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Time/Data/Visualisation

Time/Data/Visualisation

Time Symposium

As part of the Royal College of Art School of Design’s contribution to the Research Methods Course, we have organised a symposium dedicated to Time and Design. It takes place on the 19th of March, 10:00. If you are from outside the RCA and plan to attend, please email design-research@rca.ac.uk to let us know. Entrance is from Jay Mews.

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Time is the universal metric, a context for every object, life, event, alteration – but how do we design with time? What should time look like, how do we perceive it and how does it feed into how we live, act and remember? The symposium will set out historical, conceptual and cognitive problems that beset thinking about time, featuring the following speakers from the areas of psychology, history, engineering and design:

Claudia Hammond is an award-winning broadcaster, writer and psychology lecturer and the presenter of All in the Mind on BBC Radio 4, as well as programmes on BBC World Service and BBC World News TV. She is the author of the book “Time Warped” in which she delves into the mysteries of time perception. In her talk she shows how malleable our experience of time is and which factors influence how we perceive time.

Siân Lindley is part of the Socio-Digital Systems group at Microsoft Research, where she studies technologies in use and the practices that are built up around them. Siân presents two of her projects on using digital timelines for narrating personal histories, which yield unexpected insights into how representations of time shape our retelling of the past.

Matthew Shaw’s research in the history of the French Revolution, which he developed from his PhD into a major book, sheds light on perhaps one of the boldest reforms undertaken in Revolutionary France: the redesign of time itself. For almost a decade the French calendar had not only its own months and years but also decimalised hours and minutes.

John Taylor’s most ubiquitous invention has probably been used by anyone who ever switched on a kettle. However, it is his work on clocks that most captivates him. Turned inside out and controlled by a giant time-devouring mechanical creature, John’s Corpus clock required two hundred engineers, scientists and craftsmen, five years of his time, one million pounds and one Stephen Hawking for its unveiling on the wall of Corpus Christi College, Cambridge University.

Peter Bennett introduces physicality in how we interact with computers through his research on Tangible Interfaces at the Bristol Interaction & Graphics Lab. A physical interface for time however proved to be problematic. Is time flexible or solid? Is time a single object or many? Is time a line, circle, spiral or even a shape at all? It is this ambiguous nature of how time can be physically represented and controlled that Peter explores in his work.

Tree of Time: ChronoZoom

I have sacrificed parts of my Christmas break to develop something for Visualizing.org’s Visualizing Time challenge: A Hierarchical Time Tree visualisation which offers new insights into the dataset that powers the original ChronoZoom interface.

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In 2009, Walter Alvarez was looking for a way to communicate the enormous time frames that make up the history of the universe and conceived the idea of ChronoZoom, which was released as an early prototype in 2010. Since then, the visualisation evolved quite a bit and by now contains a rich collection of data. What I only found out recently, is that all of this data is also accessible separate from the visualisation through a dedicated API.

Recently, the makers of ChronoZoom launched a contest in collaboration with the platform Visualizing.org in order to, I’d guess, promote the use of this API and at the same time tackle some of the problems they encountered.
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When data turns digital

Data is often said to “speak for itself” and an empirical finding needs to be “supported by data”. But when the data is digital, does it still possess the objective rigour and the uninterpreted directness that these statements suggest?

Oil Refinery, Houston, TX

From Peder Norrby’s collection of surreal cityscapes resulting from automated 3D digitising in Apple Maps

Last week I had a chat with Stuart Dunn over at King’s College London, where he is part of the Digital Humanities Centre for e-Research. I contacted him to learn more about the kind of digital tools he and his colleagues are using, particularly those related to time and visualisation.

Stuart’s background is in archeology and he entered the digital field through the use of geographic information systems (GIS). Hence, he could tell me a lot about the potential of digital mapping tools and their shortcomings, especially when using systems intended for the ‘exact sciences’ in a context where more fine grained statements than true or false as well as the interpretation of the researcher play a major role in the construction of a narrative. This quickly lead us on to questions about the role of subjectivity in data, how to encode certainty in data and what do we actually mean by the word ‘data’?

This last question should be the first one to be asked whenever we talk about data, as all too often we end up meaning different things or, even worse, we might not even know what it really is we’re talking about. When I talk about data, I usually imply digital data, or more precisely digitally stored data. This necessarily means that data is already structured in some way to fit the confinements of a digital framework.

While this does not yet define data, it distances digital data from data as it can be understood, for example, in Ackoff’s [1] DIKW model: data, in its original latin meaning, as something given, a fact that may be observed, but is yet uninterpreted and unorganised. In this model, information is something that is derived from data (through the act of interpretation) while digital data can very well be digitally stored information – of course here we would need another discussion on what we mean by ‘information’.

