Six questions with… Amanda Hobbs

In order to sprinkle some star dust into the contents of my book I’ve been doing a few interviews with various professionals from data visualisation and related fields. These people span the spectrum of industries, backgrounds, roles and perspectives. I’ve only scratched the surface with those I have interviewed so far, there’s a long wish list of other people who I haven’t approached yet but will be doing so. My aim is to publish a new interview each week through to the publication of my book around April/May 2016 so look out for updates!

I gave each interviewee a selection of questions from which to choose six to respond. This latest interview is with Amanda Hobbs, formerly an Art Research Editor at National Geographic, now an independent researcher, writer, and visual content editor. Thank you, Amanda!


Q1 | What was your entry point into the field: From what education/career background did you transition into the world of data visualisation/infographics?

A1 | Prior to becoming the Art Research Editor at National Geographic magazine, I earned my BA/MA in History and worked for an historic preservation firm, an art museum, and a fine art gallery.

Though some may wonder how I ended up working in visual journalism, to me it always seemed like a natural extension of my professional and academic training. Storytelling, whether via words on a page, information graphics, data visualizations, museum exhibitions, or any other form of media, is about conveying a narrative to your chosen audience. In order to tell that story you must research, interpret, synthesize, and present large amounts of information in a responsible, yet engaging way. Over the years I’ve fulfilled various different professional roles, but all had certain key elements in common: research, writing, art, and communicating with the public. And all have benefitted from my training as an historian.

Q2 | We are all influenced by different principles, formed through our education, experience and/or exposure to others in the field – if you had to pick one guiding principle that is uppermost in your thoughts as you work on a visualisation or infographic, what would it be?

A2 | As a researcher: be thorough, and be organized. I’m fond of saying that the information you collect is only as good as your method of organizing it. In other words, as a researcher you must be thorough, but you must also be meticulously organized if you want all of that research to make sense. As an information designer: strive for clarity, not simplicity. It’s easy to “dumb something down,” but extremely difficult to provide clarity while maintaining complexity.

Q3 | How do you mitigate the risk of drifting towards content creep (eg. trying to include more dimensions of a story or analysis than is necessary) and/or feature creep (eg. too many functions of interactivity)?

A3 | This is highly dependent on the subject matter and the proposed goal of the infographic/data viz. In essence: too little information/interpretation, and your graphic has no meaning; too much, and you cloud its understanding. Striking that balance is what all visual journalists are constantly striving to do.

Q4 | Can you describe what role research plays in the process of creating a data visualisation/infographic and why it is so important? What capabilities make a good researcher in this context?

A4 | Research is key. Data, without interpretation, is just a jumble of words and numbers – out of context and devoid of meaning. If done well, research not only provides a solid foundation upon which to build your graphic/visualization, but also acts as a source of inspiration and a guidebook for creativity. A good researcher must be a team player with the ability to think critically, analytically, and creatively. They should be a preemptory problem solver, identifying potential pitfalls and providing various roadmaps for overcoming them. In short, their inclusion should amplify, not restrain, the talents of others.

SIDE NOTE: In my career, I’ve met many people who view the role of researcher as an antiquated, academic profession, or even worse, a glorified fact-checker who delights in pointing out the deficiencies of others. Nothing could be further than the truth! Most often, when I begin a project I have no more than a basic description of what the story will be about. Clients enlist my help because they want to know what the possibilities are and what sorts of graphics they should pursue. In order to do my job, and do it well, I have to provide them with research, but also with ideas of what to do with that research. Hardly the realm of a “fact checker.”

Q5 | When you have worked on larger projects (complex, large times, long durations) how much does planning and project management play a role or do you find that creative freedom tends to dominate most workflows?

A5 | Planning and project management are essential when working on large projects with long timeframes, and good project management should foster creative freedom, rather than constrain it. Of course, this all depends on your definition of freedom. If you’d like to be able to change the focus, design, and implementation of a graphic at any point in the development process as an exercise in “freedom” . . . well, that will definitely have an effect on your deadlines, and your future work prospects.

Q6 | Do you have some advice on what helps you demonstrate such a strong capability to take complex and/or complicated subject matters and make them accessible and interesting to your audience? Linked to this, how do judge the sweet spot of accessibility – not oversimplified or dumbed-down but still understandable to non-specialists?

A6 | I hate the word “simplify.” In many ways, as a researcher, it is the bane of my existence. I much prefer “explain,” “clarify,” or “synthesize.” If you take the complexity out of a topic, you degrade its existence and malign its importance. Words are not your enemy. Complex thoughts are not your enemy. Confusion is. Don’t confuse your audience. Don’t talk down to them, don’t mislead them, and certainly don’t lie to them.

