DataVisualisation_AHandbookforDataDrivenDesign.pdf

story – its underlying structure and arrangement are in abstract space.Just as you do when showing friends a photograph from your holiday,you might use this chart as a prop to explain how relationshipsbetween some of the different entities presented are significant.Making the chart a prop allows you to provide a narrative. In this caseit is the setting and delivery that are consistent with the notion ofstorytelling, not the chart itself. I made a similar observation aboutthe role of exhibitory visualisations used as props within explanatorysettings.A further distinction to make is between stories as being presentedand stories as being interpreted. The famous six-word story ‘for sale:baby shoes, never worn’ by Ernest Hemingway is not presented as astory, the story is triggered in our mind when we dissect this passageand start to infer meaning, implication and context. The imagined barchart I mentioned earlier in the book that could show the 43 whitepresidents and 1 black president is only presenting a story if it isaccompanied by an explanatory narrative (in which case the chart wasagain really just a prop) or if you understand the meaning of thesignificance of this statistic without this description and are able toform the story in your own mind.

Charts Comparisons

Bar chart

ALSO KNOWN AS Column chart, histogram (wrongly)

REPRESENTATION DESCRIPTION

A bar chart displays quantitative values for different categories. Thechart comprises line marks (bars) – not rectangular areas – with the sizeattribute (length or height) used to represent the quantitative value foreach category.

241

EXAMPLE Comparing the number of Oscar nominations for the 10actors who have received the most nominations without actuallywinning an award.

Figure 6.8 The 10 Actors with the Most Oscar Nominations but NoWins

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know with which categorical value each bar isassociated and what the range of the quantitative values is (min to max).Think about what high and low values mean: is it ‘good’ to be large orsmall? Glance across the entire chart to locate the big, small andmedium bars and perform global comparisons to establish the high-level ranking of biggest > smallest. Identify any noticeable exceptionsand/or outliers. Perform local comparisons between neighbouring bars,to identify larger than and smaller than relationships and estimate therelative proportions. Estimate (or read, if labels are present) the absolutevalues of specific bars of interest. Where available, compare thequantities against annotated references such as targets, forecast, lastyear, average, etc.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like tick marks and gridlines,in particular, can be helpful to increase the accuracy of the reading ofthe quantitative values. If you have axis labels you should not needdirect labels on each bar – this will lead to label overload, so generallydecide between one or the other.

242

COMPOSITION: The quantitative value axis should always start fromthe origin value of zero: a bar should be representative of the true, fullquantitative value, nothing more, nothing less, otherwise the perceptionof bar sizes will be distorted when comparing relative sizes. There is nosignificant difference in perception between vertical or horizontal barsthough horizontal layouts tend to make it easier to accommodate andread the category labels for each bar. Unlike the histogram, there shouldbe a gap, even if very small, between bars to keep each category’s valuedistinct. Where possible, try to make the categorical sorting meaningful.

VARIATIONS & ALTERNATIVES

A variation in the use of bar charts is to show changes over time. Youwould use a bar chart when the focus is on individual quantitativevalues over time rather than (necessarily) the trend/change betweenpoints, for which a line-chart would be best. ‘Spark bars’ are mini barcharts that aim to occupy only a word’s length amount of space. Theyare often seen in dashboards where space is at a premium and there is adesire to optimise the density of the display. To show furthercategorical subdivisions, you might consider the ‘clustered bar chart’ ora ‘stacked bar chart’ if there is a part-to-whole angle. ‘Dot plots’ offer aparticularly useful alternative to the bar chart for situations where youhave to show large quantitative values with a narrow range ofdifferences.

Charts Comparisons

Clustered bar chart

ALSO KNOWN AS Clustered column chart, paired bar chart

REPRESENTATION DESCRIPTION

A clustered bar chart displays quantitative values for different majorcategories with additional categorical dimensions included for further

243

breakdown. The chart comprises line marks (bars) – not rectangularareas – with the size attribute (length or height) used to represent thequantitative value for each category and colours used to distinguishfurther categorical dimensions.

EXAMPLE Comparing the number of Oscar nominations with thenumber of Oscar awards for the 10 actors who have received the mostnominations.

Figure 6.9 The 10 Actors who have Received the Most OscarNominations

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know with which categorical value each bar isassociated and what the range of the quantitative values is (min to max).Learn about the colour associations to understand what sub-categoriesthe bars within each cluster represent. Glance across the entire chart tolocate the big, small and medium bars and perform global comparisonsto establish the high-level ranking of biggest > smallest. Identify anynoticeable exceptions and/or outliers. Perform local comparisons withinclusters to identify the size relationship (which is larger and by howmuch?) and estimate (or read, if labels are present) the absolute valuesof specific bars of interest.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like tick marks and gridlines,

244

in particular, can be helpful to increase the accuracy of the reading ofthe quantitative values. If you have axis labels you should not needdirect labels on each bar – this will lead to label overload, so generallydecide between one or the other.

COMPOSITION: The quantitative value axis should always start fromthe origin value of zero: a bar should be representative of the true, fullquantitative value, nothing more, nothing less, otherwise the perceptionof bar sizes will be distorted when comparing relative sizes. If yourcategorical clusters involve a breakdown of more than three bars, itbecomes a little too busy, so you might therefore consider giving eachcluster its own separate bar chart and using small multiples to show achart for each major category. Sometimes one bar might be slightlyhidden behind the other, implying a before and after relationship, oftenwhen space is at a premium – just do not hide too much of the back bar.There is no significant difference in perception between vertical orhorizontal bars though horizontal layouts tend to make it easier toaccommodate and read the category labels for each bar. The individualbars should be positioned adjacent to each other with a noticeable gapand then between each cluster to help direct the eye towards theclustering patterns first and foremost. Where possible try to make thecategorical sorting meaningful.

VARIATIONS & ALTERNATIVES

Clustered bar charts are also sometimes used to show how twoassociated sub-categories have changed over time (like the LionelMessi bar chart discussed in Chapter 1). Alternatives would include the‘dot plot’ or, if you have just two categories forming the clusters andthese categories have a binary state (male, female or yes %, no %), the‘back-to-back bar chart’ would be effective.

Charts Comparisons

Dot plot

245

ALSO KNOWN AS Dot chart

REPRESENTATION DESCRIPTION

A dot plot displays quantitative values for different categories. Incontrast to the bar chart, rather than using the size of a bar, point marks(typically circles but any ‘symbol’ is legitimate) are used with theposition along a scale indicating the quantitative value for eachcategory. Sometimes an area mark is used to indicate one value throughposition and another value through size. Additional categoricaldimensions can be accommodated in the same chart by includingadditional marks differentiated by colour or symbol.

EXAMPLE Comparing the number and percentage of PhDs awardedby gender across different academic subjects.

Figure 6.10 How Nations Fare in PhDs by Sex

HOW TO READ IT & WHAT TO LOOK FOR

For single-series dot plots (i.e. just one dot per row), look at the axes soyou know with which categorical value each row is associated and whatthe range of the quantitative values is (min to max). Where you havemultiple series dot plots (i.e. more than one dot), establish what thedifferent colours/symbols represent in terms of categorical breakdown.Glance across the entire chart to locate the big, small and mediumvalues and perform global comparisons to establish the high-levelranking of biggest > smallest. Identify any noticeable exceptions and/or

246

outliers. Where you have multiple series look across each series of dotvalues separately and then perform local comparisons within rows toidentify the relative position of each dot, observing the gaps, big andsmall. Estimate the absolute values of specific dots of interest. Whereavailable, compare the quantities against annotated references such astargets, forecast, last year, average, etc.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like tick marks and gridlines,in particular, can be helpful to increase the accuracy of the reading ofthe quantitative values.

COMPOSITION: Given that the quantitative value axis does not needto commence from a zero origin it is important to label clearly the axisvalues when the baseline is not commencing from a minimum of zero.There is no significant difference in perception between vertical orhorizontal arrangement though horizontal layouts tend to make it easierto accommodate and read the category labels for each row. Wherepossible try to make the categorical sorting meaningful, maybeorganising values in ascending/descending size order.

VARIATIONS & ALTERNATIVES

Alternatives would include the ‘bar chart’, to show the size ofquantitative values for different categories. The ‘connected dot plot’would be used to focus on the difference between two measures. The‘univariate scatter plot’ would be used to show the range of multiplevalues across categories, to display the diversity and distribution ofvalues.

Charts Comparisons

Connected Dot Plot

247

ALSO KNOWN AS Barbell chart, dumb-bell chart

REPRESENTATION DESCRIPTION

A connected dot plot displays absolute quantities and quantitativedifferences between two categorical dimensions for different majorcategories. The display is formed by two points (normally circles butany ‘symbol’ is legitimate) to mark the quantitative value positions fortwo comparable categorical dimensions. There is a row of connecteddots for each major category. Colour or difference in symbol isgenerally used to distinguish these points. Joining the two pointstogether is a connecting line which effectively represents the ‘delta’(difference) between the two values.

EXAMPLE Comparing the typical salaries for women and men acrossa range of different job categories in the US.

Figure 6.11 Gender Pay Gap US

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know with which major categorical values eachrow is associated and what the range of the quantitative values is (minto max). Determine which dots resemble which categorical dimension(could be colour, symbol or a combination) and see if there is anymeaning behind the colouring of the connecting bars. Think about whatthe quantitative values mean to determine whether it is a good thing tobe higher or lower. Glance across the entire chart to locate the big,

248

small and medium connecting bars in each direction. Perform globalcomparisons to establish the high-level ranking of biggest > smallestdifferences as well as the highest and lowest values. There may bedeliberate sorting of the display based on one of the quantitativemeasures. Identify any noticeable exceptions and/or outliers. Estimate(or read, if labels are present) the absolute values, direction and size ofdifferences for specific categories of interest.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like tick marks and gridlines,in particular, can be helpful to increase the accuracy of the reading ofthe quantitative values. Consider labelling categories adjacent to theplotted points rather than next to the axis line (and possibly far awayfrom the values) to make it easier for the reader to understand thecategory–row association.

COLOUR TIPS: Colour may be used to indicate and emphasise thedirectional basis of the connecting line differences.

COMPOSITION: If the two plotted measures are very similar, and thepoint markers effectively overlap, you will need to decide which shouldbe positioned on top. As the representation of the quantitative values isthrough position along a scale and not size (it is the difference that issized, not the absolutes) the quantitative axis does not need to have azero origin. However, a zero origin can be helpful to establish the scaleof the differences. Where possible try to make the sorting meaningfulusing any one of the three quantitative measures to optimise the layout.

VARIATIONS & ALTERNATIVES

Variations in the use of the ‘connected dot plot’ would show before andafter analysis between two points in time, possibly using the ‘arrowchart’ to indicate the direction of change explicitly. Similarly, the‘carrot chart’ uses line width tapering to indicate direction, the fatterend the more recent values. The ‘univariate scatter plot’ would be usedto show the range of multiple values across categories, to display thediversity and distribution of values rather than comparing differencesbetween values.

Charts Comparisons

249

Pictogram

ALSO KNOWN AS Isotype chart, pictorial bar chart, stacked shapechart, tally chart

REPRESENTATION DESCRIPTION

A pictogram displays quantitative values for different major categorieswith additional categorical dimensions included for further breakdown.In contrast with the bar chart, rather than using the size of a bar,quantities of point marks, in the form of symbols or pictures, arestacked to represent the quantitative value for each category. Each pointmay be representative of one or many quantitative units (e.g. a singleshape may represent 1000 people) but note that, unless you use symbolportions, you will not be able to represent decimals. Pictograms may beused to offer a more emotive (humanising or more light-hearted)display than a bar can offer. Additional categorical dimensions can beaccommodated in the same chart by using marks differentiated byvariations in colour, symbol or picture. Always ensure the markers usedare as intuitively recognisable as possible and consider minimising thevariety as this makes it cognitively harder for the viewer to identifyassociations easily and make sense of the quantities.

EXAMPLE Comparing the number of players with different facial hairtypes across the four teams in the NHL playoffs in 2015.

Figure 6.12 Who Wins the Stanley Cup of Playoff Beards?

250

HOW TO READ IT & WHAT TO LOOK FOR

Look at the major categorical axis to establish with which category eachrow is associated. Establish the mark associations to understand whatcategorical dimensions each colour/shape variation represents. Glanceacross the entire chart to locate the big, small and medium stacks ofshapes and perform global comparisons to establish the high-levelranking of biggest > smallest. Identify any noticeable exceptions and/oroutliers. Perform local comparisons between neighbouring categories,to identify larger than and smaller than relationships and estimate therelative proportions. Estimate (or read, if labels are present) the absolutevalues of specific groups of markers of interest.

PRESENTATION TIPS

ANNOTATION: The choice of symbol/ picture should be asrecognisably intuitive as possible and locate any legends as close aspossible to the display.

COLOUR TIPS: Maximise the variation in marker by using differentcombinations in both colour and shape, rather than just variation of oneattribute.

COMPOSITION: If the quantities of markers exceed a single row, tryto make the number of units per row logically ‘countable’, such asdisplaying in groups of 5, 10 or 100. To aid readability, make sure thereis a sufficiently noticeable gap between rows, otherwise sometimes theeye struggles to form the distinct clusters of shapes for each categorydisplayed. Where possible try to make the categorical sortingmeaningful, maybe organising values in ascending/descending sizeorder.

251

VARIATIONS & ALTERNATIVES

Extending the idea of using repeated quantities of representativesymbols, some applications take this further by using large quantities ofindividual symbols to get across the feeling of magnitude and scale.When showing a part-to-whole relationship, the ‘waffle chart’ can usesimple symbol devices to differentiate the constituent parts of a whole.

Charts Comparisons

Proportional shape chart

ALSO KNOWN AS Area chart (wrongly)

REPRESENTATION DESCRIPTION

A proportional shape chart displays quantitative values for differentcategories. The chart is based on the use of different area marks, one foreach category, sized in proportion to the quantities they represent. Byusing the quadratic dimension of area size rather than the lineardimension of bar length or dot position, the shape chart offers scope fordisplaying a diverse range of quantitative values within the same chart.Typically the layout is quite free-form with no baseline or centralgravity binding the display together.

EXAMPLE Comparing the market capitalisation ($) of companiesinvolved in the legal sale of marijuana across different industry sectors.

