The Design Space of Typographic Data Visualization

There are many possible new visualizations using typography, some of which I’ve previously discussed in posts on this blog. One way to consider this design space is to decompose it into the different elements that can be used to assemble visualizations. These elements include:

  1. Typographic attributes. This is all the variation within type that can be used to create differentiation and encode information. This includes the literal alphanumeric glyphs as well as font weight, italic, case, typeface (e.g. Helvetica, Times), underline, width, baseline shifts (e.g. superscript), delimiters, x-height, as so on. Of course, other visual attributes such as color, size and outline can also be used.
  2. Data Encoding. Type can encode different kinds of data. Labels on maps use type to indicate different types of data as shown in the example below. The names of areas in this map use type to indicate: a) literal data, such as the name of the town or region; b) categoric data, such as whether the area is a country, province or city; and c) quantities, such as the population.

    SteilerAtlas

    Steiler’s Atlas (1920). Labels indicate place (literal text), categorize the type of place (typeface), indicate the level of political administration (underlines ordered dash, single, double), and population size (ordering of case, italics and size). From davidrumsey.com

  3. Scope. Type attributes may extend across a sequence of letters. They scope of the type attributes may apply to whole words (as on the map); could apply to a subset of letters within word (for example, to indicate silent letters in words such as though and answer); extend across multiple words (e.g. “There goes the HMS Titanic.”); or even extend across lines, paragraphs or portions of a document.

So What?

This creates a multi-dimensional space for design exploration: attribute x data-type x scope, which we can then use to consider some interesting new kinds of visualizations. For example, we could apply literal text to a line in a line chart (alphanumeric text x literal data x sentence). Why bother using a tooltip or creating a visually separate legend, when the content can be directly embedded in the line?

Line chart showing retweets over time for some top tweets about Trump from late Aug 2015.

Line chart showing retweets over time for some top tweets about Trump from late Aug 2015.

Or, we can vary a type attribute, such as weight to indicate word frequency. For example, the chart below indicates how frequently adjectives are associated with characters from Grimms’ Fairy Tales (i.e. font weight x quantitative data x word).

Font weight indicates the frequency of adjectives associated with characters from Grimms Fairy Tales. Kings are old, princesses are beautiful and girls are little.

Font weight indicates the frequency of adjectives associated with characters from Grimms Fairy Tales. Kings are old, princesses are beautiful and girls are little.

How I got to this framework and lots of other examples – both historic and new types of visualizations – are discussed in more detail in this journal article Using Typography to Expand the Design Space of Data Visualization (html version, PDF version), which was just published in the open-access journal She Ji: The Journal of Design, Economics and Innovation (here).  

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500+ years of increasing separation of text from visualization

In the beginning typography and infovis were tightly integrated. In this illuminated manuscript from circa 1480, the biblical text and the genealogical tree interwoven. Text in the visualization is not just simple labels. While bold, italics and sans serif don’t exist yet to create differentiation, here the text varies in color. Some node labels are plain black, some are red, some start with a red initial (and node size, outline color, outline shape all vary too). Similarly, the explanatory text is woven around the graph: it’s not separate to the visualization. These same kinds of relationships between text, typographic attributes and visualization can be seen in other medieval visualizations and tables (e.g. see more examples at Bodleian).

Genealogical tree from late 1400's. Note graph nodes use of image (people, shield) or text, where text may be black, red or start with a red initial. The nodes can vary in size, color, or shape (circle, crescent, shield). Textual commentary is intertwined throughout.

Genealogical tree from late 1400’s. Note graph nodes use of image (people, shield) or text, where text may be black, red or start with a red initial. The nodes can vary in size, color, or shape (circle, crescent, shield). Textual commentary is intertwined throughout. Via Bodleian library.

Step forward a century to the proliferation of the printing press and movable type. With movable type it is easier to set an entire page of type, but more difficult to set type and images together. It’s hard to get the image (an engraving such as a woodcut) to work together with movable type. It’s difficult to configure a page to get the components to lock together, difficult to get the ink to spread evenly, difficult to set everything to the same height. It’s hard to use color – that’s two separate pressings or laborious masking of different areas for ink to be spread. So images start to move into separate blocks or separate pages. Words within images now need to be carved, in reverse. It becomes simpler to create an image without text (or very little) and make reference to the image from the text or with captions.