But I digress… So, how do you explain complex topics? First, you have to understand it yourself. If you don’t, talk to someone who does (i.e. a researcher, scientist, or expert in the field). Ask them how they break it down and explain it to individuals who don’t have a background in the subject matter. Second, try explaining it (in your own words or sketches) to someone else. If you can’t, go back to step one. If you can, but afterwards they have questions, then talk to them about their confusion. What elements could you add/change that would address their questions and help foster understanding? Third, show your newly developed graphic or visualization to a specialist in the field. Ask them if it is an accurate interpretation – something, in effect, that they could see themselves using to explain their work to others. If they think it’s an oversimplification, ask why. There could be many reasons for this: the size of the graphic is too small, there’s not enough room for explanatory text, it would be better to have 2 graphics (or 3 or 4).

SIDE NOTE: If you’re working on a story that has been reported on in the past, you could always discuss that previous coverage with an expert in the field – what do they think could had been done better? Explaining complex topics to non-specialists is a challenge. It’s also probably the number one reason why people hire a researcher (like me) to help them develop information graphics and data visualizations. Every topic is different, and requires a different approach.

Six questions with… Gregor Aisch

In order to sprinkle some star dust into the contents of my book I’ve been doing a few interviews with various professionals from data visualisation and related fields. These people span the spectrum of industries, backgrounds, roles and perspectives. I’ve only scratched the surface with those I have interviewed so far, there’s a long wish list of other people who I haven’t approached yet but will be doing so. My aim is to publish a new interview each week through to the publication of my book around April/May 2016 so look out for updates!

I gave each interviewee a selection of questions from which to choose six to respond with quick, instinctive answers. This latest interview is with Gregor Aisch, graphics editor at The New York Times. Thank you, Gregor!


Q1 | What is the single best piece of advice you have been given, have heard or have formed yourself that you would be keen to pass on to someone getting started in a data visualisation/infographics-related discipline?

A1 | Labelling is the black magic of data visualization.

Q2 | When you begin working on a visualisation task/project, typically, what is the first thing you do?

A2 | Get the data in shape! If it comes in a weird format, like thousands of little JSON files, I will write a quick script (usually Python or Node) to get it into a nice structured format. The next step would be to fire up R Studio and do some quick plots to see where this could go.

Q3 | With deadlines looming, as you head towards the end of a task/project, how do you determine when something is ‘complete’? What judgment do you make to decide to stop making changes?

A3 | When a project drags on for too long, at some point you just want to publish the thing and move on with your life. That’s the time when I stop thinking about what niceties could be added and just finish it. There is usually tons of work left at this point.

Q4 | Given the rich capabilities you and your colleagues possess, the bar is set quite high in terms of the ambitions of what form and what function your visualisation and infographic work can take. Innovation is important, but sometimes you see work out there that is evidence of people doing things just because they can, rather than because they should. How do you (individually and within the team) maintain clarity of focus and preserve the discipline of avoiding unnecessarily innovation?

A4 | I don’t think there is such a thing as “unnecessary innovation”. If someone wants to try something new. It would be quite boring to produce only bar charts for the rest of your life. The publishing medium changes rapidly, web browsers evolve, new devices change the way people consume stories. With every project you learn something new, so it’s only natural to re-think what you do once you start over again.

Q5 | As you know (more than most!) there is a lot of science underpinning the use of colour in data visualisation. There is also, however, a lot that can be achieved through applying common sense. What is the most practical advice you’ve read, heard or have for relative beginners in respect of their application of colour?

A5 | If you want a nice custom color palette, use my color palette tool. The bottom line is that this tool will correct the steps in perceived lightness, which is what you want in a balanced palette. It also allows you to experiment with bezier interpolation to make intermediate color steps look better. Ah, and you should definitely check the palette with ColorOracle, a tool that shows you how your graphic looks like for colorblind readers.

Q6 | What advantages do you think working in a journalistic setting has in terms of ongoing development of your data visualisation/infographic capabilities?

A6 | There are tons of advantages of working in a newsroom with some of the smartest people in the world! In terms of my data vis capabilities I would say that learning the journalistic “mindset” is something that has helped me the most. While I was freelancing I had started dozens of projects that I never completed. The reason is probably that most of them where way too ambitious or completely unrealistic. Being a journalist means that no matter what you do, you focus on publishing from day one. You want to do amazing work, but you also want to get it done! Finding the right balance between these two goals is something journalists are incredibly good at!