Figure 6.13 For These 55 Marijuana Companies, Every Day is 4/20

252

HOW TO READ IT & WHAT TO LOOK FOR

Look at the shapes and their associated labels so you know with whatmajor categorical values each is associated. If there are only directlabels, find the largest shape to establish its quantitative value as themaximum and do likewise for the smallest – this will help calibrate thesize judgements. Otherwise, if it exists, acquaint yourself with the sizekey. Glance across the entire chart to locate the big, small and mediumshapes and perform global comparisons to establish the high-levelranking of biggest > smallest. Identify any noticeable exceptions and/oroutliers. Perform local comparisons between neighbouring shapes toidentify larger than and smaller than relationships and estimate therelative proportions. Estimate (or read, if labels are present) the absolutevalues of specific shapes of interest.

PRESENTATION TIPS

ANNOTATION: Sometimes a quantitative size key will be includedrather than direct labelling (usually when there are many shapes andlimited empty space) though direct labels will help overcome some ofthe limitations of judging area size. You will have to decide how tohandle label positioning for those shapes with exceptionally small sizes.

COLOUR TIPS: Colours are not fundamentally necessary to encodecategory (the position/separation of different shapes achieves thatalready) but they can be useful as redundant encodings to make thecategory even more immediately distinguishable.

COMPOSITION: Estimating and comparing the size of areas with

253

accuracy is not as easy as it is for judging bar length or dot position, soonly use this chart type if you have a diverse range of quantitativevalues. The geometric accuracy of the size calculations is paramount.Mistakes are often made, in particular, with circle size calculations: it isthe area you are modifying, not the diameter/radius. Arrangementapproaches vary: sometimes you see the shapes anchored to a commonbaseline (bottom or central alignment) while on other occasions theymight just ‘float’. If you use an organic shape, like a human figure, torepresent different quantities you need to adjust the entire shape area,not just the height. Often the approach for this type of display is to treatthe figure as a rudimentary rectangular shape. Sometimes the volume ofa shape is used rather than area to represent quantitative values(especially if there are almost exponentially different values to show)but this increases the perceptual difficulty in estimating and comparingvalues. Where possible try to make the categorical sorting meaningful,maybe organising values in ascending/descending size order.

VARIATIONS & ALTERNATIVES

The ‘bubble chart’ uses clusters of sized bubbles to compare categoricalvalues and, sometimes, to represent part-to-whole analysis. The ‘nestedshape chart’ might include secondary, smaller area sizes nested withineach shape to display local part-to-whole relationships.

Charts Comparisons

Bubble chart

ALSO KNOWN AS Circle packing diagram

EXAMPLE Comparing the Public sector capital expenditure (£million) on services by function of the UK Government during 2014/15.

REPRESENTATION DESCRIPTION

254

A bubble chart displays quantitative values for different majorcategories with additional categorical dimensions included for furtherbreakdown. It is based on the use of circles, one for each category, sizedin proportion to the quantities they represent. Sometimes severalseparate clusters may be used to display further categorical dimensions,otherwise the colouring of each circle can achieve this. It is similar inconcept to the proportional shape chart but differs through the typicallayout being based on clustering, which therefore also enables it as adevice for showing part-to-whole relationships as well.

Figure 6.14 UK Public Sector Capital Expenditure, 2014/15

HOW TO READ IT & WHAT TO LOOK FOR

Look at the shapes and their associated labels so you know with whatmajor categorical values each is associated, noting any size and colourlegends to assist in forming associations. If there are multiple clusters,learn about the significance of the grouping/separation in each case. Ifthere are direct labels, find the largest shape to establish its quantitativevalue as the maximum and do likewise for the smallest – this will helpcalibrate other size judgements. Glance across the entire chart to locatethe big, small and medium shapes and perform global comparisons toestablish the high-level ranking of biggest > smallest. Identify anynoticeable exceptions and/or outliers. Perform local comparisonsbetween neighbouring shapes to identify larger than and smaller thanrelationships and estimate the relative proportions. Estimate (or read, iflabels are present) the absolute values of specific shapes of interest. Ifthere are multiple clusters, note the general relative size and number ofmembers in each case.

255

PRESENTATION TIPS

INTERACTIVITY: Bubble charts may often be accompanied byinteractive features that let users select or mouseover individual circlesto reveal annotated values for the quantity and category.

ANNOTATION: If interactivity is not achievable, a quantitative sizekey should be included or direct labelling; the latter may make thedisplay busy (and be hard to fit into smaller circles) but will helpovercome some of the limitations of judging area size.

COLOUR TIPS: Colours are sometimes used as redundant encodingsto make the quantitative sizes even more immediately distinguishable.

COMPOSITION: Estimating and comparing the size of areas withaccuracy is not as easy as it is for judging bar length or dot position, soonly use this chart type if you have a diverse range of quantitativevalues. The use of this chart will primarily be about facilitating a gist, ageneral sense of the largest and smallest values. The geometricaccuracy of the circle size calculations is paramount. Mistakes are oftenmade with circle size calculations: it is the area you are modifying, notthe diameter/radius. If you wish to make your bubbles appear as 3Dspheres you are essentially no longer representing quantitative valuesthrough the size of a geometric area mark; rather the mark will be a‘form’ and so the size calculation will be based on volume, not area.There is no categorical or quantitative sorting applied to the layout ofthe bubble chart, instead the tools that offer these charts will generallyuse a layout algorithm that applies a best-fit clustering to arrange thecircles radially about a central ‘gravity’ force.

VARIATIONS & ALTERNATIVES

When the collection of quantities represents a whole, this evolves into achart known as a ‘circle packing diagram’ and usually involves manyparts that pack neatly into a circular layout representing the whole.Another variation of the packing diagram is when the adjacencybetween circle ‘nodes’ indicates a connected relation, offering avariation of the node–link diagram for showing networks ofrelationships. The bubble plot also uses differently sized circles but theposition in each case is overlaid onto a scatter plot structure, based ontwo dimensions of further quantitative variables. Removing the sizeattribute (and effectively replacing area with point mark) you couldsimply use the quantity of points clustered together for differentcategories to create a ’tally chart’.

256

Charts Comparisons

Radar chart

ALSO KNOWN AS Filled radar chart, star chart, spider diagram, webchart

EXAMPLE Comparing the global competitive scores (out of 7) across12 ‘pillars’ of performance for the United Kingdom.

REPRESENTATION DESCRIPTION

A radar chart shows values for three or more different quantitativemeasures in the same display for, typically, a single category. It uses aradial (circular) layout comprising several axes emerging from thecentre-like spokes on a wheel, one for each measure. The quantitativevalues for each measure are plotted through position along each scaleand then joined by connecting lines to form a unique geometric shape.Sometimes this shape is then filled with colour. A radar chart shouldonly be considered in situations where the cyclical ordering (andneighbourly pairings) has some significance (such as data that might beplotted around the face of a clock or compass) and when thequantitative scales are the same (or similar) for each axis. Do not plotvalues for multiple categories on the same radar chart, but use smallmultiples formed of several radar charts instead.

Figure 6.15 Global Competitiveness Report 2014—2015

257

HOW TO READ IT & WHAT TO LOOK FOR

Look around the chart and acquaint yourself with the quantitativemeasure represented by each axis and note the sequencing of themeasures around the display. Is there any significance in thisarrangement that can assist in interpreting the overall shape? Note therange of values along each independent axis so you understand whatpositions along the scales mean in a value sense for each measure. Scanthe shape to locate the outliers both towards the outside (larger values)and inside (smaller values) of the scales. It is more important to payattention to the position of values along an axis than the nature of theconnecting lines between axes, unless the axis scales are consistent or atleast if the relative position along the scale has the same impliedmeaning. If the variable sequencing has cyclical relevance, the spiking,bulging or contracting shape formed will give you some sense of thebalance of values. Perform local comparisons between neighbouringaxes to identify larger than and smaller than relationships. Estimate (orread, if labels are present) the absolute values of specific shapes ofinterest.

PRESENTATION TIPS

258

ANNOTATION: The inclusion of visible annotated features like axislines, tick marks, gridlines and value labels can naturally aid thereadability of the radar chart. Gridlines are only relevant if there arecommon scales across each quantitative variable. If so, the gridlinesmust be presented as straight lines, not concentric arcs, because theconnecting lines joining up the values are themselves straight lines.

COLOUR TIPS: Often the radar shapes are filled with a colour,sometimes with a degree of transparency to allow the backgroundapparatus to be partially visible.

COMPOSITION: The cyclical ordering of the quantitative variableshas to be of optimum significance as the connectors and shape changefor every different ordering permutation. This will have a major impacton the readability and meaning of the resulting chart shape. As the axeswill be angled all around the radial display, you will need to make sureall the associated labels are readable (i.e. not upside down or at difficultangles).

VARIATIONS & ALTERNATIVES

A ‘polar chart’ is an alternative to the radar chart that removes some ofthe main shortcomings caused by connecting lines in the radar chart. Ifyou have consistent value scales across the different quantitativemeasures, a ‘bar chart’ or ‘dot plot’ would be a better alternative. Whilenot strictly a variation, ‘parallel coordinates’ display a similar techniquefor plotting several independent quantitative measures in the samechart. The main difference is that parallel coordinates use a linear layoutand can accommodate many categories in one display.

Charts Comparisons

Polar chart

ALSO KNOWN AS Coxcomb plot, polar area plot

259

REPRESENTATION DESCRIPTION

A polar chart shows values for three or more different quantitativemeasures in the same display. It uses a radial (circular) layoutcomprising several equal-angled circular sectors like slices of a pizza,one for each measure. In contrast to the radar chart (which uses positionalong a scale), the polar chart uses variation in the size of the sectorareas to represent the quantitative values. It is, in essence, a radiallyplotted bar chart. Colour is an optional attribute, sometimes usedvisually to indicate further categorical dimensions. A polar chart shouldonly be considered in situations where the cyclical ordering (andneighbourly pairings) has some significance (such as data that might beplotted around the face of a clock or compass) and when thequantitative scales are the same (or similar) for each axis.

EXAMPLE Comparing the quantitative match statistics across 14different performance measures for a rugby union player.

Figure 6.16 Excerpt from a Rugby Union Player Dashboard

HOW TO READ IT & WHAT TO LOOK FOR

260

Look around the chart and acquaint yourself with the quantitativemeasures each sector represents and note the sequencing of themeasures around the display. Is there any significance in thisarrangement that can assist in interpreting the overall shape? Note therange of values included on the quantitative scale and acquaint yourselfwith any colour associations. Glance across the entire chart to locate thebig, small and medium sectors and perform global comparisons toestablish the high-level ranking of biggest > smallest. Identify anynoticeable exceptions and/or outliers. Perform local comparisonsbetween neighbouring variables to identify the order of magnitude andestimate the relative sizes. Estimate (or read, if labels are present) theabsolute values of specific sectors of interest. Where available, comparethe quantities against annotated references such as targets, forecast, lastyear, average, etc. If there is significance behind the sequencing of thevariables, look out for any patterns that emerge through spiking,bulging or contracting shapes.

PRESENTATION TIPS

ANNOTATION: The inclusion of visible annotated features like tickmarks and value labels can naturally aid the readability of the polarchart. Gridlines are only relevant if there are common scales acrosseach quantitative variable. If so, the gridlines must be presented as arcsreflecting the outer shape of each sector. Connecting lines joining upthe values are themselves straight lines. Each sector typically uses thesame quantitative scale for each quantitative measure but, on theoccasions when this is not the case, each axis will require its own, clearvalue scale.

COLOUR TIPS: Often polar chart sectors are filled with a meaningfulcolour, sometimes with a degree of transparency to allow thebackground apparatus to be partially visible.

COMPOSITION: The cyclical ordering of the quantitative variableshas to be of some significance to legitimise the value of the polar chartover the bar chart. As the sectors will be angled all around the radialdisplay, you will need to make sure all the associated labels arereadable (i.e. not upside down or at difficult angles). The quantitativevalues represented by the size of the sectors need to be carefullycalculated. It is the area of the sector, not the radius length, that will bemodified to portray the values accurately. If you make maximumquantitative value equivalent to the largest sector area, all other sectorsizes can be calculated accordingly. Knowing how many differentquantitative variables you are showing means you can easily calculate

261

the angle of any given sector. The quantitative measure axes shouldalways start from the origin value of zero: a sector should berepresentative of the true, full quantitative value, nothing more, nothingless, otherwise the perception of size will be distorted when comparingrelative sizes.

VARIATIONS & ALTERNATIVES

Unless the radial layout provides meaning through the notion of a‘whole’ or through the cyclical arrangement of measures, you might bebest using a ‘bar chart’. Variations in approach tend to seemodifications in the sector shape with measure values represented byindividual bars lengths or, in the example of the Better Life Indexproject, through variations in ‘petal’ sizes.

Charts Distributions

Range Chart

ALSO KNOWN AS Span chart, floating bar chart, barometer chart

REPRESENTATION DESCRIPTION

A range chart displays the minimum to maximum distribution of aseries of quantitative values for different categories. The display isformed by a bar, one for each category, with the lower and upperposition of the bars shaped by the minimum and maximum quantitativevalues in each case. The resulting bar lengths thus represent the rangeof values between the two limits.

EXAMPLE Comparing the highest and lowest temperatures (°F)recorded across the top 10 most populated cities during 2015.

Figure 6.17 Range of Temperatures Recorded in Top 10 MostPopulated Cities (2015)

262

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know with what major categorical values eachrange bar is associated and what the range of the quantitative values is(min to max). Glance across the entire chart to locate the big, small andmedium bars and perform global comparisons to establish the high-level ranking of biggest > smallest differences as well as the highest andlowest values. Identify any noticeable exceptions and/or outliers.Perform local comparisons between neighbouring bars, to identifylarger than and smaller than relationships and estimate the relativeproportions. There may be deliberate sorting of the display based onone of the quantitative measures. Estimate (or read, if labels arepresent) the absolute values of specific bars of interest. Whereavailable, compare the quantities against annotated references such astargets, forecast, last year, average, etc.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like tick marks and gridlines,in particular, can be helpful to increase the accuracy of the reading ofthe quantitative values. If you have axis labels you may not need directlabels on each bar – this will be lead to label overload, so generallydecide between one or the other.

COMPOSITION: The quantitative value axis does not need tocommence from zero, unless it means something significant to the

263

interpretation, as the range of values themselves does not necessarilystart from zero and the focus is more on the range and differencebetween the outer values. There is no significant difference inperception between vertical or horizontal layouts, though the latter tendto make it easier to accommodate and read the category labels. Wherepossible, try to make the categorical sorting meaningful, maybeorganising values in ascending/descending size order.

