Six questions with… Giorgia Lupi

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 gave each interviewee a selection of questions from which to choose six to respond. This latest interview is with Giorgia Lupi, co-founder of and Design Director at Accurat. Thank you, Giorgia!


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 at the university, but I have never built or designed any houses.
During my M.Arch studies I’ve always been more interested in aspects concerning the representation of information, and I tried to push all my architectural and urban projects towards working with information and mapping systems; even my M.Arch thesis (in 2006) was an urban mapping project.
For the following 4 years I’ve been collaborating with different interaction design firms in Italy, focusing my contributions on visual documentation and representation, mapping, and information architecture.

It wasn’t until lately that I started working specifically with data visualization. It came natural to me to progressively focus more on the quantitative side within the information design field, and when I got to understand the true potential of working visually with structured data to convey information about phenomena or contexts, I simply felt in love with this world and the realm of possibility it opens. Then, in 2011 I both co-founded my on information design company (Accurat), and started a PhD in communication design at DensityDesign Lab at Milan Politecnico.

As for the job that I am doing today: at Accurat we rely on building multidisciplinary teams to work on our projects. Our team is made of designers, developers, data analysts, interaction designers, and also interestingly one of my partners, Simone, is a sociologist. I am coordinating the design teams for the representation part.

I believe my background in architecture has influenced my production considerably. The very fact that I spent almost 5 years of my life designing, “composing” and manually drawing architectural and urban plans shaped my mind and my visual aesthetics a lot. Moreover, I have played the piano for a long time, and I’ve been always very fascinated by the “repetitive” aesthetic of musical scores and intrigued by the contemporary music notation style; I believe this fascination of mine is reflected in my work as well.

Lastly, as a human being, I have a very visual mind, and I need to draw and sketch to understand my surroundings. In fact, I’m not usually able to perfectly get and define what I’m thinking, or what pops up in my mind about a design problem, I usually say that I cannot think about a project without a pen and some paper. I know drawing is my way to understand I had an idea in the first place.

Besides, I take an incredible pleasure in drawing, in tracing lines on paper and seeing abstract shapes come alive and I’ve came to realize this practice gives shape to my inner thoughts, and influenced my visual design production consistently over years.

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 | The short answer is: it is something you feel, it is completed when it feels “just right”.
The long answer considers of course many other aspects. I believe it’s a matter of setting the right priorities every time. We all wish we could have unlimited time to refine our projects, but we all need to deal with deadlines. The questions I usually ask myself when a deadline is hanging over my head are very practical: does it miss anything absolutely necessary for its comprehension? Is it polished and refined enough that the hierarchies of information stand out?

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 | I would say the pursue for beauty. I’ve come to believe that pure beautiful visual works are somehow relevant in everyday life, because they can become a trigger to get people curious to explore the contents these visuals convey. I like the idea of making people say “oh that’s beautiful! I want to know what this is about!”

And also, not being a data scientist or a statistician my self, I see the focus of my persona work with Accurat on designing pleasant aesthetics that tell data-stories; I like, thus, to describe my approach and our work as an attempt to “compose” aesthetically beautiful and multilayered images with data. Of course, the accuracy of information representation principles should be followed, and we try to do it while always pushing a little bit farther the boundaries of what we can produce, visually speaking.

I think that probably (or, at least, lots of people pointed that out to us) being Italians plays its role on this idea of “making things not only functional but beautiful”. I also believe that well balanced and harmonious aesthetics can add a human touch to the world of data, and thus potentially interest and attract a wider audience.

Q4 | How important to you is the idea of establishing a workflow/process that you can adopt on any new task you work on? Alternatively, does your experience give you the confidence to be able approach tasks with a greater sense of freestyling, not being constrained by a sequenced approach to thinking?

A4 | Building an approach, a recognized method and a workflow is especially useful when you don’t work on your own but with a team of people. At Accurat, for example, we now are 20 people, and we spent a good bit to time during the last couple of years in establishing our workflow, and our principles for designing and developing our projects. A method that can be taught serves as the foundation for the collaborations among designers, and designers and developers; and it also helps new comers to embrace our design philosophy and our style.

When I work on my personal projects, or on projects on my own at Accurat, I definitely leave my self a lot more freedom. As I already mentioned, my approach consists of a lot of sketching. I draw to freely explore possibilities, I draw to visually understand what I am thinking.
I draw to evaluate my ideas and intuitions by seeing them coming to life on paper, I draw to help my mind thinking without limitations, without boundaries.

Q5 | 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?

A5 | I would say, first of all, every designer should learn how to “see”, to understand and be aware of what are the aesthetic qualities that attract us in all kind of visuals we like. Learning to see means observing which details make the difference in the visual aesthetics even by starting with replicating those images (and not necessarily data visualizations) that your eyes are fascinated from.

Then, play a lot with visual hierarchies, try to cut and remove all the visual clutter, make only important things stand out and leave the rest for the background.
Choose a color palette that feels beautiful (and appropriate) beforehand rather than picking random colors one after the other while designing.

Also, consider white space as a design space, and don’t necessarily try to fill the whole piece with details and elements: white space and general “air” of the compositions are key elements to its perceived elegance.

Q6 | Beyond the world of infographics/visualisation what other disciplines/subject areas/hobbies/interests do you feel introduce valuable new ingredients and inspire ongoing refinement of your techniques?

A6 | Personally I think that (again!) drawing by hand and playing an instrument helps my mind to stay prolific. But more generally speaking, I believe that engaging in (i.e. making time for) personal projects is necessary.