Six questions with… Isabel Meirelles

In order to sprinkle some star dust into the contents of my book I’ve been doing a few interviews with various professionals from data visualisation and related fields. These people span the spectrum of industries, backgrounds, roles and perspectives. I’ve only scratched the surface with those I have interviewed so far, there’s a long wish list of other people who I haven’t approached yet but will be doing so. My aim is to publish a new interview each week through to the publication of my book around April/May 2016 so look out for updates!

I gave each interviewee a selection of questions from which to choose six to respond. This latest interview is with Isabel Meirelles, a Professor in the Faculty of Design at OCAD University in Toronto, Canada. Thank you, Isabel!


Q1 | What was your entry point into the field: From what education/career background did you transition into the world of data visualisation/infographics?

A1 | I studied architecture and my first master was in history and theory of architecture. After working as architect and later in different capacities in art museums, I transitioned to graphic design, more specifically editorial design, and I worked for almost six years in magazines in São Paulo, Brazil. In the late 90s, with the advent of the internet, I became fascinated by the potential of incorporating time and interactivity into the communication of information. To scrutinize the possibilities offered by dynamic media, I went back to school to pursue a second master later in life. My thesis “Dynamic Visual Formation: Theory and Practice” examined the creative process of image-making in computational media toward a theory of dynamic visual language. One of my advisors was Krzysztof Lenk, a Polish/American pioneer in information design, who introduced me to methods of mapping information. He broadened my understanding of the field and my brief experience with devising infographics while working in magazines. That was the beginning of my ongoing examination of all aspects of information design,that includes studying the histories and theories of representing information visually.

After graduating from this second master, I started teaching at Northeastern University in Boston, which opened up opportunities to collaborate with researchers in other disciplines who saw the value in my research. As you know, my research focuses on the theoretical and experimental examination of the fundamentals underlying how information is structured, represented, and communicated in different media. It was through the collaboration with colleagues in the sciences and the humanities in interdisciplinary projects involving the visualization of information that I was finally introduced to the challenges of working with large datasets. A long story to get to the point of your question, my apologies.

Q2 | What is the single best piece of advice you have been given, have heard or have formed yourself that you would be keen to pass on to someone getting started in a data visualisation/infographics-related discipline?

A2 | The agreed upon approach of spending as much time as possible with examining the data, that includes “data sketching”, is always a great piece of advice. To that I would add that we should also pay as much attention to understanding the project’s goal in relation to its audience. This involves understanding principles of perception and cognition in addition to other relevant factors, such as culture and education levels, for example. More importantly, it means carefully matching the tasks in the representation to our audience’s needs, expectations, expertise, etc. Visualizations are human-centred projects, in that they are not universal and will not be effective for all humans uniformly. As producers of visualizations, whether devised for data exploration or communication of information, we need to take into careful consideration those on the other side of the equation, and who will face the challenges of decoding our representations.

Q3 | We are all influenced by different principles, formed through our education, experience and/or exposure to others in the field – if you had to pick one guiding principle that is uppermost in your thoughts as you work on a visualisation or infographic, what would it be?

A3 | My education as an architect provided me with several guiding principles, and rather than selecting one, I will briefly introduce three that have been influential throughout my career in information visualization. The first guiding principle derived from architecture that I would like to mention is the relevance of contextual information. We learn to consider diverse contexts surrounding architectural problems, from the functions to the people who will inhabit or use these spaces, from geography to cultural, social and economic factors. There is a well-known saying by the American architect Eliel Saarinen that says, “Always design a thing by considering it in the next larger context: a chair in a room, a room in a house, a house in an environment, an environment in a city plan.” Architecture also provided me with methodologies for examining problems in structured and systematic ways. Furthermore, it taught me to consider representation as a system of representations. Given the complexities of architecture, which is by nature three-dimensional and encompasses many layers of information, including layers of “hidden information” such as structural or electrical systems for example, we learn how to communicate using several representations, each serving different purposes and for different audiences. The set devised for electrical wiring is not the same as for structural engineering, to use the same example as before. They might share same items though with different levels of details. Finally, architecture is a collaborative endeavour, and we learn to consider several interdisciplinary approaches while solving architectural problems (e.g., sociology, ecology, etc.). One result is that most of the time architects work across disciplines and collaboratively. We see similar types of collaborations in the way we now work in large projects involving the visualization of information.

