Using font attributes to convey data in visualizations

Font differentiation is rarely used in information visualization for differentiating among categories of information. However it has been used in this way in typography, notation systems and cartography.

Historic Examples

One very early example is from Chambers’ Cyclopaedia published in 1728. Chambers uses all caps for the root of the tree, italics for titles of major branches, small caps for specific fields, superscript for chapter numbers and roman text for the other prose.


Cyclopaedia 1728. Roman, italics, uppercase, small caps and superscript are used to differentiate categories of information in this typographic hierarchy of knowledge.

Another great example is from Lavoisne’s A Complete Genealogical, Historical, Chronological and Geographic Atlas from 1820. Many typographic attributes are used to organize categories of text including 1) bold uppercase for major branches (e.g. BRANCH of VALOIS); 2) Roman uppercase for smaller branches, e.g. ANJOU; 3) Mixed case roman for direct descendants (e.g. Charles of Valois, Catherine); 4) Small caps for sovereign rulers (e.g. Philip VI); 5) Italics for spouses.

Lavoisne's genealogical tree from 1820.

Lavoisne’s genealogical tree from 1820.


Font Family

Changes in font family (e.g. Helvetica, Times, Courier, Garamond) today are commonly used to differentiate structural levels in text, for example titles, headings, bylines, captions, sample code, may all be different complementary fonts in a high quality publication or website (e.g.

Historically, mixing font families may be used to indicate different types of data, such as this chart from Haeckel’s Pedigree of Mammals showing a taxonomy with branches with alternative classification naming systems differentiated by roman, italics, slab-serif and blackletter.

Haeckel's Pedigree of Mammals.

Haeckel’s Pedigree of Mammals.

Haeckel might be following an established pattern of using different fonts for multilingual labels. For example, see the multilingual captions on this illustration from Henry Overton’s 1733 book, ‘The Cryes of the City of London Drawne After Life’

The Cryes of the City of London Drawn after Life - Henry Overton, 1733

This same approach can be used for different speakers in a text, for example, in the graphic novel, Asterios Polyp by David Mazzucchelli, each character speaks with a unique font family, the rigid title character in a straight-line font always in authoritative uppercase, his artistic spouse in a softer rounded font always in mixed case, and various other characters in other fonts, such as the vet in a serif font.


Each character in the graphic novel Asterios Polyp speaks with a unique font.

From a labeling perspective, while maps typically use italics – roman; serif – sans-serif; to create visually diffentiated categories of labels, other font families may also be used. For example, some Ordance Survey maps use blackletter to label historic sites. Or, in pre-GPS days, my father used to mark-up his paper maps with his own unique hand writing when planning family road trips.


Ordinance Survey map with unique fonts for middle age antiquities (blackletter) and Roman antiquities (square uppercase font).

While much discussion and many fonts installed on a system may simply be classified as sans serif or serif with many subtle differences, there are much larger diffferences – I.e. more easily visually perceived differences – if one is willing to consider a wider range of type. While some fonts are rather ornate, there are clean, readable font families available in styles such as blackletter, handletter, script, slab-serif, etc.


About richardbrath

Richard is a long time visualization designer and researcher. Professionally, I am one of the partners of Uncharted Software Inc. I have recently completed a PhD in data visualization at LSBU. The opinions on this blog are related to my personal interests in data visualization, particularly around research interests related to my PhD work- this blog is about exploratory aspects of data visualization not proven principles.
This entry was posted in Font Visualization, Text Visualization, Uncategorized, Visual Attributes and tagged . Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s