1573: Image separate from text. from William Bullein, A Dialogue… Against the Fever Pestilence. @ Bodleian.

1573: Image separate from text. from William Bullein’s A Dialogue… Against the Fever Pestilence. Author photo from Bodleian exhibition “Shakespeare’s Dead“.

By the time of the Enlightenment, images have become beautifully engraved plates executed by skilled engravers. Diderot’s famous Encyclopedia (1751-1777) has beautiful images and a wealth of text – completely separated. Plate numbers and key letters on the images provide the sole reference to relate the detailed text pages to the plates, which are in separate sections in the bound volume.

Diderot's Encyclopedia has great illustrations of various occupations - all neatly labeled, but the viewer has to cross-reference the text to understand.

Diderot’s Encyclopedia has great illustrations of various occupations – all neatly labeled, but the viewer has to cross-reference the text to understand.

 

Bring that forward another century and you see statistical graphics. Like the earlier Enlightenment illustrations, text is separate from charts. Within charts, text is minimal – pushed to the edges (title, axis labels) and maybe the occasional label on a line internally, carefully placed to avoid colliding with a grid line.

1930 book explaining charts. Text is pushed to the periphery of the chart. (T.G. Rose, Business Charts, 1930)

1930 book explaining charts. Text is pushed to the periphery of the chart. (T.G. Rose, Business Charts, 1930)

Information visualization utilizes many of the techniques from charting and statistical graphics. In general, most of the text is pushed to the edge in information visualizations. Yes, there are many news infographics where text is integrated into the visualization. And there are text visualizations where the entire visualization is made of text (e.g. tag clouds) or perhaps labels on markers (e.g. graphs). But there’s still gaps. From the infographics perspective, text is typically hand-crafted annotations carefully placed around the visualization. From the information visualization perspective, the text is limited – i.e. usually labels. Detailed text might be accessible via a tooltip, but tooltips are slow and if you don’t focus on the particular item then the tooltip content is not available. Detailed text might be visible in another linked panel (think Google finance charts), but this requires cross-referencing back and forth between two different visuals. This cross-referencing is a point of slowness (e.g. see Larkin and Simon’s Why a picture is sometimes worth 10,000 words). In a few instances, a full sentence might make it into an information visualization (e.g. newsmap.jp), but even these have various issues (e.g. newsmap has many headlines too small to read) .

Should the medieval visualization be dismissed as an early visualization created with limited tools; or should it be considered an exemplar of how visualization, text, imagery and typographic attributes can all be used together to create a clear communication of complex data. And furthermore, the medieval scribe achieved this using only a pen while we have incredible computing resources. If the medieval example is considered a goal, then the question is:
How can we move towards automated information visualization with rich textual information directly integrated into visualizations?

(This post was inspired by discussions at TDi2016 Reading University and exhibits at Bodleian Library).

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Venn Diagrams enhanced with Typographic Attributes

Here is an example visualization illustrating a potential use case where typographic attributes add functionality and usefulness beyond a familiar representation such as a Venn diagram. In the case of set visualization, typography has many different attributes (e.g. weight, italic, case, font family, underlines, capitalization, and so on) that can be combined together to indicate membership in multiple different sets. Furthermore, attributes such as font-family, can be used to indicate membership across different categories within a set, not just binary membership.

U.S. House of Representatives 4-Way Venn Diagram

Below is a 4-way Venn diagram that includes the name of every member of the U.S. House of Representatives. At a high-level there are 4 bubbles indicating 4 different sets: gender (blue/pink); party affiliation (red stripes for Republicans); race (freckle dots for white) and multiple terms (light green):

4-way Venn diagram of U.S. House of Representatives with members indicated in stacks of text.

