The current paper focuses on information visualization projects related to the topic of economic inequality which can, in this particular case, be more specifically categorized under “speculative visualization” as defined by the authors Tanyoung Kim and Carl DiSalvo in their 2010 article “Speculative Visualization: A New Rhetoric for Communicating Public Concerns”. According to these academics, speculative visualization “represents socially and politically meaningful data in aesthetic ways to provoke viewer’s interpretation and further elicit discussions”.

[This paper was written within the theoretical framework provided in the 2012/13 New Media Project course]

Alternatively, you can continue reading the article on Scribd

The way things are organized in today’s publishing industry are not what they should be. Prices are high, much higher than is necessary to keep the authors alive. Where’s the money going? Definitely into the pockets of the greedy publishers.

I am doing a masters program to become an editor, part of that evil industry, and during my study I had a publisher explain the three main markets for books. First, you have the general book market: here you find the books for the public, fiction and non-fiction, from novel to cookbook. Second, there’s the scientific market: those are the books for the scholars in the university libraries. Third, there’s the educational market: those are the books for the classrooms.

Next, the publisher explained the economical difference between them. The general book market is really risky: there is a huge audience, but no-one can predict what they want to buy. You have some clues, but overall it’s more diffuse. The scientific market is much better for the publisher. ‘Just start a journal,’ the publisher said, ‘get some good articles about the subject and you can name the price: the libraries will pay almost everything.’ The educational market seemed to be even more profitable.

You might think publishers are greedy and evil, and maybe they are, at a certain height. But they also have a wrong reaction to the things that are happening in the market. Due to the economic crisis, universities are spending less money on new books and journals. Instead of ordering one copy for every library they have, they order one copy for the whole university, allowing libraries to share their collection with each other. In the Netherlands (university) libraries are looking more and more to ‘de collectie Nederland’ than to their own collection alone.

The reaction of the publishing industry is just to raise the prices. They want to make 100 out of it, first it cost 10 and they sold 10 copies, now with 5 copies they just ask 20 for it. It’s a nice model, except that it will not hold very long. Universities will even buy less copies, until there’s just one copy available that’s unaffordable. Because that’s what is happening: copies becoming unaffordable.

In the newly industrialized countries, there’s less science than in Europe and the USA. Part of it because they can’t afford all of the journals and books they need to study. In order to get educated, they make use of initiatives like Library.nu, sites where they can find thousands of digital books online. Unfortunately for them, the Euro-American publishing industry killed Library.nu, thinking they would sell more legal copies by doing that. But can the students afford the legal copies?

Instead of raising the price of publications, the publishing industry should lower the prices, so they can open a broader market.

And what if the publishing industry doesn’t do anything? For the newly industrialized countries, it would be better to just ignore the western copyright for some years. The USA did something like it with works from England, and China is doing really well by not recognizing western copyright. They need(ed) that knowledge to create their own knowledge. In the end, the world as a whole would benefit from it.

Maybe the ones that have power over the knowledge nowadays don’t want to give it to those new countries, because they are afraid of loosing their power. But isn’t it that competition makes us stronger?

When you take economics to the basic, it’s all about doing things together. We have earth and nature, and to survive we have to get certain things out of it: food, shelter, that kind of stuff. During the centuries that we’re on this planet, more needs have been created. If we want information, someone has to gather that information, meaning he cannot search for food, so we have to search for food for him. Time is money, they tend to say, but if you see it this way, money is time is a better phrase.

This is the principle behind the scientists and the journalists: we pay them to gather information for us. The scientist is more on the creating side of information: he looks for new knowledge, adding it to the pile of existing information about a certain topic. A journalist gets also paid for his information, but he’s more on the collection side of information: he brings information together into one, broader understandable piece of writing. (And yeah, scientists do report in writing, journalists do their research.)

The information created and gathered by scientists and journalists isn’t free, because they have to eat: someone should pay their bill. A cheaper model of information gathering is Wikipedia. In here, everybody can write, research and gather information. One can argue that Wikipedia is not free, because they have their servers to pay (that’s why they have their donation request every year or so). But besides from that, the real gathering of information isn’t free here also. The ones that are writing, still need food. It’s just that they pay themselves: they’re writing for Wikipedia in their spare time, they have to do another job for living. That’s free for the reader, but not free in general.

