconstructivist Telephone Book:

RonellTelephoneBook.png

Font size varied to create a rhythm separate from words in Ronell’s Telephone Book.

Ronell’s formatting isn’t constrained to whole words but runs across sequences of letters creating a rhythm separate from the text. In general, being able to tweak and adjust font attributes opens up new creative possibilities and new kinds of data visualizations. Like Ronell, data visualization could apply font attributes like x-height, or width or curviness or other attribute to subsets of words. Suppose you had a dataset that indicated which letters in English words are silent – you could then draw words such that the silent letters are shown differently, say, with a low x-height:

 

 

SilentLetters2.PNG

Silent letters indicated with low x-height letters.

Those are just a couple of quick examples – many of the other font visualization examples in this blog could be adapted to better utilize some of these font parameters. And, of course, there are many other visualization possibilities yet to be considered.

Parametric vs. Variable Fonts?

So which is better – parametric or variable? I have used Prototypo and created a few variants and a few visualizations. I haven’t used variable fonts yet – but I like the specifications.

Both parametric and variable fonts have limitations. As discussed earlier, with parametric fonts in current technologies, a font file needs to be generated for each variant required, which means managing a lot of font files and dealing with inefficient use of application memory.

With variable fonts, however, the font user has to rely on all the variants created by the font designer. If no condensed/expanded variant was created by the font designer, then that axis will not be available to manipulate. Linear interpolation of shapes could also run into issues as well: for example, consider these three widths of Gill Sans (normal, condensed and extra condensed) – for example, the a and g completely change shape, the openings on S, 3, 5, get bigger, at some point the edges of the M start to slope,  and so on. These sorts of sharp changes presumably won’t be captured in variable fonts. In theory, a parametric font might be able to have some rules to accommodate this, but that would depend on how complex the parametric design rules are.

gillsans.png

Gill Sans in 3 widths. Note how heights and letter shapes change with increasing narrowness.

I’m looking forward to experimenting more with OpenType Variable Fonts: it could make font manipulation in visualization much easier to do. I’m hoping that variable fonts won’t go the way of multiple master fonts. We’ll need a couple things to happen to make sure that we have a solid foundation for variable fonts. First, we’ll need browser and application support – and there is already some indication that there will be browser support in Chrome. Then, we’ll need to see font families created that support variable fonts. Ideally, these variants won’t be restricted to typical attributes such as weight or width, but hopefully we’ll see variants that have multiple x-heights, or serif styles, or slopes or other parameters.  Then, on the data visualization side, we’ll need to invent new types of useful visualizations that use these new font capabilities.

Here’s hoping that variable fonts will become a well supported standard.

Posted in Data Visualization, Font Visualization, parametric fonts, Text Skimming, Text Visualization, Variable Fonts | Tagged , | Leave a comment

The Origin of Thematic Maps — and the problem with base maps

Why is there such a big gap between thematic maps and label maps? Both types of maps show data about places. Thematic maps typically use lots of color to show data about places; whereas label maps use a lot of labels to indicate the names of places — plus they use typographic formats such as bold, italics, caps and so on – to show extra data about places.  Compare these two US maps (both from the National Atlas of the United States of America 1,2):

US-choroplethMap

Left: Choropleth map of US counties with color indicating presidential vote in 2012; Right: Atlas map of US with various text labels and formats. Inset shows city labels using size and italics to indicate additional data.

On the left, counties are color coded, indicating one data attribute per county (and tiny counties may not be visible). On the right, cities are indicated with text, plus the population is indicated by text size, plus italics are used to indicate if the city is the capitol of a state or country.

Obviously, both maps serve different purposes, but in both cases additional data about places is getting encoded into visual attributes. The difference between thematic and labelled maps is entrenched in our thinking about maps. In cartography textbooks (e.g. Tyner, Brewer, etc), thematic maps are discussed in completely different chapters than labels: text labels aren’t considered to be thematic.

Why?

Perhaps it is useful to look back in time to figure out where this split first occurred to provide some insight. Thematic maps have been around for a very long time. Here’s a pair of maps from the 1850’s. On the left is a thematic map by Minard (link) with circle sizes indicating shipments per port. On the right is a contemporary map from an atlas by Heinrich Keipert (link) where city labels indicate information via text, font size, bold, underline and capitalization.