By using stacks of names, you don’t even need to read the names to make high-level macro-comparisons. It’s easy to visually compare the relative heights of stacks to gauge the approximate number of representatives in each set and set intersection. So, this Venn diagram indicates some discrepancies between the proportions of the elected representatives and the general population. For example, women (in pink) seem to be under-represented, assuming that women make up approximately 50% of the population. There is also a bias in ethnicity. Democrats (top half) have a larger number of ethnic minorities, particularly in the far right column (serving more than a single term), whereas Republicans have few ethnic minorities.

Micro-details. Close-up you can read the name of each member. Unlike a simple Venn diagram, all the individual elements are visible (congress members), and named. Read names directly without relying on tooltips. Search for a specific name (browser search e.g. ctrl+f in Chrome on Windows). Click any name for details. I.e. there are lots of benefits to making names available.

Plus, with all the detailed names, we can also use font attributes to reinforce the Venn memberships, plus add additional data. So, for example, Republicans are right leaning italics and Democrats are left leaning italics. Here’s a closeup:

Closeup of names on Venn diagram. Left leaning indicates Democrats, right leaning indicates Republican. Plain text indicates white ethnicity, etc.

Gender is pink/blue; party affiliation is left leaning for Democrats and right leaning for Republicans. Members serving multiple terms are bold. Those with white ethnic background are in a plain sans-serif font. But there’s more…

Beyond 4 sets. Venn diagrams do have limitations: they can be difficult to show more than 4 different sets. While feasible, it may be difficult to distinguish set membership following complex outlines. Instead, additional font attributes can be used.  For example, those over 65 years of age are all caps; and those with higher education are indicated with underlines. Note that there isn’t a separate Venn bubble for these attributes – these represent memberships in 5th and 6th sets.

Beyond binary membership. Venn diagrams also do not qualify between different categories within a set. For example, for ethnicity, different ethnic backgrounds are differentiated in the data, but a Venn only indicates on category (e.g. white or not-white). In this example, white politicians are in a plain sans serif font, while non-whites have more diversity: a script font for Latino, a serif font for Asian Americans and a block font for African Americans. An additional level of information is revealed.

Demo! Here’s the URL for the interactive version: http://codepen.io/Rbrath/full/QEGBOo/

The demo version lets you toggle on/off different ways of showing set membership. Do you notice the difference in font? Toggle any text feature button and notice how the labels adjust appropriately.

About the code and demo. This is the first post with a functioning demo. It’s is not meant to be an example of good programming – the code is prototype-grade code, which means just enough coding to get it running and not bothering to go back and clean it up. It should ideally be more flexible with data, for example, allowing the user to pick and choose which attributes to use for any Venn set. Nice future features would be to use a more generalized Venn diagram, capable of using circles or ellipses rather than rounded rectangles. This would require some more intensive computational geometry which is an exercise left to the reader – I haven’t looked into it, but Jonathan Feinberg’s approach used in Wordle may be a good starting point.

Data is from Measure of America and Wikipedia . Feel free to reuse any concepts. Please cite: Richard Brath. Typographic Sets: Labelled Set Elements with Font Attributes. in SetVR 2016 International Workshop on Set Visualisation and Reasoning (2016).

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Noticing a Difference vs. Decoding

I’ve had a number of papers rejected where I’ve varied multiple (font) attributes within a single visualization – jump ahead to fig. 6 for an example. There are members in the visualization community and the typography community who have reservations about varying too many things at once. However, there can be some cases where multiple variations are actually useful to tasks such as noticing similarities and differences between elements, as will be explored in this post.

Noticing a Difference

There have been many psychology experiments looking at preattentive perception. Healey has a great summary.  When presented with some kind of visual search task, the requirement is to determine (quickly) whether or not a particular target exists. This is a bit like looking for the thing that is different among all the other things. There’s a lot of nuances in this research, for example some types of visual cues can be perceived more quickly than others. Some types of cues are not symmetic, e.g. finding an Q in a field of O’s is faster than finding an O in a field of Q’s:

A Q in a field of O's is easier to find than an O in a field of Q's.

A Q in a field of O’s is easier to find than an O in a field of Q’s.