At last, there is what some call The Internet of Things. By this, they mean that computers have to gather information themselves, and by linking them to the internet, we can get an interesting network of knowledge. No one has to eat in this case, but the computers don’t collect their information for nothing: they eat electricity. Also, someone has to create those computers, and this human has to eat as well. There’s no such thing as a free lunch.

Thus, real free information does not exist. There’s always someone who has to pay for the creation of information. In some cases, like Wikipedia, the information creator pays itself, which is as close to free you can get. The drawback of this situation is that Wikipedia writers are not specially trained to write. The scientist and the journalist get paid for their jobs because they’re good at it and they get even better and better, for they have all day to do the job. A Wikipedia writer has less time to become a professional (although sometimes already professional writers write for Wikipedia as well).

When we have to pay, the options are countless. Paywall, subscription, advertisement, funding by research groups itself, donations or some hybrid form: it’s all possible and for every situation there can be found a right way to reserve time for those who have to write. But someone has to pay, real free information does not exist.

“WE ARE IN THE MIDST OF A COLD WAR BETWEEN THE LARGEST TECHNOLOGY COMPANIES IN THE INDUSTRY*”, boldly states tech journalist Dieter Bohn in his overview of Apple’s, Google’s and Microsoft’s battling ecosystems [1].

This “war” won’t be won by whoever sells more phones, tablets or computers, though. The tech behemoths are wrestling each other for control (and eventual domination) of content distribution channels. The fight will be decided by the ones that impose their own file formats, standards and “ecosystems” on the industry. Domination of the market, be it apps, music, film, television or literature, then allows these companies to measure it with great precision, profile user’s tastes and consumption practices, and even use this data to influence the production of content.

Winning the battle of the book

Literature and e-book distribution, is a heavily monopolized market. In fact, there is really only one company dominating the digital literature marketplace – Amazon, even if other big companies like Apple and Google are trying to compete. And this is not because the Kindle portable e-readers are universally widespread, but because Amazon has made its Kindle software available for installation on pretty much every modern computational device. Just like Apple in their quest to dominate music sales through iTunes, Amazon doesn’t mind what device the content is being consumed on at the user’s end.

Amazon is winning the battle of the book, because it was the first company to offer a reliable, usable and cheap device for reading digital literature. The other big reason for its hefty market share is the speed with which publishers embraced the idea of selling more books at virtually no added distribution cost. This allowed Amazon to develop the platform, or “ecosystem”, that powers the Kindle and makes it what it is.

Right from the start, the company realized that it needs a strongly walled garden in order to maintain itself as the market leader. It achieved that by keeping its proprietary file formats closed, effectively preventing unintended distribution and piracy. This is where Kindle e-books take the upper hand against the competition of openly distributable formats like .pdf or .epub, naturally making Amazon’s platform more attractive to publishers and content producers alike.

Tethered forever

Real power, however, lies elsewhere – in the ability to keep track and record reader’s tastes, their reading practices and the frequency and speed of their content consumption [2]. Amazon is able to do all this because books never actually leave its grasp. A user’s library is still stored in a controlled “cloud” environment on the company’s servers, which maintains constant back-end communication with whatever device content is being viewed on.

Illustration by Boing Boing

To take things even further, Amazon is able to control content even after it’s been purchased – something that is unthinkable in print publishing. The company, for example, can censor certain passages of a book in a country after a request from the government. What is more, all purchased content can even be remotely deleted or made inaccessible [3]. Amazon’s grip over its products is so tight that it even poses the question of the ownership of books – can users really call an e-book their own after they’ve bought it, if the content can still be altered and the book removed?

These practices exemplify Alexander Galloway’s concept of power within protocols – the ubiquity of the Kindle service and its file formats, along with being both the pioneers of the market and its leaders, has put Amazon in the position to shape the future of e-book publishing standards [4].

A monopolized market is not suitable habitat for innovation [5]. A scenario in which one ecosystem conquers an entire market would leave its users exposed to all its flaws (in Amazon’s case – privacy, ownership) and without the ability to choose an alternative.

*Original capitalization kept for emphasis.