With Dear Data for example, (a year long collaborative laborious analog data drawing effort) I realized the importance of experimenting on making things when no client is judging you and when there’s none looking over your shoulder. You can try things, you can take risks and explore hunches, you ultimately get to do the kind do work that you want to.

We are all busy (I personally don’t like the word busy), but I would encourage anyone to make a little of spare time for projects that are outside our day job. We all have a passion for what we do, we’re lucky we are in a very interesting industry, but we easily end up only making the work that helps to pay the rent, procrastinating personal ideas and projects to nobody-knows-when. I guess the most of us has a resolution list of things we want to do for ourselves but we just have a hard time making the time for that. I believe it’s just a matter of starting, and starting can be as simple as “I will go to a cafe and sit with my notebooks for just an hour every Tuesday after work”. With Dear Data we’ve created a habit for our non-demand work, it helped us staying prolific, producing more consistently and it opened up unexpected new and exciting directions.

Views from ‘Seeing Data’ research (Part 2)

This is the second in a series of three blogposts about the Seeing Data project. The first post was written by Professor Helen Kennedy, director of Seeing Data, and discussed some of the findings and what they meant for how we think about ‘effective’ visualisations. In this second post, I reflect on one of the research methods that we used, called ‘Talking Mats’, which we think offers some really exciting possibilities for measuring the effectiveness of data visualisations.


Part 2: Talking mats as a visual method for assessing and discussing data visualisations

One of the central research activities during this project involved the nine focus groups, with a total of 46 participants involved. The purpose of these focus groups was to invite the participants to spend time looking at, exploring and reflecting on their experiences of working with up to eight different visualisation projects.

SeeingDataVis

The projects were selected according to a range of different characteristics in order to expose participants to a diverse set of subject matters, chart types, and formats. They were asked to spend up to 40 minutes experiencing as many of the projects as possible but with no fixed time limit – they could stay immersed for longer in some and quickly move on from others should they wish to.

In addition to a capturing their responses about their experiences across different prompts via a written template, we also invited participants to log their perceptions about the projects they had spent time with using a technique called a ‘Talking Mat’ (developed by Murphy and Cameron at the University of Stirling, 2008).

The Talking Mat is a 2×2 grid on which participants could position a representative thumbnail image of the visualisations they had looked at to express their feelings and reactions across two dimensions of assessment: like and learn. The ‘like’ dimension would allow them to express what level of appeal they subjectively felt towards the work (like to the right, dislike to the left). The ‘learn’ dimension was a term to capture the participants impression of whether they felt the visualisation had facilitated understanding for them, whether through new understanding or through confirming existing understanding (learnt to the top, didn’t learn to the bottom).

SampleGrid

We used the terms ‘like’ and ‘learn’ to reflect the everyday language of people who are not expert in data visualisation (inspired by Andrew Sayer’s Why Things Matter to People’ in which he encourages researchers to do this). It also enabled participants to really grasp the type of judgments we were seeking of them.

VISUALISATION RATINGS

Below you see the collection of ratings formed by our focus group participants about the visualisation projects used. The size of the bubble indicates the number of responses where participants shared the same rating location (the circle labels also indicate this value). Note that not all participants managed to find time to explore all visualisations so, with the sequence of visualisations to assess preserved for all involved, the sample size of ratings diminishes as you reach the lower projects in this list. The coloured background regions simply emphasise the sense that the top-right quadrant is the ‘ideal’ location for a project to be rated, the top-left and bottom-right demonstrate a perceived shortcoming in one dimension of judgment, and the bottom-left would reflect a general view of a project being less than effective.

As you will see some projects were perceived more favourably, across both dimensions, than others, but, critically, there were no universal opinions, showing again that the notion of ‘perfect’ in visualisation is a permanently elusive state. One could argue that even if a project was ‘disliked’, if a participant felt they had learnt something (top-left) then that could be seen as a better outcome than a ‘liked’ project that did not appear to facilitate ‘learning’ (bottom-right). Click on the thumbnails to view each project.

1. Your Olympic Athlete Body Match, BBC online

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GRID2-BBCOlympics

2. Migration In The Census, produced for The Migration Observatory, University of Oxford, by Clever Franke

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GRID1-MigrationMap

3. Migration In The News, produced for The Migration Observatory, University of Oxford, by Clever Franke

5-Migration-in-the-News

GRID5-MigrationCorpus

4. The Clicks Don’t Lie, The Metro newspaper

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GRID7-ClicksDontLie

5. The Ebb & Flow of Box Office Receipts, 1986-2008, New York Times

3

GRID3-EbbAndFlow

6. Top Ten Freshwater Consumers, Scientific American magazine

4

GRID4-Water

7. Better Life Index, The Organisation for Economic Co-operation and Development (OECD)

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GRID6-BetterLife

8. Non-UK Born Census Data, Office for National Statistics (ONS)

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GRID8-CensusBubbles

EVALUATING VISUALISATIONS WITH TALKING MATS

The 2×2 grid helped us capture the emotional nature of responses to data visualisations and the relationship between the feelings visualisations evoke (like) and the thoughts they instigate (learn). It captured these precisely through the gradation of locations that it offered. But our use of the Talking Mat also raises this question: do people respond viscerally/affectively to visualisations, or did this method draw out the visceral/affective? Does the method play a role in prioritising certain (visceral/affective) responses, or ways of responding, over others?