VARIATIONS & ALTERNATIVES

‘Connected dot plots’ will also emphasise the difference between twoselected measure values (as opposed to min/max) or where theunderlying data is a change over time between two observations. ‘Bandcharts’ will often be used to show how the range of data values haschanged over time, displaying the minimum and maximum bands ateach time unit. These are often used in displays like weather forecasts.

Charts Distributions

Box-and-whisker plot

ALSO KNOWN AS Box plot

REPRESENTATION DESCRIPTION

A box-and-whisker plot displays the distribution and shape of a seriesof quantitative values for different categories. The display is formed bya combination of lines and point markers to indicate (through positionand length), typically, five different statistical measures. Three of thestatistical values are common to all plots: the first quartile (25thpercentile), the second quartile (or median) and the third quartile (75thpercentile) values. These are displayed with a box (effectively a widebar) positioned and sized according to the first and third quartile valueswith a marker indicating the median. The remaining two statisticalvalues vary in definition: usually either the minimum and maximum

264

values or the 10th and 90th percentiles. These statistical values arerepresented by extending a line beyond the bottom and top of the mainbox to join with a point marker indicating the appropriate position.These are the whiskers. A plot will be produced for each majorcategory.

EXAMPLE Comparing the distribution of annual earnings 10 yearsafter starting school for graduates across the eight Ivy League schools.

Figure 6.18 Ranking the Ivies

HOW TO READ IT & WHAT TO LOOK FOR

Begin by looking at the axes so you know with which category eachplot is associated and what the range of quantitative values is (min tomax). Establish the specific statistics being displayed, by consulting anylegends or descriptions, especially in order to identify what the‘whiskers’ are representing. Glance across the entire chart to locate themain patterns of spread, identifying any common or noticeably different

265

patterns across categories. Look across the shapes formed for eachcategory to learn about the dispersal of values: starting with the median,then observing the extent and balance of the ‘box’ (the interquartilerange between the 25th and 75th percentiles) and then check the‘whisker’ extremes. Is the shape balanced or skewed around themedian? Is the interquartile range wide or narrow? Are the whiskerextremes far away from the edges of the box? Then return to comparingshapes across all categories to identify more precisely any interestingdifferences or commonalities for each of the five statistical measures.

PRESENTATION TIPS

ANNOTATION: If you have axis labels you may not need direct labelson each bar – this will lead to label overload, so generally decidebetween one or the other.

COMPOSITION: The quantitative value axis does not need tocommence from zero, unless it means something significant to theinterpretation, as the range of values themselves do not necessarily startfrom zero and the focus is on the statistical properties between the outervalues. There is no significant difference in perception between verticalor horizontal box-and-whisker plots, though horizontal layouts tend tomake it easier to accommodate and read the category labels. Try to keepa noticeable gap between plots to enable greater clarity in reading.When you have several or many plots in the same chart, where possibletry to make the categorical sorting meaningful, maybe organising valuesin ascending/descending order based on the median value.

VARIATIONS & ALTERNATIVES

Variations involve reducing the number of statistical measures includedin the display by removing the whiskers to just show the 25th and 75thpercentiles through the lower and upper parts of the box. The‘candlestick chart’ (or OHLC chart) involves a similar approach and isoften used in finance to show the distribution and milestone values ofstock performances during a certain time frame (usually daily), plottingthe opening, highest, lowest and closing prices, using colour to indicatean up or down trend.

Charts Distributions

266

Univariate scatter plot

ALSO KNOWN AS 1D scatter plot, jitter plot

REPRESENTATION DESCRIPTION

A univariate scatter plot displays the distribution of a series ofquantitative values for different categories. In contrast to the box-and-whisker plot, which shows selected statistical values, a univariatescatter plot shows all values across a series. For each category, a rangeof points (typically circles but any ‘symbol’ is legitimate) are used tomark the position along the scale of the quantitative values. From thisyou can see the range, the outliers and the clusters and form anunderstanding about the general shape of the data.

EXAMPLE Comparing the distribution of average critics score (%)from the Rotten Tomatoes website for each movie released across arange of different franchises and movie theme collections.

Figure 6.19 Comparing Critics Scores for Major Movie Franchises

267

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know what each scatter row/column relates toin terms of which category it is associated with and what the range ofthe quantitative values is (min to max). If colour has been used toemphasise or separate different marks, establish what the associationsare. Also, learn about how the design depicts multiple marks on thesame value – these may appear darker or indeed larger. Glance acrossthe entire chart to observe the main patterns of clustering and identifyany noticeable exceptions and/or outliers across all categories. Thenlook more closely at the patterns within each scatter to learn about eachcategory’s specific dispersal of values. Look for empty regions whereno quantitative values exist. Estimate the absolute values of specificdots of interest. Where available, compare the quantities againstannotated references such as the average or median.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like gridlines can be helpfulto increase the accuracy of the reading of the quantitative values. Directlabelling is normally restricted to including values for specificallynoteworthy points only.

COLOUR: Colour may be used to establish focus of certain points

268

and/or distinction between different sub-category groups to assist withinterpretation. When several points have the exact same value youmight need to use unfilled or semi-transparent filled circles to facilitatea sense of value density.

COMPOSITION: The representation of the quantitative values isbased on position and not size, therefore the quantitative axis does notneed to have a zero origin. There is no significant difference inperception between vertical or horizontal arrangement, thoughhorizontal layouts tend to make it easier to accommodate and read thecategory labels. Where possible try to make the categorical sortingmeaningful, maybe organising values in ascending/descending sizeorder.

VARIATIONS & ALTERNATIVES

To overcome occlusion caused by plotting several marks at the samevalue, a variation of the univariate scatter plot may see the pointsreplaced by geometric areas (like circles), where the position attribute isused to represent a quantitative value along a scale and the size attributeis used to indicate the frequency of observations of similar value.Adding a second quantitative variable axis would lead to the use of a’scatter plot’.

Charts Distributions

Histogram

ALSO KNOWN AS Bar chart (wrongly)

REPRESENTATION DESCRIPTION

A histogram displays the frequency and distribution for a range ofquantitative groups. Whereas bar charts compare quantities for differentcategories, a histogram technically compares the number of

269

observations across a range of value ‘bins’ using the size of lines/bars(if the bins relate to values with equal intervals) or the area ofrectangles (if the bins have unequal value ranges) to represent thequantitative counts. With the bins arranged in meaningful order (thateffectively form ordinal groupings) the resulting shape formed revealsthe overall pattern of the distribution of observations.

EXAMPLE Comparing the distribution of movies released over timestarring Michael Caine across five-year periods based on the date ofrelease in the US.

Figure 6.20 A Career in Numbers: Movies Starring Michael Caine

HOW TO READ IT & WHAT TO LOOK FOR

Begin by looking at the axes so you know what the chart depicts interms of the categorical bins and the range of the quantitative values(zero to max). Glance across the entire chart to establish the mainpattern. Is it symmetrically shaped, like a bell or pyramid (around amedian or average value)? Is it skewed to the left or right? Does it dipin the middle and peak at the edges (known as bimodal)? Does it haveseveral peaks and troughs? Maybe it is entirely random in its pattern?

270

All these characteristics of ‘shape’ will inform you about the underlyingdistribution of the data.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like tick marks and gridlinesin particular can be helpful to increase the accuracy of the reading ofthe quantitative values. Axis labels more than direct value labels tend tobe used so as not to crowd the shape of the histogram.

COMPOSITION: Unlike the bar chart there should be no (or at most avery thin) gap between bars to help the collective shape of thefrequencies emerge. The sorting of the quantitative bins must be inascending order so that the reading of the overall shape preserves itsmeaning. The number of value bins and the range of values covered byeach have a prominent influence over the appearance of the histogramand the usefulness of what it might reveal: too few bins may disguiseinteresting nuances, patterns and outliers; too many bins and the mostinteresting shapes may be abstracted by noise above signal. There is nosingular best approach, the right choice simply arrives throughexperimentation and iteration.

VARIATIONS & ALTERNATIVES

For analysis that looks at the distribution of values across twodimensions, such as the size of populations for age across genders, a‘back-to-back histogram’ (with male on one side, female on the other),also commonly known as a ‘violin plot’ or ‘population pyramid’, is auseful approach to see and compare the respective shapes. A ‘box-and-whisker plot’ reduces the distribution of values to five key statisticalmeasures to describe key dimensions of the spread of values.

Charts Distributions

Word cloud

271

ALSO KNOWN AS Tag cloud

REPRESENTATION DESCRIPTION

A word cloud shows the frequency of individual word items used intextual data (such as tweets, comments) or documents (passages,articles). The display is based around an enclosed cluster of words withthe font (not the word length) sized according to the frequency of usage.In modifying the size of font this is effectively increasing the area sizeof the whole word. All words have a different shape and size so this canmake it quite difficult to avoid the prominence of long words,irrespective of their font size. Word clouds are therefore only usefulwhen you are trying to get a quick and rough sense of some of thedominant keywords used in the text. They can be an option for workingwith qualitative data during the data exploration stage, more so as ameans for reporting analysis to others.

EXAMPLE Comparing the frequency of words used in Chapter 1 ofthis book.

Figure 6.21 Word Cloud of the Text from Chapter 1

HOW TO READ IT & WHAT TO LOOK FOR

The challenge with reading word clouds is to avoid being drawn to thelength and/or area of a word – they are simply attributes of the word,not a meaningful representation of frequency. It is the size of the fontthat you need to focus on. Scan the display to spot the larger textshowing the more frequently used words. Consider any words ofspecific interest to see if you can find them; if they are not significantlyvisible, that in itself could be revealing. While most word cloud

272

generators will dismiss many irrelevant words, you might still need tofilter out perceptually the significance of certain dominantly sized text.

PRESENTATION TIPS

INTERACTIVITY: Interactive features that let users interrogate, filterand scrutinise the words in more depth, perhaps presenting examples oftheir usage in a passage, can be quite useful to enhance the value of aword cloud.

ANNOTATION: While the absolutes are generally of less interest thanrelative comparisons, to help viewers get as much out of the display aspossible a simple legend explaining how the font size equates tofrequency number can be useful.

COLOUR: Colours may be used as redundant encoding to accentuatefurther the larger frequencies or categorically to create useful visualseparation.

COMPOSITION: The arrangement of the words within a word cloudis typically based on a layout process. Although not random, this willgenerally prioritise the placement of words to occupy optimumcollective space that preserves an overall shape (with essentially acentral gravity) over and above any arrangement that might betterenable direct comparison.

VARIATIONS & ALTERNATIVES

The alternative approach would be to use any other method in thiscategorical family of charts that would more usefully display the countsof text, such as a bar chart.

Charts Part-to-whole

Pie chart

273

ALSO KNOWN AS Pizza chart

REPRESENTATION DESCRIPTION

A pie chart shows how the quantities of different constituent categoriesmake up a whole. It uses a circular display divided into sectors for eachcategory, with the angle representing each of the percentageproportions. The resulting size of the sector (in area terms) is a spatialby-product of the angle applied to each part and so offers an additionalmeans for judging the respective values. The role of a pie chart isprimarily about being able to compare a part to a whole than being ableto compare one part to another part. They therefore work best whenthere are only two or three parts included. There are a few importantrules for pie charts. Firstly, the total percentage values of all sectorvalues must be 100%; if the aggregate is greater than or less than 100%the chart will be corrupted. Secondly, the whole has to be meaningful –often people just add up independent percentages but that is not what apie chart is about. Finally, the category values must represent exclusivequantities; nothing should be counted twice or overlap across differentcategories. Despite all these warnings, do not be afraid of the pie chart –just use it with discretion.

EXAMPLE Comparing the proportion of eligible voters in the 2015UK election who voted for the Conservative Party, for other parties andwho did not vote.

Figure 6.22 Summary of Eligible Votes in the UK General Election2015

274

HOW TO READ IT & WHAT TO LOOK FOR

Begin by establishing which sectors relate to what categories. This mayinvolve referring to a colour key legend or through labels directlyadjacent to the pie. Quickly scan the pie to identify the big, medium andsmall sectors. Notice if there is any significance behind the ordering ofthe parts. Unless there are value labels, you next will attempt to judgethe individual sector angles. This usually involves mentally breakingthe pie into 50% halves (180°) or 25% quarters (90°) and using thoseguides to perceptually measure the category values. Comparing partsagainst other parts with any degree of accuracy will only be possibleonce you have formed estimates of the individual sector sizes. If youare faced with the task of judging the size of many parts it is quiteunderstandable if you decide to give up quite soon.

PRESENTATION TIPS

ANNOTATION: The use of local labelling for category values can beuseful but too many labels can become cluttered, especially whenattempting to label very small angled sectors.

COLOUR: Colour is generally vital to create categorical separation

275

and association of the different sectors so aim to use the difference incolour hue and not colour saturation to maximise the visible difference.

COMPOSITION: Positioning the first slice at the vertical 12 o’clockposition gives a useful baseline to help judge the first sector anglevalue. The ordering of sectors using descending values or ordinalcharacteristics helps with the overall readability and allocation of effort.Do not consider using gratuitous decoration (like 3D, gradient colours,or exploding slices).

VARIATIONS & ALTERNATIVES

Sometimes a pie chart has a hole in the centre and is known as a‘doughnut chart’, continuing the food-related theme. The function isexactly the same as a pie but the removal of the centre, often toaccommodate a labelling property, removes the possibility of the readerjudging the angles at the origin. One therefore has to derive the anglesfrom the resulting arc lengths. If you want to display multiple parts(more than three) the bar chart will be a better option and, for manyparts, the ‘treemap’ is best. Depending on the allocated space, a‘stacked bar chart’ may provide an alternative to the pie. Unlike mostchart types, the pie chart does not work well in the form of smallmultiples (unless there is only a single part being displayed). A ‘nestedshape chart’, typically based on embedded square or circle areas,enables comparison across a series of one-part-to-whole relationshipsbased on absolute numbers, rather than percentages, where the wholesmay vary in size.

Charts Part-to-whole

Waffle chart

ALSO KNOWN AS Square pie, unit chart, 100% stacked shape chart

REPRESENTATION DESCRIPTION

276

A waffle chart shows how the quantities of different constituentcategories make up a whole. It uses a square display usuallyrepresenting 100 point ‘cells’ through a 10 × 10 grid layout. Eachconstituent category proportion is displayed through colour-coding aproportional number of cells. Difference in symbol can also be used.The role of the waffle chart is to simplify the counting of proportions incontrast to the angle judgements of the pie chart, though the display islimited to rounded integer values. This is easier when the grid layoutfacilitates quick recognition of units of 10. As with the pie chart, thewaffle chart works best when you are showing how a single partcompares to the whole and perhaps offers greater visual impact whenthere are especially small percentages of a whole. Rather than justcolouring in the grid cells, sometimes different symbols will be used toassociate with different categories. For example, you might see figuresor gender icons used to show the makeup of a given sample population.