References

[1] Dieter Bohn, “First strike: how tech’s superpowers could start an ecosystem war”, The Verge, November 20, 2012

[2] Ted Striphas, “The Abuses of Literacy: Amazon Kindle and the Right to Read”, Communication and Critical/Cultural Studies 7.3 (2010)

[3] Jonathan Zittrain, “Tethered Devices, Software as Service, and Perfect Enforcement”, The Future of the Internet – And How to Stop It, New Haven: Yale University Press, 2008

[4] Alexander Galloway, “Protocol vs. Institutionalization”, New Media, Old Media: A History and Theory Reader, London: Routledge, 2006

[5] Justin Fox, “The Web’s New Monopolists”, The Atlantic, January/February 2013

New media technologies as they function today perform specific social roles that unmistakably alter cultural traditions and practices. The specific form of collage as an artistic practice finds itself in this wave of shifting ideologies as it weaves together both relics of old media into realms of new media. As it has realized itself within a select periods of history as an artistic practice, collage has always popped up in the midst of major cultural transitions. First known recordings of collage appear in the Dadaist movement at the beginning of the twentieth century as a medium that extended the role of painting. Collage was practiced by artists experimenting with various forms within the Cubist movement exploring the possibilities outside of the canvas that restricted their conceptual dealings with their art. Parallel to this experimentation, the new abundance of paper made available in the midst of a massive industrial boom in Western nations. Another notable moment of collage appears in the punk-rock movement of 70’s with artists such as Linder Sterling most notably using collage to produce the album cover of the band Buzzcocks. This aesthetic carried over to a number of pop art pieces and eventually gained mainstream acceptance in the contemporary art circuit. The most recent movement of collage which I will discuss further involves the current historical moment beginning from the early 2000′s continuing to the present as a growing medium potentially working as a catalyst towards a renewed understanding of the medium.

Contemporary media theorist Fredric Jameson characterizes postmodernity as “the total saturation of cultural space by the image, whether at the lands of advertising, communications media, or hyperspace. This complete image-permeation of social and daily life means, he says, that aesthetic experience is now everywhere” (Kraus, 56). What Jameson ascribes to contemporary culture is the ways in which artistic practice today has left the confines of the artists studio, the gallery and museum and has drifted to every facet of life. From billboards to television screens made available in public spaces to advertisements, visual art has poured into the ethos of significant spaces. The proliferation of digital technology have allowed for what Jameson goes on to say “an expansion of culture that has not only made the notion of an individual work of art wholly problematic, but has also emptied out the very concept of aesthetic autonomy” (Krauss, 56). Aesthetic autonomy, known to occupy a single aesthetic has now traversed commercial platforms as art has found a variety of environments to occupy and thrive in. Advertising is one such example but the Internet and especially Web 2.0 has offered a wealth of opportunities to communicate and utilize the art.

The debate over the future of the musical industry began early in 2003, when Apple released iTunes which allowed people to download songs over a standard fee. The producers and the artists then began to state that this was going to be the demise of their financial revenues; however, ten years later, the recent study by Luis Aguiar and Bertin Martens – Digital Music Consumption on the Internet: Evidence from Clickstream Data – comes to prove the opposite.

The study shows how illegal music downloads don’t do almost any harm to the legal downloads form platforms such as iTunes, Spotify or Zonga and that 57% of the consumers have accessed one of the legal music services at least once and that 73% engaged in music piracy. Piracy is referred to in the study as being educational on the basis that it enlarges perspectives for the music fans. The study also shows how users still prefer to download a song as opposed to just listen to it in an online player because of the feeling of possession.

“It seems that most of the illegal music content consumed by the subjects we have analyzed would not have been legally bought if illegal services had not made it available on the Internet”, state the researchers. The study concludes that “although we are discussing the trespassing of intelectual property, it is highly unlikely that legal revenues had to suffer from illegal downloads.”

The study was developed over the course of an entire year and has also proven that streaming services such as Pandora or Spotify also offer a large boost to the sales in the musical industry. “According to our report, a 10% growth of the legal streaming websites led to a 0,7% growth of the legal music downloads” However, the International Federation of Phonographic Industry (IFPI) questions the study, stating that it is “foul and malicious”. The IFPI criticizes the report, stating in a press release that “these conclusions seem completely unrelated to the comercial reality. If a large part of the online ilegal downloaders do not buy our music and still they have access to it, it can’t possibly be a logical fact that an illegal behavior is able to help legal sales and boost the musical industry.” The conclusion of the reports shows how little influence does piracy have over music production in general as the digital revenues grew 1000% in the last 10 years.