The process of positioning the visualisations on the mats was fun for our participants; they enjoyed comparing their responses to other participants and were interested in the differences and similarities. As we had hoped, our use of Talking Mats enabled discussion. By allowing participants to look at their own responses as a whole, it enabled them to evaluate their responses to data visualisations intertextually (that is, in relation to other visualisations). For example, participant Robert, a business analyst, held up his Talking Mat and said ‘I must be in really bad mood today’, because of his positionings of all the visualisations.

Talking Mats do have some limitations though. The language of ‘like/learn’ on the grid framed focus group discussion about what participants liked and learned, perhaps directing them away from other interesting things they might have said. As mentioned, ‘like’ and ‘learn’ are vague and fluid terms, and represent only two possible responses and reactions to data visualisations. It might be hard for participants to classify their responses in these simultaneously narrow and ill-defined terms.

The grids didn’t allow for nuance or ambivalence: participants may have liked bits of visualisations, but not others (they might be interested in the subject matter but not like the aesthetic style), but the Talking Mat didn’t allow them to record this. Participants are pushed to commit to a decision; using Talking Mats might close down the possibility of changing position later.

‘Despite these limitations, as a research team, we feel that Talking Mats offer a practical, relevant and valuable way for gathering useful ratings of the effectiveness of a project as perceived by its user or reader. Because of our positive experience of using Talking Mats during the research, we developed an application/module on our Seeing Data project website, where visitors can record their responses to visualisations and compare these with the responses of other visitors. Additionally, in my capacity as a lecturer and trainer, I have found these approaches valuable for conducting evaluation exercises with my students and delegates.

Our website application has been extremely well received, by our project advisory board, visualisation experts at the University of Oxford and others. We think there is lots of potential to develop it further, so we’re trying to secure funding to develop an open source widget that can be used across visualisations, platforms and devices and which is freely available to all visualisation designers. Watch this space, as we might be asking you to help us beta test it some time soon!

Six questions with… Jan Willem Tulp

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 gave each interviewee a selection of questions from which to choose six to respond. This latest interview is with Jan Willem Tulp, a freelance Data Experience Designer. Thank you, Jan Willem!


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 come from a software engineering background. At least, that’s what I’ve done professionally before I started my freelancing career. But I come from a very creative family (my dad is a visual artist, my brother is a character designer) and I too wanted to become an artist when I was in high school. I even went to a different high school so that art and art history could be part of my exam, which was not offered at my other high school. But I also liked technology a lot. So, ideally I wanted a job where I could work on both, but at that time it was hard to find it. I studied interaction design, with that intention in mind: during my studies we both learned how to program and to make visual designs. So, for me that was the perfect education. However, at that time companies were either looking for graphic designers or programmers, not someone who does both. So, I ended up in programming jobs, as I turned out to be a fairly good programmer. In my spare time I still dabbled in Photoshop, 3D Studio Max, web design instead of web programming, etc. So, when data visualization started to become a commercially interesting opportunity, I took the leap to start my freelancing career as a data experience designer, creating data visualizations.

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 | My guiding principle is actually that you should focus on learning principles. Becoming a specialist also has it’s value, but even if you’re in a niche like data visualization, it’s very valuable to focus on principles. In my case on programming principles, design principles, interaction principles, visualization principles, etc. The thing is, this world, especially the digital data visualization world, is changing rapidly: new technologies, new tools and frameworks are being developed constantly. So, you need to be able to adapt. But principles are much more timeless. If you know what you want to create, then using technology is just the means to create what you have in mind. If you’re too fixed on one type of technology, you may be out of a job soon. So, keep learning new technologies, but more importantly, know your principles, as they will allow you to make the right decisions.

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 a very challenging one. I recently changed my process because of scope creep I experienced. I must say that the risk of scope creep is larger with clients who are not very experienced with data visualization projects than people who are more used to doing these kinds of projects, simply because the former has a much harder time to understand at the beginning of a project what a possible end result can be. And therefore they get a lot of ideas once they see the first concepts. The big ‘problem’ with data visualization is that, if you would compare it to traditional graphic design, you are not entirely free in what you can design; with traditional graphic design, even though you have constraints there too, you are not tied to a dataset. In other words, for a data visualization project, part of the process is figuring out what a good visualization is, taking in to account the goal, target audience, intention, vision, constraints, etc. for the project. And so during the process you will find out what works and what doesn’t. In my experience, if you have too much communication about the visualization itself throughout the process, it is very easy to lose focus, and to extend or drastically change the scope. So, what I now prefer in most of my projects is to have a fixed number of review moments during the project. During this time I will present the progress to them first. This allows me to explain my rationale behind my design decisions, and what I think works and doesn’t work. Then I also explain what my vision is for the next revision and the remainder of the project. And then the client can ask questions and we can discuss changes. If these changes are deviating too much and involve too much work, then I will make a separate estimate for that. Not coming from a graphic design background the concept of ‘client presentation’ was kind of new to me, but I began to realize more and more that data visualization is also very much a design job, and a client presentation can really help having a smoother process (and having fixed revision moments!).

Q4 | As you will fully appreciate, the process of gathering, familiarising with, and preparing data in any visualisation/infographic design task is often a sizeable but somewhat hidden burden – a task that can occupy so much time and effort but is perhaps ultimately invisible to the ultimate viewer. Obviously, pressures during this stage can come in the shape of limited timescales, data that doesn’t quite reveal what you expected and/or substantial data that offers almost too many possibilities. Have you got any stand out pieces of practical advice to share about your practices at this stage?

