2 Composition Composition concerns making careful decisions about the physical attributes of, and relationships between, every visual property to ensure the optimum readability and meaning of the overall, cohesive project. Composition is the final layer of your design anatomy, but this should not imply that it is the least important part of your design workflow. Far from it. It is simply that now is the most logical time to think about this, because only at this point will you have established clarity about what content to include in your work. As I explained, this final layer of design thinking, along with colour, is no longer about what elements will be included but how they will appear. Composition is a critical component of any design discipline. The care and attention afforded in the precision of your composition thinking will continue until the final dot or pixel has been considered. Visual assets such as your chart(s), interactive controls and annotations all occupy space. In this chapter you will be judging what is the best way to use space in terms of the position, size and shape of every visible property. In many respects these individual dimensions of thought are inseparable and so, similar to the discussion about annotation, the division in thinking is separated between project- and chart-level composition options: Project composition: defining the layout and hierarchy of the entire visualisation project. Chart composition: defining the shape, size and layout choices for all components within your charts. 2.1 Features of Composition: Project Composition This first aspect of composition design concerns how you might lay out and size all the visual content in your project to establish a meaningful hierarchy and sequence. Content, in this case, means all of your charts, interactive operations and elements of annotation. Where will you put all of this, what size will it be and why? How will the hierarchy (across views) and sequencing (within a view) best fit the space you have to work in? How will you convey the relative importance and provide a connected narrative where necessary? I will shortly run through all the key factors that will influence your decisions, but it is worth emphasising that so much about composition thinking is rooted in common sense and involves a process of iteration towards what feels like an optimum layout. Of course, there are certain established conventions, such as the positioning of titles first or at the top (usually left or centrally aligned). Introductions are inevitably useful to offer early, whereas footnotes detailing data sources and credits might be of least importance, relatively speaking. You might choose to show the main features first, exploiting the initial attention afforded by your audience, or you may wish to build up to this, starting off with contextual content before the big ‘reveal’. F igure 10.1 City of Anarchy 360 361 The hierarchy of content is not just a function of relative position through layout design, it can also be achieved through the relative variation in size of the contents. Just as variation in colour implies significance, so too does variation in size: a chart that is larger than another chart will imply that the analysis it is displaying carries greater importance. The ‘City of anarchy’ infographic demonstrates a clear visual hierarchy across its design. There is a primary focal point of the main subject ‘cutaway’ illustration in the centre with a small thumbnail image above it for orientation. At the bottom there are small supplementary illustrations to provide further information. It is clear through their relative placement at the bottom of the page and their more diminutive stature that they are of somewhat incidental import compared with the main detail in the centre. There are generally two approaches for shaping your ideas about this project-level composition activity, depending on your entry-point perspective: w irefram ing and s to ry b o arding. I profiled these at the start of this part of the book, but it is worth reinforcing their role now you are focusing on this section of design thinking. Wireframing involves sketching the potential layout and size of all the major contents of your design thinking across a single-page view. This might be the approach you take when working on an infographic or any digital project where all the interactive functions are contained within a single-screen view rather than navigating users elsewhere. Any interactive controls included would have a description within the wireframe sketch to explain the functions they would trigger. Figure 10.2 is an early wireframe drawn by Giorgia Lupi when shaping up her early thoughts about the potential layout of a graphic exploring various characteristics of Nobel prizes and laureates between 1901 and 2012. F igure 10.2 Wireframe Sketch 362 Storyboarding is something you would undertake with wireframing if you have a project that will entail multiple pages or many different views and you want to establish a high-level feel for the overall architecture of content, its navigation and sequencing. This would be an approach relevant for linear outputs like discrete sequences in reports, presentation slides or video graphics, or for non-linear navigation around different pages of a multi-faceted interactive. The individual page views included as cells in this big-picture hierarchy will each merit more detailed wireframing versions to determine how their within-page content will be sized and arranged, and how the navigation between views would operate. With both wireframing and storyboarding activities all you are working towards, at this stage, are lowfidelity sketched concepts. Whether this sketching is on paper or using a quick layout tool does not matter; it just needs to capture with moderate precision the essence of your early thinking about the spatial consequence of bringing all your design choices together. Gradually, through further iteration, the precision and finality of your solution will emerge. 2.2 Features of Composition: Chart Composition After establishing your thoughts about the overall layout, you will now need to go deeper in your composition thinking and contemplate the detailed spatial matters local to each chart, to optimise its legibility and meaning. There are many different components to consider. C hart s iz e: Do not be afraid to shrink your charts. The eye can still detect at quite small resolution and with great efficiency chart attributes such as variation in size, position, colour, shape and pattern. 363 This supports the potential value of the small-multiples technique, an approach that tends to be universally loved in data visualisation. As I explained earlier, this technique offers an ideal solution for when you are trying to display the same analysis for multiple categories or multiple points in time. Providing all the information in a simultaneous view means that viewers can efficiently observe overall patterns as well as perform a more detailed inspection. Figure 10.3 provides a single view of a rugby team’s match patterns across the first 12 matches of a season. Each line chart panel portrays the cumulative scoring for the competing teams across the 80 minutes of a match. The 12 match panels are arranged in chronological order, from top left to bottom right, based on the date of the match. F igure 10.3 Example of the Small Multiples Technique The main obstacle to shrinking chart displays is the impact on text. The eye will not cope too well with small fonts for value or category labels, so there has to be a trade-off, as always, between the amount of detail you show and the size you show it. C hart s cales : When considering your chart-scales try to think about how you might use these to tell the viewer something meaningful. This can be achieved through astute choices around the maximum value ranges and also in the choice of suitable intervals for labelling and gridline guides. The maximum values that you assign to your chart scales, informed by decisions around editorial framing, can be quite impactful in surfacing key insights. You may recall the chart from earlier that looked at the disproportionality of women CEO’s amongst the S&P 1500 companies. Figure 10.4 is another graphic on a similar subject, which contextualises the relative progress in the rise of women CEOs amongst the Fortune 500 companies. By setting the maximum y-axis value range to reflect the level at which equality would exist, the resulting empty space emphasises the significant gap that still persists. F igure 10.4 Reworking of ‘The Glass Ceiling Persists’ 364 F igure 10.5 Fast-food Purchasers Report More Demands on Their Time 365 Figure 10.5 shows how the lack of careful thought about your scales can undermine the ease of readability. This chart shows how American adults spend their time on different activities. The analysis is broken down into minutes and so the maximum is set at 1440 minutes in a day. For some reason, the y-axis labels and the associated horizontal gridlines are displayed at intervals of 160 minutes. This is an entirely meaningless quantity of time so why divide the day up into nine intervals? To help viewers perceive the significance and size of the different stacked activities it would have been far more logical to use 60-minute time intervals as that is how we tend to think when dividingour daily schedule. C hart o rientatio n: Decisions about the orientation of your chart and its contents can sometimes help squeeze out an extra degree of readability and meaning from your display. F igure 10.6 Illustrating the Effect of Chart orientation Decisions 366 The primary concern about chart orientation is towards the legibility of labels along the axis. A vertical bar chart, with multiple categories along the x-axis, will present a challenge of making the labels legible and avoiding them overlapping. Ideally you would want to preserve label reading in line with the eye, but you might need to adjust their orientation to either 45° or 90°. My preference for handling this with bar charts is to switch the orientation of the chart and to then have much more compatible horizontal space to accommodate the labels. The meaning of your subject’s data may also influence your choice. While there may have been constraints on the dimension of space in its native setting, Figure 10.6, portraying the split of political parties in Germany, feels like a missed opportunity to display a political axis of the Left and the Right through using a landscape rather than portrait layout. As you saw earlier, the graphic about ‘Iraq’s bloody toll’ (Figure 1.11) uses an inverted bar chart to 367 create a potent display of data that effectively conveys the subject matter, but importantly does so without introducing any unnecessary obstacles in readability. In the previous section I presented a wireframe sketch of a graphic about Nobel prize winners. Figure 10.7 shows the final design. Notice how the original concept of the novel diagonal orientation was accomplished in the final composition, exploiting the greater room that this dimension of space offers within the page. It feels quite audacious to do this in a newspaper setting. F igure 10.7 Nobels no Degrees F igure 10.8 Kasich Could Be The GOP’s Moderate Backstop 368 Figure 10.8, from FiveThirtyEight, rotates the scatter plot by 45° and then overlays a 2 × 2 grid which helps to guide the viewer’s interpretation by making it easier to observe which values are located in each quadrant. It is also used to emphasise the distinction between location in the top and bottom halves of the chart along the axis of popularity, essentially the primary focus of the analysis. Although the LATCH and CHRTS acronyms share some similarities, the application of each concerns entirely different aspects of your design thinking. They are independent of one another. A bar chart, which belongs to the categorical (C) family of charts, could have its data potentially sorted by location, alphabet, time, category or hierarchy. C hart v alue s o rting: Sorting content within a chart is important for helping viewers to find and compare quickly the most relevant content. One of the best ways to consider the options for value sorting comes from using the LATCH acronym, devised by Richard Saul Wurman, which stands for the five ways of organising displays of data: Location, Alphabet, Time, Category or Hierarchy. Location sorting involves sequencing content according to the order of a spatial dimension. This does not refer to sorting data on a map locations are fixed, rather it could be sorting data by geographical spatial relationships (such as presenting data for all the stops along a subway route) or a non-geographical spatial relationship (like a sequence based on the position of major parts of the body from head to toe). 