EXAMPLE Comparing the proportion of total browser usage forInternet Explorer and Chrome across key milestone moments.

Figure 6.23 The Changing Fortunes of Internet Explorer and GoogleChrome

HOW TO READ IT & WHAT TO LOOK FOR

Begin by establishing how the different shapes or colours are associatedwith different categories. Assess the grid layout to understand thedimension of the chart and the quantity of cell ‘units’ forming thedisplay (e.g. is it a 10 x 10 grid?). Quickly scan the chart to identify thebig, medium and small sectors. Notice if there is any significance

277

behind the ordering of the parts. Unless there are value labels, you willneed to count/estimate the number of units representing each categoryvalue. Comparing parts against other parts will only be possible onceyou have established the individual part sizes. If several related wafflecharts are shown, possibly for different categories or points in time,identify the related colours/shapes in each chart and establish thepatterns of size between and across the various charts, looking fortrends, declines and general differences.

PRESENTATION TIPS

ANNOTATION: Direct labelling can become very cluttered and hardto incorporate elegantly without the need for long arrows.

COLOUR: Borders around each square cell are useful to help establishthe individual units, but do not make the borders too thick to the pointwhere they dominate attention.

COMPOSITION: Always start each row of values from the same side,for consistency and to make it easier for people to estimate the values.When you have several parts in the same waffle chart, where possibletry to make the categorical sorting meaningful, maybe organising valuesin ascending/descending size order or based on a logical categoricalorder.

VARIATIONS & ALTERNATIVES

Sometimes the waffle chart approach is used to show stacks of absoluteunit values and indeed there are overlaps in concept between thisvariation in the waffle chart and potential applications of the pictogram.Aside from the pie chart, a ‘nested shape chart’ will provide analternative way of showing a part-to-whole relationship while alsooccupying a squarified layout.

Charts Part-to-whole

Stacked bar chart

278

ALSO KNOWN AS

REPRESENTATION DESCRIPTION

A stacked bar chart displays a part-to-whole breakdown of quantitativevalues for different major categories. The percentage proportion of eachcategorical dimension or ‘part’ is represented by separate bars,distinguished by colour, that are sized according to their proportion andthen stacked to create the whole. Sometimes the whole is standardisedto represent 100%, at other times the whole will be representative ofabsolute values. Stacked bar charts work best when the parts are basedon ordinal dimensions, which enables ordering of the parts within thestack to help establish the overall shape of the data. If the parts arerepresentative of nominal data, it is best to keep the number ofconstituent categories quite low, as estimating the size of individualstacked parts when there are many becomes quite hard.

EXAMPLE Comparing the percentage of adults (16–65 year olds)achieving different proficiency levels in literacy across differentcountries.

Figure 6.24 Literarcy Proficiency: Adult Levels by Country

279

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know with what major categorical values eachbar is associated and what the quantitative values are, determining if itis a 100% stacked bar or an absolute stacked bar (in which case identifythe min and the max). Establish the colour association to understandwhat categories the bars within each stack represent. Glance across theentire chart. If the categorical data is ordinal, and the sorting/colour ofthe stacks is intuitive, you should be able to derive meaning from theoverall balance of colour patterns, especially where any annotatedgridlines help to guide your value estimation. If the categorical data isnominal, seek to locate the dominant colours and the least noticeableones. Comparing across different stacked bars is made harder by thelack of a common baseline for anything other than the bottom stack onthe zero baseline (and for 100% stacked bars, those final ones at thetop) and so a general sense of magnitude will be your focus. Studycloser the constituent parts within each stack to establish the high-levelranking of biggest > smallest. Estimate (or read, if labels are present)

280

the absolute values of specific stacked parts of interest.

PRESENTATION TIPS

ANNOTATION: Direct value labelling can become very clutteredwhen there are many parts or stacks and you are comparing severaldifferent major categories. You might be better with a table if that isyour aim. Definitely include value axis labels with logical intervals andit is very helpful to annotate, through gridlines, key units such as the25%, 50% and 75% positions when based on a 100% stacked bar chart.

COLOUR: If you are representing categorical ordinal data, colour canbe astutely deployed to give a sense of the general balance of valueswithin the whole, but this will only work if their sorting arrangementwithin the stack is logically applied. For categorical nominal data,ensure the stacked parts have sufficiently different colours so that theirdistinct bar lengths can be efficiently observed.

COMPOSITION: Across the main categories, once again consider theoptimum sorting option, maybe organising values inascending/descending size order or based on a logical categorical order.Judging the size of the stacks with accuracy is harder for those that arenot on the zero baseline, so maybe consider which ones are of mostimportance to be more easily read and place those on the baseline.

VARIATIONS & ALTERNATIVES

The main alternative would be to use ‘multi-panel bar charts’, whereseparate bar charts each include just one ’stack’/part and they are thenrepeated for each subsequent constituent category. In the world offinance the ‘waterfall chart’ is a common approach based on a singlestacked bar broken up into individual elements, almost like a step-by-step narrative of how the components of income look on one side andthen how the components of expenditure look on the other, with theremaining space representing the surplus or deficit. Like their unstackedsiblings, stacked bar charts can also be used to show how categoricalcomposition has changed over time.

Charts Part-to-whole

281

Back-to-back bar chart

ALSO KNOWN AS Paired bar chart

REPRESENTATION DESCRIPTION

A back-to-back bar chart displays a part-to-whole breakdown ofquantitative values for different major categories. As with any bar chart,the length of a bar represents a quantitative proportion or absolute valuefor each part and across all major categories. In contrast to the stackedbar chart, where the constituent bars are simply stacked to form awhole, in a back-to-back bar chart the constituent parts are based ondiverging categorical dimensions with a ‘directional’ essence such asyes/no, male/female, agree/disagree. The values for each dimension aretherefore presented on opposite sides of a shared zero baseline to helpreveal the shape and contrast differences across all major categories.

EXAMPLE Comparing the responses to a survey question asking foropinions about ‘the government collection of telephone and Internetdata as part of anti-terrorism efforts’ across different demographiccategories.

Figure 6.25 Political Polarization in the American Public

282

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know with which major categorical values eachbar is associated and what the range of the quantitative values is (min tomax). Establish what categorical dimensions are represented by therespective sides of the display and any colour associations. Glanceacross the entire chart to locate the big, small and medium bars andperform global comparisons to establish the high-level ranking ofbiggest > smallest. Repeat this for each side of the display, noticing anypatterns of dominance of larger values on either side. Identify anynoticeable exceptions and/or outliers. Perform local comparisons foreach category value to estimate the relative sizes (or read, if labels arepresent) of each bar.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like tick marks and gridlinesin particular can be helpful to increase the accuracy of the reading ofthe quantitative values.

COLOUR: The bars either side of the axis do not need to be colouredbut often are to create further visual association.

COMPOSITION: The quantitative value axis should always start fromthe origin value of zero: a bar should be representative of the true, fullquantitative value, nothing more, nothing less, otherwise the perception

283

of bar sizes will be distorted when comparing relative sizes. There is nosignificant difference in perception between vertical or horizontal bars,though horizontal layouts tend to make it easier to accommodate andread the category labels. Where possible try to make the categoricalsorting meaningful, maybe organising values in ascending/descendingsize order or based on a logical categorical order.

VARIATIONS & ALTERNATIVES

Back-to-back bar charts facilitate a general sense of the shape ofdiverging categorical dimensions. However, if you want to facilitatedirect comparison, a ‘clustered bar chart’ showing adjacent bars helpsto compare respective heights more precisely. For analysis that looks atthe distribution values across two dimensions, such as the size ofpopulations for age across genders, a ‘back-to-back histogram’ (withmale on one side, female on the other), also commonly known as a‘violin plot’ or ‘population pyramid’, is a useful approach to see andcompare the respective shapes. Some back-to-back applications do notshow a part-to-whole relationship but simply compare quantities fortwo categorical values. Further variations may appear as ‘back-to-backarea charts’ showing mutual change over time for two contrastingstates.

Charts Part-to-whole

Treemap

ALSO KNOWN AS Heat map (wrongly)

REPRESENTATION DESCRIPTION

A treemap is an enclosure diagram providing a hierarchical display toshow how the quantities of different constituent parts make up a whole.It uses a contained rectangular layout (often termed ‘squarified’)representing the 100% total divided into proportionally sized

284

rectangular tiles for each categorical part. Colour can be used torepresent an additional quantitative measure, such as an indication ofamount of change over a time period. The absolute positioning anddimension of each rectangle is organised by an underlying tilingalgorithm to optimise the overall space usage and to cluster relatedcategories into larger rectangle-grouped containers. Treemaps are mostcommonly used, and of most value, when there are many parts to thewhole but they are only valid if the constituent units are legitimatelypart of the same ‘whole’.

EXAMPLE Comparing the relative value of and the daily performanceof stocks across the S&P 500 index grouped by sectors and industries.

Figure 6.26 FinViz: Standard and Poor’s 500 Index

HOW TO READ IT & WHAT TO LOOK FOR

Look at the high-level groupings to understand the different containingarrangements and establish what the colour association is. Glanceacross the entire chart to seek out the big, small and medium individualrectangular sizes and perform global comparisons to establish a generalranking of biggest > smallest values. Also identify the largest throughto smallest container group of rectangles. If the colour coding is basedon quantitative variables, look out for the most eye-catching patterns atthe extreme end of the scale(s). If labels are provided (or offeredthrough interactivity), browse around the display looking for categoriesand values of specific interest. As with any display based on the size ofthe area of a shape, precise reading of values is hard to achieve and so itis important to understand that treemaps can only aim to provide a

285

single-view gist of the properties of the many components of the whole.

PRESENTATION TIPS

INTERACTIVITY: Typically, a treemap will be presented withinteractive features to enable selection/mouseover events to revealfurther annotated details and/or drill-down navigation.

ANNOTATION: Group/container labels are often allocated a cell ofspace but these are not to be read as proportional values. Effectivedirect value labelling becomes difficult as the rectangles get smaller, sooften only the most prominent values might be annotated. Interactivefeatures will generally offer visibility of the relevant labels wherepossible.

COLOUR: Colour can also be used to provide further categoricalgrouping distinction if not already assigned to represent a quantitativemeasure of change.

COMPOSITION: As the tiling algorithm is focused on optimising thedimensions and arrangement of the rectangular shapes, treemaps maynot always be able to facilitate much internal sorting of high to lowvalues. However, generally you will find the larger shapes appear in thetop left of each container and work outwards towards the smallerconstituent parts.

VARIATIONS & ALTERNATIVES

A variation of the treemap sees the rectangular layout replaced by acircular one and the rectangular tiles replaced by organic shapes. Theseare known as ‘Voronoi treemaps’ as the tiling algorithm is informed bya Voronoi tessellation. The ‘circle packing diagram’, a variation of the‘bubble chart’, similarly shows many parts to a whole but uses a non-tessellating circular shape/layout. The ‘mosaic plot’ or ‘Marimekkochart’ is similar in appearance to a treemap but, in contrast to thetreemap’s hierarchical display, presents a detailed breakdown ofquantitative value distributions across several categorical dimensions,essentially formed by varied width stacked bars.

Charts Part-to-whole

286

Venn diagram

ALSO KNOWN AS Set diagram, Euler diagram (wrongly)

REPRESENTATION DESCRIPTION

A Venn diagram shows collections of and relationships betweenmultiple sets. They typically use round or elliptical containers torepresent all different ‘membership’ permutations to include allindependent and intersecting containers. The size of the contained areais (typically) not important: what is important is in which containingregion a value resides, which may be represented through the mark of atext label or ‘point’.

EXAMPLE Comparing sets of permutations for legalities aroundmarijuana usage and same-sex marriage across states of the USA.

Figure 6.27 This Venn Diagram Shows Where You Can Both SmokeWeed and Get a Same-Sex Marriage

287

HOW TO READ IT & WHAT TO LOOK FOR

To read a Venn diagram firstly establish what the different containersare representative of in terms of their membership. Assess themembership of the intersections (firstly ‘all’, then ‘partial’ intersectionswhen involving more than two sets) then work outwards towards theindependent container regions where values are part of one set but notpart of others. Occasionally there will be a further grouping stateoutside of the containers that represents values that have nomembership with any set at all.

PRESENTATION TIPS

ANNOTATION: Unless you are using point markers to representmembership values, clear labels are vital to indicate how many or whichelements hold membership with each possible set combination.

COLOUR: Colour is often used to create more immediate distinctionbetween the intersections and independent parts or members of each

288

container.

COMPOSITION: As the attributes of size and shape of the containersare of no significance there is more flexibility to manipulate the displayto fit the number of sets around the constraint of real estate you arefacing and to get across the set memberships you are attempting toshow. The complexity of creating containers to accommodate allcombinations of intersection and independence states increases as thenumber of sets increases, especially to preserve all possiblecombinations of intersections between and independencies from all sets.As the number of sets increases, the symmetry of shape reduces and thecircular containers are generally replaced with ellipses. While it istheoretically possible to exceed four and five set diagrams, the ability ofreaders to make sense of the displays diminishes and so they commonlyinvolve only two or three different sets.

VARIATIONS & ALTERNATIVES

A common variation or alternative to the Venn (but often mistakenlycalled a Venn) is the ‘Euler diagram’. The difference is that an Eulerdiagram does not need to present all possible intersections with andindependencies from all sets. A different approach to visualising sets(especially larger numbers) can be achieved using the ‘UpSet’technique.

Charts Hierarchies

Dendrogram

ALSO KNOWN AS Node–link diagram, layout tree, cluster tree, treehierarchy

REPRESENTATION DESCRIPTION

A dendrogram is a node–link diagram that displays the hierarchical

289

relationship across multiple tiers of categorical dimensions. It displays ahierarchy based on multi-generational ‘parent-and-child’ relationships.Starting from a singular origin root node (or ‘parent’) each subsequentset of constituent ‘child’ nodes, a tier below and represented by points,is connected by lines (curved or straight) to indicate the existence of arelationship. Each constituent node may have further sub-constituenciesrepresented in the same way, continuing down through to the lowest tierof detail. Each ‘generational’ tier is presented at the same relativedistance from the origin. The layout can be based on either a linear treestructure (typically left to right) or radial tree (outwards from thecentre).