Q4 | As somebody who is involved in educating others, what are your observations about what attributes separate the successful students from the rest of the pack? What capabilities are you most eagerly looking for as they enter the programme – or during – to decide if that person has got ‘it’?

A4 | Similar to other design practices, information design requires a lot of discipline and perseverance. On the other hand, I also believe that flexibility and curiosity are essential to the design process of visualizing data, in that information design is both systematic and iterative.

Q5 | What do you feel is still the big unknown in data visualisation? If you could undertake one research project (assume any funding needed, plenty of time, good collaborators, justification are all in place) what do you feel would make the biggest difference to the field at large?

A5 | The more I think about your question, the more I come to the conclusion that there are many open areas, which is great, because there is room for everyone, and we can continue working as well as educating future generations to join our efforts. In any case, I will give you two examples that interest me. Interestingly, visualizations of textual data are not as developed as one would expect. On the other hand, there is a great need for such visualizations given the amount of textual information we generate daily, from social media to news media and so on, not to mention all the material generated in the past and that are now digitally available. There are opportunities to contribute to the research efforts of humanists as well as social scientists by devising ways to represent not only frequencies of words and topics, but also semantic content. However, this is not at all trivial. Another area that I think requires further work is the representation of spatio-temporal data. Human mobility, diffusion of information or the spread of diseases are examples that involve changes over time and space and present challenges in how we represent them.

Q6 | If you could somehow secure 3 months to do anything you wanted, what would you love to be able to spend your time doing to enhance your data visualisation capabilities further? (Eg. Reading, making, learning new tools etc.)

A6 | All of the above! I have so many things to learn. I would love to spend more time reading, especially about cognition, but also all the research that is conducted in our field, which is a lot! I would also love to learn new skills, especially programming languages, as I feel I am getting behind in this aspect. Our field moves very rapidly and I always have a feeling I am lagging behind…

Six questions with… John Nelson

In order to sprinkle some star dust into the contents of my book I’ve been doing a few interviews with various professionals from data visualisation and related fields. These people span the spectrum of industries, backgrounds, roles and perspectives. I’ve only scratched the surface with those I have interviewed so far, there’s a long wish list of other people who I haven’t approached yet but will be doing so. My aim is to publish a new interview each week through to the publication of my book around April/May 2016 so look out for updates every Thursday!

I gave each interviewee a selection of questions from which to choose six to respond. This latest interview is with John Nelson, Director of Visualization at IDV Solutions. Thank you, John!


Q1 | When you begin working on a visualisation task/project, typically, what is the first thing you do?

A1 | I kick it over into a rough picture as soon as possible. When I can see something then I am able to ask better questions of it –then the what-about-this iterations begin. I try to look at the same data in as many different dimensions as possible. For example, if I have a spreadsheet of bird sighting locations and times, first, I like to see where they happen, previewing it in some mapping software. I’ll also look for patterns in the timing of the phenomenon, usually using a pivot table in a spreadsheet. The real magic happens when a pattern reveals itself only when seen in both dimensions at the same time.

Q2 | With deadlines looming, as you head towards the end of a task/project, how do you determine when something is ‘complete’? What judgment do you make to decide to stop making changes?

A2 | I have the benefit of, for the most part, making visualizations for fun, as time permits (my official capacity revolves around software user experience and information architecture). As such, I’m generally the only one who has to decide if when something is done, and there really are no looming deadlines, other than my impatience to share the results on the web. When I step back and think, ‘Ok, they are really going to like this’ then I know I’m in an alright spot. Invariably I have a spelling mistake in there though, that a reader will point out early on, so there is usually one last unfortunate revision before I am actually done.

Q3 | How do you mitigate the risk of drifting towards content creep (eg. trying to include more dimensions of a story or analysis than is necessary) and/or feature creep (eg. too many functions of interactivity)?

A3 | Once I heard a quote by Billy Wilder, on effective storytelling, where he said something like “…give them two and two, and let them add it up.” The most exciting dialogues I’ve had around visualizations was when I just show the data with no commentary or explanation. Visualization designers are almost never experts in the topic we design for; I learn the most when I like to stick to the showing and then listen. Also, this way you engage visualization participants, rather than pitch to visualization readers.

Q4 | At the start of a design process we are often consumed by different ideas and mental concepts about what a project ‘could’ look like. How do you maintain the discipline to recognise when a concept is not fit for purpose (for the data, analysis or subject you are ultimately pursuing)?