369 You should order by location only when you believe it offers the most logical sequence for the readability of the display or if there is likely to be interest or significance in the comparison of neighbouring values. An example of location sorting is displayed in ‘On Broadway’ (Figure 10.9) on the following page, an interactive installation that stitches together a sequenced compilation of data and media related to 30 metre intervals of life along the 13 miles (21 km) of Broadway that stretches across the length of Manhattan. This continuous narrative offers compelling views of the fluctuating characteristics as you transport yourself down the spine of the city. F igure 10.9 On Broadway Alphabetical sorting is a cataloguing approach that facilitates efficient lookup and reference. Only on rare occasions, when you are especially keen to offer convenient ordering for looking up categorical values, will you find that alphabetical sorting alone offers the best sequence. In Figure 10.10, investigating different measures of waiting times in emergency rooms across the United States, the bar charts are presented based on the alphabetical sorting of each state. This is the default setting but users can also choose to reorder the table hierarchically based on the increasing/decreasing values across the four columns. Data representation techniques that display overlapping connections, like Sankey diagrams, slope graphs and chord diagrams, also introduce the need to contemplate value sorting in the z-dimension: that is, which of these connections will be above and which will be below, and why. Alphabetical sorting might be seen as a suitably diplomatic option should you not wish to imply any ranking significance that would be displayed when sorting by any other dimension. Additionally, there is a lot of sense in employing alphabetical ordering for values listed in dropdown menus as this offers the most immediate way for viewers to quickly find the options they are interested in selecting. F igure 10.10 ER Wait Watcher: Which Emergency Room Will See You the Fastest? 370 Time-based sorting is used when the data has a relevant chronological sequence and you wish to display and compare how changes have progressed over time. In Figure 10.11, you can see a snapshot of a graphic that portrays the rain patterns in Hong Kong since 1990. Each row of data represents a full year of 365/366 daily readings running from left to right. The subject matter and likely interest in the seasonality of patterns make chronological ordering a common-sense choice. F igure 10.11 Rain Patterns Categorical sorting can be usefully applied to a sequence of categories that have a logical hierarchy implied by their values or unique to the subject matter. For example, if you were presenting analysis about football players you might organise a chart based on the general order of their typical positions in a team (goalkeeper > defenders > midfielders > forwards) or use seniority levels as a way to present analysis about staff numbers. Alternatively, if you have ordinal data you can logically sort the values according to their inherent hierarchy. In Figure 10.12, that you saw earlier in the profile of ordinal colours, the columns are sequenced left to right in order from ‘major deterioration’ to ‘major improvement’, to help reveal the balance of treatment outcomes from a sample of psychotherapy clients. F igure 10.12 Excerpt from ‘Pyschotherapy in The Arctic’ 371 Hierarchical sorting organises data by increasing or decreasing quantities so a viewer can efficiently perceive the size, distribution and underlying ranking of values. In Figure 10.13, showing the highest typical salaries for women in the US, based on analysis of data from the US Bureau of Labour Statistics, the sorting arrangement presents the values by descending quantity to reveal the highest rankings values. F igure 10.13 Excerpt from ‘Gender Pay Gap US’ In Figure 10.12 the bubbles in each column do not need to be coloured as their position already provides a visual association with the ‘deterioration’ through to ‘improvement’ ordinal categories. The attribute of colour, specifically, can therefore be considered redundant encoding. However, you might still choose to include this redundancy if you believed it aided the immediacy of association and distinction. In this case, the chart was part of a larger graphic that employed the same colour associations across several different charts and therefore it made sense to preserve this association. 2.3 Influencing Factors and Considerations 372 You are now familiar with the array of various aspects of composition thinking. At this point you will need to weigh up your decisions on how you might employ these in your own work. Here are some of the specific factors to bear in mind. Formulating Your Brief F o rm at: Naturally, as composition is about spatial arrangement, the nature and dimensions of the canvas you have to work with will have a fundamental bearing on the decisions you make. There are two concerns here: what will be the shape and size of the primary format and how transferable will your solution be across the different platforms on which it might be used or consumed? Another factor surrounding format concerns the mobility of viewing the work. If the form of your output enables viewers to easily move a display or move around a display in a circular plane (such as looking at a printout or work on a tablet) this means that issues such as label orientation can be largely cast aside. If your output is going to be consumed in a relatively fixed setting (desktop/laptop or via a presentation) the flexibility of viewing positions will be restricted. Working With Data D ata ex am inatio n: Not surprisingly, the shape and size of your data will directly influence your chart composition decisions. When discussing physical properties in Chapter 4, I described the influence of quantitative values with legitimate outliers distorting ideal scale choices. One solution for dealing with this is to use a non-linear logarithmic (often just known as a ‘log’) scale. Essentially, each major interval along a log scale increases the value at that marked position by a factor of 10 (or by one order of magnitude) rather than by equal increments. In Figure 10.14, looking at ratings for thousands of different board games, the x-axis is presented on a log scale in order to accommodate the wide range of values for the ‘Number of ratings’ measure and to help fit the analysis into a square-chart layout. Had the x-axis remained as a linear scale, to preserve a square layout would have meant squashing values below 1000 into such a tightly packed space that you would hardly see the patterns. Alternatively, a wide rectangular chart would have been necessary but impractical given the limitations of the space this chart would occupy. I have great sympathy for the challenges faced by designers like Zimbabwe-based Graham van de Ruit, when working on typesetting a book titled Millions, Billions, Trillions: Letters from Zimbabwe, 2005−2009 in 2014. The book was all text, apart from one or two tables. One of the tables of data supplied to Graham showed Zimbabwe’s historical monthly inflation rates, which, as you can see (Figure 10.15), included some incredibly diverse values. I love the subtle audacity of Graham’s solution. Even though it is presented in tabular form there is a strong visual impact created by allowing the sheer spatial consequence of the exceptional mid-2008 numbers to cause the awkward widening of the final column. I think this makes the point much more effectively than a chart might, in this case. F igure 10.14 The Worst Board Games Ever Invented 373 F igure 10.15 From Millions, Billions, Trillions: Letters from Zimbabwe, 2005−2009 374 ‘I thought that a graph might be more effective, but I quickly realised that the scale would be a big challenge… The whole point of graphing would have been to show the huge leap in 2008, something that I felt the log scale would detract from and was impractical with the space constraints. I also felt that a log scale might not be intuitive to the target audience.’ Graham van d e R u it, Ed itorial and Information Desig ner Establishing Your Editorial Thinking Angles : The greater the number of different angles of analysis you wish to cover in your work, the greater the challenge will be to seamlessly accommodate the resulting chart displays in one view. The more content you include increases the need to contemplate reductions in the size of charts or a nonsimultaneous arrangement, perhaps through multi-page sequences with interactive navigation. In defining your editorial perspectives, you will have likely established some sense of hierarchy that might inform which angles should be more prominent (regarding layout position and size) and which less so. There might also be some inherent narrative binding each slice of analysis that lends itself to being presented in a deliberate sequence. Data Representation C hart ty pe cho ice: Different charts have different spatial consequences. A treemap generally occupies far more space than a pie chart simply because there are many more ‘parts’ being shown. A polar chart is circular in shape, whereas a waffle chart is squared. With each chart you include you will 375 have a uniquely shaped piece that will form part of the overall jigsaw puzzle. Inevitably there will be some shuffling of content to find the right size and placement balance. The table in Figure 10.16 summarises the main chart structures and the typical shapes they occupy. This list is based only on the charts included in the Chapter 6 gallery but still offers a reasonable compilation of the main structures. These are ordered in descending frequency as per the distribution of the different structures of charts in the gallery. F igure 10.16 List of chart structures Trustworthy Design C hart- s cale o ptim is atio n: Decisions about chart scales concern the maximum, minimum and interval choices that ensure integrity through the representation as well as optimise readability. Firstly, let’s look at decisions around minimum values used on the quantitative value axis, known as the origin, and the reasons why it is not OK for you to truncate the axis in methods like the bar chart. Any data representation where the attribute of size is used to encode a quantitative value needs to show the full, true size, nothing more and nothing less. The origin needs to be zero. When you truncate a bar chart’s quantitative value axis you distort the perceived length or height of the bar. Visualisers are often tempted to crop axis scales when values are large and the differences