Master students from UvA & maHKU
proudly present seven multidisciplinary data visualization projects
at SHOW ME THE DATA 2013

This year with three special guest speakers:

John Verhoeven, Inspiring Data
http://inspiring-data.com/

Arjan Scherpenisse, MiracleThings
http://miraclethings.nl

Jelle Kamsma, Datajournalist

When: March 28, 16:30 – 19:00
Where: UvA, Room OMHP F0.01, Oudemanhuispoort 4-6, 1012CN Amsterdam

Documentation on the previous years and additional information can be found on the website: showmethedata.nl. There is also a Facebook event page.

The main goal of data visualization is to communicate information clearly and effectively through graphical means. (Friedman 2008)

Friedman’s definition is simple and concise, yet broad and applicable to any type of visualization. What it fails to provide is what happens after the communication act. Would it be reasonable to say that some data visualizations have the goal to be aesthetically pleasing, as expert David McCandless mentions in a TED Talk on The Beauty of Data Visualization? Possibly, although much of the visual data art I have witnessed was not just art for art’s sake – there were underlying meanings, debates. Does data visualization stop at the level of informing then, making readers acknowledge bits and thus live the illusion that mere understanding means making a change? In the same TED Talk, David points out how we have become accustomed to demand a visual aspect to information. My worry here would be the risk that online journalism, if it fails to produce valuable and interactive visualizations, will only become the same cold medium that makes the reader passively absorb information (remember McLuhan?). In yet another activity field, for-profit companies are picking up on infographics and the likes in their marketing strategy; ideally, for them this has a return of investment, making their target take action by buying.

What can one say about the various goals that data visualization has in different fields of activity? Does it aim at informing, at pleasing the eye, at shocking and surprising, at simply adding cultural capital? The goal, therefore, is a matter of defining the threshold where the impact of data visualization on its readers ends. Current academic literature, I believe, falls short of detailing at this and I will not take the task on myself.

What I do wish to discuss, however, is a case of data visualization whose goal is specifically to have a strong call-to-action and thus real-world reactions. This, I believe, is especially the case with non-profit actors using data visualizations in their campaigning. These actors ultimately ask for people to take actions, either by joining a movement, donating, reporting etc.  I have recently discovered that the NGOs sector is empowering its campaigning by the use of data visualization. Even data visualization projects that do not present themselves as campaigns often have the right ingredients to make users take action.

With non profit data visualization, one could see a pattern evolving: a call for action visualization needs first to inform, make an emotional appeal, convince. A yard before convincing, it needs to be visible, to be understandable and perhaps interactive enough to keep the user exploring. Therefore, we are looking at a sort of ladder with different steps, with data visualization goals aiming to go ever higher, from mere visibility to the final call to action.

In the other fields, campaigns are structured in these types of steps. In communication theory (and applied practically in advertising,marketing and social media campaigns) the model is called “hierarchy of effects”or “communication ladder”. Each step is an objective which, if the campaign offers to the readers, then it will lead to the desired goal; otherwise, not only can it lose them, but create adverse opinions. The model is not fixed and other industries have appropriated it accordingly.The Fledging Fund, a creative foundation supporting documentaries and their makers, visualize the impact of films and media slightly different.

Communication Ladder in Advertising and Marketing. Source: clarityincommunication.com

Communication Ladder in Creative Media. Source: Thefledglingfund.org

If we take Friedman’s definition of data visualization as a communication act, then I argue data visualization, especially in a campaign context, is efficient if it attends to the hierarchy of effects. There is no model available for data visualization, therefore I am proposing an exercise in outlining one:

1. The step from seeing to exploring is made by a good choice of representation for the given data set and good balance between author and reader driven approaches (Segel  and  Heer 2010)
2. The step from exploring to understanding (awareness) is done through good design interface, usability, aesthetics, clarity of data, good encoding, acknowledge of limitations, the overall conveying of a story.
3. The step from understanding to conviction is made through trust in data sets and in the visuals.
4. The step from conviction to action is made by the smoothness with which the data visualization directs you to immediately-available action points (click, donate, submit…)

In the following, I will take two examples of data visualizations as part of non-profit campaign and analyze them accordingly to the steps.