A4 | I have the lazy benefit of being a specialist. If the data isn’t spatial, it is unlikely I’ll dig into it for a visualization. Most of my head-scratching comes when trying to determine what complimentary graphics to include or not to include. I have had some projects where I found the analytical method I was walking down yielded beautiful, though misleading, visuals. Then I had to throw it away and start over (see “False Start” section)

Q5 | Whilst there is a great deal of science underpinning the use of colour in data visualisations, a lot can be achieved through applying common sense. What is the most practical advice you’ve read, heard or have for relative beginners in respect of their application of colour?

A5 | Too many colors! Keep the palette simple and clean. If the background is dark, use brightness to denote higher values; if the background is light, darker values. The phenomenon and the background lightness should oppose each other; contrast is the vehicle for magnitude.

Q6 | Presenting spatial data is a specialist discipline and one that involves a lot of different challenges to other types of charts and graphics. Not wishing to reduce this discipline to a diluted bullet point list but what would be 2/3 key pieces of advice – or pitfalls to avoid – you would offer folks facing this kind of challenge?

A6 | When making a choropleth map, chose your range breaks carefully. There is an elusive balance between choosing color ranges that respect the user and the integrity of the phenomenon, and in teasing out the story the phenomenon has to tell. That balance lies somewhere between mathematically dogmatic range breaks that communicate little and creative range breaks that propagandize. (see article “Telling the Truth“)

Most maps are now made with readily-available software that lowers the barrier to entry for many designers, and this is terrifically exciting to me. It does, however, also give me the opportunity to encourage more novice map-makers to avoid the defaults. Each factor should be an active choice by the designer, rather than automated passive acceptance (see “Avoid the Defaults” section)

Six questions with… Kennedy Elliott

In order to sprinkle some star dust into the contents of my book I’ve been doing a few interviews with various professionals from data visualisation and related fields. These people span the spectrum of industries, backgrounds, roles and perspectives. I’ve only scratched the surface with those I have interviewed so far, there’s a long wish list of other people who I haven’t approached yet but will be doing so. I’m aiming to publish a new interview every week through to the publication of my book around April/May 2016, we’ll see how that goes…

I gave each interviewee a selection of questions from which to choose six to respond. The first interview is with Kennedy Elliott, Visual Journalist par excellence at The Washington Post. Thank you, Kennedy!


Q1 | What was your entry point into the field: From what education/career background did you transition into the world of data visualisation/infographics?

A1 | I specialized in Media Studies (media theory and criticism) in undergrad and coded a bit for fun while I was there. After working for a few years editing scientific publications, I decided to go to grad school full time for journalism and be a developer. I thought I’d want to work on infrastructure in newsrooms, but instead I discovered data visualization, and realized I quite liked telling stories in visual ways. After grad school, I moved to New York and worked in industry for a while as a data visualist, but then moved back to journalism, working at the Associated Press, the Guardian US and the Washington Post.

Q2 | What is the single best piece of advice you have been given, have heard or have formed yourself that you would be keen to pass on to someone getting started in a data visualisation/infographics-related discipline?

A2 | Be a great journalist. You might think that learning to code, understanding visual design, user experience or human perception would be the most important part of data visualization, but it’s not. Understanding data is the most important part. There can be so many issues with the information you’re trying to convey, like biased sources, incorrect methodology, incomplete or insufficient data collection, that are necessary to resolve before you begin visualizing. Even if your data is thorough and unbiased, it might not be the correct metric by which to evaluate your thesis. It might not tell the complete story, or even a part of the story accurately. It’s absolutely necessary to investigate the data itself, understand its flaws and report what you can within the proper context before visualizing it.

Q3 | We are all influenced by different principles, formed through our education, experience and/or exposure to others in the field – if you had to pick one guiding principle that is uppermost in your thoughts as you work on a visualisation or infographic, what would it be?

A3 | My main goal is to represent information accurately and in proper context. This spans from data reporting and number crunching to designing human-centered, intuitive and clear visualizations. This is my sole approach, although it is always a work in progress.

Q4 | Given the rich capabilities you and your colleagues possess, the bar is set quite high in terms of the ambitions of what form and what function your visualisation and infographic work can take. Innovation is important, but sometimes you see work out there that evidences people doing things just because they can, rather than because they should. How do you (individually and within the team) maintain clarity of focus and preserve the discipline of avoiding unnecessarily innovation?

A4 | I think having a consistent body of solid work is important here, because I truly feel that experimentation (even for the sake of experimentation) is important, and I wouldn’t discourage it. I think a good rule of thumb is to never allow your design or implementation to obscure the reader understanding the point of your piece. However, I’d be willing to forsake this, at a very minimal level and very infrequently, to allow for the occasional innovation or experiment. It ends up moving us all forward, in some way or another. If you have a reputation for consistent work, then you have earned your right to take risks!