between categories are small. However, as you can see in Figure 10.17, the consequence is that it creates the impression of highly noticeable relative difference between values when the absolute values do not support this. 376 F igure 10.17 Illustrating the Effect of Truncated Bar Axis Scales The single instance in which it is remotely reasonable to truncate an axis would be if you had a main graphic which effectively offered a thumbnail view of the whole chart for orientation positioned alongside a separate associated chart (similar to that on the right). This separate chart might have a truncated axis that would provide a magnified view of the main chart, showing just the tips of the bar, to help viewers see the differences close up. In contrast to the bar chart, a line chart does not necessarily need always to have a zero origin for the value axis (normally the y-axis). A line chart’s encoding involves a series of connected lines (marks) joining up continuous values based on their absolute position along a scale (attribute). It therefore does not encode quantitative values through size, like the bar chart does, so the truncation of a value axis will not unduly impact on perceiving the relative values against the scale and the general trajectory. For some data contexts the notion of a zero quantity might be impossible to achieve. In Figure 10.18, showing 100m sprint record times, no human is ever going to be able to run 100m in anywhere near zero seconds. Times have improved, of course, but there is a physical limit to what can be achieved. To show this analysis with the y-axis starting from zero would be unnecessary and even more so if you plotted similar analysis for longer distance races. However, if you were to plot the 100m results and the 400m results on the same chart, you would need to start from zero to enable orientation of the scale of comparable values. This sense of comparable scale is missing from the next chart, whereby including the full quantitative value range down to zero would be necessary to perceive the relative scale of attitudes towards same-sex marriage. The chart’s y-axis appears to start from an origin of 20 but as we are looking at part-to-whole analysis, the y-axis should really be displayed from an origin of zero. The maximum doesn’t need to go up to 100%, the highest observed value is fine in this case, but it could be interesting to set the maximum range to 100% in order to create a similar sense of the gap to be bridged before 100% of respondents are in agreement. F igure 10.18 Excerpt from ‘Doping under the Microscope’ 377 F igure 10.19 Record-high 60% of Americans Support Same-sex Marriage As pect ratio s : The aspect ratio of a line chart, as derived from the height and width dimensions of the chart area, can have a large impact on the perceived trends presented. If the chart is too narrow, the steepness of connections will be embellished and look more significant; if the chart is stretched out too wide, the steepness of slopes will be much more dampened and key trends may be somewhat disguised. There is no absolutely right or wrong approach here but clearly there is a need for sensitivity to avoid the possibility of unintended deception. A general rule of thumb is to seek a chart area that enables the average slope to be presented at 45°, though this is not something that can be easily and practically applied, especially as there are many other variables at play, such as the range of quantitative and time values and the scales being used. My advice is just to make a pragmatic judgement by eye to find the ratio that you think is faithful to the significance of the trends in your data. M apping pro jectio ns : One of the most contentious matters in the visual representation of data relates to thematic mapping and specifically to the choice of map projection used. The Earth is not flat (hopefully no contention there, otherwise this discussion is rather academic), yet the dominant form 378 through which maps are presented portrays the Earth as being just that. Features such as size, shape and distance can be measured accurately on Earth but when projected on a flat surface a compromise has to occur. Only some of these qualities can be preserved and represented accurately. I qualify this with ‘dominant’ because, increasingly, advances in technology (such as WebGL) mean we can now interact with spherical portrayals of the Earth within a 2D space. There are lots of exceptionally complicated calculations attached to the variety of spatial projections. The main things you need to know about projection mapping are that: every type of map projection has some sort of distortion; the larger the area of the Earth portrayed as a flat map, the greater the distortion; there is no single right answer – it is often about choosing the least-worst case. Thematic mapping (as opposed to mapping spatially for navigation or reference purposes) is generally best portrayed using mapping projections based on ‘equal-area’ calculations (so the sacrifice is more on the shape, not the size). This ensures that the phenomena per unit – the values you are typically plotting – are correctly represented by proportion of regional area. For choosing the best specific projection, in the absence of perfect, damage limitation is often the key: that is, which choice will distort the spatial truth the least given the level of mapping required. There are so many variables at play, however, based on the scope of view (world, continent, or country/sub-region), the potential distance from the equator of your region of focus and whether you are focusing on land, sea or sky (atmosphere), to name but a few. As with many other topics in this field, a discussion about mapping projections requires a dedicated text but let me at least offer a brief outline of five different projections to begin your acquaintance: Many tools that offer rudimentary mapping options will tend to only come with a default (non-adjustable) projection, often the Mercator (or Web Mercator). The more advanced geospatial analysis tools will offer pre-loaded or add-in options to broaden and customise the range of projections. Hopefully, in time, an increasing range of the more pragmatic desktop tools will enhance projection customisations. F igure 10.20 A Selection of Commonly Deployed Mapping Projections 379 Accessible Design G o o d des ign is uno b trus iv e: One of the main obstructions to facilitating understanding through a visualisation design is when viewers are required to rely on their memory to perform comparisons between non-simultaneous views. When the composition layout requires viewers to flick between pages or interactively generated views, they have to try store one view in their mind and then mentally compare that against the live view that has arrived on the screen. This is too hard and too likely to fail given the relatively weak performance of the brain’s working memory. Content that warrants direct comparison should be enabled through proximity to and alignment with related items. I mentioned in the section on animation that if you 380 want to compare different states over time, rather than see the connected system of change, you will need to have access to the ‘moment’ views simultaneously and without a reliance on memory. ‘Using our eyes to switch between different views that are visible simultaneously has much lower cognitive load than consulting our memory to compare a current view with what was seen before.’ Tamara M u nzner taken from Visua liza tion Ana lysis a nd Design Elegant Design ‘I’m obsessed with alignments. Sloppy label placement on final files causes my confidence in the designer to flag. What other details haven’t been given full attention? Has the data been handled sloppily as well? … On the flip side, clean, layered and logically built final files are a thing of beauty and my confidence in the designer, and their attention to detail, soars.’ Jen C hristiansen, Grap hics Ed itor at Scientific America n U nity : As I discussed with colour, composition decisions are always relative: an object’s place and its space occupied within a display immediately create a relationship with everything else in the display. Unity in composition provides a similar sense of harmony and balance between all objects on show as was sought with colour. The flow of content should feel logical and meaningful. The enduring idea that elegance in design is most appreciated when it is absent is just as relevant with composition. Look around and open your eyes to composition that works and does not work, and recognise the solutions that felt effortless as you read them and those that felt punctured and confusing. This is again quite an elusive concept and one that only comes with a mixture of common-sense judgement, experience and exposure to inspiration from elsewhere. T ho ro ughnes s : Precision positioning is the demonstration of thoroughness and care that is so important in the pursuit of elegance. You should aim to achieve pixel-perfect accuracy in the position and size of every singleproperty. Think of the importance of absolute positioning in the context of detailed architectural plans that outline the position of every fine detail down to power sockets, door handles and the arc of a window’s opening manoeuvre. A data visualiser has to commit to ultimate precision and consistency because any shortcomings will be immediately noticeable and will fundamentally impact on the function of the work. If you do not feel a warm glow from every emphatic snap-to-grid resize operation or upon seeing the results of a mass alignment of page objects, you are not doing it right. (Honestly, I am loads of fun to be around. Summary: Composition P ro ject co m po s itio n defines the layout and hierarchy of the entire visualisation project and may include the following features: Visual hierarchy – layout: how to arrange the position of elements? Visual hierarchy – size: how to manage the hierarchy of element sizes? Absolute positioning: where specifically should certain elements be placed? C hart co m po sitio n defines the shape, size and layout choices for all components within your charts and 381 may include the following features: Chart size: don’t be afraid to shrink charts, so long as any labels are still readable, and especially embrace the power of small multiple. Chart scales: what are the most meaningful range of values given the nature of the data? Chart orientation: which way is best? Chart value sorting: consider the most meaningful sorting arrangement for your data and editorial focus, based on the LATCH acronym. Influencing Factors and Considerations Formulating the brief: what space have you got to work within? Working with data: what is the shape and size of your data and how might this affect your chart design architecture? Establishing your editorial thinking: how many different angles (charts) might you need to include? Is there any specific focus for these angles that might influence a sequence or hierarchy between them? Data representation: any chart has a spatial consequence – different charts have different structures that will create different dimensions that will need to be accommodated. Trustworthy design: the integrity and meaning of your chart scale, chart dimensions, and (for mapping) your projection choices areparamount. Accessible design: remember that good design is unobtrusive – if you want to facilitate comparisons between different chart displays these ideally need to be presented within a simultaneous view. Elegant design: unity of arrangement is another of the finger-tip sense judgments but will be something achieved by careful thinking about the relationships between all components of your work. Tips and Tactics You will find that as you reach the latter stages of your design process, the task of nudging things by fractions of a pixel and realigning features will dominate your attention. As energy and attention start to diminish you will need to maintain a commitment to thoroughness and a pride in precision right through to the end! Empty space is like punctuation in visual language: use it to break up content when it needs that momentary pause, just as how a comma or full stop is needed in a sentence. Do not be afraid to use empty space more extensively across larger regions as a device to create impact. Like the notes not played in jazz, effective visualisation design can also be about the relationship between something and nothing. 