This article is specifically about the balance and relationship between auditory and visual information - but the introduction is a good summary of lots of research around presentation - Partial Verbal Redundancyin Multimedia Presentations for Writing Strategy Instruction Rod D. Roscoe, Matthew E. Jacovina, Danielle Harry, Devin G. Russell and Danielle S. McNamara Article first published online: 24 JUL 2015 | DOI: 10.1002/acp.3149
This infokit will provide guidance on best approaches in using presentation software such as PowerPoint, Prezi and Slideshare, looking at their advantages and disadvantages, using multimedia to enhance the content as well as sharing with students and learners. It will also explore the possibilities of using new tools that have now emerged to further enhance presentations."
BBC television seems to have embraced informing people of the power (and dangers) of infographics. Several months before Hans Rosling's television documentary 'The Joy of Stats', they even took up data visualization and infographics as a subject of intense debate. More specifically, on a episode of News Night, Information is Beautiful author David McCandless dueled with 'Anti Design' initiator Neville Brody, a 'legendary designer who is the original art director of The Face'.
Interestingly, the actual discussion topic quickly focused on the potential misuse of beauty in data visualization, which ultimately might make them 'too mesmerizing, too beguiling, too pretty' (I confess, I had to look the 2nd verb up). Without much consideration, the moderator put up several infographics of one of the two guests and invited the other one to vent some critiques. What started off with a friendly 'Congratulations David! I would like that on my wall!' quickly shifted into an intellectual argument that nailed the work as the epitome of what should not come out after '25 years of Thatcher locking up culture'. While no-one took the trouble of asking what actually should come out instead, the moderator was quick to remark: 'Are you more coffee table graphics?'
So, in short, if you want to see the utter surprise when a talented and acclaimed information designer is so openly criticized on national television, then watch the movie below.
What should David McCandless have answered instead?
Here is David's own take, as he recently mentioned in an interesting interview at Visualising Data: 'I forgot how TV journalism reduces debate down to two opposing polarities: for and against. Which I think for a topic like information design is a lame approach. How can you be against information design? It's just a technique! So I was caught on the hop a bit and felt quite bemused by what was going on. I thought we might have a debate about its potential and its limitations. But no.'.
The following post describes not only one of my favorite papers from VisWeek this year, but I have the feeling it will be one of those papers which has the potential to lead to a big impact in the way we will use, and see, information visualization in the future.
The whole trend behind storytelling with vis is, at least in my opinion, one of the biggest changes we are experiencing in visualization since many years. We can see its effects everywhere: taking inspiration from the NYTimes visualizations and the infographics that appear in several other international journals, many other press outlets and advocacy groups followed the route of data-driven journalism, which is slowly but surely becoming a rather successful visualization subfield of its own. It seems that after several decades of research in visualization being focused on data exploration, academics and practitioners alike are re-discovering what the fathers of Infovis have always been saying: "visualization is a great tool for telling stories".
Method: What the authors did
Edward and Jeff analyzed a large set of interactive visualizations for storytelling and, through an iterative process, identified common patterns and built a classification on top of it. Their paper is very rich and I heartedly suggest you to read it all if you are interested, because it is quite "thick" and I will not be able to convey all the useful details it contains in this single post.
In this post, I rather will focus on the parts of the models I deem useful and personally appreciated the most. I will try to suggest, as usual in these guest posts of mine, how you can use the results in the visualization practice.
The paper contains a classification of the design space organized around the following components:
Genres - 7 generes of story telling vis exist.
Visual Narrative Tactics - Visual devices that assist or facilitate the narrative: visual structuring, highlighting, transition guidance
Narrative Structure Tactics - Non-visual mechanisms to assist and facilitate the narrative: ordering, interactivity, and messaging.
For a encompassing picture of these components, the paper offers a big overview table (click on it for a larger version).
Tacit Tutorial and Stimulating Default Views
These 2 patterns refer to how a new visualization is introduced to the reader and are of enormous importance. The problems of gently introducing a person into reading a visualization has been neglected for a long time and and it is great to see some suggested practical solutions here.
Tacit tutorial refers to the strategy used by some to gently introduce users to the interactive functionalities, for instance by starting with guided examples. Stimulating default views refers to the strategy of providing initial views that stimulate curiosity and encourage to explore further.
The identification of several genres can function as a starting point for any new story telling visualization, and help designers decide which style better suites the need of the story. The image on the top of this post is a visual summary taken directly from the article.
Messaging and Interactivity
Messaging and interactivity refer to the way the author sends messages in the visualization and how interaction is provided into the user's hands. They are somewhat in contrast, as the more messaging you provide, the less freedom is given to the user. Messaging can generate clutter, but interactivity can detract from the intended message. The existing tension between the two is one of the major issues and trade-offs an visualization designer has to take into account when developing visualizations for storytelling. So please take a note: you will probably have to introduce messaging and interactivity in your visualization, but you will be better off if you carefully balance them and understand when they support and when they hinder its intended purpose.