Q5 | What advantages do you think working in a journalistic setting introduces to your visual work?

A5 | I think working in a journalistic setting is the perfect place for visual critique and inspiration. Everyone has varied backgrounds and therefore varied opinions, but we all want the same thing, and that is to tell a great digital story with high journalistic integrity. I think when everyone has the same goal but different experiential wisdom, constructive feedback leads you to discovering new and better things.

Q6 | The judgment of how to elegantly compose and layout a piece is possibly one of the least (publicly) discussed aspects of visualisation and infographic design thinking. Do you have any tips or tactics you can pass on to others about how you approach this?

A6 | The exciting thing about information design is that it is always evolving. It’s a discipline that borrows from sister disciplines – graphic design, product design, interface design. I’m not sure if there are many rules that will still be true in ten years, but I’ll give it a shot. Right now, we try to make things that are intuitive and thusly minimal. We rely on a reader’s previous experience with technology to inform them on how to interact with our work. We use traditional graphic design techniques, like typographical hierarchy, to direct the eye. We use color to communicate meaning when we can, so that design becomes more of a practical endeavor than an ornamental flourish (although we do save room on some projects for ornamental flourishes!). And because technology is always changing, we always have to adapt to how we know (or how we think we know) humans interact with interfaces and process information.

Seeking input for book: Have struggles with data?

As visitors know, I’m in the key stages of book writing. I won’t bore you by repeating the background of that anymore.

As you might anticipate, in a book about data visualisation, there is a chapter about data, describing the mechanics for gathering, familiarising with, preparing, analysing etc.

One of the easy assumptions to make is that anybody who is interested about visualisation – and certainly enough to buy a book about it – is going to arrive with at least a basic understanding of data and numerical literacy (inc. some stats knowledge).

That’s not always the case. Plenty of people have little knowledge of data but are keen to learn about – and do – visualisation. Indeed, for some, the ‘little’ knowledge they do have is a shortcoming that can seem hard to overcome. They might not know the difference between quantitative and qualitative or discrete and continuous data, for example. This naturally leads to a certain anxiety and perhaps, in the extreme cases, fear.

Experience from meeting delegates on my training courses and teaching modules down the years reveals as much. It is a generalisation but I would suggest this is more prevalent amongst people who have a dominant background in a creative environment but little experience in more analytical pursuits.

I’m keen to make the contents of my book as accessible as possible to as wide a group of people as possible and this is therefore a key section to cover.

To help fine tune my coverage of this particular topic, I would therefore appreciate the opportunity to hear from people who might identify with having (currently) or having had (in the past) a deficit in their abilities around working with data.

To be clear, this is not related to any anxiety over how people will use your data, how their lack of chart literacy might lead to misinterpretations, it is simply your confidence in working with data yourself.

If anyone has any insights to share I would really value you adding comments below. This input will really help me check I have something approaching the right content and tone for my book. Thank you in advance.

Creativity and science in data visualisation

When observing the trajectory and development of the data visualisation field I often contemplate the dual role of the scientific and creative communities: I believe scientists tell us what we should do and creatives tell us what we could do. Alone, the field leans towards one direction but together they take us forward in the most positive direction.

In the past few days I’ve come across nice examples of both communities in action and thought I’d summarise them in a combined post. Firstly two examples of innovation from creative thinkers and then two valuable products of research from scientific minds.

‘Draw your chart’, The Upshot

Today we have seen a terrific demonstration of the compelling nature of participatory visualisations. Amanda Cox, Kevin Quealy and Gregor Aisch from TheUpshot have launched a brilliantly engaging ‘draw your own chart‘ project that allows users to draw a line chart that you feel represents the shape of how family income affects children’s college chances. Not only that, but depending on the ‘fit’ of the line you draw (and I won’t share a screen grab of my guess and ruin the surprise) the resulting text is customised to comment on how well or poorly you did, compared to the actual and to everyone else.

UpShotDrawChart

‘The Fallen of World War II’, Neil Halloran

This is a fascinating looking interactive documentary by Neil that “examines the human cost of the second World War and the decline in battle deaths in the years since the war”. It exists as a 15-minute video visualisation using “cinematic storytelling techniques to provide viewers with a fresh and dramatic perspective of a pivotal moment in history”. The mix of video-based explainers and interactively explorable charts is typical of a slowly emerging recent trend to combine experiences. I’ve only had chance to watch the first minute or so but it is already clear that its an excellent piece of work.