CASE STUDIES:

1.      Amnesty International: Countries practising death penalty

Amnesty International is powerful NGO which intervenes in world-wide issues. Fore some years, it has been employing data visualizations for its major campaigns. The visualization presented here explores which countries practice death penalty and in what numbers.

Click here to view the embedded video.

The visualization debuts with a two-minute video that acts as a splash screen for the user, providing context and numbers for the hot topic, as well as stating the NGOs official opposing to such measures. The video thus introduces the story; it is entirely author-driven, a tactic which leaves out details and raises questions to be explored by the user in the data visualization itself. After the video (itself an animation using interactive forms and icons), the user can explore the actual visualization and build on the story. The visualization is a world map with each country encoded by color, according to the numbers of executions per year. The data is available from 2007 to 2011 and can be opened as an Excel.

And here it stops. Although the map has so many opportunities for encoding further information, all it offers is a report on how many executions and death penalties each country holds for a period of 5 years. The reason for this is that much data is actually surrounding the visualization …as text. The site itself plays the major role.  A 74 pages report offers an extensive analysis of the issue, and furthermore the left side of the page comprises further sections with data expressed in tables. Why has this data not been used for visualization?

Somewhere on the goal, this project is promising to reach to action with the video, while the map falls short of conveying any complex data, story or trust.

What could have been done for an improved impact? Have a look at several infographics The Guardian presents, based on the same data sets but revealing more information.

2. Janaagraha (non profit NGO) - I paid a Bribe

When I attended the Be Good Be Social event in Amsterdam earlier this month, open data enthusiast and speaker, Pelle Aardema , discussed a project entitled I paid a bribe, a site dedicated to Indian citizens anonymously reporting cases of bribing (developed by NGO Janaagraha, whose dedicated role is  ”improving the quality of citizenship and infrastructure and services in India’s cities”). Thus, the visualization’s role is to “uncover the  market price of corruption” in country. All this crowd sourced data goes into the section “Bribing trends”, a constantly updated data visualization on analytics reports.

The first visualization gives overall information on what the reader is looking at: 305 cities and 23 departments in the Indian state, with a top of 5 most corrupt cities by bribe numbers – a good hint at what the story will reveal. The second slide of the visualization allows for in depth exploration with a mix of author and reader driven approach, analyzing either a city’s statistics or a department’s. The visualization is split in 3 dials: the first is a bar graph encoding the not paid, not asked and paid bribes (encoded by color) in each city/department. The second reveals the amount of money lost in bribe amount overall, as well as in each city and each department. The third acts as a timeline: showing trends in bribing in cities and departments since the project’s start. Exploration is further enhanced by choice of manual or automated presentation and an upper search box.

Apart from effectively communicating a story (and an unfolding one too!), the visualization has the powerful motivation to urge people in submitting a contribution – hopefully, the story will end when paid bribes on bar graphs reach 0.
The site itself supports the whole project very well, as anyone can actually read the submitted reports of bribery or good ethics. The site further includes articles on best practices in relation to authorities, awareness of laws – restrictions and rights – which makes this visualization useful not only to citizens, but also to authorities. The project is easy scalable and could be implemented in every country – in fact, some countries already have their own “I paid a bribe” platform.

CONCLUSION

Having worked in the non profit sector myself, I think it could benefit hugely from using data visualization for campaigning, transparency in reports or communication of goals and results. However, I see two important issues:

1. Lack of theoretical development on data visualization impact: how can we make data visualization reach the goal of action? There is no similar model to the ladder of communication, hence my experiment with the proposing one in this blog post.

2. Lack of skills in making data visualization. At the Big, Open and Beautiful conference last evening in Amsterdam, the moderator had a recurrent question for his speakers (mostly data journalists): what skills do you need to do this? The replies were either “one man good for everything: researcher, storyteller, programmer and designer” or team work with separated roles. Another option is simply outsourcing to a design/programming company. This is truly an issue for NGOs in terms of affording and evening finding the right collaborators. Bad visualizations are much worse than clean PDF reports in text, unless one is a perfectionist and manages well with online courses teaching the skills.

All in all, I believe NGOs could leverage the benefits of data visualizations in their campaigns, but are facing difficulties in making it right as the field is still developing theoretically, competitively and professionals that make them are just rising.