The difference between digital data (data as structure) and data as ‘facts’ may not be obvious when the two appear to be closely correlated, such as recorded readouts from a thermometer. As Stuart points out, in such a case the data structure logically follows from the data inputs, but with the humanities you have to take decisions when creating a database. “Creating a database is an act of interpretation”, he explains.

As someone who sits at the receiving end of such databases I often encounter additional levels of interpretation, namely when the data does not neatly fit the pre-determined structure. In this case the authors often try to describe the data (as facts) in a different way to still be recorded in the data (as structure). While this interpretative step remains visible in the original database format, it often fails to be reproduced in a visualisation which expects the data to be structured in a certain way.

And this is where for me the main problem lies. Not in the fact that (digital) data is always interpreted, filtered and distorted, but in the failure of communicating this process or by mistakenly treating digital data as if it were factual data. Digital data is always made to fit a certain structure that someone somewhen decided to be suitable and while quantitative data visualisations may not be that vulnerable to the consequences, we certainly need to be aware of this when visualising data for the humanities.


[1] R Ackoff, From Data to Wisdom, 1989

Uncertain times need uncertain measures

Data visualisations should represent their underlying data as accurate as possible, and timelines are no exception. However in many cases, temporal data is not accurate in the first place, as it can not easily be measured or counted. In order to represent such uncertain data accurately, we have to allow for ambiguousness in the visual representation of it.



Friedrich Strass’s Strom der Zeiten (1849, left) draws the world’s history as a fluid stream of empires in and out of each other, while Edward Lee’s History’s Largest Empires (2011, right) represents them as solid, discrete entities.

A visualisation should make understandable through an image, what is difficult to grasp in words, and enable new discoveries through visual analysis. Data visualisation in particular enables abstract numbers to be compared visually, or patterns to emerge from what might be just a list of measurements. It is always a translation, a representation of information in a graphical format, which by itself does not contain any new information in the strict sense 1 that was not contained in the raw data already. In the first instance, it makes existing information accessible for visual exploration.

Joseph Priestly stresses this aspect of his work in his Description of a Chart of Biography[1]. The text accompanies what can be considered one of the first graphical timelines (after the pioneering work by Jacques Barbeau-Dubourg): A chart depicting the lives of about two thousand individuals, represented by lines on a linear scale of years ranging from 1200 to 1800.

It is of course an understatement when Priestley declares himself simply “to be an assistant to the great Historians, Chronologers, and Biographers” (p.4), whose work forms the foundation of his timeline. What he means, is that he did not himself produce any new knowledge, but assembled the research of others in one coherent visualisation — laborious and painstakingly of course. In reward for his undertaking, he was now able to see and also show to others, the relation and succession of historic figures, their contemporaries with whom they might have conversed, and the periods when cultural life flourished or, symbolised by emptiness, stalled. The representation of chronological information in graphical form opened it up for visual analysis and exploration, which enables new hypotheses to be developed from which ultimately new knowledge can be gained.

I mention this historic example of a graphical timeline, because it exhibits awareness for key requirements a timeline needs to fulfil, which are still relevant today. Being one of the first to ever create a timeline, Priestley gave careful consideration to all of his design decisions, which he documented in his Description.

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[1] J Priestley, A Description of a Chart of Biography, 1764

Notes:

  1. According to Shannon’s Theory of Information

Exploring Digital Collections

A growing number of museums and other holders of cultural datasets are making their collections accessible to the public via online interfaces. Not always does accessible also mean approachable. Often, the interfaces are about as attractive as Excel sheets and as much fun to use. What we need is interfaces that support exploration and discoveries in digitised cultural data.

Most of the cultural institutions who have their collections online, provide a basic search field, sometimes with options to further specify and filter results. Some even fully subscribe to the Open data philosophy and expose their entire collection via a SPARQL interface. Both interfaces however do not function if a user does not formulate a specific question. They immediately ask the user “what are you looking for?” without first presenting a glimpse of what can actually be found. All too often, a user is not specifically looking for a certain thing, but simply wants to look around.

I would like to look at examples, where this behaviour is supported. Where collections are exposed via rich interactive interfaces, that encourage to browse, learn and discover things. Interfaces that provide one with questions, to which one likes to find answers — as opposed to search fields that ask for questions where one inherently knows the answer already.

Originally I intended to write a blog post on some of such examples I have collected. However, it would not really do them justice to summarise them in one single post, or it would just be a really long article that no one would read anyway. So instead, I will write one article per example, in which I will provide an in depth description and impression. I will update this post and the articles that follow as I go along and maybe discover things that are worth to compare among the examples already presented.

So here we go.
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