Fallen

A second interesting innovation is the use of an optional ‘ticket’ payment system presenting the possibility for viewers to contribute a small fee for the benefit of watching – “support will help us develop the project and create new episodes”. We see different approaches of revenue generating from upfront kickstarter type models, paywalls in journalism, and advertising space so why not offer an optional charge? People have to earn a living!

FallenPay

New Paper: ‘Impact of Visual Embellishments’

This week at EuroVis, Robert Kosara, Drew Skau, and Lane Harrison are presenting their new paperAn Evaluation of the Impact of Visual Embellishments in Bar Charts“. The title is self-explanatory so I won’t just rehash the entire abstract but this is a really valuable piece of research that offers evidence to support what we might otherwise need instinct to rely on. It also means there is a robust argument to rightly dismiss those dreadful triangle shaped bar charts.

Embellishments

New Paper: ‘Deceptive Embellishments’

This one goes back to late February, but I wanted to bring balance to this piece. Enrico Bertini, Anshul Vikram Pandey, Katharina Rall, Meg Satterthwaite and Oded Nov published a paper “How Deceptive Are Deceptive Visualizations?“. This study explored a range of classic deceptive visual distortions and tested out the impact they had on readers. Once again as you might imagine, the results show the deceptive devices we have always warned against genuinely do have the distorting influence we suspected. Like the paper above, this is another valuable and practical piece of empirical evidence to support good visualisation practices.

Deceptive

Determining the use of language: User? Reader?

As I am in the process of writing my book I find certain challenges in the use of language crop up time and time again. The main one I have difficulty with is maintaining consistency in how I term the person who reads, uses or consumes a visualisation or infographic work. It is particularly problematic when I am constructing a sentence that really needs a singular catch-all label and not a multi-comma-separated list attempting to cover all nuances. That makes it both clumsy to write and to read.

Yesterday, I asked my esteemed twitterati to suggest the language terms they use or feel most comfortable with in order to arrive at a consensus viewpoint or at least accept there are too many variations to be able to settle on one single term.

Rather than leave it buried on Twitter, I have storified a collection of the contributions people made (thank you to all again) so that others can join the debate.

However, in summary and unless I am presenting with an alternative compelling argument, my decision is to go with VIEWER. Regardless of the type and format of visualisation we are working with, we are always ‘viewing’ a visual portrayal of the subject’s data.

USER is an appropriately active term for describing those who engage with an interactive project – but even when we have the ability to interact we are not doing so constantly, we do stop to look.

READER feels more associated (in my definition) with specific acts of reading text, values and point-reading from a chart. It is clearly a key component of engaging with a visualisation but not a universal act – when we’re taking an initial ‘at a glance’ perspective, that’s not in my view a specific act of reading.

AUDIENCE would be something I would maybe use in a different written context but is problematic when referring to an individual.

CONSUMER, CUSTOMER, RECIPIENT, RECEIVER and (even) VICTIM are either too passive, too context specific or feel too harrowing.

The two winners and two losers of the UK Elections

With the dust settling after the UK elections, a brief reflection on the winners and losers from a data and visualisation perspective:

Two Winners

Cartograms

As I stated a couple of weeks ago, these things are everywhere right now…

…and they have never been deployed to such good effect and in such an across-the-board sense. During the build up to and night of the election, cartograms emerged as the real star.

The tracking of the predicted and actual outcome of the election is so well suited to the cartogram approach, sacrificing geographical precision for a more equitable visual weighting for each individual constituency, the voting outcomes of which are so critical to the ebb and flow of the overall election results. This first example comes from the Guardian:

GuardianCarto

The hexagon, with its reasonably flexible tessellating qualities, provides a great geometric option to build up the election picture, as shown by this in the Telegraph:

TelegraphCarto

Kenneth Field, of CartoNerd acclaim, is working on an interesting looking experiment to take the election hexagon bin map results into a 3D landscape, breaking down the votes of each constituency in stacked hexagon bars, creating the look of the Giant’s Causeway.

KenField

Not everything was digital. We had the BBC’s excellent and huge outdoor cartogram (that I cleverly, I’m sure you’ll agree, coined the ‘elecxagon’ map)… It’s excellence was enhanced further by confusing those cretins at the Daily Mail.