Thematic_MinardvsKiepert

The first Choropleth Map

The earliest choropleth maps (according to Michael Friendly), are from Charles Dupin in 1819 (almost 200 years ago!) with an example shown on the left below (link). Simple grey shading applied across almost equally sized regions makes for a great image showing a broad dark band across the center of France. Again, on the right is a contemporary map, this time by Carey and Buchon (link) and again, this map has variation in typography such as spacing, capitalization, italics and size.

Thematic_DupinVsCarey

Crome’s Neue Carte von Europa

So where did Dupin get the idea for a thematic map? A big influence on Dupin was the German researcher August Crome. Below is Crome’s Neue Carte von Europa from 1782. This maps shows where various commodities are produced across Europe.

Thematic_Crome
You can see that Crome starts with a base map that has the labeling conventions of the time, for example italics for rivers, all caps for country name, and color to denote country boundaries. Then he adds on top of this map all the content related to his thematic investigation: different kinds of commodities. He displays these as symbols and codes (only a small portion of the legend and map is shown – original map is here).

Base Map Pain

However, Crome can’t differentiate these symbols and codes from the base map using color, font size, case, and italics — because those have already been used in the base map. Even if he were to use them, they wouldn’t stand out because those formats would just be confused with the base map use of those same attributes.

Anyone who’s designed a map knows the pain of base maps: it’s really hard to make your data standout when the basemap is an already noisy and colorful Internet map or satellite image. And, when you’re designing a thematic map, it’s nice to have patterns in your data visually pop-out. So Crome is backed into a corner and uses different symbols for commodities as well as pairs of letters. However, all of these effectively require perception of different shapes, and different shapes don’t visually pop-out (i.e. shape is not preattentive, e.g. scholarpedia visual search, or Bertin).

So Crome’s proto-thematic map was highly popular but there are no patterns that you see – you have to inspect it closely and read all the labels. Instead, Dupin starts with a much simpler base map – outlines of regions – and his dataset is simpler too – just a single variable. As a result, he is able to use an attribute such as brightness or color. He adds labels too, but his labels are simple plain text and the labels are easily skipped by other later map makers.

What if…

Could Dupin have used text and typographic formats instead, like the other contemporary label-based maps of the time? It’s an interesting hypothetical question. Bold type has strong preattentive properties (e.g. Strobelt et al). Dupin might not have known about or had access to bold type: it was invented around the same time as his map on the other side of the English channel (1820s). And the first bold-faces were not available in a range of different weights which Dupin would have needed. Similarly, italics of varying slopes, or different styles of underlines wouldn’t have been available to him. As a result, Dupin and his engraver use intensity, which was available to them, and launching the split between thematic maps and label maps.

Here’s a thematic map, using font weight (more examples of typographic thematic maps are in the paper just published for ICC available here):

CartogramByFontWeight

Six different levels of font weight are used to convey data.

I wonder what Crome and Dupin would have thought?

 

Posted in Choropleth, Data Visualization, Font Visualization, Thematic Map | Leave a comment

Top NY Picks: Me vs. Reviews

I have a few upcoming speaking appearances, including the International Cartography Conference (DC July 6); Information Visualization 2017 (London July 13) and Strata Data Conference (NYC Sept 26-28).

Since NYC is a city I often visit, I thought I’d make a list of places I like and compare it to the ratings on a trip review website, in this case Trip Advisor. In this example, each row lists a place I like and a few words from a recent review. Then, added typographic symbols indicate the mean and one standard deviation. With the added bolding, it forms a kind of box plot made of text.  What you’ll notice is that some of the things I like are also liked by reviewers (e.g. MOMA, NYPL) but some things are not liked so well by reviewers (Lever House, Citicorp Center):

RichardNYCfaves

To provide a bit more context as to why this may be, the underlines indicate the number of reviewers. There are more than 18,000 reviews of Rockefeller Plaza – seriously? – what does the 18,001st reviewer really think they are adding to the reviews? On the other end though, Lever House and Citicorp aren’t written up by many people – they don’t score highly perhaps because no one has told the visitor to look at these places and they don’t. Some of the reviewers who do take a bit of time to review them are dismissive.

This visualization is interesting for the discrepancy between the review site and the personal favorites. It is also interesting for the insight to herd mentality to notch one’s name on a deemed cultural landmark (18,001!) versus stopping to smell the other roses that everyone else walks past. And that’s what makes cities like New York great: there are millions of interesting places in every nook, plaza and market waiting to be discovered and appreciated.