382 383 Part D Developing Your Capabilities 384 3 Visualisation Literacy This final chapter explores some of the important ingredients and tactics that will help you continue to develop and refine your data visualisation literacy. By definition, literacy is the ability to read and write. Applied to data visualisation, this means possessing the literacy both to create visualisations (write) and consume them (read). Data visualisation literacy is increasingly an essential capability regardless of the domain in which we work and the nature of our technical skills. Just as computer literacy is now a capability that is expected of everyone, one can imagine a time in the not-too-distant future when having data visualisation capabilities will be viewed as a similarly ‘assumed’ attribute across many different roles. In exploring the components of visualisation literacy across this chapter we will look at two sides of the same coin: the competencies that make up the all-round talents of a visualiser but, first, the tactics and considerations required to be an effective and efficient viewer of data visualisation. 3.1 Viewing: Learning to See Learning how to understand a data visualisation, as a viewer, is not a topic that has been much discussed in the field until recently. For many the idea that there are possible tactics and efficient ways to approach this activity is rarely likely to have crossed their mind. We just look at charts and read them, don’t we? What else is there to consider? Many of the ideas for this section emerged from the Seeing Data visualisation literacy research project (seeingdata.org) on which I collaborated. The fact is we are all viewers. Even if you never create a visualisation again you will always be a viewer and you will be widely exposed to different visual forms of data and information across your daily life. You cannot escape them. Therefore, it seems logical that optimising visualisation literacy as a consumer is a competency worth developing, Let’s put this into some sort of context. As children we develop the ability to read numbers and words. These are only understandable because we are taught how to recognise the association between numeric digits and their representation as numbers and the connection between alphabetical characters with letters and words. From there we begin to understand sentences and eventually, as we build up a broader vocabulary, we acquire the literacy of language. This is all a big effort. We are not born knowing a language but we are born with the capacity to learn one. Beyond written language, something as simple and singular as, for example, the Wi-Fi symbol is now a universally recognised form of visual language but one that only exists in contemporary culture. For millions of people today, this symbol is a signal of relief and tangible celebration – ‘Thank God, Wi-Fi is available here!’ The context of the use of this symbol would have meant nothing to people in the 1990s: it is a symbol of its time and we have learnt to recognise its use and understand its meaning. Across all aspects of our lives, there are things that once seemed complicated and inaccessible but are now 385 embedded within us as automatic competencies: driving a car, using a keyboard, cooking a meal. I often think back to growing up as a kid in the 1980s and my first (functioning) computer, the mighty Commodore 64 (C64). One of the most famous games in the UK from this period was Daley Thompson’s Decathlon. Of particular nostalgic fame was the brutally simple operation of maniacally waggling the single joystick arm left and right to control the running events (if memory serves me correctly, the single button came into use when there were hurdles to jump over). Consider the universally and immediately understandable control configuration of that game with the frankly ludicrous number of options and combinations that exist on the modern football games, such as the FIFA series on contemporary consoles like the Xbox or PS4. The control combinations required to master the array of attacking moves alone require an entire page of instruction and remarkable levels of finger dexterity. Yet young kids today are almost immediate masters of this game. I should know – I have been beaten by some awfully young opponents. It hurts. But they have simply utilised their capacity to learn through reading and repeated practice. As discussed in Chapter 1 when looking at the principle of ‘accessible’ design, many data visualisations will be intended – and designed – for relatively quick consumption. These might be simple to understand and offer immediately clear messages for viewers to easily comprehend. They are the equivalent of the C64 joystick controls. However, there will be occasions when you as a viewer are required to invest a bit more time and effort to work through a visualisation that might be based on subject matter or analysis of a more complex nature, perhaps involving many angles of analysis or numerous rich features of interactivity. This is the equivalent prospect of mastering the Xbox controls. Without having the confidence or capability to extract as much understanding from the viewing experience as possible and doing so as efficiently as possible, you are potentially missing out. ‘Though I consider myself a savvy consumer of bar charts, line graphs, and other traditional styles of data display, I’m totally at sea when trying to grasp what’s going on in, say, arc diagrams, circular hierarchy graphs, hyperbolic tree charts, or any of the seemingly outlandish visualisations … I haven’t thought much about this flip side, except that I do find I now view other people’s visualisations with a more critical eye.’ M arcia Gray, Grap hic Desig ner As viewers, we therefore need to acknowledge that there might be a need to learn and a reward from learning. We should not expect every type of visualisation to signpost every pearl of insight that is relevant to us. We might have to work for it. And we have to work for it because we are not born with the ability or the right to understand everything that is presented to us. Few of us will have ever been taught how to go about effectively consuming charts and graphics. We might be given some guidance on how to read charts and histograms, maybe even a scatter plot, if we study maths or the sciences at school. Otherwise, we get by. But ‘getting by’ is not really good enough, is it? Even if, through exposure and repetition, we hope gradually to become more familiar with the most common approaches to visualising data, this does not sufficiently equip us with the breadth and range of literacy that will be required. I mentioned earlier the concept, proposed by Daniel Kahneman, of System 1 and System 2 thinking. The distinctions of these modes of thought manifest themselves again here. Remember how System 1 was intuitive and rapid whereas System 2 was slow, deliberate and almost consciously undertaken? For example, you are acutely aware of thinking when trying to run a mathematical calculation through your mind. That is System 2 at work. In part, due to the almost hyperactive and instinctive characteristics of System 1, when there is a need 386 for System 2 thinking to kick into action, we might try to avoid whatever that activity entails. We get lazy and resort to shortcut solutions or decisions based on intuition. System 1 almost persuades System 2 to sit back and let it look after things. Anything to avoid having to expend effort thinking deeply and rationally. The demands of learning anything new or hard can trigger that kind of response. It is understandable that somebody facing a complex or unfamiliar visualisation that needs learning might demonstrate antipathy towards the effort required to learn. Of course, there are other factors involved in learning, such as having the time, receiving assistance or tuition, and recognising the incentive. These are all enablers and therefore their absence can create obstacles to learning. Without assistance from the visualiser, viewers are left to fend for themselves. The role of this book has primarily been to try to raise the standard of the design choices that visualisers make when creating visualisations. Visualisers do not want to obstruct viewers from being able to read, interpret and comprehend. If work is riddled with design errors and misjudgements then viewers are naturally going to be disadvantaged. However, even with a technically perfect design, as I explained in the definition section of the first chapter, we as visualisers can only do so much to control this experience. There are things we can do to make our work as accessible as possible, but there is also a partial expectation of the viewer to be willing to make some effort (so long as it is ‘proportional’) to get the most out of the experience. The key point, however, is that this effort should be rewarded. Many of the visualisations that you will have seen in this book, particularly in Chapter 6, may have been unfamiliar and new to you. They need learning. Your confidence in being able to read different types of charts is something that will develop through practice and exposure. It will be slow and deliberate at first, probably a little consciously painful, but then, over time, as the familiarity increases and the experiential benefits kick in, perceiving these different types of representations will become quite effortless and automatic. System 2 thinking will then transform into a reliably quick form of System 1 thinking. Over the next few pages I will present a breakdown of the components of effectively working with a visualisation from the perspective of being a viewer. This demonstration will provide you with a strategy for approaching any visualisation with the best chance of understanding how to read it and ensure you gain the benefit of understanding from being able to read it. To start with I will outline the instinctive thoughts and judgements you will need to make before you begin working with a visualisation. I will then separate the different features of a visualisation, first by considering the common components that sit outside the chart and then some pointers for how to go about perceiving what is presented inside the chart. This part will also connect with the content included in the chart type gallery found in Chapter 6 describing how to read each unique chart type. Finally, I will touch on the attributes that will lead you, in the longer term, to becoming a more sophisticated viewer. It is important to note that not all data visualisation and infographic designs will have all the design features and apparatus items that I describe over the next few sections. There may be good reasons for this in each case, depending on the context. However, if you find there are significant gaps in the work you are consuming, or features of assistance have been deployed without real care or quality, that would point to flawed design. In these cases the viewer is not really being given all the assistance required: the visualiser has