…and then there was Tom Katsumi’s almost-live cross-stitched cartograms

_82869419_stitchfinal

High quality UK visualisation work

There was some very high calibre visualisation coverage across many different news and media outlets but the standout work (in the UK at least) emerged, perhaps unsurprisingly. from the Guardian, the BBC and the Financial Times. These three organisations are at the top of their game right now and leading the UK data journalism and visualisation landscape.

*There is a nice round-up of some of the election visualisations on BuzzFeed*

Two losers

Liberal Democrats’ Visualisation Integrity

Whilst there were surprisingly few examples of corrupt visualisation work, the Liberal Democrats – the big losers in the election itself – offered up the dodgiest data visualisation work, a theme that has continued on from their efforts back in 2010. I’m not saying that their political performance is linked to their visualisation output but…

LDCrime

Attribution

There have been many recent examples of twitter users taking other peoples’ work and ideas and passing it off as their own on tweets that then generate traffic and attention, blatantly failing to attribute the original author.

Many you will have seen the pattern formed by the predicted GB (not UK, as Northern Ireland missed off) political map compared to Maggie Simpson. I first saw this in a tweet dated 29th April.

This might not be the original, but it was certainly shared enough and predates the endless copycat tweets that went viral after the results came in, with @serialsockthief and @suffragentleman just two of many others who failed to acknowledge where they’d seen the original. Maybe they are unfortunate exhibits to pick on and perhaps they independently came up with the very same idea…

Whilst the Maggie Simpson thing is more comedic than visualisation, there was another example that really caught my attention. This astute piece of analysis by Vaughan Roderick, looks at the patterns of voting matching some of the traditional coal mining areas of the country.

Once again, this has been blatantly ripped off by others without the slightest hint of acknowledgement. @Amazingmaps and @Bowgroup should hang their heads in shame. Particularly as both were told who did the analysis and who should be attributed. Amazing Maps even faved the tweet telling them who the author was!

I appreciate there are character restrictions on a tweet but a follow up tweet with details of where the original came from is surely the least that can be done.

Data is your raw material, not your ideas

Back in January I claimed that I would be hitting the new year with plans for more frequent, smaller blog posts to offer ‘some practical tidbits most probably relating to quite narrow design considerations’. That lasted for about a week, so its certainly long overdue that I pick this back up.

The small nugget of advice I want to share today is about the relationship between your data and your vision.

Whenever we start a visualisation task there will inevitably be ideas that form in your mind about what this thing might look like. It will be a mental slideshow of different imagery comprising keywords, colours and forms, metaphors, maybe cliches, things that you’ve seen before, things that have inspired you and things that you’ve maybe worked on before.

There is no ‘perfect’ in visualisation: there are better and worse solutions but no absolute path to perfection. It is therefore important to embrace these instinctive reactions we have to the subject and task we’re working on. These mental manifestations inject imagination and creativity into our work and this is important, without question.

However, our ideas only act as initial possible signposts and they should only play the role of background inspiration. They cannot be the leader. We can’t afford to commit ourself to such a narrow aperture in our thinking.

Our ideas are not the raw material, the data is.

Take the example below. This is a piece I’m working on as a demonstration project to accompany the central workflow discussed in my upcoming book. The focus of the project is about the differing career stories of various movie stars. The tentative title is ‘Filmographics’ (that’s a clever wordplay combining films and infographics, in case you were wondering) and looks at the relationship between an actor’s career and the relative success of their movies in terms of critical reception and box office.

When I first had the idea, the very first image that formed was something like the sketch below, captured in my notebook on a particularly bouncy train journey back from London one evening. I had this vision of a forest of trees, with the height being the critical review, the size of the bubbles being the takings and the colours maybe representing the genre.

Trees

The reality, when using real data, was that a movie career is not organised in perfect intervals, with consistent reviews and takings: it is up and down, big and small, densely packed and then sparse. There are so many genres, and derivatives, that there aren’t enough colours to suitably distinguish each one. There are things from my initial idea that I can preserve going forward – and that in itself can be quite rare – but the initial idea of that neat forest was quickly shown up by the data to be redundant.

Data

An important discipline you have to show as a data visualisation designer is NOT to be servant to just pursuing your initial idea (or even more starkly important, those of your client/customer). Early ideas and sparks of creativity are really valuable and, particularly as we become more experienced, our instincts are worth tapping in to. Just don’t be precious or stubborn, always maintain an open mind. Ultimately you need to be respectful to the shape, size and conversation emerging from your data. That is the true raw material.

“Good ideas are in abundance. We all have them. Implementations on the other hand, are not. I admire implementations far more than great ideas”, Julian Oliver