A4 | I primarily try to solve this in the process itself. Although, you get a better understanding of the data throughout the process, so you can only deal with it to some extent. In my process I first want to do some exploration of the data, even in situations where other people have already done some analysis and know the key insights. I still need to know the structure of the data, the quality, and its potential for a visualization. If insights are not known before the start of the project, then it is important to communicate at the beginning of the project that if you have a certain goal in mind for the projects, or some ideas of tasks a user should be able to do, that after the data exploration phase you may come to the conclusion that the data does not support the goal of the project. The thing is: data is leading in a data visualization project — you cannot make up some data just to comply with your initial ideas. So, you need to have some kind of an open mind and ‘listen to what the data has to say’, and learn what it’s potential is for a visualization. Sometimes this means that a project has to stop if there is too much of a mismatch between the goal of the project and the available data. In other cases this may mean that the goal needs to be adjusted and the project can continue.

Q5 | Whilst there is a great deal of science underpinning the use of colour in data visualisations, 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 | My advice would be to use the HSL color space to come up with colors that match well. There are many tools, online like Kuler from Adobe, but also just a regular desktop color picking tool that offers HSL color picking. The trouble with an RGB color picker is that it is really hard to influence individual components of a color. With HSL you can separately set the Hue, Saturation and Lightness of a color. This is also useful because people distinguish colors primarily based on contrast. So, if you want to make a contrasting color palette, set a few lightness levels but keep the rest the same. Also, if you want some categorical colors, you can leave the lightness the same, and pick some hues that look nice together. But from a visualization point of view, they have more or less the same visual emphasis, which is great if the categories are of equal (semantical) value. A final benefit of using HSL color space is that you can programmatically easily generate colors that would work together. In general, it’s much easier to work with than trying to accomplish the same thing with RGB color pickers.

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 | A key factor here is the target audience. I make drastically different design decisions if the target audience is the general public who will look at the visualization one time and move on, or if the target audience is a group of experts that will work with the visualization on a daily basis. The general public might need some more introduction, guidance and annotations, whereas the experts know what it is about, and they may not need it at al. In my experience it is therefor an additional challenge if your visualization needs to support both the general public and experts. Usually they have different questions and needs, so that’s very tricky to do in a nice way. I don’t think there is a general rule to find the sweet spot, but I do think it has everything to do with your target audience. I do however try to keep being critical of my own work. I constantly ask myself: can I read this label? Is it too small? Are these elements overlapping to much? Is there enough contrast between these elements? etc. I think a lot of work goes into the tweaking of the visualization: getting the basic concept right is one thing, but to make it look good and work well and make it easy to understand involves a lot of tweaking, being critical (and creative!) and also apply general interaction and design rules. But as an advice: keep questioning your own work, constantly, all the time!

Six questions with… Katie Peek

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 gave each interviewee a selection of questions from which to choose six to respond. This latest interview is with Katie Peek, Information Graphics Editor at Popular Science magazine. Thank you, Katie!


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 | My journey was circuitous but ultimately pretty logical. I started as a scientist. Well, technically, my first job out of college was teaching high-school physics. Then I did a Ph.D. in astronomy. Then I shifted into science journalism, doing a second grad degree for that. The design part came last. That I learned while working as a designer at Popular Science, where I commissioned illustrations and laid out pages. But information visualization was a constant throughout this journey, actually. As an astrophysicist, had extraordinarily good-looking charts my published academic papers. And in journalism grad school, I honed my interviewing and writing skills, for sure, but I knew by then that data-viz was where I wanted to be, so I also took every opportunity to expand my practice. I started in the design gig at Popular Science because I had the visual acumen to explain the science clearly to the technical illustrators who work for the magazine. Finally, I pulled all that together and started in my current role, where I’m the editor at Popular Science who conceives and commissions or makes our data visualizations, charts, infographics, and maps.

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 | Our audience is not a captive one. They’re choosing to read our magazine or web site, but if the visualization fails them in some way, they will move on. Fast. So the subject needs to be gripping, or the presentation needs to be enticing, or the text needs to sail flawlessly, or the interface needs to be nearly invisible. If the point is simple, we need to strip away everything that might slow the reader down in understanding it. If the point is complex or subtle, that’s fine—but all the introduction and explanation the reader will need has to be seamlessly easy to follow. Somehow, they have to want to get there. My main job is to figure out how to help them want it.

Q3 | As Information Graphics Editor one of your frequent roles will be to commissioning and coordinating others to contribute designs to the magazine. As you operate at the junction between designers/developers and domain experts, what would you describe as being the most important attributes that help make the experience as effective and efficient as possible?

A3 | Having experience in both. I often make graphics myself, and I do a lot of coding myself, so I know pretty well what’s possible in that realm, even if the stuff I commission is beyond my own technical capabilities. And I’ve got background as a domain expert, so I know just how much insight the person who created the data has to offer. (Imagine me sitting night after night at a telescope, slowly building up a record of a star’s motion over months and years—that was my experience in graduate school. You know the insights and limitations of data so well if you’ve collected it yourself.) Because I speak the language of data, I can talk pretty efficiently with the experts who made it. It doesn’t take them long, even if the subject is new to me, for them to tell me any important caveats or trends. I also think that’s because I approach that conversation as a journalist, where I’m mostly there to listen. I find if you listen, people talk. (It sounds so obvious but it is so important.) But going into that conversation, I have in mind what I want to get out of it—by which I mean, I know where I think I’d like the graphic to go, what trends I think it should highlight. And that lets me ask smart questions. I find if you ask an insightful question, something that makes them say “oh, that’s a good point,” the whole conversation opens up. Now you’re both on the same side, trying to get this great data to the public in an understandable way. I promise I’m not just saying “be brilliant!” I mean that if you’re really listening when you talk to the expert, but you also know what you want out of the graphic, the good questions will come up. And working together toward the same goal makes everything more efficient. The flip side of that is being totally willing to throw out your original idea and go with a different angle if the expert convinces you your first idea is flawed. And actually, in working with designers, pretty much the same thing applies. I know what’s best for my readers, and I go in with a design approach I think will work, but I absolutely respect the designer’s take on the data. I think all that boils down to: have your own ideas first, but then, listen.