failed to facilitate understanding. F igure 11.1 The Pursuit of Faster 387 To illustrate this process I will refer to a case-study project titled ‘The Pursuit of Faster: Visualising the Evolution of Olympic Speed’. As the title suggests, the focus of this work was to explore how results have changed (improved or declined) over the years of the Olympics for those events where speed (as measured by a finishing time) was the determinant of success. Before You Begin Here are some of the instinctive, immediate thoughts that will cross your mind as soon as you come face to face with a data visualisation. Once again, these are consistent with the impulsive nature of the System 1 thoughts mentioned earlier. S etting: Think about whether the setting you are in is conducive to consuming a visualisation at that moment in time. Are you under any pressure of time? Are you on a bumpy train trying to read this on your smartphone? V is ual appeal? In this early period of engaging with the work you will be making a number of rapid judgements to determine whether you are ‘on board’. One of the ingredients of this is to consider whether the look and feel (the ‘form’) of the visualisation attract you and motivate you to want to 388 spend time with it. R elev ance? In addition to the visual appeal, the second powerful instinct is to judge whether the subject matter interests you. You might have decided you are on board with your instinctive reaction to the visuals but the key hurdle is whether it is even interesting or relevant to you. Ask yourself if this visualisation is going to deliver some form of useful understanding that confirms, enlightens or thrills you about the topic. If you respond positively to both those considerations you will likely be intent on continuing to work with the visualisation. Even if you are just positive about one of these factors (form or subject) you will most probably persevere despite the indifference towards the other. If your thoughts are leaning towards a lack of interest in both the relevance of this work and its visual appeal then, depending on circumstances, your tolerance may not be high enough to continue and it will be better to abandon the task there and then. Initial s can? It is inevitable that your eyes will be instinctively drawn to certain prominent features. This might be the title or even the chart itself. You may be drawn to a strikingly large bar or a sudden upward rise on a line chart. You might see a headline caption that captures your attention or maybe some striking photo imagery. It is hard to fight our natural instincts, so don’t. Allow yourself a brief glance at the things you feel compelled to look at – these are likely the same things the visualiser is probably hoping you are drawn to. Quickly scanning the whole piece, just for an initial period of time, gives you a sense of orientation about what is in store. In ‘The Pursuit of Faster’ project you might find yourself only drawn to this if you have a passing interest in the Olympics and/or the history of athletic achievement. On the surface, the visuals might look quite analytical in nature, which might turn some people off. The initial scan probably focuses on elements like the Olympic rings and the upward direction of the lines in the chart which might offer a degree of intrigue, as might the apparent range of interactive controls. Outside the Chart Before getting into the nuts and bolts of understanding the chart displays, you will first need to seek assistance from the project at large to understand in more detail what you are about to take on and how you might need to go about working with it. The Proposition Considering the proposition offered by the visualisation is about determining how big a task of consuming and possibly interacting you have ahead of you. What is its shape, size and nature? F o rm at: Is it presented in a print, physical or digital format and what does this make you feel about your potential appetite and the level of your engagement? Is it static or interactive and what does this present in terms of task? If it is a static graphic, how large and varied is the content – is it a dense display with lots of charts and text, or quite a small and compact one? Does the sequence of content appear logical? If it is interactive, how much potential interactivity does there appear to be – are there many buttons, menus, options, etc.? Where do the interactive events take you? Are there multiple tabs, pages or layers beneath this initial page? Have a click around. S hape and s iz e: Do you think you will probably to have to put in a lot of work just to scan the surface insights? Is there a clear hierarchy or sequence derived through the size and position of elements 389 on the page? Does it feel like there is too much or too little content-wise? If the project layout exceeds the dimensions of your screen display, how much more scrolling or how many different pages will you have to look through to see the whole? This initial thinking helps you establish how much work and effort you are going to be faced with to explore the visualisation thoroughly. In ‘The Pursuit of Faster’ project, it does not feel like there is too much content and all the possible analysis seems to be located within the boundaries of the immediate screen area. However, with a number of different selectable tabs, interactive options and collapsible content areas lurking beneath the surface, it could be more involving than it first appears. What’s this Project About? Although you have already determined the potential relevance of this subject matter (or otherwise) you will now look to gain a little more insight into what the visualisation is specifically about. T itle: You will have probably already glanced at the title but now have another look at it to see if you can learn more about the subject matter, the specific angle of enquiry or perhaps a headline finding. In the sample project (Figures 11.2 and 11.3), the presence of the Olympic rings logo on the right provides an immediate visual cue about the subject matter, as you might have observed in the initial scan. The title, ‘The Pursuit of Faster’, is quite ambiguous, but as the supporting subtitle reveals, ‘Visualising the evolution of Olympic speed’ helps to explain what the visualisation is about. F igure 11.2 Excerpt from ‘The Pursuit of Faster’ S o urce: If it is a web-based visualisation the URL is worth considering. You might already know where you are on the Web, but if not you can derive plenty from the site on which this project is being hosted. An initial sense about trust in the data, the author and the possible credibility of insights can be drawn from this single bit of information. This particular project is hosted on my website, visualisingdata.com, and so may not carry the same immediate recognition that an established Olympics or sport-related site might command. There is nothing provided in the main view of the visualisation that informs the viewer who created the project. Normally this might have been detailed towards the bottom of the display or underneath the title, but in this case viewers have to click on a ‘Read more…’ link to find this out. If there are no details provided about the author/visualiser, as a viewer, this anonymity might have any affect on your trust in the work’s motives and quality. Intro ductio n: While some visualisation projects will be relatively self-explanatory, depending on the familiarity of the audience with the subject matter, others will need to provide a little extra guidance. The inclusion of introductory text will often help ‘set the scene’, providing some further background about the project. If, as the viewer, the introduction fails to equip you with all the information you feel you need about the visualisation, then the visualiser has neglected to include all the assistance that might be necessary. In ‘The Pursuit of Faster’ project, the introductory text provides sufficient initial information about the 390 background of the project based on a curiosity about what improvements in speed have been seen throughout the history of the Olympics. As mentioned, there is a ‘Read more…’ link to find more information that was perhaps too much to include in the main opening paragraph. This includes a comprehensive ‘How to use it’ guide providing a detailed account about the content and role of each section of the project, including advice on how to read the chart and utilise the interactive features. F igure 11.3 Excerpt from ‘The Pursuit of Faster’ What Data? Any visualisation of data should include clear information to explain the origin of the data and what has been done with it in preparation for its visual portrayal. D ata s o urce: Typically, details of the data source will be located in the introduction, as a footnote beneath a chart or at the bottom of a page. It is important to demonstrate transparency and give credit to the origin of your data. If none is provided, that lowers trust. D ata handling: It is also important to explain how the data was gathered and what, if any, criteria were applied to include or exclude certain aspects of the subject matter. These might also mention certain assumptions, calculations or transformations that have been undertaken on the data and are important for the reader to appreciate. In ‘The Pursuit of Faster’ project, the link you saw earlier to ‘Read more …’ provides details about the origin of the data and the fact that it only includes medal winners from summer Olympic events that have a timebased measure. What Interactive Functions Exist? 391 As you have seen in Chapter 8, interactive visualisations (typically hosted on the Web or in an app) aim to provide users with a range of features to interrogate and customise the presentation of the data. Sometimes, interactive features are enabled but not visible on the surface of a project. This might be because visualisers feel that users will be experienced enough to expect certain interactive capabilities without having to make these overly conspicuous by labelling or signposting their presence. For example, rather than show all the value labels on a bar chart you might be able to move the mouse over a bar of choice and a pop-up will reveal the value. The project might not tell you that you can do this, but you may intuitively expect to. Always fully explore the display with the mouse or through touch in order to gain a sense of all the different visible and possibly invisible ways you can interact with the visualisation. In ‘The Pursuit of Faster’ project (Figure 11.4), you will see multiple tabs at the top, one for each of the four sports being analysed. Clicking on each one opens up a new set of sub-tabs beneath for each specific event within the chosen sport. F igure 11.4 Excerpt from ‘The Pursuit of Faster’ Choosing an event will present the results in the main chart area (Figure 11.5). Once a chart has loaded up, you can then filter for male/female and also for each of the medals using the buttons immediately below the chart. Within the chart, hovering above a marker on the chart will reveal the specific time value for that result. Clicking on the marker will show the full race results and offer further analysis comparing those results with the all-time results for context. F igure 11.5 Excerpt from ‘The Pursuit of Faster’ 392 Finally, the collapsible menus below the chart show further detailed analysis and comparisons within and between each sporting event (Figure 11.6). The location of this implies that it is of lower relative importance than the chart or maybe is a more detailed view of the data. F igure 11.6 Excerpt from ‘The Pursuit of Faster’ Inside the Chart Now you have acquainted yourself with the key features of a visualisation outside