Posted in Alphanumeric Chart, Data Visualization | Leave a comment

50 categoric colors, inspired by telephone wires


25 pair color-coded telephone wires

Using colors to represent different categories of data is challenging to scale past ten unique colors. Many authors and researchers recommend against it. Colobrewer2 doesn’t go past 12 unique colors. D3.js offers three variants of color scales with 20 unique colors. But what if you want more? Suppose you have a line chart with 50 lines? Or some kind of choropleth map with more than 20 categories?

Dashes Don’t Work

With line charts, one obvious answer is to alter the line style, for example some continuous lines and some dashed lines. In this way a color can be reused, once in a normal style, again as a dash variant, again as a dotted variant. I used to use this approach, but found out that users don’t like dash lines. Why? Dashes might have a gap at a turning point: many financial users want certainty at the local highs and lows. Dashes are also confusable with gaps: in real data there may be a gap and you want to explicitly depict the gap because a gap in data is meaningful.

Cartographic Lines

Like many things in data visualization, researchers want to invent new techniques. But first, it may be more constructive to see how other people solved the problem.

I like to look at cartographers, because there’s 500 years of printed maps to consider for inspiration. There are many variants on line styles: dots, dashes, combinations of dots and dashes and pairs of lines:

Many different simple geometric glyphs can be embedded in lines to differentiate between them: dots, circles, stars, diamonds, boxes, and so on.

Charts, of course, borrow the idea, as seen in Excel’s charts.

These glyph-based line styles could be combined with various colors to create a wide number of different lines, e.g., orange line with stars, orange line with circles, blue line with stars, blue line with circles, and so on.

But, lines with glyphs create visual noise. The high point of a line is confounded with a star making the highest point a few pixels higher. And the entire scene becomes cluttered with circles, dots, stars and diamonds. One suspects that cognitive load may be increased, but some independent studies should be done to confirm the hypothesis.

25 Pair Color-coded Telephone Wires

Instead of glyphs, this post is focused on alternating colors on lines, borrowing an idea from telephone wires.

Interestingly, wire-based telephony had the same problem as line charts. You need a get a pair of wires to each household. Those wires need to be bundled together back to the telephone exchange. You need to be able to visually distinguish between the wires when you open up the bundle at any point. You could interactively determine this by successively testing each wire, but that would be slow and cumbersome. And there’s definitely more than 5 houses per exchange, so you need to create some kind of categorization scheme to differentiate among many different lines.

I remember as a kid in our suburban house we had open floor joists in the basement and my dad had wired up phone jacks in each room (this is pre-wireless technology). Unlike 120 volt power wires (black and white), each telephone wire was predominantly one color, say red, with a second color as a small dash every half centimetre, say yellow. So, you could have red with a bit of green or orange with a bit of yellow, green with white and so on. The telephone standard supports 25 pairs of wires, which results in 50 unique color codes. For example, in the top image in this post, the yellow wire with bits of blue can be clearly distinguished from the blue wire with bits of yellow paired beside it.

This color patterning means that a wire can be visually traced through a spaghetti jumble of wires or decoded at either end without needing to see the middle. Presumably the colors were chosen and standardized to meet the needs of telephone repair, for example, under poor lighting conditions. And, given the standard has been around for many decades with worldwide use, it is probably safe to assume that it has some degree of effectiveness.

Using the 25 Pair Color Code with Visualization

What does this mean for data visualization? Using the same approach, line charts could be created with 50 uniquely identifiable lines. Using this approach, the clutter associated with glyphs on lines does not occur and the objection to gaps associated with dash styles is no longer an issue.

Areas could be filled with 50 uniquely identifiable color combinations, and the pattern orientation, size and glyph remain open to either express other data or allow for aesthetics. Points, such as a scatterplot, however, won’t work, unless those points are larger and given a lack of association between adjacent points, the approach might not work well perceptually.

Color-coded lines are easy to implement in SVG (and D3). A line can be plotted twice: the under-line in the dominant color followed by a second line drawn overtop with the secondary color and a dasharray. Similarly, for areas, SVG patterns can be created.

Note that the colors in the telephone wire standard include colors such as black and white. Given that visualizations are typically on a white or black background, the initial colors need to be tweaked for visualization. Perhaps pink instead of white when used on a white background.

So here are 50 lines in a pseudo-random line chart, where each line is colored using a 25-pair color encoding:

25pairColorLines2

A couple of things worth noticing:

  1. The approach does work in that there are no gaps and each line is clearly and uniquely encoded. Yellow with a bit of green hits the lowest low on this chart.
  2. A line chart with 50 lines is very crowded. You can see sort-of macro-patterns, with a density of lines starting a bit higher and trending down.
  3. You can identify where lines appear and reappear, such as the purple line with a bit of pink at the top left, reappears at the top again the right side. You can even visually trace a line, but that requires considerably more effort particularly through an area of congestion.