Q4 | You will also play a key role in evaluating work that you commission. What are some of the key components of assessment you are making when determining if a design is at the right level to be published?

A4 | Well, I work out what the level and angle should be well in advance of commissioning anything. I spend a lot of time with the data myself, exploring (and cleaning). Once I have a few ideas for trends that would be interesting to highlight, I talk to the people who made the data, other experts in the field, and my fellow Popular Science editors to hone the angle further. Usually I make a sketch of the layout, too. Then, I go to the design studio with all that in hand, but also very willing for them to find a totally new way to visualize the data that I hadn’t thought of. I love when that happens! But as long as I know the single most important point the graphic needs to make—usually, something simple enough that it can become a headline for the page—I can assess whether the commissioned work is at the right level.

Q5 | Do you have any 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 you judge the sweet spot of accessibility – not oversimplified or dumbed-down but still understandable to non-specialists?

A5 | I choose my angles very carefully. We’re a general-interest science and technology magazine, with a fair number of teenage kids and laypeople who read it, so I always keep them in mind when I’m designing something. I picture the reader who’s picking up Popular Science on a newsstand for the first time, and just flipping through it for a minute and a half. I want those readers to get something out of the graphic. But I also want to satisfy what I think of as the retired-engineer part of our audience. These are the readers who are very knowledgeable scientists or science enthusiasts. (We get plenty of mail from these folks if we make a mistake!) My goal is to create a graphic that satisfies both. Even if the subject-matter expert doesn’t necessarily learn something, I still want them to respect what we’re doing and the trends we’ve chosen to highlight. Some of that comes from my background as a professional scientist. As anyone who’s an expert in anything knows, it’s pretty common to pick up a news article about something in your field and feel that while the reporter is technically right, they’ve somehow missed the big picture, the important trends or gaps in our understanding. (They’re more not-wrong than they are right.) So in a sense, I serve two masters: I want the expert to respect our choices, even if they don’t learn anything new, and I want the teenager who’s just encountering the subject for the first time to be able to understand point the graphic is making. I was a high-school physics teacher at earlier in my career, and I still lean on that experience to remember the level and mindset of a novice. (I think my two imaginary readers are very similar to Shan Carter’s Bart-and-Lisa-Simpson framework.) Do I have any advice? Think of the reader—a specific reader, like a friend who’s curious but a novice to the subject and to data-viz—when designing the graphic. That helps. And I rely pretty heavily on that introductory text that runs with each graphic—about 100 words, usually, that should give the new-to-the-subject reader enough background to understand why this graphic is worth engaging with, and sets them up to understand and contextualize the takeaway. (We often run a nerd box as well, that gets into more detail and caveats. That satisfies the retired engineers.) And annotate the graphic itself. If there’s a particular point you want the reader to understand, make it! Explicitly! I often run a few captions typeset right on the viz, with lines that connect them to key elements in the design.

Q6 | From your experience of publishing for print, what are some of the key tips you would offer to people creating visualisation work designed for print output?

A6 | The beauty and frustration of print is the lack of a time dimension. We have to guide readers through the static graphic very carefully, by making conscious graphic design choices that create a visual hierarchy of information. When I’m designing a big piece for print, before I even start to sketch, I begin with the question “If a reader learns just one thing on this page, what should that one thing be?” And then I try to design a graphic that highlights that single takeaway as strongly as possible. Then, with that structure in place, I can add additional layers of information to satisfy the curious reader who engages with the page more completely. It’s a useful framework for any piece, of course—static or interactive—but with print we need to be extra disciplined about it.

Six questions with… Nigel Holmes

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 gave each interviewee a selection of questions from which to choose six to respond. This latest interview is with the legendary Nigel Holmes. Thank you, Nigel!


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 | (For clarification all my answers come from the perspective of journalism/art, rather than data analysis – infographics rather than data visualization – and mostly from print). After 3 years at Hull art school, I went to the Royal College of Art in 1963. The best part of my time at the RCA was not the classes there, but doing summer internships at the Sunday Times Magazine with Brian Haynes, the art director, who was busily breaking down the wall between art (pictures) and edit (words). He was way ahead of his time in that respect—there’s still a wall (albeit less rigid) in most magazines and newspapers, even today. I was studying to be an illustrator, but Brian told me I wasn’t a very good one. He encouraged me to use whatever illustration skills I had to explain things to people. Although it wasn’t called information graphics (or anything) then, that’s when I started in the field, and i’ve never done anything else. Thanks Brian!