the chart, the next stage is to start the process of deriving understanding from the chart. The process of consuming a chart varies considerably between different chart types: the approach to drawing observations from a chart showing trends over time is very different from how you might explore a map-based visualisation. The charts I profiled in Chapter 6 were each accompanied by detailed information on the type of observations you should be looking to extract in each case. In Chapter 1 you learnt how there were three elements involved in the achievement of understanding a chart: perceiving, interpreting and comprehending. Let’s work through these steps by looking at the analysis shown for the 100m Finals. P erceiv ing: The first task in perceiving a chart is to establish your understanding about the role of every aspect of the display. Here we have a line chart (Figure 11.7) which shows how quantitative values for categories have changed over time. This chart is structured around a horizontal x-axis showing equal intervals from the earliest Olympics (1896) on the left through to the most recent (2012) on the right, although the latest values in the data only seem to reach 2008. Depending on your interest in this topic, the absence of data for the more recent Olympics may undermine your sense of its completeness and 393 representativeness. F igure 11.7 Excerpt from ‘The Pursuit of Faster’ The vertical y-axis is different from what you might normally see for two reasons. Firstly, it moves downwards below the x-axis (rather than upwards, as is more common), and secondly, there is no labelling, either of the variable plotted or of scale values. I can see that the encoding is formed by points (marking the race results) and connecting lines showing the change over time. Through the use of colour there are plotted lines for the gold, silver and bronze medal winning times for each Olympics. There are two sets of medal lines but there is no obvious distinction to explain what these are. With no direct labelling of the values I hover over the point (‘medal’) markers and a tooltip annotation comes up with the athlete’s name and time in a medalcoloured box. I compare tooltip info for the lines at the top and those below and discover the lower lines are the women’s results and the upper lines are the men’s results. From the tooltip info I can determine that the quicker times (the gold medal line) are at the top so this suggests that the y-axis scale is inverted with quicker (smaller) times at the top and slower (larger) times at the bottom. This also reveals that there is no origin of zero in the vertical axis; rather the quickest time is anchored just below the top of the chart, the slowest stretches down to the bottom of the chart, and then all the values in between are distributed proportionally. Interjecting as the visualiser responsible for this project, let me explain that the focus was on patterns of relative change over time, not necessarily absolute result times. As every different event has a different distance and duration behind the final timed results, a common scale for all results needed to be established, which is why this decision was taken to standardise all results and plot them across the vertical chart space provided. Inside the chart I now try clicking on the markers and this brings up details about the event (for that gender), including the three medal winners, their times and small flags for the countries they represented. I can also read an interesting statistic that explains if the time for the medallist I selected had been achieved throughout the event’s history, it would have secured gold, silver or bronze medals on x number of occasions. I now know enough about the chart’s structure and encodings to be able to start the process of perceiving the patterns to make some observations about what the data is showing me: 394 I can see that there is a general rise across all Olympics for the event in both men’s and women’s results. It feels like the women’s times are getting closer to the men’s, with Florence Griffith Joyner’s victory time in 1988 being the closest that the respective times have been – her result there would have been good enough for a men’s bronze in 1956. There are no real patterns between medal times; they are neither always more packed closely together, nor always spread out – it changes on each occasion. I notice the gaps where there were no events, during the First and Second World Wars, and also the presence of an obscure 1906 event, the only Olympic Games that did not follow the fouryear interval. Interpreting: As someone who follows a lot of sport and, like most people, is particularly familiar with the 100m event, I feel there is a lot of information I can get out of this display at both a general level, looking at the relative patterns of change, and a local level, checking up on individual medallists and their absolute values. Thinking about what these patterns mean, on looking at the times from the first Olympic Games in 1896 until the 1960s there was a lot of improvement and yet, since the 1960s, there is generally a much flatter shape – with only a gradual improvement in the times for both genders. This tells me that maybe the threshold for the capacity of athletes to run faster is getting closer. Even with all the contributions of sports science over the past few generations, the increase in speed is only ever marginal. That was until Usain Bolt blew the world away in 2008 and, likewise for women, Shelly-Ann Fraser improved the women’s results for the first time in 20 years. C o m prehending: What does this all mean to me? Well it is interesting and informative and, while I have no direct investment in this information in terms of needing to make decisions or it triggering any sense of emotion in me, in outcome terms I feel I have learnt more about a topic through this chart than I would have done just looking at the data. My understanding of the history of the Olympic 100m final has been expanded and, in turn, I have a better appreciation of the advancements in speed across and between both genders. Becoming a More Sophisticated Consumer Effective visualisation requires the visualiser and viewer to operate in harmony, otherwise the possibility of facilitating understanding is compromised. Beyond the mechanics of perceiving a visualisation, there are softer ‘attitudinal’ differences you can make to give yourself even more of a chance of gaining understanding. This is about modifying your mindset to be more critically appreciative of the challenges faced by the visualiser responsible for producing the work as well as its intended purpose. It is about showing empathy in your critical evaluation which will markedly help you become an increasingly sophisticated consumer. Appreciatio n o f co ntex t: When consuming a visualisation try to imagine some of the circumstances and constraints that might have influenced the visualiser’s decisions: You might not find the subject matter interesting, but other people might. You have the right not to read or interact with a visualisation that has no relevance to you. If it should have relevance, then that’s when there may be some problems! If you are struggling to understand a visualisation it could be that the project was aimed more at specialists, people with specific domain knowledge. Your struggles are possibly not a reflection of an ineffective visualisation or any deficit in your expected knowledge – it just was not intended 395 for you. If the size of the text is frustratingly tiny on your screen, maybe it was intended primarily for printing as a poster and would have been the right size if consumed in its native format? When criticising a work, spare a thought for what could have been done differently. How would you imagine an alternative way to represent the data? What other design solutions would you have tried? Sometimes what is created is a reflection of crippling constraints and might more closely resemble the least-worst solution than the best. O v erv iew firs t, details if pro v ided: Sometimes a visualiser only aims to offer a sense of the big picture – the big values, the medium and the small ones. Just because we cannot instantly read precise values from a chart it is important to avoid getting frustrated. Our default state as viewers is often to want every detail available. Sometimes, we just need to accept the idea that a gist of the hierarchy of values is of more worth than the precise decimal point precision of specific values. It may be that it was not feasible to use a chart that would deliver such detailed reading of the data – many charts simply cannot fulfil this. We might not even realise that we are just a mouseover or click away from bringing up the details we desire. F als e co ns cio us nes s : Do you really like the things you like? Sometimes we can be too quick to offer a ‘wow’ or a ‘how cool is that?’ summary judgement before even consuming the visualisation properly. It is quite natural to be charmed by a superficial surface appeal (occasionally, dare I say it, following the crowd?). Ask yourself if it is the subject, the design or the data you like? Could any portrayal of that compelling data have arrived at an equally compelling presentation of that content? C urio s ities ans w ered, curio s ities no t ans w ered: Just because the curiosity you had about a subject is not answerable does not make the visualisation a bad one. Statements like ‘This is great but I wish they’d shown it by year …’ are valid because they express your own curiosity, to which you are entirely entitled. However, a visualiser can only serve up responses to a limited number of different angles of analysis in one project. The things you wanted to know about, which might be missing, may simply have not been possible to include or were deemed less interesting than the information provided. If you are thinking ‘this would have been better on a map’, maybe there was no access to spatial data? Or maybe the geographical details were too vague or inaccurate to generate sufficient confidence to use them? 3.2 Creating: The Capabilities of the Visualiser Now that you are reaching the end of this journey, it will be quite evident that data visualisation design is truly multidisciplinary. It is the variety that fuels the richness of the subject and makes it a particularly compelling challenge. To prepare you for your ongoing development, the second part of this final chapter aims to help you reflect on the repertoire of skills, knowledge and mindsets required to achieve excellence in data visualisation design. The Seven Hats of Data Visualisation Inspired by Edward de Bono’s Six Thinking Hats, the ‘Seven hats of data visualisation’ is a breakdown of the different capabilities that make up the multi-talented visualiser. The attributes listed under each of these hats can be viewed as a wish-list of personal or team capabilities, depending on the context of your data visualisation work. 396 Project Manager The co o rdinato r – oversees the project Initiates and leads on formulating the brief Identifies and establishes definitions of key circumstances Organises the resources according to the ambition of a project Manages progress of the workflow and keeps it cohesive Has a ‘thick skin’, patience and empathy Gets things done: checks, tests, finishes tasks Pays strong attention to detail Communicator The b ro k er – manages the people relationships Helps to gather and understand requirements Manages expectations and presents possibilities Helps to define the perspective of the audience Is a good listener with a willingness to learn from domain experts Is a confident communicator with laypeople and non-specialists Possesses strong copy-editingabilities