References:

McCandless, David. “The Beauty of Data Visualization”. TED.com. August 2010. 15 March 2013 <http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html>

Friedman, Vitaly. “Data Visualization and Infographics”. Smashing Magazine. 2008. 15 March 2013 <http://www.smashingmagazine.com/2008/01/14/monday-inspiration-data-visualization-and-infographics/>

Segel, Edward, and Heer, Jeffrey. “Narrative visualization: Telling stories with data.” Visualization and Computer Graphics, IEEE Transactions 16.6 (2010): 1139-1148

The title of this blogpost is part of a somewhat famous quote in the world of information visualization from Moritz Stefaner. The full quote “Position is everything, color is difficult” is especially promoted by Noah Iliinsky (co-author of the books Designing Data Visualizations and Beautiful Visualization) in different talks held by him as a summary of how to visually encode data well. With these six words Iliinsky explains that, as a rule of thumb, the most important data variables in information visualization should always be encoded by some form of positioning. Spatial dimensions like placement, length and size differences are easier for humans to perceive and to compare than differences in color. This more or less corresponds with a more analytic principle of infovis identified by Lev Manovich; that infovis privileges spatial dimensions over other visual dimensions. So what follows for Iliinsky is that the use of color to encode properties of the data should only be used for secondary variables because color is difficult. That color is difficult can also be learned from visualization guru Edward Tufte in stating that:

“[…] even putting a good color in a good place is a complex matter. Indeed, so difficult and subtle that avoiding catastrophe becomes the first principle in bringing color to information: Above all, do no harm.”
from: Envisioning Information, Edward Tufte, Graphics Press, 1990

This blogpost is about the several ways in which color is difficult and provides some tips as well to avoid color catastrophe.

Color is not ordered in the brain
As Noah Iliinsky explains it, some visual encodings are ordered in the brain and others are not (Iliinsky has a useful diagram on his website on different visual encodings and their properties). If a particular visual encoding is ordered in the brain, it means that it is not something that has to be learned or that can be unlearned. Visual encodings that are ordered in the brain are automatically processed with very high precision. Automatically we our brains can deduct hierarchies and orderings from these visual encodings.

Color as a visual encoding for data is not ordered in the brain. This is the main reason why color is difficult. Although the spectrum of colors visible for humans can be ordered according to wavelength, this ordering makes no sense to the human brain until it is learned. Spotting color variations is not something that is automatically processed in the brain and hence not something which humans are very good at. This makes color not so useful for encoding quantitative or ordinal data. For categorical data color can effectively applied. But what colors to choose for the different categories is complicated by the fact that also the symbolic and emotional meaning of different color hues are not hardwired in human brains, they have to be learned.

Color and meaning
The fact that color meanings are learned introduces a second reason why color is difficult. Color is learned differently by different people and hence there are no universal meanings of colors. Color meanings vary among cultures age groups, professions, sexes and so on. Furthermore are color meanings affected by differences in experiences and tastes from person to person. And to complicate things even more, color meanings are also context specific. Below is a (somewhat difficult to read) visualization of differences in color associations.

source: www.informationisbeautiful.net

Another compelling example of how color is learned and can be very profession specific, is discussed a great datastori.es podcast by Enrico Bertini and Moritz Stefaner on color. They talk about how color gradients encompassing the whole color spectrum (like in the example below) should never be used. Interestingly enough they found out that scientist who do use these, because it happens to be a default in their software, have become very skilled in reading these full color spectrum scales.

source: eusoils.jrc.ec.europa.eu

So using color in design is a complex task, like for example logo designers know all to well. Color use in information visualizations can lead to different interpretations by different viewers. If color is used it is therefore good to know the targeted audience and their color associations. This is of course easier when an audience can be clearly defined. Furthermore it is important to provide context for the visualization, so that certain associations can be stimulated.

Perceiving color
A last reason why color is difficult that I want to mention is about how humans perceive color. Or how people don’t perceive color. If color is used in a visualization to show differences in the data it is good to make sure that people with color blindness can also perceive the differences in color and hence the differences in the data. A useful tool for non-colorblind people to test if a design is colorblind safe is Color Oracle. This software allows for altering the screen display according to the various forms of colorblindness.

chroma.js

Another difficulty involving color perception is the problem of equidistance. When encoding quantitative data with color in equal subgroups it is important that the different colors used represent the data categories well. So that the perceived distance between colors corresponds with difference between the data values. The javascript library chroma.js by Gregor Aisch helps generating a good equidistant color palette. The HCL colorpicker and the iWantHue tool (that both use chroma.js) make it even easier by providing direct output of color values of a created palette.