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 | (I’m allowing Nigel more to share than one guiding principle. Why? Because its Nigel Holmes and he’s earned the right to write his own rules round here!) Here’s a three part answer:

(a) When making statistical charts, I ask: What would Otto and Gerd do? (WWO&GD?…Otto Neurath and Gerd Arntz. Both long dead now, but I’m on a first name basis with them!) I respect their mantra as far as possible: make charts statistically accountable, but with pictures. In other words, make it possible to SEE the subject of the chart by recognizing the meaning of pictorial icons (rows of little people, or houses or dollars) and being able to COUNT those icons. Neurath (1882-1945) was an early adviser against increasing the size of an icon (a person for example, representing population) to show a comparative increase in the population from a previous, smaller icon.

(b) Start with black and white, and only introduce color when it has relevant meaning.

(c) In general, use color very sparingly.

Q3 | If you had the time and resources (perhaps more skills, new tools) to revisit one project from your past and make improvements to certain features, which project would it be and what would you change?

A3 | There are too many examples from Time Magazine to pick just one. However, I will say I believe that those over-illustrated examples were right for their times (1977-c.1990), and they helped a lot of readers to become engaged with the sometimes dry financial info, and to better understand the (sometimes dry!) articles they accompanied. What would I change? Then: nothing. Now: less illustration. Because readers are more sophisticated, and I don’t need to try so hard to attract their attention.

Q4 | One of the trademarks of your work is your ability to incorporate fun into your infographic designs. This of course helps to generate strong appeal amongst those viewing the work. How do you determine the sweet-spot, when is enough, enough and not too much? What has experience taught you to be able to safeguard against such ideas becoming potentially overly indulgent or gimmicky?

A4 | Early on I was quite severely criticised in US academic circles. (But strangely, never in England where I was actually doing the same kind of stuff for the Radio Times as later appeared in Time.) Trouble was, readers loved it, which meant the editors loved it too. I got fan mail! Sometimes I did overdo the imagery, but in all the charts the data/numbers from the map and chart department’s researchers was always solid. My idea was to attract readers, true, but then to very quickly deliver the facts/numbers.

A confession: sometimes I was thinking of an image first; and that’s the wrong way round, of course. The data must come first. However, I still firmly believe in the power of pictures to convey information (as opposed to completely neutral, abstract shapes). The danger is that one can easily slip into editorialising, into having an opinion about the data…but I can argue that such an approach is also acceptable in certain situations. It depends on the audience. And sometimes “fun” is just not right: death, abortion, guns, etc. Play those straight! (and Play is the wrong word!)

Q5 | What are the main characteristics of infographic/visualisation work you like (done by others)? Particularly, what traits of ‘excellence’ might you see (given your experience and empathy about the challenges with this type of design) that more novice viewers would possibly under appreciate?

A5 | Clean, simple overall design; an ability to read the information/data; clear labelling of the elements. A natural hierarchy of information, starting with a headline/title, followed by a brief description (subheading); logical left-to-right, top-to-bottom numbering in how-to-do-it, or how-it-happened infographics. Color only used to inform.

Q6 | Beyond the world of infographics/visualisation what other disciplines/subject areas/hobbies/interests do you feel introduce valuable new ingredients and inspire ongoing refinement of your techniques?

A6 | Many different thoughts about this:

Look at how other designers solve visual problems (but don’t copy the look of their solutions).

Look at art to see how great painters use space, and oragnize the elements of their pictures.

Look back at the history of infographics. It’s all been done before, and usually by hand!

Draw something with a pencil (or pen…but NOT a computer!) Sketch often: the cat asleep. The view from the bus. The bus.

Personally, I listen to music—mostly jazz—a lot. There are parallels between making music and making art (and I know that’s not a new idea). Differentiating between “voices”—in music that’s instruments, in infographics, it’s graph lines or other graphic marks, or just colors — so that the various voices can be heard as one, or separate from one another, while contributing to the whole sound (in music) or image (infographics). Listening to just the bass, or the drums, or the piano in a jazz trio opens up ways of thinking about how 3 different elements of a data- or infographic might mesh together or be intentionally held apart.

Colour swatch alternatives to green and red

Yesterday I posted on Twitter a set of colour swatch pairings that offer colour-blind safe alternatives to the default greens and reds often used. They are from the chapter in my upcoming book where I talk about the impact of colourblindness and the recommended alternatives to consider using. The colours shown were generated from the wonderful ColorBrewer website.

They seemed popular on Twitter so I thought I should really share them with others who are not on social media. Also, and perhaps most importantly, there was a typo in one of the HEX codes so I need to correct that.

The colours on the LEFT are the alternatives to the default green, the colours on the RIGHT are the alternatives to the default red. The bottom pairing switches red to be a ‘positive’ colour based on the metaphor of red = hot = good, blue = cold = bad.

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Views from ‘Seeing Data’ research (Part 1)

This is the first in a series of three blogposts about the Seeing Data project. The first post is guest written by Helen Kennedy, Professor of Digital Society at the University of Sheffield and director of Seeing Data. Part one discusses some of the findings and what this means for how we think about ‘effective’ visualisations. The next two posts will focus on our ‘talking mats’ method (part two) and what our findings mean for visualisation designers (part three).


Part 1: What we found and what this means for how we think about ‘effective’ visualisations?

WHAT WE DID

Regular readers of this blog will know that Andy and I have been working with Rosey Hill (University of Leeds) and Will Allen (University of Oxford) on Seeing Data, a research project which had two core aims. The first was to improve our understanding of the factors that affect people’s engagement with data visualisations and the second was to think about the implications of our findings for how we define and measure effectiveness in visualisations. We went about our research using this mix of methods:

WHAT WE FOUND

Two points stand out from our research: the profound way that a range of social, human factors affect people’s encounters with visualisations, and the importance of emotions when engaging with datavis.