Launches and promotes the final solution Scientist The think er – provides scientific rigour Brings a strong research mindset to the process Understands the science of visual perception Understands visualisation, statistical and data ethics Understands the influence of human factors Verifies and validates the integrity of all data and design decisions Demonstrates a system’s thinking approach to problem solving Undertakes reflective evaluation and critique Data Analyst The w rangler – handles all data work Has strong data and statistical literacy Has the technical skills to acquire data from multiple sources Examines the physical properties of the data Undertakes initial descriptive analysis Transforms and prepares the data for its purpose Undertakes exploratory data analysis Has database and data modelling experience Journalist The repo rter – pursues the scent of an enquiry 397 Defines the trigger curiosity and purpose of the project Has an instinct to research, learn and discover Driven by a desire to help others understand Possesses or is able to acquire salient domain knowledge Understands the essence of the subject’s data Has empathy for the interests and needs of an audience Defines the editorial angle, framing and focus Designer The co nceiv er – provides creative direction Establishes the initial creative pathway through the purpose map Forms the initial mental visualisation: ideas and inspiration Has strong creative, graphic and illustration skills Understands the principles of user interface design Is fluent with the full array of possible design options Unifies the decision-making across the design anatomy Has a relentless creative drive to keep innovating Technologist The dev elo per – constructs the solution Possesses a repertoire of software and programming capabilities Has an appetite to acquire new technical solutions Possesses strong mathematical knowledge Can automate otherwise manually intensive processes Has the discipline to avoid feature creep Works on the prototyping and development of the solution Undertakes pre- and post-launch testing, evaluation and support Assessing and Developing Your Capabilities Data visualisation is not necessarily a hard subject to master, but there are plenty of technical and complicated matters to handle. A trained or natural talent in areas like graphic design, computer science, journalism and data analysis is advantageous, but very few people have all these hats. Those that do cannot be exceptional at everything listed, but may be sufficiently competent at most things and then brilliant at some. Developing mastery across the full collection of attributes is probably unachievable, but it offers a framework for guiding an assessment of your current abilities and a roadmap for the development of any current shortcomings. I am painfully aware of the things I am simply not good enough at (programming), the things I have no direct education in (graphic design) and the things I do not enjoy (finishing, proofreading, note-taking). Compromise is required with the things you do not like – there are always going to be unattractive tasks, so just bite the bullet and get on with them. Otherwise, you must seek either to address your skills gap through learning and/or intensive practice, finding support from elsewhere through collaboration, or to simply limit your ambitions based on what you can do. 398 Regardless of their background or previous experience, everyone has something to contribute to data visualisation. Talent is important, of course, but better thinking is, in my view, the essential foundation to focus on first. Mastering the demands of a systems’ thinking approach to data visualisation – being aware of the options and the mechanics behind making choices – arguably has a greater influence on effective work. Thereafter, the journey from good to great, as with anything, involves hard work, plenty of learning, lots of guidance and, most importantly, relentless practice. ‘Invariably, people who are new to visualisation want to know where to begin, and, frankly, it’s understandably overwhelming. There is so much powerful work now being done at such a high level of quality, that it can be quite intimidating! But you have to start somewhere, and I don’t think it matters where you start. In fact, it’s best to start wherever you are now. Start from your own experience, and move forward. One reason I love this field is that everyone comes from a different background – I get to meet architects, designers, artists, coders, statisticians, journalists, data scientists … Data vis is an inherently interdisciplinary practice: that’s an opportunity to learn something about everything! The people who are most successful in this field are curious and motivated. Don’t worry if you feel you don’t have skills yet; just start from where you are, share your work, and engage with others.’ Scott M u rray, Desig ner The Value of the Team The idea of team work is important. There are advantages to pursuing data visualisation solutions collaboratively, bringing together different abilities and perspectives to a shared challenge. In workplaces across industries and sectors, as the field matures and becomes more embedded, I would expect to see a greater shift towards recognising the need for interdisciplinary teams to fulfil data visualisation projects collectively. The best functioning visualisation team will offer a collective blend of skills across all these hats, substantiating some inevitably, but also, critically, avoiding skewing the sensibilities towards one dominant talent. Success will be hard to achieve if a team comprises a dominance in technologists or a concentration of ‘ideas’ people whose work never progresses past the sketchbook. You need the right blend in any team. We have seen quite a lot of great examples of visualisation and infographic work from newspaper and media organisations. In the larger organisations that have the fortune of (relatively) large graphics departments, team working is an essential ingredient behind much of the success they have had. Producing relentlessly highquality, innovative and multiple projects in parallel, within the demands of the news environment, is no mean feat. Such organisations might have the most people and also some of the best people, but their output is still representative of their punching above their weight, no matter how considerable that base. Developing Through Evaluating There are two components in evaluating the outcome of a visualisation solution that will help refine your capabilities: what was the outcome of the work and how do you reflect on your performance? O utco m e: Measuring effectiveness in data visualisation remains an elusive task – in many ways it is the field’s ‘Everest’ – largely because it must be defined according to local, contextual measures of success. This is why establishing an early view of the intended ‘purpose’, and then refining it if circumstances change, was necessary to guide your thinking throughout this workflow. Sometimes effectiveness is tangible, but most times it is entirely intangible. If the purpose of the work is to further the debate about a subject, to establish one’s reputation or voice of authority, then those are 399 hard things to pin down in terms of a yes/no outcome. One option may be to flip the measure of effectiveness on its head and seek out evidence of tangible ineffectiveness. For example, there may be significant reputation-based impacts should decisions be made on inaccurate, misleading or inaccessible visual information. There are, of course, some relatively free quantitative measures that are available for digital projects, including web-based measures such as visitor counts and social media metrics (likes, retweets, mentions). These, at least, provide a surface indicator of success in terms of the project’s apparent appeal and spread. Ideally, however, you should aspire also to collect more reliable qualitative and value-added feedback, even if this can, at times, be rather expensive to secure. Some options include: capturing anecdotal evidence from comments submitted on a site, opinions attributed to tweets or other social media descriptors, feedback shared in emails or in person; informal feedback through polls or short surveys; formal case studies which might offer more structured interviews and observations about documented effects; experiments with controlled tasks/conditions and tracked performance measures. Yo ur perfo rm ance: A personal reflection or assessment of your contribution to a project is important for your own development. The best way to learn is by considering the things you enjoyed and/or did well (and doing more of those things) and identifying the things you did not enjoy/do well (and doing less of those things or doing them better). So look back over your project experience and consider the following: Were you satisfied with your solution? If yes, why; if no, why and what would you do differently? In a different context, what other design solutions might you have considered? Were there any skill or knowledge shortcomings that restricted your process and/or solution? Are there aspects of this project that you might seek to recycle or reproduce in other projects? For instance, ideas that did not make the final cut but could be given new life in other challenges? How well did you utilise your time? Were there any activities on which you feel you spent too much time? Developing effectiveness and efficiency in your data visualisation work will take time and will require your ongoing efforts to learn, apply, reflect and repeat again. I am still learning new things every day. It is a journey that never stops because data visualisation is a subject that has no ending. ‘There is not one project I have been involved in that I would execute exactly the same way second time around. I could conceivably pick any of them – and probably the thing they could all benefit most from? More inter-disciplinary expertise.’ Alan Smith OBE, Data Visu alisation Ed itor, Fina ncia l T imes However, to try offer a suitable conclusion to this book, at least, I will leave you with this wonderful bit of transcribed from a video of Ira Glass, host and producer of ‘This American Life’. Nobody tells this to people who are beginners, I really wish someone had told this to me. All of us who do creative work, we get into it because we have good taste… [but] there is this gap and for the first couple of years that you’re making stuff, what you’re making is just not that good… It’s trying to be good, it has potential, but it’s not. But your taste, the thing that got you into the game, is still killer. And your taste is why your work disappoints you. A lot of people never get past this phase, they quit. Most people I know who do interesting, creative work went through years of this. We know our work 400 doesn’t have this special thing that we want it to have. We all go through this. And if you are just starting out or you are still in this phase, you gotta know it’s normal and the most important thing you can do is do a lot of work. Put yourself on a deadline so that every week you will finish one story. It is only by going through a volume of work that you will close that gap, and your work will be as good as your ambitions. And I took longer to figure out how to do this than anyone I’ve ever met. It’s gonna take awhile. It’s normal to take awhile. You’ve just gotta fight your way through. Summary: Visualisation Literacy Viewing: Learning to See Before You Begin Setting: is the situation you are in conducive to the task of consuming a visualisation? In a rush? Travelling? Visual appeal: are you sufficiently attracted to the appearance of the work? Relevance: do you have an interest or a need to engage with this topic? Initial scan: quickly orientate yourself around the page or screen, and allow yourself a brief moment to be drawn to certain features. Outside the Chart The proposition: what task awaits? What format, function, shape and size of visualisation have you got to work with? What’s the project about?: look at the titles, source, and read through any introductory explanations. What data?: look for information about where the data has originated from and what might have been done to it. What interactive functions exist?: if it is a digital solution browse quickly and acquaint yourself with the range of interactive devices. Ins ide the C hart Refer to the Chart Type Gallery in Chapter 6 to learn about the approaches to perceiving and interpreting different chart types. Perceiving: what does it show? Interpreting: what does it mean? Comprehending: what does it mean to me? Becoming a More Sophisticated Consumer Appreciation of context: what circumstances might the visualiser have been faced with that are hidden from you as a viewer? Overview first, details if provided: accept that sometimes a project only aims to (or maybe only can) provide a big-picture gist of the data, rather than precise details. False consciousness: don’t be too quick to determine that you like a visualisation. Challenge yourself, do you really like it? Do you really gain understanding from it? Curiosities answered, curiosities not answered: just because it does not answer your curiosity, it might 401 answer those of plenty of others. Creating: The Capabilities of the Visualiser The Seven Hats of Data Visualisation Design Project Manager: the coordinator – oversees the project. Communicator: the broker – manages the people relationships. Scientist: the thinker – provides scientific rigour. Data analyst: the wrangler – handles all the data work. Journalist: the reporter – pursues the scent of enquiry. Designer: the conceiver – provides creative direction. Technologist: the developer – constructs the solution. Assessing and Developing Your Capabilities The importance of reflective learning: evaluating the outcome of the work you have created and assessing your own performance during its production. Tips and Tactics The life and energy of data visualisation are online: keep on top of blogs, the websites of major practitioners and agencies creating great work. On social media (especially Twitter, Reddit) you will find a very active and open community that is willing to share and help. Practise, practise, practise: experience is the key – identify personal projects to explore different techniques and challenges. Learn about yourself: take notes, reflect, self-critique, recognise your limits. Learn from others: consume case studies and process narratives, evaluate the work of others (‘what would I do differently?’). Expose yourself to the ideas and practices of other related creative and communication fields: writing, video games, graphic design, architecture, cartoonists. 1 Chapter 1: Defining Data Visualization Summary In Chapter 1, the author Mr. Kirk describes about the concept of Data Visualization. Data visualization was defined as the visual analysis and communication of data. The chapter also 2 included the historical background survey definition of data visualization by various other authors. Also, in the book was a set of fascinating recipes that of the components in that involve in the definition. The type of data that is required to be visually analyzed is important before it is being subjected to further processing before visualization. Mr. Kirk also emphasized the significance of the art and science of making data analysis a fun filled technical and an analytical reading that encourages the use of human perception to make decisions in assistance of visual treats that come in the form of graphs, pie charts among others. The science of data visualization is defined with the implication of truth, evidence and rules that govern the process of visualizing a set of data that can be quintessential in determining the path of an enterprise or an organization. Highlights: Upon reading the chapter 1 in this book that was in depth into data visualization, I was able to grasp essential technical and analytical definitions and can say they are quiet telling in terms of the importance on the concept and visual representation of the definitions. The use of some of the citations was a key indicator that data visualization can be defined in various ways and can assist in technical improvements if used in way that is beneficial to all parties. Ideas and thoughts: The chapter was a thorough analysis of the concept. However, I was also keen on looking for live examples of visual tools or results of analysis inculcated in this defining place of the book. The big positive is the use of the concept of science and art that can be implemented in the day to day activities to introduce data visualization in any area and can help in making decisions that 3 can set a trend for the growth of an organization. In terms of the course, it was a great read to write this review journal and can hopefully add a firm base to the things to come. Application: The concept of data visualization can be implemented in my current work environment. As an IT personnel, I deal with the network infrastructure and constantly come across large chunk of data that will need to be analyzed for its usage stats, bandwidth, performance and benefits of choosing the hardware or software accordingly. To best impact this, the monitoring tools such a s NetFlow helps us in verifying bandwidth over utilization or underutilization to perform a set of tasks before troubleshooting any related issues. Now, the concept of data visualization can be implemented here to introduce business analysis visualization tools such as Tableau to measure the weekly, bi-weekly, monthly statistics to make decisions. The visual analysis shows the decision maker to stick to the current bandwidth, hardware etc. or upgrade as necessary. Chapter 2: Visualization Workflow Summary In chapter, Mr. Kirk explains about the workflow and path of visually analyzing data, the visualization workflow is a key concept in implementing a data visualization tool in an enterprise and the chapter benefits the reader with typical representations of the concept in mutual 4 combination of theoretical definitions. The conceptual workflow involves around the decision forensics, assessment of the current workflow and a final analysis of potential problems. The decision forensics speaks about sample visualization and forensically decipher the designs and pattern of data and deconstruct a puzzle to get to the root of the theme under consideration. The tactics involved is explained and the need to find hidden contexts and behind the scenes data is important. The stage of current workflow talks of the existing setup. Advantages, disadvantages, need to improve and the benefits of improved visualization analysis. Highlights: the author emphasizes an activity involving brainstorming the reader to perform data gathering, ideas to implement a project plan with the manager at an enterprise and to learn the underlying concept of data visualization. This provided a learning opportunity to the reader to engage in the book and analyze their situation based on this concept. Ideas and thoughts: The author presents us a unique way of representing data visualization through workflow models that can highly impact the decision maker to choose a path that can be totally different to the existing setup. Upon reading the chapter 2, I was able to gather info about the use of data gathering and arrangement before processing. A quick thought on this provided the possibility of segregating data beforehand in order make the process smooth and to eliminate unusable data. This can save a lot of time and money when the size of data is large. A further benefit was to improvise the existing setup by going through the existing setup and acquire hidden data. However, this needs to be done without unintended downtime and loss to an organization. 5 Application: The workflow can be implemented in my current personal space while assessing the amount of data stored in my emails coming from credit card transactions. Upon logging into my credit card activity statement, I can filter thrones that are needed. This can help benefit me to keep track of the required ones and delete the rest of junk. Nowadays, since this is visually available through graphs, it makes life easier to organize the data before acting. Meanwhile, in an enterprise, the importance of workflow cannot be emphasized enough. The need to gather historical data is quintessential in terms of auditing and cost analysis. The most important part is the effect this has in future decision-making processes. The next application is to perform thorough research on the hidden data that can go missing but can have a significant impact on the outcome of a project. For example, if the data usage over a weekend is not captured as it was a long weekend, it affects the next part of the report and can mislead a user to perform wrong analysis. Chapter 3: Formulaing Your Brief Summary In Chapter 3, the author brings a fascinating idea of Formulation of your thoughts in brief and to analyze the context that revolves around the curiosity ad the purpose and eventually concluding with ideas and maps that define the purpose of doing the activity. 6 The author begins with his fascination of movies – what makes a big movie, the cast involved, technical aspects, background score, set and the need to entertain the audience in a visually pleasing way. Key decisions are to be made for each of these to make it a blockbuster. Highlights: The key highlights on the chapter in terms of the activity was the representation of circumstances involved around a big movie – stakeholders and audience; the constraints; consumables – frequency and setting to inculcate the rest; delivery – importance of time and format and the major part was the required resources. All these required skilled labors to assess the data and provide suitable suggestions to bring a visual spectacle to the screens. Ideas and thoughts: The key ideas and thoughts while reading the chapter was the unique way of analysis of the author to bring about his point of view. In conjunction to the above points, the vision was clear through the purpose maps and ideas. The purpose was to present in a way that is appealing through caption, overlays, dialogues and balancing it with values and logic. The lollipop maps provide a key mental visualization analysis. I find that if this can be used elsewhere, it can benefit organizations to make quarterly decisions. Application: The concept can be applied and understood when we read the influence and inspiration of visualization explained in the chapter. The author of such vast knowledge gets inspiration and 7 writes books, and we as readers can make use of the quantitative and qualitative variables and can implement it in our daily activities. The overall concept of delving deep into a plan and to research the key factors and to analyses situations that stimulate the process can be crucial in determining the type of decisions that can be undertaken. In our workplace, a lot of work goes through tiers and hierarchical models and requires analysis at each stage. The concept of formulating a brief can make sure the owners can keep track Nd govern the activities in suitable fashion that can steer the growth of the organization. Reference: Kirk, A. (2016). Data Visualization: A Handbook for Data Driven Design. Thousand Oaks, CA: Sage Publications, Ltd. ISBN: 978-1-4739-1214-4
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