EXAMPLE Showing a breakdown of the 200+ beer brands belongingto SAB InBev across different countries grouped by continent.

Figure 6.28 The 200+ Beer Brands of SAB InBev

HOW TO READ IT & WHAT TO LOOK FOR

290

Reading a dendrogram will generally be a highly individual experiencebased on your familiarity with the subject and your interest in exploringcertain hierarchical pathways. The main focus of attention will likely beto find the main clusters from where most constituent parts branch outand to contrast these with the thinner, lighter paths comprising fewerparts. Work left to right (linear) or in to out (radial) through thedifferent routes that stoke your curiosity.

PRESENTATION TIPS

ANNOTATION: With labelling required for each node, depending onthe number of tiers and the amount of nodes, the size of the text willneed to be carefully considered to ensure readability and minimise theeffect of clutter.

COLOUR: Colour would be an optional choice for accentuating certainnodes or applying some further visual categorisation.

COMPOSITION: There are several different layout options to displaytree hierarchies like the dendrogram. The common choice is a clusterlayout based on the ‘Reingold–Tilford’ tree algorithms that offers atidying and optimisation treatment for the efficiency of the arrangementof the nodes and connections. The sequencing of sub-constituenciesunder each node could be logically arranged in some more meaningfulway than just alphabetical, though the cataloguing nature of A–Z maysuit your purpose. The choice of a linear or radial tree structure will beinformed largely by the space you have to work in as well as by thecyclical or otherwise nature of the content in your data. The main issueis likely to be one of legibility if and when you have numerous layers ofdivisions and many constituent parts to show in a single view.

VARIATIONS & ALTERNATIVES

More advanced applications of dendrograms are used to presenthierarchical clustering (in fields such as computational biology) andapply more quantitative meaning to the length of the links and thepositioning of the nodes. The ‘tree hierarchy diagram’ offers a similartree structure but introduces quantitative attributes to the nodes usingarea marks, such as circles, sized according to a quantitative value. Analternative approach to the dendrogram could involve a ‘linear bracket’.This might show hierarchical structures for data-related sportingcompetitions with knock-out format. The outer nodes would be thestarting point representing all the participating competitors/teams. Eachsubsequent tier would represent those participants who progressed tothe next round, continuing through to the finalists and eventual victors.

291

Charts Hierarchies

Sunburst

ALSO KNOWN AS Adjacency diagram, icicle chart, multi-level piechart

EXAMPLE Showing a breakdown of the types of companiesresponsible for extracting different volumes of carbon-based fuelsthrough various activities.

REPRESENTATION DESCRIPTION

A sunburst chart is an adjacency diagram that displays the hierarchicaland part-to-whole relationships across multiple tiers of categoricaldimensions. In contrast to the dendrogram, the sunburst uses layers ofconcentric rings, one layer for each generational tier. Each ring layer isdivided into parts based on the constituent categorical dimensions atthat tier. Each part is represented by a different circular arc section thatis sized (in length; width is constant) according to the relativeproportion. Starting from the centre ‘parent’ tier, the outward adjacencyof the constituent parts of each tier represents the ‘parent-and-child’hierarchical composition.

Figure 6.29 Which Fossil Fuel Companies are Most Responsible forClimate Change?

292

HOW TO READ IT & WHAT TO LOOK FOR

Reading a sunburst chart will be a highly individual experience basedon your familiarity with the subject and your interest in exploringcertain hierarchical pathways. The main focus of attention will likely beto find the largest arc lengths, representing the largest single constituentparts, and those layers or tiers with the most constituent parts. Workfrom the centre outwards through the different routes that stoke yourcuriosity. Depending on the deployment of colour, this may help youidentify certain additional categorical patterns.

PRESENTATION TIPS

INTERACTIVITY: Often interactive mouseover/selection events arethe only way to reveal the annotations here.

ANNOTATION: Labelling can be quite difficult to fit into the narrowspaces afforded by small proportion ‘parts’. If interactivity is not an

293

option you may decide to label only those parts that can accommodatethe text space.

COLOUR: Colours are often used to achieve further categoricaldistinction.

COMPOSITION: Sometimes the parent–child (and other generational)relationships could be legitimately reversed, so decisions need to bemade about the best hierarchy sequencing to suit the curiosities of theaudience. The sequencing of sub-constituencies under each node couldalso be logically arranged in a meaningful way, more so than justalphabetical, unless the cataloguing nature of A–Z ordering suits yourpurpose.

VARIATIONS & ALTERNATIVES

Where the sunburst chart uses a radial layout, the ‘icicle chart‘ uses avertical, linear layout starting from the top and moving downwards. Thechoice of a linear or radial tree structure will be informed largely by thespace you have to work in as well as by the legitimacy of the cyclicalnature of the content in your data. A variation on the sunburst chartwould be the ‘ring bracket’. This might show a reverse journey forhierarchical data based on something like sporting competitions withknock-out formats. The outer concentric partitions would represent theparticipant competitors/teams at the start of the process. The length ofthese arc line parts would be equally distributed across all constituentparts with each subsequent tier representing ‘participants’ who progressforward to the next ‘round’, continuing through to the finalists andeventual victors in the centre.

Charts Correlations

Scatter plot chart

ALSO KNOWN AS Scatter graph

294

REPRESENTATION DESCRIPTION

A scatter plot displays the relationship between two quantitativemeasures for different categories. Scatter plots are used to explorevisually the potential existence, extent or absence of a significantrelationship between the plotted variables. The display is formed bypoints (usually a dot or circle), representing each category and plottedpositionally along quantitative x- and y-axes. Sometimes colour is usedto distinguish categorical dimensions across all the points. Scatter plotsdo not work too well if one or both of the quantitative measures haslimited variation in value as this especially causes problems of‘occlusion’, whereby multiple instances of the similar values are plottedon top of each other and essentially hidden from the reader.

EXAMPLE Exploring the relationship between life expectancy and thepercentage of healthy years across all countries.

Figure 6.30 How Long Will We Live — And How Well?

HOW TO READ IT & WHAT TO LOOK FOR

Learn what each quantitative axis relates to and make a note of therange of values in each case (min to max). Look at what category orobservation each plotted value on the chart refers to and look up anycolour associations being used for categorical distinction. Scan the chartlooking for the existence of any diagonal trends that might suggest a

295

linear correlation between the variables, or note the complete absenceof any pattern, to mean no correlation. Annotations will often assist indetermining the significance of any patterns like this. Identify anyclusters of points and also look at the gaps, which can be just asrevealing. Some of the most interesting observations come fromindividual outliers standing out separately from others. Look out for anypatterns formed by points with similar categorical colour. One approachto reading the ‘meaning’ of the plotted positions involves trying tobreak down the chart area into a 2 × 2 grid translating what markspositioned in those general areas might mean – which corner is ‘good’or ‘bad’ to be located in? Remember that ruling out significantrelationships can be just as useful as ruling them in.

PRESENTATION TIPS

ANNOTATION: Gridlines can be useful to help make the valueestimates clearer and reference lines (such as a trend line of best fit)might aid interpretation. It is usually hard to make direct labelling of allvalues work well. Firstly, it can be tricky making it clear which valuerelates to which point, especially when several points may be clusteredtogether. Secondly, it creates a lot of visual clutter. Labelling choicesshould be based on values that are of most interest to include editoriallyunless interactive features enable annotations to be revealed throughselection or mouseover events. If possible, you might consider putting anumber inside the marker to indicate a count of the number of points atthe same position if this occurs.

COLOUR: If colours are being used to distinguish the differentcategories, ensure these are as visibly different as possible. On theoccasion where multiple values may be plotted close to or on top ofeach other, you might need to use semi-transparency to enableoverlapping of points to build up a recognisably darker colourcompared to other points, indicating an underlying stack of values at thesame location on the chart.

COMPOSITION: As the encoding of the plotted point values is basedon position along an axis, it is not necessary to start the axes from azero baseline, so just make the scale ranges as representative as possibleof the range of values being plotted. Ideally a scatter plot will have a1:1 aspect ratio (equally as tall as it is wide), creating a squared area tohelp patterns surface more evidently. If one quantitative variable (e.g.weight) is likely to be affected by the other variable (e.g. height), it isgeneral practice to place the former on the y-axis and the latter on the x-axis. If you have to use a logarithmic quantitative scale on either or both

296

axes, you need to make this clear to readers so they avoid makingincorrect conclusions from the resulting patterns (that might implycorrelation if the values were linear, for example).

VARIATIONS & ALTERNATIVES

A ‘ternary plot’ is a variation of the scatter plot through the inclusion ofa third quantitative variable axis. The ‘bubble plot’ also incorporates athird quantitative variable, this time through encoding the size of ageometric shape (replacing the point marker). A ‘scatter plot matrix’involves a single view of multiple scatter plots presenting differentcombinations of plotted quantitative variables, used to explore possiblerelationships among larger multivariate datasets. A ‘connected scatterplot’ compares the shifting state of two quantitative measures over time.

Charts Correlations

Bubble plot

ALSO KNOWN AS Bubble chart

REPRESENTATION DESCRIPTION

A bubble plot displays the relationship between three quantitativemeasures for different categories. Bubble plots are used visually toexplore the potential existence, extent or absence of a significantrelationship between the plotted variables. In contrast to the scatter plot,the bubble plot plots proportionally sized circular areas, for eachcategory, across two quantitative axes with the size representing a thirdquantitative measure. Sometimes colour is used to distinguishcategorical dimensions across all the shapes.

EXAMPLE Exploring the relationship between rates of murders,burglaries (per 100,000 population) and population across states of theUSA.

297

Figure 6.31 Crime Rates by State

HOW TO READ IT & WHAT TO LOOK FOR

Learn what each quantitative axis relates to and make a note of therange of values in each case (min to max). Look at what category orobservation each plotted value on the chart refers to. Establish thequantitative size associations for the bubble areas and look up anycolour associations being used for categorical distinction. Scan the chartlooking for the existence of any diagonal trends that might suggest alinear correlation between the variables, or note the complete absenceof any pattern, to mean no correlation. Annotations will often assist indetermining the significance of any patterns like this. Identify anyclusters of points and also look at the gaps, which can be just asrevealing. Some of the most interesting observations come fromindividual outliers standing out separately from others. Look out for anypatterns formed by points with similar categorical colour. What can youlearn about the distribution of small, medium or large circles: are theyclustered together in similar regions of the chart or quite randomlyscattered? One approach to reading the ‘meaning’ of the plottedpositions involves trying to break down the chart area into a 2 × 2 gridtranslating what marks positioned in those general areas might mean –which corner is ‘good’ or ‘bad’ to be located in? Remember that rulingout significant relationships can be just as useful as ruling them in.

298

Estimating and comparing the size of areas is not as easy as it is forjudging bar length or dot position. This means that the use of this charttype will primarily be about facilitating a gist – a general sense of thehierarchy of the largest and smallest values.

PRESENTATION TIPS

ANNOTATION: Gridlines can be useful to help make the valueestimates clearer and reference lines (such as a trend line of best fit)might aid interpretation. It is usually hard to make direct labelling of allvalues work well. Firstly, it can be tricky making it clear which valuerelates to which point, especially when several points may be clusteredtogether. Secondly, it creates a lot of visual clutter. Labelling choicesshould be based on values that are of most interest to include editoriallyunless interactive features enable annotations to be revealed throughselection or mouseover events.

COLOUR: If colours are being used to distinguish the differentcategories, ensure these are as visibly different as possible. When acircle has a large value its size will often overlap in spatial terms withother values. The use of outline borders and semi-transparent colourshelps with the task of avoiding occlusion (visually hiding values behindothers).

COMPOSITION: As the encoding of the plotted area marker values isbased on position along an axis, it is not necessary to start the axes froma zero baseline – just make the scale ranges as representative as possibleof the range of values being plotted. Make sensible decisions about howlarge to make the maximum bubble size; this will usually require trialand error experimentation to find the right balance. Ideally a bubble plotwill have a 1:1 aspect ratio (equally as tall as it is wide), creating asquared area to help patterns surface more evidently. If one quantitativevariable (e.g. weight) is likely to be affected by the other variable (e.g.height), it is general practice to place the former on the y-axis and thelatter on the x-axis. Geometric accuracy of the circle size calculations isparamount, since mistakes are often made with circle size calculations:it is the area you are modifying, not the diameter/radius. If you wish tomake your bubbles appear as 3D spheres you are essentially no longerrepresenting quantitative values through the size of a geometric areamark, rather the mark will be a ‘form’ and so the size calculation willbe based on volume, not area.

VARIATIONS & ALTERNATIVES

If the third quantitative variable is removed, the display would just

299

become a ‘scatter plot’. Variations on the bubble plot might see the useof different geometric areas as the markers, maybe introducing extrameaning from the underlying data through the shape, size anddimensions used.

Charts Correlations

Parallel coordinates

ALSO KNOWN AS Parallel sets

REPRESENTATION DESCRIPTION

Parallel coordinates display multiple quantitative measures for differentcategories in a single display. They are used visually to explore therelationships and characteristics of multi-dimensional, multivariate data.Parallel coordinates are based on a series of parallel axes representingdifferent quantitative measures with independent axis scales. Thequantitative values for each measure are plotted and then connected toform a single line. Each connected line represents a different categoryrecord. Colour may be used to differentiate further categoricaldimensions. As more data is added the collective ’shape’ of the dataemerges and helps to inform the possibility of relationships existingamong the different measures. Parallel coordinates look quiteoverwhelming but remember that they are almost always only used toassist in exploratory work of large and varied datasets, more so thanbeing used for explanatory presentations of data. Generally the greaterthe number of measures, the more difficult the task of making sense ofthe underlying patterns will be, so be discerning in your choice ofwhich variables to include. This method does not work for showingcategorical (nominal) measures nor does it really offer value with theinclusion of low-range, discrete quantitative variables used (e.g. numberof legs per human). Patterns will mean very little when intersecting withsuch axes (they may be better deployed as a filtering parameter or acoloured categorical separator).

300

EXAMPLE Exploring the relationship between nutrient contents for 14different attributes across 1,153 different items of food.

Figure 6.32 Nutrient Contents — Parallel Coordinates

HOW TO READ IT & WHAT TO LOOK FOR

Look around the chart and acquaint yourself with what eachquantitative measure axis represents. Also note what kind of sequencingof measure has been used: are neighbouring measures significantlypaired? Note the range of values along each independent axis so youunderstand what positions along the scales represent and can determinewhat higher and lower positions mean. If colour has been used to grouprelated records then identify what these represent. Scan the overall massof lines to identify any major patterns. Study the patterns in the spacebetween each pair of adjacent axes. This is where you will really see thepotential presence or absence of, and nature of, relationships betweenmeasures. The main patterns to identify involve the presence of parallellines (showing consistent relationships), lines converging in similardirections (some correlation) and then complete criss-crossing (negativerelationship). Look out for any associations in the patterns across colourgroupings. Remember that ruling out significant relationships can bejust as useful as ruling them in.