First, the social/human factors. These are:

Subject matter: Visualisations do not exist in isolation of the subject matter that they represent. When subject matter spoke to participants’ interest, they were engaged; when it didn’t, they weren’t.

“I didn’t like what the topic was. What was the point?”

Source/location: The source of a visualisation is important: it has implications for whether users trust them. When visualisations were encountered in already-trusted media that participants view or read regularly, they were more likely to trust them.

“You see more things wrong or printed wrong in The Sun I think (said one participant who usually reads The Daily Mail.”

Beliefs and opinions: Participants trusted the newspapers they regularly read and therefore trusted the visualisations in these newspapers, because both the newspapers and the visualisations fitted with their views of the world. But it’s not just when visualisations confirm existing beliefs that beliefs matter. Some participants liked data in visualisations that called into question their beliefs, because they provoke and challenge horizons. So beliefs and opinions matter in this way too.

“It was surprising, it was something I hadn’t even thought of and it was like, ‘Wow!’. […] it was something I didn’t expect.”

Time: Engaging with visualisations can be seen as work, or laborious, by people for whom doing so does not come easily. Because of this, having time available is crucial in determining whether people are willing to do this ‘work’.

“Because I don’t have a lot of time to like read things and what have you, so if it’s kept simple and easy to read, then I’m more likely to be interested in it and reading it all and, and you know, to look at it, have a good look at it really.”

Confidence and skills: Participants needed to feel confident in their ability to make sense of a visualisation, in order to be willing to give it a go. This usually meant feeling confident that they had some of these skills (which many participants doubted).

Language skills, to be able to read the text within visualisations (not always easy for people for whom English is not their first language).

Mathematical or statistical skills, for knowing how to read particular chart types or what the scales mean.

“How would you know what that is and what all this is unless you’ve got a certain level of maths skills or English skills as well?”

Visual literacy skills, for understanding meanings attached to the visual elements of datavis.

“It was all these circles and colours and I thought that looks like a bit of hard work; don’t know if I understand.”

Computer skills, to know how to interact with a visualisation on screen, where to input text, and so on.

Critical thinking skills, to be able to ask what has been left out of a visualisation, or what point of view is being prioritised.

Of course, visual elements, style and arrangements also played a role in determining whether participants felt engaged with the visualisations we showed them. These sometimes appealed to participants, but sometimes they were deemed unfamiliar and off-putting.

“It was a pleasure to look at this visual presentation because of the co-ordination between the image and the message it carries.”

“Frustrated. It was an ugly representation to start with, difficult to see clearly, no information, just a mess.”

The quotes in this post also show that all of the factors (subject matter, media location, beliefs and opinions, skills) provoked strong emotional reactions amongst participants. We found that people reacted emotionally to:

During our interviews with month-long diary keepers, we asked what these participants remembered about the visualisations that they had seen during the focus groups. We were struck by the fact that none of them could remember any specific data from the visualisations they looked at there, but they could remember the overall impressions that the visualisation made and, importantly, the way that the visualisations had made them feel. Again, this shows the importance of emotions in engaging with data through visualisations.

This might all seem like common sense, but these are things that don’t get talked about much amongst visualisation designers and researchers. The challenge for such folks is how to translate these findings into practice and research – Andy will say more about this in one of the next two Seeing Data blogs. I’ll end this one with some reflections about what our findings mean for how we should define effectiveness in relation to data visualisations.

DEFINING EFFECTIVENESS

Based on our findings, we think that in the visualisation field, definitions of effectiveness need to be broadened. Such definitions need to take into account the fact that people don’t always look at visualisations with the aim of accessing specific information quickly or remembering it forever. Visualisations in the media that are targeted at non-specialists might aim to persuade, and they all need to attract in order for people to commit time to finding out about the data. This means that effectiveness can be defined in many different ways, including:

What this means for visualisation designers will be the subject of one of the next posts by Andy. In the meantime, if you’re interested in whether visualisations can make a positive contribution to society, given all of these complexities, sign up for this event!:

We aim to publish a longer article which says more about our findings in the next couple of months. Follow @visualisingdata or @seeing_data to find out when it’s out!


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If your visuals deceive, your message deceives

In case you’ve missed the coverage this week, there has been a lot of discussion about an enormously misleading graphic relating to the activities of an organisation called Planned Parenthood. The graphic was presented in US Congress by a Republican Congressman and was created by ‘American United for Life’. Here is the offending specimen:

PlannedParenthood

It doesn’t matter which side of the argument you believe in (I’m not close to the subject so can’t and don’t intend to offer observations about the specific issues at stake) this is a visual lie. End of discussion. Others (MSNBC, Vox, Alberto Cairo) have already covered off the necessary head-shaking commentary about how badly wrong this chart is. People like Emily Schuch have offered a helpful redesign showing what and how it should look.

My main interest in this post is in part to just to spread the word further about examples of bad practice in visualisation/infographic design. Whether intentional or otherwise, a demonstration of appalling standards like this in such a public arena is irresponsible – it has a powerfully deceptive influence. Regardless of the contention of your message, integrity and accuracy should never be compromised either in the visual or beneath the surface in the subject’s data and statistics.

Thanks to the absurd response by the those responsible for the graphic, not only are we witnessing a modern classic demonstration of the impact of visual distortions, we also have some quite outrageous attempts to defend this practice. I encourage you to read this piece on the Federalist website, defending the integrity of the chart’s message and slamming its critics.