Author-driven, Reader-driven and Hybrid Models
Visualizations for storytelling are author-driven when there is no freedom for the user in the process of exploring the data. That is, there is only one clear and predefined path to follow. In reader-driven visualizations the user has, on the contrary, total freedom to follow any path.
Narrative visualizations are in general in between these 2 extreme cases, and the paper describes a series of hybrid models. Again, keeping these models in mind can help in reflecting what is the best way to convey a given message when designing a novel visualization.
Martini Glass Structure: it starts with an author-driven approach as a way to introduce the reader to the story and the functionalities and at the end it goives total freedom to expolore the data further.
Interactive Slideshow: it follows the traditional slideshow structure but it gives freedom to further explore and interact with the data in the scope of each single slide.
Drill-Down Story: it shows a general theme organized by content, and visually by author, but then it gives total freedom to the reader to choose where to drill down to get more information.
How do you put this into practice?
I gave some suggestions already inline together with the descriptions of the various patterns and parts of the model. But let me summarize.
The table with the design space can help you during the design phase to understand what components you can use in your solution. Choose a genre, a visual narrative tactic, and a narrative structure. You can also think creatively and see if there are useful combinations of these elements that have never appeared before. Alternatively, you can think out-of-the-box, and come up with new solution altogether.
Be careful with the level of interactivity and messaging you provide. This is probably a choice that you have to take early in your visualization project: do you want to give lots of freedom and risk that the message is not conveyed clearly or you prefer to guide the reader but remove the thrill of exploration?
The hybrid models are ready-cooked solutions that you can use right away. Again, these can both serve as a way to find a model that suits your message, or just as a starting point to create new hybrid models.
In any case, I hope you enjoyed this post. As I said, given the complexity of this article the post is necessarily reductive. If you are really interested be sure to give a look to the paper itself.
Over the last two days at ACTEM's annual conference I've shared Google Fusion Tables to great response. Many people commented that they had never heard of it, but really liked it and plan to explore it some more on their own. This post is a follow-up to yesterday's conversations. I originally wrote most ofthis post last winter.
Google Fusion Tables is a neat spreadsheet application that makes it easy to create visualizations of data sets. Fusion Tables can also be used to create visualizations of data set comparisons. At its most basic level Fusion Tables can be used to visualize existing data sets with one click. At a deeper level, Fusion Tables can be used to compare your own data sets and create visualizations of those comparisons. The types of visualizations available include tables, maps, charts, and graphs. As a Social Studies teacher, I really like the map visualization options.
Applications for Education For the visual learners in your classroom, Google Fusion Tables could be an excellent tool for showing the various ways that data can be interpreted. Fusion Tables also provides students with a fairly easy way to compare their own data sets.
A few days ago, I came across a drag’n'drop, wire it together visualisation and data analysis tool called Orange.
Here’s a quick run through of some of the basics (at least, a run through of the first few things I tried to do with the tool…)
First off, we need some data. Orange likes TSV (tab separated values) rather than CSV, so I grabbed some TSV from one of the Guardian Datastore spreadheets on Google Docs (use “Save as Text” to get the tab separated value format…)
Orange is a canvas based visual programming environment, in which functional blocks are added the the canvas and certain parameters set within the block. Here’s how we get some data into Orange from a TSV file:
The File icon is giving me a warning (no dependent variable) but I’m not sure why…? I’m sure Orange has managed to detect labels and quantities correctly from other files I’ve tried?
Anyway… we can inspect the data by looking at it in a data table widget – just wire one in:
The table is sortable by column, and the Report button can be used to save a version of the table. Looking t the data table, we see it has identified columns with missing entries. We can clean these from out data set using the Preprocessing widget:
If we now wire the output of the Processing widget into the Scatterplot widget, we can generate a variety of scatterplots:
If you want to save a copy of the chart, it’s easy enough to do so. (I can’t get colour palettes to work on my Mac, so I’m stuck with greyscale displays. Also, the blob sizing doesn’t seem very responsive…)
The Report tool allows us to create a report from various bits of the dataflow, including adding information from several widgets to either separate report pages or the same report page.
Saving a Report saves all the report pages to a navigable set of HTML pages that resemble the Orange Report viewer.
Here are a couple of other things we can do with the data, this time using a data set that isn’t throwing the “dependent variable missing” error, in particular the distribution of comments in a small Friendfeed network…
So for example, here’s how the number of comments made by members of the network is distributed:
Alternatively, we may look at the distribution in a more “statistical” way:
(Remember, we can generate these reports interactively, and then add them to a growing report.)