PRESENTATION TIPS

INTERACTIVITY: Parallel coordinates are particularly useful whenoffered with interactive features, such as filtering techniques, enablingthe user to interrogate and manipulate the display to facilitate visualexploration. Additionally, the option to rearrange the sequence of themeasures can be especially useful.

301

ANNOTATION: The inclusion of visible annotated features like axislines, tick marks, gridlines and value labels can naturally aid thereadability of the data but be aware of the impact of clutter.

COLOUR: When you are plotting large quantities of records,inevitably there will be over-plotting and this might disguise the realweight of values, so the variation in the darkness of colour can be usedto establish density of observations.

COMPOSITION: The ordering of the quantitative variables has to beof optimum significance as the connections between adjacent axes willoffer the main way of seeing the local relationships: the patterns willchange for every different ordering permutation. Remember that theline directions connecting records are often inconsequential in theirmeaning unless neighbouring measures have a common scale andsimilar meaning: the connections are more about establishingcommonality of pattern across records, rather than there being anythingtoo significant behind the absolute slope direction/length.

VARIATIONS & ALTERNATIVES

The ‘radar chart’ has similarities with parallel coordinates in that theyinclude several independent quantitative measures in the same chart buton a radial layout and usually only showing data for one record in thesame display. A variation on the parallel coordinate would be the‘Sankey diagram’, which displays categorical composition andquantitative flows between different categorical dimensions or ‘stages’.

Charts Correlations

Heat map

ALSO KNOWN AS Matrix chart, mosaic plot

REPRESENTATION DESCRIPTION

302

A heat map displays quantitative values at the intersection between twocategorical dimensions. The chart comprises two categorical axes witheach possible value presented across the row and column headers of atable layout. Each corresponding cell is then colour-coded to represent aquantitative value for each combination of category pairing. It is noteasy for the eye to determine the exact quantitative values representedby the colours, even if there is a colour scale provided, so heat mapsmainly facilitate a gist of the order of magnitude.

EXAMPLE Exploring the connections between different Avengerscharacters appearing in the same Marvel comic book titles between1963 and 2015.

Figure 6.33 How the ‘Avengers’ Line-up Has Changed Over the Years

HOW TO READ IT & WHAT TO LOOK FOR

Learn what each categorical dimension relates to and make a note of therange of values in each case, paying attention to the significance of anyordering. Establish the quantitative value associations for the colourscales, usually found via a legend. Glance across the entire chart tolocate the big, small and medium shades (generally darker = larger) andperform global comparisons to establish the high-level ranking ofbiggest > smallest. Scan across each row and/or column to see if thereare specific patterns associated with either set of categories. Identifyany noticeable exceptions and/or outliers. Perform local comparisonsbetween neighbouring cell’s areas, to identify larger than and smaller

303

than relationships and estimate the relative proportions. Estimate (orread, if labels are present) the absolute values of specific colour scalesof interest.

PRESENTATION TIPS

ANNOTATION: Direct value labelling is possible, otherwise a clearlegend to indicate colour associations will suffice.

COLOUR: Sometimes multiple different colour hues may be used tosubdivide the quantitative values into further distinct categoricalgroups. Decisions about how many colour-scale levels and whatintervals each relates to in value ranges will affect the patterns thatemerge. There is no single right answer – you will arrive at it largelythrough trial and error/experimentation – but it is important to consider,especially when you have a diverse distribution of values.

COMPOSITION: Logical sorting (and maybe even sub-grouping) ofthe categorical values along each axis will aid readability and may helpsurface key relationships.

VARIATIONS & ALTERNATIVES

A ‘radial heat map’ offers a structure variation whereby the table maybe portrayed using a circular layout. As with any radial display this isonly really of value if the cyclical ordering means something for thesubject matter. A variation would see colour shading replaced by ameasure of pattern density, using a scale of ‘packedness’ to indicateincreasing quantitative values. An alternative approach would be the‘matrix chart’ using size of a shape to indicate the quantitative or arange of point marker to display categorical characteristics.

Charts Connections

Matrix chart

304

ALSO KNOWN AS Table chart

REPRESENTATION DESCRIPTION

A matrix chart displays quantitative values at the intersection betweentwo categorical dimensions. The chart comprises two categorical axeswith each possible value presented across the row and column headersof a table layout. Each corresponding cell is then marked by ageometric shape with its area sized to represent a quantitative value andcolour often used visually to distinguish a further categoricaldimension. While they are most commonly seen using circles, you canuse other proportionally sized shapes.

EXAMPLE Exploring the perceived difficulty of fixtures across theseason for teams in the premier league 2013–14.

Figure 6.34 Interactive Fixture Molecules

HOW TO READ IT & WHAT TO LOOK FOR

Learn what each categorical dimension relates to and make a note of therange of values in each case, paying attention to the significance of anyordering. Establish the quantitative size associations for the area marksand look up any colour associations being used, both usually found viaa legend. Glance across the entire chart to locate the big, small andmedium areas and perform global comparisons to establish the high-level ranking of biggest > smallest. Scan across each row and/or columnto see if there are specific patterns associated with either set ofcategories. Identify any noticeable exceptions and/or outliers. Performlocal comparisons between neighbouring circular areas, to identifylarger than and smaller than relationships and estimate the relativeproportions. Estimate (or read, if labels are present) the absolute values

305

of specific geometric areas of interest.

PRESENTATION TIPS

ANNOTATION: Direct value labelling is possible, otherwise be sureto include a clear size legend. Normally this will be more than sufficientas the reader may simply be looking to get a gist of the order ofmagnitude.

COLOUR: If colours are being used to distinguish the differentcategories, ensure these are as visibly different as possible.

COMPOSITION: If there are large outlier values there may beoccasions when the size of a few circles outgrows the cell it occupies.You might editorially decide to allow this, as the striking shape maycreate a certain impact, otherwise you will need to limit the largestquantitative value to be represented by the maximum space availablewithin the table’s cell layout. Logical sorting (and maybe even sub-grouping) of the categorical values along each axis will aid readabilityand may help surface key relationships. The geometric accuracy of thecircle size calculations is paramount. Mistakes are often made withcircle size calculations: it is the area you are modifying, not thediameter/radius.

VARIATIONS & ALTERNATIVES

A variation may be to remove the quantitative attribute of the areamarker, replacing it with a point marker to represent a categorical statusto indicate simply a yes/no observation through the presence/absence ofa point or through the quantity of points to represent a total. Anapplication of this might be in calendar form whereby a marker in adate cell indicates an instance of something. It could also employ abroader range of different categorical options; in practice any kind ofmarker (symbol, colour, photograph) could be used to show acharacteristic of the relationship at each coordinate cell. An alternativemight be the ‘heat map’ which colour-codes the respective cells toindicate a relationship based on a quantitative measure.

Charts Connections

306

Node–link diagram

ALSO KNOWN AS Network diagram, graph, hairballs

REPRESENTATION DESCRIPTION

Node–link diagrams display relationships through the connectionsbetween categorical ‘entities’. The entry-level version of this type ofdiagram displays entities as nodes (represented by point marks andusually including a label) with links or edges (represented by lines)depicting the existence of connections. The connecting lines will oftendisplay an attribute of direction to indicate the influencer relationship.In some versions a quantitative weighting is applied to the showrelationship strength, maybe through increased line width. Replacingpoint marks with a geometric shape and using attributes of size andcolour is a further variation. Often the complexity seen in these displaysis merely a reflection of the underlying complexity of the subject and/orsystem upon which the data is based, so oversimplifying cancompromise the essence of such content.

EXAMPLE Exploring the connections of voting patterns forDemocrats and Republicans across all members of the US House ofRepresentatives from 1949 to 2012.

Figure 6.35 The Rise of Partisanship and Super-cooperators in the U.S.

307

HOW TO READ IT & WHAT TO LOOK FOR

The first thing to consider is what entity each node (point or circulararea) represents and what the links mean in relationship terms. Theremay be several other properties to acquaint yourself with, includingattributes like the size of the node areas, the categorical nature ofcolouring, and the width and direction of the connections. Across thegraph you will mainly be seeking out the clusters that show the nodeswith the most relationships (representative of influencers or hubs) andthose without (including outliers). Small networks will generally enableyou to look closely at specific nodes and connections and easily see theemerging relationships. When datasets are especially large, consistingof thousands of nodes and greater numbers of mutual connections, thedisplays can seem overwhelmingly cluttered and will be too dense tomake many detailed observations at node–link level. Instead, just relaxand know that your readability will be about a higher level sense-making of the clusters/hubs and main outliers.

PRESENTATION TIPS

INTERACTIVITY: Node–link diagrams are particularly useful whenoffered with interactive features, enabling the user to interrogate andmanipulate the display to facilitate visual exploration. The option toapply filters to reduce the busy-ness of the visual and enable isolation ofindividual node connections helps users to focus on specific parts of thenetwork of interest.

308

ANNOTATION: The extent of annotated features tends to be throughthe inclusion of value labels for each entity. Accommodating therelative word sizes on each node can be difficult to achieve with realelegance (once again that is where interactivity adds value, through theselect/mouseover event to reveal the label).

COLOUR: Aside from the possible categorical colouring of each node,decisions need to be made about the colour of the connecting lines,especially on deciding how prominent these links will be in contrast tothe nodes.

COMPOSITION: Composition decisions are where most of thepresentation customisation exists. There are several commonalgorithmic treatments used to compute custom arrangements tooptimise network displays, such as force-directed layouts (using thephysics of repulsion and springs to amplify relationships) andsimplifying techniques (such as edge bundling to aggregate/summarisemultiple similar links).

VARIATIONS & ALTERNATIVES

There are many derivatives of the node–link diagram, as explained,based on variations in the use of different attributes. ‘Hive plots’ and‘BioFabric’ offer alternative approaches based on replacing nodes withvertices.

Charts Connections

Chord diagram

ALSO KNOWN AS Radial network diagram, arc diagram (wrongly)

REPRESENTATION DESCRIPTION

A chord diagram displays relationships through the connectionsbetween and within categories. They are formed around a radial display

309

with different categories located around the edge: either as individualnodes or proportionally sized segments (arcs) of the circumferenceaccording to a part-to-whole breakdown. Emerging inwards from eachorigin position are curved lines that join with other related categoricallocations around the edge. The connecting lines are normallyproportionally sized according to a quantitative measure and adirectional or influencing relationship is often indicated. The perceivedreadability of the chord diagram will always be influenced by thequantity and range of values being plotted. Small networks will enable areader to look closely at specific categories and their connections to seethe emerging relationships easily; larger systems will look busy throughthe network of lines but they can still provide windows into complexnetworks of influence. Often the complexity seen in these displays ismerely a reflection of the underlying complexity of the subject and/orsystem upon which the data is based, so oversimplifying cancompromise the essence of such content.

EXAMPLE Exploring the connections of migration between andwithin 10 world regions based on estimates across five-year intervalsbetween 1990 and 2010.

Figure 6.36 The Global Flow of People

310

HOW TO READ IT & WHAT TO LOOK FOR

First determine how categories are displayed around the circumference,either as nodes or part-to-whole arcs, and identify each oneindividually. Consider the implication of the radial sorting of thesecategorical values and, if based on part-to-whole sizes, establish a senseof the largest > smallest arc lengths. Colour-coding may be applied tothe categories so note any associations. Look inside the display todetermine what relationships the connecting lines represent and checkfor any directional significance. Look closer at the tangled collection oflines criss-crossing this space, noting the big values (usually throughline weight or width) and the small ones. Avoid being distracted by thedistance a line travels, which is just a by-product of the outercategorical arrangement: a long connecting line is just as significant arelationship as a short one. For this reason, pay close attention to anyconnecting lines that have very short looping distances to adjacentcategories. Are there any patterns of lines heading towards or leavingcertain categories?

311

PRESENTATION TIPS

INTERACTIVITY: Chord diagrams are particularly useful whenoffered with interactive features, enabling the user to interrogate andmanipulate the display to facilitate visual exploration. The option toapply filters to reduce the busy-ness of the visual and enable isolation ofindividual node connections helps users to focus on specific parts of thenetwork of interest.

ANNOTATION: Annotated features tend to be limited to valuelabelling of the categories around the circumference and, occasionally,directly onto the base or ends of the connecting lines (usually just thosethat are large enough to accommodate them).

COLOUR: Aside from the categorical colouring of each node,decisions need to be made about the colour of the connecting lines,especially on deciding how prominent these links will be in contrast tothe nodes. Sometimes the connections will match the origin ordestination colours, or they will combine the two (with a start and endcolour to match the relationship).

COMPOSITION: The main arrangement decisions come throughsorting, firstly by generating as much logical meaning from thecategorical values around the edge of the circle and secondly bydeciding on the sorting of the connecting lines in the z-dimension – ifmany lines are crossing, there is a need to think about which will be ontop and which will be below. Showing the direction of connections canbe difficult as there is so little room for manoeuvring many more visualattributes, such as arrows or colour changes. One common, subtlesolution is to pull the destination join back a bit, leaving a small gapbetween the connecting line and the destination arc. This then contrastswith connecting lines that emerge directly from the categorical arcs,showing it is their origin.

VARIATIONS & ALTERNATIVES

The main alternatives would be to consider variations of the ‘node–linkdiagram’ or, specifically, the ‘arc diagram’, which offers a furthervariation on the theme of networked displays, placing all the nodesalong a baseline and forming connections using semi-circular arcs,rather than using a graph or radial layout.

Charts Connections

312

Sankey diagram

ALSO KNOWN AS Alluvial diagram

REPRESENTATION DESCRIPTION

Sankey diagrams display categorical composition and quantitativeflows between different categorical dimensions or ‘stages’. The mostcommon contemporary form involves a two-sided display, with eachside representing different (but related) categorical dimensions ordifferent states of the same dimension (such as ‘before and after’). Oneach side there is effectively a stacked bar chart displayingproportionally sized and differently coloured (or spaced apart)constituent parts of a whole. Curved bands link each side of the displayto represent connecting categories (origin and destination) with theproportionally sized band (its thickness) indicating the quantitativenature of this relationship. Some variations involve multiple stages andmight present attrition through the diminution size of subsequent stacks.Traditionally the Sankey has been used as a flow diagram to visualiseenergy or material usage across engineering processes. It is closelyrelated to the ‘alluvial diagram’, which tends to show changes incomposition and flow over time, but the Sankey label is often applied tothese displays also.