The same kind of nuances apply to font attributes such as bold or italic as well.  Here’s an interesting example using font weight.  In fig. 2, finding the different name in the first and second examples is easy because the difference in weight between plain and bold in the font family Segoe UI is significant. However in the third column, weights used are plain and light variants in the font family Segoe UI, which don’t have as much differentiation. It’s reasonable to assume that finding the light text using the font Segoe will be slower than finding the bold text.

Find bold in plain or vice versa is fast; but finding lightweight in plain will be harder.

Fig. 2. Find bold in plain or vice versa is fast; but finding lightweight in plain will be harder.

In addition to the font attribute, the choice of font family may impact the degree of notice-ability. Fig 3 shows italics. Typographers already think italics are a quieter form of emphasis than bold. And Stroebelt’s research seems to confirm this too.  On the left, italics are used in the sans-serif font Segoe UI, wherein the italicized form of the font is very similar in shape to original font with an oblique skew, i.e. a geometric transformation of the letters* (see footnote). At the far right is an example using Garamond, which, like most serif fonts, supports true italics, wherein the shapes of the italic letters are different from their upright counterparts. Presumably italics set in Garamond pop-out more quickly than italics set in a sans-serif. This is purely conjecture and not actually proven. It would be very interesting to test and see if the results confirm the hypothesis.

Presumably the noticing the difference in the sans-serif font Segoe (left) is not as fast and effective as noticing the italics in the serif Garamond font.

Fig. 3. Presumably the noticing the difference in the sans-serif font Segoe (left) is not as fast and effective as noticing the italics in the serif Garamond font.

This can be explored with each font attribute. Fig. 4. shows some examples using font family and case. Noticing blackletter in the middle of sans serif (left top row) is a lot easier than trying to find the serif in the middle of sans serif (right top row).

Spot the difference in font family (top row) and case (lower row).

Fig. 4. Spot the difference in font family (top row) and case (lower row).

Interference among multiple attributes

Search tasks become more complicated when multiple attributes are varied among each element. Some visual attributes can interfere with the ability to quickly detect the target attribute. Search for a combination of attributes is difficult. The classic example is the interference between shape and color. Fig. 5. asks you to find a specific club in a field of spades. Mixing multiple attributes can make it harder to locate the target.

Finding a combination of attributes can be difficult.

Fig. 5. Finding a combination of attributes can be difficult.

Difficulty decoding multiple attributes

Another challenge with multiple attributes is the ability to decode. If only a single attribute varies, like the examples in fig. 1 -4, remembering what the attribute means is easy. However, when many different data attributes vary, it can be more difficult to remember, given the limitations of short term memory. E.g. fig. 5. right could be described as a collection 35 people, with red indicating republicans, spades indicating men, and underlines indicating high wealth. Seeing a specific marker, e.g. red club with underline, requires one to recall each mapping to decode. With each additional data attribute the cognitive load increases, the time to decode increases and the chance of error increases.

Spotting Differences

However, the task may not be searching and locating targets, nor deciphering the encoding for a particular glyph. Sometimes the task may simply be to assess whether an item is the same or different compared to its neighbors. The alphanumeric map in figure 6 of UK postcodes varies italics, font weight and case.

UK post code areas indicating data via font weight, italic, and case.

Fig. 6. UK post code areas indicating data via font weight, italic, and case.

Even without knowing the encodings, we can ask simple questions such as whether a particular location is similar to its neighbors. For example, NG is similar to S and LE (near center top) or CA is similar to LA, DL, TD and FY (near top left).

We may also get a sense of the degree of difference between items. Near center bottom we can see wc. Immediately above is nw, in a slightly heavier font, while to the right is EC, in an upper case font. Above left from wc is HA, varying in both weight and case, indicating more difference than the previous two comparisons. This notion of degree of difference is also completely untested. Some differences may not even be noticed (see research on change blindness).

A Difference is Insightful Information

Depending on the task, seeing differences may adequately solve requirements, as illustrated in the previous example. There may be various applications where noticing differences is a relevant tasks, for example, understanding differences between elements in a items in a scatterplot, glyphs on a map, nodes on a graph, or possibly infographics (think Isotype, where pictographs may be combined of a number of elements).