The survey plot gives us a macroscopic birds eye view over the whole of the data set:
Okay, that’s enough for starters – hopefully you get the idea: wire stuff together and generate visual reports… So why not go and download Orange now?!;-)
There are a whole range of clustering tools, too, which look like they could be interesting…
And I think the platform is extensible, which means there’s a way of adding your own widgets (written in Python, maybe..?)
Last month Hans Rosling, the Swedish global health professor, statician and sword swallower released a desktop version of Gapminder World, his mesmerizing data visualization tool. Named one of Foreign Policy's top 100 global thinkers in 2009, the information design visionary co-founded Gapminder.org with his son and daughter-in-law aiming to make the world's most important trends accessible and digestible to global leaders, policy makers and the general public.
The software they developed, Trendalyzer, (acquired by Google in 2007) translates static numbers into dynamic, interactive bubbles moving through time. The desktop version of Gapminder, which is still in beta, allows you to create and present graphs without an Internet connection.
Emily Cunningham is a research intern at ReadWriteWeb and a design and user experience intern at OpenMRS.org. She is pursuing a Master in Information Management at University of Washington in Seattle, WA. Emily is continually fascinated by the way social technologies are enabling collective action in new forms and on a scale we've never seen before. You can contact her at firstname.lastname@example.org or via Twitter: @emahlee.
What is Awesome
Many of same things that make the Web-based version of Gapminder a great tool applies to the desktop version:
Graphs are highly dynamic and yet easy to understand and create. Presenting data as animated colored dots allows you to show the relationship between sets of information at a level of complexity that's impossible in your typical graphing program.
The interactive nature of the graphs lets users' curiosity lead them to more discoveries about the data. You might, for instance, click on one or two dots representing countries and all the other country dots fade in the background. You can then compare the life expectancy and income per person of say, India and Vietnam.
Accessing the raw data for the graph is as easy as clicking a button.
The example graphs are fun to browse and play with. For instance, "USA or China, who emits the most CO2?" and "Who has the most Internet users?"
Benefits specific to Gapminder Desktop include:
For presentation purposes, having the ability to bookmark graphs for easy reference is invaluable. (And with automatic software you never have to worry if your data is out of date.)
Obviously not needing a Internet connection is a big bonus. Especially if you're giving a lecture in a resource-constrained part of the world with unreliable Internet connectivity.
Definitely don't miss Rosling's screencast (below) explaining how to use the application. Bonus: He gives five good tips on how to give a successful presentation with the software. Double bonus: Rosling is an endearing, quirky guy and is entertaining to listen to even in a "how to" screencast. (See his answer to "What's it like knowing so many on reedit (sic) have intense nerd crushes on you?" in this screencast for more Rosling goodness.)
What Needs Work
In a word: social. I would love to see the most shared and viewed graphs as well as new graphs and the people who made them. Peering in on conversations to see how people are using the graphs in their own work would be interesting and might help spark more ideas on how to use the content. I'm thinking of the way Wordnik displays the most recent tweets of a given looked-up word to give users more context to its meaning. I can also imagine people discovering one another as well as important discussions and collaborations coming out of a more social Gapminder.
Right now, Gapminder World has a "share" button, but all it does is give users a shortened bit.ly URL instructing them to "copy the short link below and paste it in a blog or send it by e-mail." (Though not necessarily in real-time, you can imagine the desktop version of the app getting the same information as the website version.)
Another benefit to having more social information displayed: It gives users more hooks to play with the tool. Yes, the data is very interesting. But with over 600 indicators, I found myself not going as deep into the data as I probably could have; it just got overwhelming at some point. Seeing the titles of other people's graphs probably would have kept me tinkering around a bit longer. I played around with the pre-made "example" graphs more than making my own graphs, in fact.
The Larger Picture
You won't comprehend the full power of Gapminder unless you watch at least one of Rosling's TED talks. His passion and style makes him a joy to watch. As TED notes, "With the drama and urgency of a sportscaster, statistics guru Hans Rosling debunks myths about the so-called 'developing world.'"
Rosling is a master at understanding global statistics and communicating the underlining trends they reveal. But what I find most admirable about Rosling is his commitment to making important information and insights understandable by everyone. Freeing public data from database hell so that it can be used by experts and laypersons alike is one of his goals. It was also the impetus behind creating Gapminder.
In a Gapcast titled "Free Statistics for Democracy," Rosling explains in his typical humorous style that his target audience consists of "children below 12 and heads of state. Because they both, they don't want people to show them boring graphs, you know and pretend. They want to see for themselves."
While children and global leaders are on his target list, his real audience is you and me: "The main decision makers we have aren't government, it's not policy makers; it's the public... Decision making, policy setting is done by the public in a democracy. So we have to change. Statistics means bookkeeping of the state. Serving the decision makers within the state."