EXAMPLE Exploring the seat changes among political partiesbetween the 2010 and 2015 UK General Elections.

Figure 6.37 UK Election Results by Political Party, 2010 vs 2015

313

HOW TO READ IT & WHAT TO LOOK FOR

Based on the basic two-sided version of the Sankey diagram, look downboth sides of the chart to learn what states are represented and what theconstituent categories are. Pay close attention to the categorical sortingand pick out the large and small values on each side. Then look at theconnecting lines, making observations about the largest and narrowestbands and noting any that seem to be mostly redistributed into adifferent category compared to those that just join with the same. Noticeany small break-off bands that seem to cross the height of the wholechart, perhaps representing a more dramatic change or diversionbetween states. As with most network-type visualisations, the perceivedreadability of the Sankey diagram will always be influenced by thequantity and range of values being plotted, as well as the number ofdifferent states presented.

PRESENTATION TIPS

INTERACTIVITY: Sankey diagrams are particularly useful whenoffered with interactive features, enabling the user to interrogate andmanipulate the display to facilitate visual exploration. The option toapply filters to reduce the busy-ness of the visual and enable isolation of

314

individual node connections helps users to focus on specific parts of thenetwork of interest.

ANNOTATION: Annotated features tend to be limited to valuelabelling of the categories that make up each ‘state’ stack.

COLOUR: Colouring is often used visually to indicate the categoriesof the connecting bands, though it can get a little complicated whentrying to combine a sense of change through an origin category colourblending with a destination category colour when there has been aswitch.

COMPOSITION: The main arrangement decisions come throughsorting, firstly by generating as much logical meaning from thecategorical values within the stacks and, secondly, by deciding on thesorting of the connecting lines in the z-dimension – if many lines arecrossing, there is a need to think about which will be on top and whichwill be below. There is no significant difference between a landscape orportrait layout, which will depend on the subject matter ‘fit’ and thespace within which you have to work. Try to ensure that the sorting ofthe categorical dimensions is as logical and meaningful as possible.

VARIATIONS & ALTERNATIVES

The concept of a Sankey diagram showing composition and flow canalso be mapped onto a geographical projection as one of the variationsof the ‘flow map’. You could use a ‘chord diagram’ as an alternative toshow how larger networks are composed proportionally and in theirconnections. Showing how component parts have changed over timecould just be displayed using a ‘stacked area chart’. A ‘funnel chart’ is amuch simplified display to show how a single value changes (usuallydiminishing) across states, for topics like sales conversion. This often isbased on a funnel-like shape formed by a wide bar at the top (thoseentering the system) and then gradually narrower bars, stage by stagetowards the end state.

Charts Trends

315

Line chart

ALSO KNOWN AS Fever chart, stock chart

REPRESENTATION DESCRIPTION

A line chart shows how quantitative values for different categories havechanged over time. They are typically structured around a temporal x-axis with equal intervals from the earliest to latest point in time.Quantitative values are plotted using joined-up lines that effectivelyconnect consecutive points positioned along a y-axis. The resultingslopes formed between the two ends of each line provide an indicationof the local trends between points in time. As this sequence is extendedto plot all values across the time frame it forms an overall linerepresentative of the quantitative change over time story for a singlecategorical value. Multiple categories can be displayed in the sameview, each represented by a unique line. Sometimes a point (circle/dot)is also used to substantiate the visibility of individual values. The linesused in a line chart will generally be straight. However, sometimescurved line interpolation may be used as a method of estimating valuesbetween known data points. This approach can be useful to helpemphasise a general trend. While this might slightly compromise thevisual accuracy of discrete values if you already have approximations,this will have less impact.

EXAMPLE Showing changes in percentage income growth for the Top1% and Bottom 90% of earners in the USA between 1917 and 2012.

Figure 6.38 The Fall and Rise of U.S. Inequality, in 2 Graphs

316

HOW TO READ IT & WHAT TO LOOK FOR

Firstly, learn about the axes: what is the time period range presented onthe x-axis (and in what order) and what is the range of quantitativevalues shown on the y-axis, paying particular attention to the originvalue (which may not be zero)? Inside the chart, determine whatcategories each line represents: for single lines this will usually be clearfrom the chart title, for multiple lines you might have direct labelling ora legend to learn colour associations. Think about what high and lowvalues mean: is it ‘good’ to be large/small, increasing or decreasing?Glance at the general patterns (especially if there are many) looking forobservations such as any trends (short or long term), any suddenmoments of a rise or fall (V- or W -shapes, or inverted), any sense ofseasonal or cyclical patterns, any points of interest where lines crosseach other or key thresholds that are reached/exceeded. Can youmentally extrapolate from the values shown any sense of a forecastedtrend? Avoid jumping to spurious interpretations if you see two lineseries following a similar pattern; this does not necessarily mean thatone thing has caused the other, it might just be coincidence. Then lookmore closely at categories of interest and at patterns around specificmoments in time, and pick out the peak, low, earliest and latest valuesfor each line. Where available, compare the changing quantities againstannotated references such as targets, forecast, previous time periods,range bands, etc.

PRESENTATION TIPS

317

INTERACTIVITY: Interactivity may be especially helpful if you havemany categories and wish to enable the user to isolate (in focus terms) acertain line category of interest.

ANNOTATION: Chart apparatus devices like tick marks and gridlinesin particular can be helpful to increase the accuracy of the reading ofthe quantitative values. If you have axis labels you should not needdirect labels on each value point – this will be label overload. Youmight choose to annotate specific values of interest (highest, lowest,specific milestones). Think carefully about what is the most useful andmeaningful interval for your time axis labelling. When severalcategories are being shown, if possible, try directly to label thecategories shown by each line, maybe at the start or end position.

COLOUR: When many categories are shown it may be that onlycertain emphasised lines of interest possess a colour and a label – therest are left in greyscale for context.

COMPOSITION: Composition choices are mostly concerned with thechart’s dimensions: its aspect ratio, how high and wide to make it. Thesequencing of values tends to be left to right for the sequence of thetime-based x-axis and low rising to high values on the y-axis; you willneed a good (and clearly annotated) reason to break this convention.Line charts do not always need the y-axis to start at zero, as we are notjudging the size of a bar, rather the position along an axis. You shouldexpect to see a zero baseline if zero has some critical significance in theinterpretation of the trends. If your y-axis origin is not going to be zero,you might include a small gap between the x-axis and the minimum sothat it is not implied. Be aware that the upward and downward trends ona line chart can seem more significant if the chart width is narrow andless significant if it is more stretched out. There is no single rule tofollow here but a useful notion involves ‘banking to 45°’ whereby theaverage slope angle across your chart heads towards 45°. While it isimpractical to actually measure this, judging by eye tends to be morethan sufficient.

VARIATIONS & ALTERNATIVES

Variations of the line chart may include the ‘cumulative line chart’ or‘step chart’. ‘Spark lines’ are mini line charts that aim to occupy almostonly a word’s length amount of space. Often seen in dashboards wherespace is at a premium and there is a desire to optimise the density of thedisplay. ‘Bar charts’ can also be used to show how values look overtime when there is perhaps greater volatility in the quantitative valuesacross the time period and when the focus is on the absolute values at

318

each point in time, more so than trends. Sometimes a line chart canshow quantitative trends over continuous space rather than time. Forshowing ranking over time, consider the ‘bump chart’, and for beforeand after comparisons, the ‘slope graph’.

Charts Trends

Bump chart

ALSO KNOWN AS

REPRESENTATION DESCRIPTION

A bump chart shows how quantitative rankings for categories havechanged over time. They are typically structured around a temporal x-axis with equal intervals from the earliest to latest point in time.Quantitative rankings are plotted using joined-up lines that effectivelyconnect consecutive points positioned along a y-axis (typically top =first). The resulting slopes formed between the two ends of each lineprovide an indication of the local ranking trends between points in time.As this sequence is extended to plot all values across the time frame itforms an overall line representative of the ranking story for a singlecategorical value. Multiple categories are often displayed in the sameview, showing how rankings have collectively changed over time.Sometimes a point (circle/dot) mark is also used to substantiate theconnected visibility of category lines, as is colour (for the lines and/orthe points).

EXAMPLE Showing changes in rank of the most populated US citiesat each census between 1790 and 1890.

Figure 6.39 Census Bump: Rank of the Most Populous Cities at EachCensus, 1790—1890

319

HOW TO READ IT & WHAT TO LOOK FOR

Firstly, you need to learn about the axes. What is the time period rangepresented on the x-axis (and in what order)? What are the range ofquantitative rankings shown on the y-axis (check that the ranks start at 1from the top downwards)? Inside the chart, determine what categorieseach line represents: this might be explained through direct labelling, acolour legend, interactivity or through differentiating point markerattributes of colour/shape/pattern. Think about what high and low ranksmean: is it ‘good’ to be high up the rankings and is it better to bemoving up or down? Consider the general patterns to look forobservations such as consistent trends (largely parallel lines) orcompletely non-relational patterns (lines moving in all directions). Arethere any prominent stories of categories that have had a sudden rise orfall (V- or W-shapes, or inverted)? Is there any evidence of seasonal orcyclical patterns, any key points of interest where lines cross each otheror key thresholds that are reached/exceeded? Next, look more closely atcategories of interest and at patterns around specific moments in time,and pick out the peak, low, earliest and latest values for each line.

PRESENTATION TIPS

INTERACTIVITY: Interactivity is usually necessary with bumpcharts, especially if you have many categories and wish to enable theuser to isolate (in focus terms) a certain line category of interest.

320

ANNOTATION: The ranking labels can be derived from the verticalposition along the scale so direct labelling is usually unnecessary. Youmight choose to annotate specific values of interest (highest, lowest,specific milestones). Think carefully about what is the most useful andmeaningful interval for your time axis labelling.

COLOUR: Often, with many categories to show in the same chart, thebig challenge is to distinguish each line, especially as they likely criss-cross often with others. Using colour association can be useful for lessthan 10 categories, but for more than that you really need to offer theinteractivity or maybe decide that only certain emphasised lines ofinterest will possess a colour and the rest are left in greyscale forcontext.

COMPOSITION: The sequencing of values tends to be left to right forthe sequence of the time-based x-axis with high rankings (low number)on the y-axis moving downwards. You will therefore need a good (andclearly annotated) reason to break this convention.

VARIATIONS & ALTERNATIVES

Alluvial diagrams (similar to Sankey diagrams) can show how rankingshave changed over time while also incorporating a component ofquantitative magnitude. This approach is effectively merging the ‘bumpchart’ with the ‘stacked area chart’. Consider ‘line charts’ and ‘areacharts’ if the ranking is of secondary interest to the absolute values.

Charts Trends

Slope graph chart

ALSO KNOWN AS Slope chart

REPRESENTATION DESCRIPTION

A slope graph shows a ‘before and after’ display of changes in

321

quantities for different categories. The display is based on (typically)two parallel quantitative axes with a consistent scale range to cover allpossible quantitative values. A line is plotted for each categoryconnecting the two axes together with the vertical position on each axisrepresenting the respective quantitative values. Sometime a dot is alsoused to further substantiate the visibility of the value positions. Theseconnecting lines form slopes that indicate the upward, downward orstable trend between points in time. The resulting display incorporatesabsolute values, reveals rank and, of course, shows change betweentime. Colours are often used visually to distinguish different categoricallines, otherwise this can be used to surface visibly the major trend states(up, down, no change). A slope graph works less well when all values(or the majority) are going in the same direction; consider alternatives ifthis is the case.

EXAMPLE Showing changes in the share of power sources across allUS states between 2004 and 2014.

Figure 6.40 Coal, Gas, Nuclear, Hydro? How Your State GeneratesPower

HOW TO READ IT & WHAT TO LOOK FOR

322

Firstly, learn about the axes: what are the two points in time beingpresented and what is the possible range of quantitative values shownon the y-axis, checking that the ranks start from the top down? Insidethe chart, learn what each category line relates to and determine whatcategories each line represents: this might be explained through directlabelling, a colour legend, or through interactivity. Think about whatupward, downward and stable trends mean: is it ‘good’ to be moving upor down? Is it more interesting to show no change? Look at the generalpatterns to observe such things as consistent trends (largely parallellines in either direction) or completely non-relational patterns (linesmoving in all directions). Colour may be used to accentuate thedistinction between upward and downward trends. Are there anyprominent stories of categories that have had a dramatic rise or fall?Even if no values have dramatically altered, that in itself can be animportant finding, especially if change was expected. Next, look moreclosely at categories of interest and pick out the highest and lowestvalues on each side to learn about those stories. Look for the gapswhere there are no values, and at outlier values too, to see if some sitoutside the normal value clusters.

PRESENTATION TIPS

INTERACTIVITY: Depending on the number of category valuesbeing presented, slope graphs can become quite busy, especially if thereare bunches of similar values and slope transitions. This also causes aproblem with accommodating multiple labels on the same value. Onthese occasions you might find interactive slope graphs to helpfilter/exclude certain values.

ANNOTATION: Labelling of each category will get busy, especiallywhen there are shared values, so you might choose to annotate specificvalues of interest (highest, lowest, of editorial interest).

COLOUR: Often when you have many categories to show in the samechart the big challenge is to distinguish each line, especially as theylikely criss-cross often with others. Using colour association can beuseful for less than 10 categories usually with direct labelling on the leftand/or right of the chart.

COMPOSITION: The aspect ratio of the slope graph (height andwidth) will often be determined by the space you have to work with.

VARIATIONS & ALTERNATIVES

Rather than showing a before and after story, some slope graphs are

323

used to show the relationship between different quantitative measuresfor linked categories. In this case the connecting line is not indicative ofa directional relationship, just the relationship itself. An alternativeoption would be the ‘connected dot plot’ which can also show beforeand after stories and is a better option when all values are moving in thesame direction.

Charts Trends

Connected scatter plot

ALSO KNOWN AS Trail chart

REPRESENTATION DESCRIPTION

A connected scatter plot displays the relationship between twoquantitative measures over time. The display is formed by plottingmarks like a dot or circle for each point in time at the respectivecoordinates along two quantitative x- and y-axes. The collection ofindividual points is then connected (think of a dot-to-dot drawingpuzzle) using lines joining each consecutive point in time to form asequence of change. Generally there would only be a single connectedline plotted on a chart to avoid the great visual complexity of overlayingseveral in one display. However, if multiple categories are to beincluded, colour is typically used to distinguish each series.