Sometimes there may be additional tasks, such as a task that requires accurate decoding. This can be facilitated in many ways, such as providing a legend or providing interactions such as tooltips.

One could make a similar argument to use interactivity to reveal similarities and making all the visual attributes uniform. For example, pointing at NG in figure 6 could be used to highlight the similar S, LE, B and CF – no need to adjust italic, weight and case. While this is feasible, the viewer loses the ability to see patterns serendipitously. By making the differences visible, one can see patterns much more readily than relying on slow mouse movements across all the possible combinations and permutations. Of course, both techniques (visual encoding and interactive highlighting) could be used together to improve the overall effectiveness.

*Note 1: Segoe UI, unlike many sans serif, does have italics which vary letter form. Compare Segoe UI lowercase a and l in their plain and italic forms to see the difference. In this example, however, the variation between Segoe plain/italic is not as pronounced as the variation between Garamond plain/italic.  

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Alphanumeric Financial Charts

Financial charting has long used alphanumerics as point indicators in charts. One of the oldest I can find is Hoyle’s Figure Chart (from The Game in Wall Street and How to Play it Successfully: 1898) which essentially plots individual security prices in a matrix organized by time (horizontally) and price (vertically).

An early figure chart. Time is implied horizontally, price vertically. A numeric "figure" is recorded for each price that occurs for each day.

An early figure chart (from Hoyle: 1898). Time is implied horizontally, price vertically. A numeric “figure” is recorded for each price that occurs for each day.

This textual representation evolved over the decades. By 1910, Wyckoff (Studies in Tape Reading: 1910) was creating charts where x and y are still time and price, but he was writing down volumes instead of prices, and connecting together subsequent observations with a line.

Wyckoff's figure chart records rising and falling prices in adjacent columns. For each price level he records the volume figures and connects together the sequence with a line.

Wyckoff’s figure chart records rising and falling prices in adjacent columns. For each price level he records the volume figures and connects together the sequence with a line.

By the 1930’s these had evolved into early point and figure charts, such as can be seen in DeVilliers and Taylor (Devilliers and Taylor on Point and Figure Charting: 1933).  Columns use X’s to plot prices and other characters to denote particular price thresholds.

DeVilliiers and Taylor's Point and Figure chart (1933).

DeVilliiers and Taylor’s Point and Figure chart (1933).

These charts look pretty close to modern financial point and figure charts. Now we typically use X’s for a column of rising prices and O’s for a column of falling prices, and other character may be used to denote particular time thresholds (e.g. 1-9, A-C to indicate the start of each month).

Modern Point and Figure chart, via Wikipedia.

Modern Point and Figure chart, via Wikipedia.

Other alphanumeric charts evolved along the way as well. Here’s an interesting depression era chart plotting a histogram of states based on state unemployment rates. Like Wyckoff, the author seems to be interested to keep the alphanumerics inside circles. Also, note standardized 2 letter codes for states did not yet exist – states are numbered instead. (from W.C.Cope’s book Graphic Presentation: 1939).

Distribution Chart made of stacked characters. Note additional information encoded in shading and added markers.

Distribution Chart made of stacked characters. Note additional information encoded in shading and added markers.

Fast forward to the 1980’s, and we have Peter Steidlmayer’s Market Profile (R) charts that appear reminiscent to the alphanumeric distributions seen in the depression era chart. In these distributions, the alphanumeric value represent times when a security traded at a specific price. Depending on the timeframe of the chart different mappings may be used. One common intraday convention is to use characters A-X and a-x to represent half hour intervals throughout the day, with a split from uppercase to lowercase at noon.