Does 25 pair color coding really work? The result above seems promising but inconclusive. More tests would need to be done. And more importantly, what are the tasks that a user might need to do on a 50 line line chart? Tracing is an interesting task to consider. What are the other tasks?

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Readability

Readability

and what it means for data visualization

Type can be legible, but still unreadable. Consider this image:

The letters are perfectly legible, but the text, upside down and mirrored is unreadable.

Beautiful Helvetica, mirrored and rotated 180 degrees. Two highly common words from the English language. Letters are perfectly legible and even turn into other letters: p becomes b, m turns into a strange w. But it’s unreadable (thanks truetypestories).

Legibility is concerned with the clear delineation of the individual letterforms and their separability from one another.

  • Are the individual letters clearly designed?
    For example, is the opening of a c sufficient.
  • Are the letters clearly distinguishable from one another?
    For example, Helvetica uppercase I, lowercase l and numeral 1 are extremely similar.
  • Is there potential for letters to run together to be mistaken for a different letter?
    E.g. rn in Helvetica could be mistaken for an m, particularly if a drop shadow fills the gap between the r and n.
  • Do the proportions between letters make a potential letter ambiguous.?
    A letter with high x-height may not have much variation to distinguish between an h and an n (again see Helvetica).

Legibility is very much in the domain of the font designer and is very concerned with the shapes of letters, spacing, consistency across the design.

Readability, however, goes beyond type design. Readability is a comprehension issue concerned with ease of reading lines and paragraphs. Readability can be affected by many factors:

  • Line length: paragraphs that are very wide or very narrow are harder to read.
  • Spacing, kerning and leading: spacing between letters and lines, and tuning these spaces. For example too far   a p a r t  and words break apart.
  • Font weight: text that is too heavy or too light can be more difficult to read. Note that most fonts with variable weight have a “book” weight.
  • X-height: a font with a high x-height may increase legibility of words at a distance on signage, but may be more difficult to read for long paragraphs.
  • Uppercase: all uppercase is more difficult to read than type set in mixed case.  This is NOT an endorsement of readability based on word shape, rather simply that all uppercase has no ascenders or descenders, meaning that there is less shape differentiation between letterforms.

Readability is also related to cultural conventions. For example, in languages with longer/shorter words, optimal paragraph widths may be longer/shorter. Font choice is related to readability. Fonts that are more familiar are easy to read:

  • font-blackletter font  is  difficult to read these days because it is uncommon, but was used regularly in Germanic countries until the early 20th century.
  • font-baskerville was considered difficult to read when introduced (claim it hurt eyes), but would likely be unnoticed today.
  • font-neueswift is a modern font, designed by Gerard Unger in 1985. Gerard says: “When I first released Swift, people criticized it has hard to read with many angry angles: now it is a standard used in many newspapers, dictionaries and other major works.” (presentation by Gerard at University of Reading, 2016).
  • Note that there is an ongoing discussion as to whether sans serif or serif fonts are easier to read. In practice, for long printed texts, the convention tends towards serif-based fonts; while on mobile screens, the convention currently tends towards sans-serif fonts (perhaps this may change with more more devices at higher resolutions). Or perhaps a notion that sans serif is better for short bursts (headlines, narrow mobile devices) versus serifs for wide lines (Williams).

So what does readability mean to  visualization? The visualization programmer has control over choice of font, spacing, weight, shadows, and so on – so readability should be considered. Furthermore, techniques that may change font weights or other attributes in running text,  m a y  negatively impact  r e a d a b i l i t y,  particularly if  t h e r e  are multiple different attributes adjusted co-occurring within a text (Carl Dair).

There are also cases where readability is not an issue. Short snippets of text, such as headlines or text specifically designed for skimming are an example. For example, dictionaries often use a wide mix of typographic techniques to differentiate elements within each entry to facilitate ability to quickly skip across parts of a definition of interest.

Furthermore, a visualization may be interested in deliberately interrupting readability, given the appropriate application. The ideal exemplar here is Tallman lettering, used to differentiate among similar sounding medications.

(For more info and examples, see, e.g. Victoria Squire et al’s: Doing it Right with Type; Beier’s Reading Letters, Designing for Legibility; Walter Tracy: Letters of Credit; Isabel Gauthier et al, Font Tuning associated with expertise in letter perception)

 

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