The color advice tool Color Brewer is also a very useful way of obtaining a good equidistant color palette. Predefined sequential, divergent or qualitative palettes can be selected and adjusted to the specifics needs at hand. A good feature of this tool is that the selection of palettes can be narrowed down by colorblind, color print, photocopy and LCD friendliness. The downside is that the predefined palettes are a bit dull and limit freedom in design.

Use color!
That color is difficult should not discourage visualization designers to use it of course. For color is maybe the most important aspect that makes information visualizations look good and compelling. It may also be an important reason for newspaper readers for example to take a look at a particular visualization in the first place. So this post was certainly not to daunt the the use of color in information visualization, but more a encouragement to use it well and wisely.

» more on color and information visualization: datastori.es podacast by Enrico Bertini and Moritz Stefaner
» more on cultural factors and information visualization (on this blog): Cultural Bias in Data Visualization by Jorrit Schaap

Image taken from http://spitzenpopper.blogspot.nl/2010/07/classroom-of-future.html

“Data is the new oil? No: Data is the new soil.”- David McCandless, TEDGlobal, 2010 

Whatever data is, one thing is sure: we cannot overlook them. Since we live in the Big Data era,as Jaimy and Ana analyze in their posts, we should use them as efficiently we can, and seek to discover new potential uses and benefits out of them.

Nonetheless, the large quantity and the different forms that data can take, make it quite difficult for the public to get a deep insight into them, as training, knowledge, expertise and personal or even cultural bias differ from one person to another. In order to make data comprehensible and more approachable to the broad audience, visualization comes into practice, searching for ways to enhance understanding, interpretation and communication. A lot of definitions have been used for data visualization, but one of the most known, yet quite ample is the one that describes it as “the use of computer-supported, interactive, visual representations of data to amplify cognition” (Card et al. 12).

Could we extend the use of data visualization in education?

What I would like to highlight, in this post, is that no matter how many years data visualization has been used, in different forms and levels of complexity, its potential still remains  strong and promising. Inspired by our group project, which deals with universities and educational decisions, I started thinking about the use of data visualization for educational purposes. In spite of the wide use of infographics, charts, maps and other forms of data visualization, as a pedagogical and educational tool, their role is-mostly-supportive to the traditional learning techniques, i.e. texts, slides or whatever is used in the different educational levels. I don’t argue that there are not institutions, colleges or universities that do use data visualization, exclusively as their teaching“language”, but beyond doubt, its use in education is not as ubiquitous, as it is in other aspects of our everyday life.

Some proof…

In the “Big Data era” we live, one can easily identify himself, as subject to an information overload, which makes his attention and memory span shorter than ever before. Human perception and cognition, thus, are subject to a change.

Dr Shaw, a neuroscience and psychology lecture, argues that this overabundance of information has an impact on our psychological situation as well and “this is a symptom of the computer age”. Dr Shaw states that “We’ve really seen this incredible amount of information flooding us constantly. The problem with information overload is really new to the human brain”. It seems, thus, that new ways of data and information presentation need to be found, in order to improve people’s perception.

Taken from http://www.bbc.co.uk/news/business-17682294

Nevertheless, I believe that what is needed is not a radical new way of presenting information and data but on the other hand,data visualization might be the answer to this problem -or to put it better challenge- of the big data.

Dr Shaw made an experiment, in order to prove, that people are overwhelmed by the huge amount of data they are being surrounded and bombarded with. She used an experiment group and investigated the effect that data visualization had in their brainwaves. The findings can be summarized, as follows:

 Data visualisation vs. text

• Individuals working with visual mapping techniques used on average 19% less cognitive resources
• They were 17% more productive and 4.5% better able to recall details than when using the equivalent traditional software
• Groups working together on a project used on average 10% less cognitive resources
• They were 8% more productive and recalled 6.5% more data when using visual mapping compared with traditional techniques.