Very basic Market Profile chart

Very basic Market Profile chart

There are many, many variants of market profile charts now e.g. sierrachart.com, windotrader.com, bluewatertradingsolutions.com, prorealtime.com, cqg.com, etc, etc. Given the many possible data attributes and analytics that one might associate with a character in a chart, it can become a challenge to encode them. As a result, one can find interesting variants. Beyond position, letters and case:

  • color: of the foreground letter or background square
  • bold: to indicate a row or potentially as a highlight to one time interval, e.g. MarketDelta
  • superscripts: e.g. eSignal.
  • added symbols: asterisks, less than, greater than, etc.
  • added shapes: circles and diamonds
Market Picture Variants

Many variants of Market Profile (R) charts by various vendors. Note all the additional information added via foreground/background color, bold, superscript, etc.

Jesse Livermore (How to Trade in Stocks: 1940created his own variant of alphanumeric charts stripped down to tracking only the minimums and maximums, discarding the intervening levels and using color and underlines to indicate information.

Livermore strips down charts to a simple table recording only the local minimums and maximums, using different colored text and different colored underlines.

Livermore strips down charts to a simple table recording only the local minimums and maximums, using different colored text and different colored underlines.

One interesting discussion point is the actual use of these charts. Whenever I show these charts to the visualization research community, people are aghast and suspect. There’s so much going on in these charts, so many different things being shown simultaneously, that they don’t believe that people actually use them or that somehow these charts can’t be perceptually efficient.

On the otherhand, I’ve talked to people who’ve traded off these charts their entire career. They see patterns and pick out things immediately at very different scales: individual outliers, columns of a particular letter, the shape of a distribution, and so on. Much like an expert chess player, these market participants have learned these charts, know how to interpret them, and use them to make trading decisions.

To be fair, not everyone in the visualization community is shocked: some are genuinely curious. Instead of reducing visualizations down to just one or two attributes, here’s something heavily loaded with a lot of visual attributes. And it’s not a static poster where you have no interaction: these are on computer screens packed with interactive features. In spite of all the computational ability to filter and reduce, here’s a community that that has these densely packed charts. People are actually using them to see macro patterns (shapes of distributions) and micro readings (individual characters), but they are also able to attend to intermediate patterns such as particular letters within a distribution. Perhaps they aren’t seeing patterns as fast as preattentive recognition, but they are still seeing patterns quickly with this external cognitive aid. There’s still more that the visualization community needs to understand about expert users.

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Text Visualization and Search

When considering text visualization or visual text analytics, search has to be considered a significant application. Tag clouds originated as a form of faceted search.

What is search?

But first, take a step back and consider what search really is. I like Card and Pirolli’s sense-making loop:

Sensemaking Loop (from Card and Pirolli)

Sensemaking Loop (from Card and Pirolli)

A key takeaway from this analysis is that “search” is really made up of many different tasks. As such, there are likely many different user components that address these different tasks. If you take a look at various search interfaces you’ll see all these components working together. Amazon has a search box of course, plus facets for refinement (on the left sidebar), plus a hierarchy of departments (in the center, for browsing down through a hierarchy). Or, if you consider a news portal, such as google or bing, elements include the search and facets; as well as individual stories including headlines, a lead sentence or paragraph, and possibly a photo. Textually, there are things happening at the level of individual words and phrases, lines, (like the headline or a keyword in context), and paragraphs.

A quick review of text visualization

There is a nice repository of text visualizations at textvis.lnu.se. In mid-January 2016, there were 250 text visualizations listed from 1976 – 2015. Looking through these visualizations, you can enumerate whether these visualizations depict text at the level of words, sentences, paragraphs, full documents or down to individual characters. Some don’t have any text at all. Here’s the results:

Representation Number of Visualizations Percent of Visualizations
None 40 19.1
Character/Syllable 2 1.0
Word 173 82.8
Line 19 9.1
Paragraph 15 7.2
Document 5 2.4

*totals do not add up to 100%: some visualizations use multiple techniques.

There’s a big discrepancy here. More than 80% of text visualizations operate at depicting words. Textual representations of lines (sentences), paragraphs, documents are uncommon – all together less common than no text at all. Slicing it another way looking at the visual representation of text we find:

Text Visualization Number
No Text 40
Plain Text 103
Tag Cloud 39
Other 68

This table shows (again) 40/250 with no text. The next number is interesting 103/250 are just plain text: i.e. label on a graph or a line of text. Plain text uses no color, no size, no enhancement, no additional encoding. This is, in some cases, a missed opportunity.