Here are some particular lowlights:

Despite this simple reality that Planned Parenthood is Abortion, Inc. – doing one in three abortions today – activist media sites exploded in a furry [sic] of smoke and mirrors, trying to critique the visual rather than discuss the mathematical reality behind it. We should not be surprised that abortion activists in the media were worried about a picture worth a thousand of their angry words.

In a piece posted today at The Federalist, AUL Attorney Anna Paprocki addressed the misinformation in media attacks of the infographic.

The infographic in question is a simple illustration created by Americans United for Life of two trends that have undeniably occurred at Planned Parenthood under Cecile Richards’ leadership: a decrease in cancer screening services and an increase in abortions. There is nothing dishonest about highlighting those opposite trends.

For anyone that is interested, Americans United for Life has produced a huge body of work analyzing Planned Parenthood’s business model, including charts of all kinds.

Of course, there is no need to quibble over an infographic — unless you intend to miss the forest for the trees…

One shudders at the prospect of the charts they’ve made across their body of work. As Alberto commented, as a professional in this field, this kind of exemplar can’t fail to make your blood boil.

Six questions with… Alyson Hurt

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 next year 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 Alyson Hurt, Graphics Editor for NPR. Thank you, Alyson!


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 | I’ll offer two, both of which I’ve heard/seen in the past year and keep coming back to:

1) From Nigel Holmes, on *editing* as the distinction between datavis and infographics: “So instead of asking ‘what’s the data?’ [data visualizers] are trying to humanize their work by asking ‘what’s the story?’ More often than not, that means editing the data.”

2) From Kat Downs of the Washington Post: (paraphrased from a talk she gave at SND) Once you’ve processed your data, made your sketches, identified your story and gotten down to make the final graphic, *start with the headline.* What is the key thing (or things) you want your users to take away from this piece? Defining that up front will help lend focus to your design.

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

A2 | At the beginning, there’s a process of “interviewing” the data — first evaluating their source and means of collection/aggregation/computation, and then trying to get a sense of what they say (and how well they say it) via quick sketches in Excel with pivot tables and charts. Do the data, in various slices, say anything interesting? If I’m coming into this with certain assumptions, do the data confirm them — or refute them?

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 | I work backwards a little bit and ask the question: What is the LEAST this can be? What is the minimum result that will be 1) factually accurate, 2) present the core concepts of this story in a way that a general audience will understand, and 3) be readable on a variety of screen sizes (desktop, mobile, etc.)? And then I judge what else can be done based on the time I have. Certainly, when we’re down to the wire it’s no time to introduce complex new features that require lots of testing and could potentially break other, working features. But it’s not uncommon for there to be lots of minor fiddling up to — and even for a short time after — the piece is published online.

Q4 | What advice would you give to anyone working under pressure of timescales: What are the compromises you are willing to make vs. those you are not? How do you juggle an ambition to innovate within the constraints you face?

A4 | At minimum, every project we produce must be:

  1. 1) Accurate
  2. Understandable to a layperson
  3. Readable/functional on a variety of screen sizes (desktop to mobile)
  4. Up to our editorial/visual standards

So, starting from that baseline, we can consider more or less ambitious treatments based on the material and time we have.

We also have a number of templates and standardized practices for our projects (large-scale and small), which means that when we start a new project, a good bit of the baseline work — the base HTML file and JS libraries, starter code for various types of charts, a hook into Google Spreadsheets, etc. — is already set up. Starting from there buys us a little more time to weigh greater “ambition” with a given project.

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

A5 | Firstly, the sheer variety and quantity of work means that I get to learn a little bit about quite a few things, and there are many opportunities to apply design/technical/workflow lessons learned from one project to the next. I’m constantly learning, and it’s fantastic. Secondly, being in a newsroom, there’s an emphasis on *story* rather than *data*. (Nigel Holmes’s comments on editing re: datavis are quite apt.) This lends a certain amount of focus to how we frame our work — identifying key concepts rather than including every possible thing.

Q6 | As you will fully appreciate, the process of gathering, familiarising with, and preparing data in any visualisation/infographic design task is often a sizeable but somewhat hidden burden – a task that can occupy so much time and effort but is perhaps ultimately invisible to the ultimate viewer. Obviously, pressures during this stage can come in the shape of limited timescales, data that doesn’t quite reveal what you expected and/or substantial data that offers almost too many possibilities. Have you got any stand out pieces of practical advice to share about your practices at this stage?

A6 | My main advice is not to be disheartened when this happens. Sometimes the data don’t show what you thought they would, or they aren’t available in a usable or comparable form. But sometimes that research still turns up threads a reporter could pursue and turn into a really interesting story — there just might not be a viz in it. Or maybe there’s no story at all. And that’s all okay. At minimum, you’ve still hopefully learned something new in the process about a topic, or a data source (person or database), or a “gotcha” in a particular dataset — lessons that can be applied to another project down the line.

Talk slides from second Tableau 2015 webinar

Yesterday I had the pleasure of being invited back by Tableau to deliver a second webinar of this year. The talk was titled ‘Data Visualisation Literacy: Learning to See’ and I discussed some of the findings and reflections from our work on the Seeing Data research project, looking at the implications for readers and creators, and integrated these with some of the notions I’ve been working on with my book about what ‘understanding’ means for visualisation.

The slides are available published on SlideShare. I will be working up a longer blog post discussing more about the findings from this research work over the come weeks and will share other outputs (articles, talks) that the rest of the team have been working hard on.