 

Taken from http://www.atlanticbt.com/blog/the-lean-content-movement/

This experiment, hence, proved what Ruddle et al. had acknowledged almost one decade earlier, that “Graphics, if well constructed, may make the human cognitive process of constructing a mental image of the route easier” (2002). Drawing from the main findings above, it can be inferred that a possible use of data visualization in the classroom would have beneficial results in the students’ learning process, as it would  make it faster and more productive, at the same time.

From computer scientists to kindergarten students?

When looking for relevant papers and concepts on the use of data visualization in the education, I came across a very interesting scientific paper , named “Exploring the Role of Visualization and Engagement in Computer Science Education” , written by 11 researchers. This work approaches the topic of data visualization in education, but it is focused on computer science students, and examines the level of engagement with the visual representation of data. The main assumption, is that the mere data visualization does not add significantly in the learning experience, unless the users are really active within it, implying that it may have to include interactivity (Naps et al.). It seems to treat the classic idea that “visualisation is an activity in which humans are engaged, as an internal construct of the mind” by Spence and Ware with some kind of cautiousness and prejudice, considering the human factor and involvement as a very important factor in the decoding and the desired engagement with the data. Personally, I believe that interaction is an important feature of data or information visualization ,but it has to be used only when it has something to add, because its aimless and excessive use can be tiring, misleading and discouraging for the user .

Data visualization tools and techniques seem to be much more frequent in universities and high schools, where students have already developed their visual perception and their learning experience, in general. Nevertheless, I stumbled upon something really interesting about kindergarten students, regarding various  effective learning techniques that a teacher tested in the classroom. One of them is called  “mind movies”, in which students had to visualize in simple drawings what they learned from their teacher. The results showed that young students tended to remember more things, after having the visual representation of the story, and that by discussing the other kids’ drawings, new perspectives of analysis and discussion were created in the class (Gregory & Cahill 516).

Visualizations for Sunflakes

 

Which dangers and fallacies need to be avoided?

After presenting the positive implications of data visualization in education, I should also mention one of the major challenges that data visualization faces and this is the missing information. In plain, the visual representation of data, if it has a problematic structure and design, it can lead its viewers to misunderstanding and misinterpretation of the information given.

Enrico Bertini writes in his blog that Telling a story doesn’t tell the whole story and he suggests that one should be really cautious when he wants to deliver a story , by using data visualization, because this is not such an easy task. In the same vein, Lev Manovich has written a paper about the reduction of the information, that may occur when one presents his data visually. The reduction in a nutshell, happens when some data are visualized,excluding or hiding at the same time some other data or information. Nonetheless, Manovich argues and provides specific case studies that support this argument, that it is possible to avoid reduction, thus missing information, by using the right design, the right presentation methods and be explicit about the narratives you want to present. If you are interested to read his paper, click here.

Conclusions

In the rear, data visualization has serious benefits for human cognition and this is something education should use, to improve its results. It would be really interesting to see how a class –explicitly organized in a visualized way, would function and to what extent it would be different from a “traditional” class. We cannot be sure that the results will be significantly  better, but we have some quite positive evidence about the visual language effectiveness. So maybe government and all the relevant institutions should take this decision and hopefully, help us reach a higher cognition-level society.

 

References

Card, Stuart K., Jock D. Mackinlay, and Ben Shneiderman. Readings in information visualization: using vision to think. Morgan Kaufmann, 1999.

“DataMarket Blog.” DataMarket Blog. N.p., n.d. Web. 20 Mar. 2013.

“Fell in Love with Data – Data Visualization Evangelism.” Fell in Love with Data RSS. N.p., n.d. Web. 20 Mar. 2013.

Graham, Fiona. “Pretty Pictures: Can Images Stop Data Overload?” BBC News. BBC, 16 Apr. 2012. Web. 20 Mar. 2013.

Gregory, Anne E., and Mary Ann Cahill. “Kindergartners can do it, Too! Comprehension strategies for early readers.” The reading teacher 63.6 (2010): 515-520.

Manovich, Lev. “What is visualization?.” Poetess Archive Journal 2.1 (2010).

Naps, Thomas L., et al. “Exploring the role of visualization and engagement in computer science education.” ACM SIGCSE Bulletin. Vol. 35. No. 2. ACM, 2002.

Ruddle, R., Brodlie, K., and Dimitrova, V. (2002). Communication, visualisation and interaction. University of Leeds, School of Computing. Handbook of the course.