Out of the remaining, one third are tag clouds. Tag clouds are popular on the web and popular in research too. There are tag clouds in bars, tag clouds in graphs.

So what?

It seems that there should be a lot of opportunity beyond words, tag clouds and facets in search interfaces and text visualization. From a search perspective, visualizing the text of lines, paragraphs and documents and should be considered. A focus on words removes them from their context. Loss of context means that the sematics of those words is lost: homonyms, ambiguity, sarcasm and other meanings are lost when words are split up.

My current research on search and text visualization is in this article: Font attributes enrich knowledge maps and information retrieval. It includes a few different visualizations at various different levels of text: words through to paragraphs.

More importantly, there is still a lot of design, experimentation and research into new text visualizations to address all the many tasks associated with search including search, filter, skimming, reading, extracting, connecting, schematizing, assembling and story telling.

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Recent Publications

I’ve been blogging and writing on font attributes, multi-attribute labels and novel text based encodings in data visualization for a while. Eventually these get published. Here are a number of recent peer-reviewed papers, chapters and books (since 2013). The first section is specifically about text, typography and font attributes:

  1. The Design Space of Typeface
    (with Ebad Banissi) @ VisWeek, 2014: short paper and poster
    very short introduction into type attributes
  2. Using Font Attributes in Knowledge Maps and Information Retrieval
    (with Ebad Banissi) @ First Workshop on Knowledge Maps and Information Retrieval, 2014: paper
    application of type attributes to search, information retrieval and knowledge maps; first publication of techniques for proportional encoding and skim formatting
  3. Evaluating Lossiness and Fidelity in Information Visualization
    (with Ebad Banissi) @ SPIE  2105: paper
    a novel technique for measuring relative lossiness of visualization design alternatives
  4. Using Text in Visualizations for Micro/Macro Readings
    (with Ebad Banissi) @ TextVis 2015: paper
    more focused on labels; first publication with variants of textual stem & leaf plots
  5. Using Type to Add Data to Data Visualizations
    (with Ebad Banissi) @ TypeCon 2015: paper
    a fairly broad introduction to the topic; first publication with variation in x-height and font-width, in this case applied to prosodic encoding
  6. Font Attributes enrich Knowledge Maps and Information Retrieval
    (with Ebad Banissi) @ International Journal on Digital Libraries, 2016: journal article in HTML or PDF
    a comprehensive discussion of application of type attributes to search, information retrieval and knowledge maps

Some other recent papers about glyphs, financial vis, 3D and sports:

  1. High-Category Glyphs in Industry
    @ VisWeek 2015: paper
  2. Challenges in Financial Visualization: Panel
    @ VisWeek 2014: paper
  3. 3D InfoVis is Here to Stay
    @ 2014 IEEE VIS International Workshop on 3DVis: paper
  4. Bloomberg Sports Visualization for Pitch Analysis
    w/ co-author Bo Moon @ 1st Workshop on Sports Data Visualization: paper

Some chapters and full books on shapes, spheres and graphs:

  1. The Multiple Visual Attributes of Shape
    chapter in book: Information Visualisation: Techniques, Usability and Evaluation, 2014. Publisher: Cambridge Scholars Publishing, Editors: Ebad Banissi, Francis T. Marchese, Camilla Forsell, Jimmy Johansson, pp.43-66. link to book (amazon) or here is a subset from an earlier paper.
  2. Information Visualization on Spheres
    w/ co-author Peter MacMurchy, chapter in the same book: Information Visualisation: Techniques, Usability and Evaluation, 2014. Publisher: Cambridge Scholars Publishing, Editors: Ebad Banissi, Francis T. Marchese, Camilla Forsell, Jimmy Johansson, pp. 24-42.
  3. Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data
    book co-authored with David Jonker, published by Wiley (2015), avail at Amazon.
Posted in Font Visualization, Text Visualization, Uncategorized | Leave a comment