EXAMPLE Showing changes in the daily price and availability ofSuper Bowl tickets on the secondary market four weeks prior to theevent across five Super Bowl finals.

Figure 6.41 Holdouts Find Cheapest Super Bowl Tickets Late in theGame

324

HOW TO READ IT & WHAT TO LOOK FOR

Learn what each quantitative axis relates to and make a note of therange of values in each case (min to max). Look at what each plottedvalue on the chart refers to in terms of its date label and determine themeaning of line direction. It usually helps to parse your thinking byconsidering what higher/lower values mean for each quantitative axisindividually and then combining the joint meaning thereafter. Try tofollow the chart from the start to the end, mapping out in your mind thesequence of a narrative as the values change in all directions and notingthe extreme values in the outer edges of your line’s reach. Look at theoverall pattern of the connected line: is it consistently moving in onedirection? Does it ebb and flow in all directions? Does it create a spiralshape? Compare consecutive points for a more focused view of changebetween two points.

PRESENTATION TIPS

INTERACTIVITY: The biggest challenge is making the connectionsand the sequence as visible as possible. This becomes much harderwhen values change very little and/or they loop back almost in spiral

325

fashion, crossing back over themselves. It is especially hard to label thesequential time values elegantly. One option to overcome this isthrough interactivity and particularly through animated sequenceswhich build up the display, connecting one line at a time and unveilingthe date labels as time progresses. It is often the case that only oneseries will be plotted. However, interactive options may allow the userto overlay one or more for comparison, switching them on and off asrequired.

ANNOTATION: Connected scatter plots are generally seen as one ofthe most complex chart types for the unfamiliar reader to work out howto read, given the amount of different attributes working together in thedisplay. It is therefore vital that as much help is given to the reader aspossible with ‘how to read’ guides and illustrations of what the differentdirections of change mean.

COLOUR: Colour is only generally used to accentuate certain sectionsof a sequence that might represent a particularly noteworthy stage ofnarrative.

COMPOSITION: As the encoding of the plotted point values is basedon position along an axis, it is not necessary to start the axes from azero baseline – just make the scale ranges as representative as possibleof the range of values being plotted. Ideally a connected scatter plot willhave a 1:1 aspect ratio (equally as tall as it is wide), creating a squaredarea to help patterns surface more evidently. If one quantitative variable(e.g. weight) is likely to be affected by the other variable (e.g. height), itis general practice to place the former on the y-axis and the latter on thex-axis.

VARIATIONS & ALTERNATIVES

The ‘comet chart’ is to the connected scatter plot what the ‘slope graph’is to the ‘line chart’ – a summarised view of the changing relationshipsacross two quantitative values between just two points in time.Naturally a reduced variation of the connected scatter plot is simply the‘scatter plot’ where there is no time dimension or elements ofconnectedness.

Charts Trends

326

Area chart

ALSO KNOWN AS

REPRESENTATION DESCRIPTION

A line chart shows how quantitative values for different categories havechanged over time. They are typically structured around a temporal x-axis with equal intervals from the earliest to latest point in time.Quantitative values are plotted using joined-up lines that effectivelyconnect consecutive points positioned along a y-axis. The resultingslopes formed between the two ends of each line provide an indicationof the local trends between points in time. As this sequence is extendedto plot all values across the time frame it forms an overall linerepresentative of the quantitative change over time story for a singlecategorical value. To accentuate the magnitude of the quantitativevalues and the change through time the area beneath the line is filledwith colour. The height of each coloured layer at each point in timereveals its quantity. Area charts can display values for severalcategories, using stacks, to show also the changing part-to-wholerelationship.

EXAMPLE Showing changes in the average monthly price ($ perbarrel) of crude oil between 1985 and 2015.

Figure 6.42 Crude Oil Prices (West Texas Intermediate), 1985—2015

327

HOW TO READ IT & WHAT TO LOOK FOR

Firstly, learn about the axes: what is the time period range presented onthe x-axis (and in what order) and what is the range of quantitativevalues shown on the y-axis, paying particular attention to whether it is apercentage or absolute based scale? Inside the chart, determine whatcategories each area layer represents: for single areas this will usuallybe clear from the chart title, for multiple areas you might have directlabelling or a nearby legend to learn colour associations. Think aboutwhat high and low values mean: is it ‘good’ to be large/small,increasing or decreasing? Glance at the general patterns (especially ifthere are many layers), looking at the visible ‘thickness’ of the colouredlayers. At what points are the values highest or lowest? When are theygrowing or shrinking as the time axis moves along? If there are multiplecategories, which ones take up the largest and smallest slices of theoverall total? Are there any trends (short or long term), any suddenmoments of a rise or fall, any sense of seasonal or cyclical patterns? Ifthere are multiple categories, look more closely at individual layers ofinterest.

PRESENTATION TIPS

ANNOTATION: Direct labelling of quantitative values will get far toobusy so you might choose to annotate specific values of interest(highest, lowest, specific milestones). Think about the most useful

328

interval for your axis labelling. As ever there is no single rule, so adoptthe Goldilocks principle of not too many, not too few. If you have astacked area chart, try directly to label the category layers shown asclosely as possible (if the heights allow it) or at least ensure any colourassociations are easily identifiable through a nearby legend. Thinkcarefully about what is the most useful and meaningful interval for yourtime axis labelling.

COLOUR: If you are using a stacked area chart, ensure the categoricallayers have sufficiently different colours so that their distinct readingcan be efficiently performed.

COMPOSITION: Similar to the line chart, the area chart’s dimensionsshould ideally utilise an aspect ratio that optimises the readabilitythrough 45° banking (roughly judging the average slope angle). Thesequencing of values tends to be left to right for the sequence of thetime-based x-axis and low rising to high values on the y-axis; you willneed a good (and clearly annotated) reason to break this convention.Unlike the line chart, the quantitative axis for area charts must start atzero as it is the height of the coloured areas under each line that helpsreaders to perceive the quantitative values. Do not have overlappingcategories on the same chart because it makes it very difficult to see(imagine hills behind hills, peaking out and then hiding behind eachother). Rather than stacking categories you might consider using smallmultiples, especially as this will present the respective displays from acommon baseline (and make reading sizes a little easier).

VARIATIONS & ALTERNATIVES

Like area charts, ‘alluvial diagrams’ display proportional stacked layersfor multiple categories showing the absolute value change over time.However, they also show the evolving ranks, switching the relativeordering of each layer of values based on the current magnitude. Somedeployments of the area chart are not plotted over time but overcontinuous dimensions of space, perhaps showing the changing natureof a given quantitative measure along a given route. When you havemany concurrent layers to show and these layers start and stop atdifferent times, a ‘slope graph’ is worth considering.

Charts Trends

329

Horizon chart

ALSO KNOWN AS

EXAMPLE Showing percentage changes in price for selected fooditems in the USA between 1990 and 2015.

REPRESENTATION DESCRIPTION

Horizon charts show how quantitative values for different categorieshave changed over time. They are valuable for showing changes overtime for multiple categories within space-constrained formats (such asdashboards). They are structured around a series of rows each showingchanges in quantitative values for a single category. The temporal x-axis has equal intervals from the earliest to latest point in time.Quantitative values are plotted using joined-up lines that connectconsecutive points positioned along a value y-axis. The resulting slopesformed between the ends of each line provide an indication of the localtrends between two points in time. As this sequence is extended to plotall values across the time frame it forms an overall line representative ofthe quantitative changes. To accentuate the magnitude of thequantitative values the area beneath the line is filled with colour.Negative values are highlighted in one colour, positive values inanother colour. Variations in colour lightness are used to indicatedifferent degrees or bands of magnitudes, with the extremes gettingdarker. Negative value areas are then flipped from underneath thebaseline to above it, joining the positive values but differentiated intheir polarity by colour. Finally, like slicing off layers of a mountain,each distinct threshold band that sits above the imposed maximum y-axis scale is chopped off and dropped down to the baseline, in front ofits foundation base. The final effect shows overlapping layers ofincreasingly darker colour-shaded areas all occupying the same verticalspace with combinations of height, colour and shade representing thevalues.

Figure 6.43 Percentage Change in Price for Select Food Items, Since

330

1990

HOW TO READ IT & WHAT TO LOOK FOR

Firstly, learn about the category rows: what do they represent and inwhat order are they presented? Next, the chart axes: what is the timeperiod range presented on the x-axis (and in what order) and what is therange of quantitative values shown on the y-axis, paying attention towhether it is a percentage or absolute value scale? Next, what are thecolour associations (for positive and negative values) and the differentshaded banding thresholds? Think about what high and low valuesmean: is it ‘good’ to be large/small, increasing or decreasing? Glance atthe general patterns over time, looking at the most visible dark areas ofeach colour polarity: where have values reached a peak in eitherdirection? Maybe then separate your reading between looking at thepositive value insights and then the negative ones: which chunks of

331

colour are increasing in value (darker) or shrinking (getting lighter) asthe time axis moves along? Where can you see most empty space,indicating low values? Are there any trends (short or long term), anysudden moments of a rise or fall, any sense of seasonal or cyclicalpatterns, any points of interest where lines cross each other or keythresholds that are reached/exceeded? Then look more closely atcategories of interest, assessing their own patterns around specificmoments in time and picking out the peak, low, earliest and latestvalues for each row.

PRESENTATION TIPS

ANNOTATION: The decisions around annotations are largely reducedto labelling the category rows. Such is the busy-ness of the chart areasthat any direct labelling is going to clutter the display too much: horizoncharts are less about precise value reading and more about getting asense of the main patterns, so avoid the temptation to over-label. Thinkcarefully about what is the most useful and meaningful interval for yourtime axis labelling.

COLOUR: Colour decisions mainly concern the choices of quantitativescale bandings to show the positive and negative value ranges.

COMPOSITION: The height of the chart area in which you canaccommodate a single row of data will have an influence on the entireconstruction of the horizon chart. It will often involve an iterative/trialand error process, looking at the range of quantitative values acrosseach category, establishing the most sensible and meaningful thresholdswithin these range and then fixing the y-axis scales accordingly. Try toensure the sorting of the main categorical rows is as logical andmeaningful as possible.

VARIATIONS & ALTERNATIVES

An alternative to the horizon chart is the entry-level single category‘area chart’, which does not suffer the same constraints of restrictions tothe vertical scale. For space-constrained displays, ‘spark lines’ wouldoffer an option suitable to such situations and easily accommodatemultiple category displays.

Charts Trends

332

Stream graph

ALSO KNOWN AS Theme river

REPRESENTATION DESCRIPTION

A stream graph shows how quantitative values for different categorieshave changed over time. They are generally used when you have manyconstituent categories at any given point in time and these categoriesmay start and stop at different points in time (rather than continuethroughout the presented time frame). As befitting the name, theirappearance is characterised by a flowing, organic display of meanderinglayers. They are typically structured around a temporal x-axis withequal intervals from the earliest to latest point in time. Quantitativevalues are plotted using joined-up lines that effectively connectconsecutive points to quantify the height above a local baseline, whichis not a stable zero baseline but rather a shifting shape formed out ofother category layers. To accentuate the size of the category’s height atany given point the area beneath the line is filled with colour. Theheight of each coloured layer at each point in time reveals its quantity.This colour is often used to further represent a quantitative value scaleor to associate with categorical colours. The stacking arrangement ofthe different categorical streams goes above and below the central axisline to optimise the layout but not with any implication of polarity.

EXAMPLE Showing changes in the total domestic gross takings ($US)and the longevity of all movies released between 1986 and 2008.

Figure 6.44 The Ebb and Flow of Movies: Box Office Receipts 1986—2008

333

HOW TO READ IT & WHAT TO LOOK FOR

Firstly, determine what is the time period presented on the x-axis (andin what order). In most stream graphs you do not see the quantitative y-axis scale because the level of reading is more about getting a gist forthe main patterns in a relative sense rather than an absolute one. Youmight find that the colouring of layers has a quantitative scale orcategorical association so look for any keys. Also, you will often findguides to help estimate the quantitative heights of each layer. Thinkabout what high and low values mean: is it ‘good’ to be large/small,increasing or decreasing? Glance at the general patterns over time.Remember that above or below means nothing in the sense of polarityof values, so your focus is on the entirety of the collective shape. Lookfor the largest peaks and the shallowest troughs, possible seasonalpatterns or the significant moments of change. Note where thesepatterns occur in relation to the timescale. Can you see any prominentlytall (big values) or wide (long-duration) layers? Notice when layers startand end, noting times when there are many concurrent categories andwhen there are few. Pick out the layers of personal interest and assesstheir patterns over time. Do not spend too much effort trying to estimateprecise values of height, but keep your focus on the bigger picture level.It is often useful to rotate the display so the streams are travellingvertically, offering a different perspective and removing the instinct tosee positive values above and negative values below the central axis.

PRESENTATION TIPS

334

INTERACTIVITY: If interactivity is a possibility, this could enableselection or mouseover events to reveal annotated values at any givenpoint in time or to filter the view.

ANNOTATION: Chart apparatus devices are generally of limited usein a stream graph with the priority on a general sense of pattern morethan precision value reading. Direct labelling of categories is likely tobe quite busy but may be required, at least to annotate the mostinteresting patterns (highest, lowest, specific milestones). Thinkcarefully about what is the most useful and meaningful interval for yourtime axis labelling.

COLOUR: Ensure any colour associations or size guides are easilyidentifiable through a nearby legend.

COMPOSITION: Composition choices are firstly concerned with thelandscape or portrait layout. This will largely be informed by the formatand space of your outputs and the meaning of the data. The streamlayers are often smoothed, giving them an aesthetically organicappearance, both individually and collectively. This is achieved viacurved line interpolation.

VARIATIONS & ALTERNATIVES

The fewer categorical series you have in your data, the more likely astacked ‘area chart’ is going to best-fit your needs. You could considera stacked ‘bar chart’ over time also, but there is less chance ofmaintaining the connected visibility of continuous categorical series viaa singular shape.

Charts Activities

Connected timeline

ALSO KNOWN AS Relationship timeline, storyline visualisations,

335

swim-lane chart

REPRESENTATION DESCRIPTION

A connected timeline displays the duration, milestones and categoricalrelationships across a range of categorical ‘activities’. It represents aparticularly diverse and creative way of showing changes over time andso involves many variations in approach. The structure is generallyformed of time-based quantitative x-axis and categorical y-axis lanes.Each categorical activity will commence at a point in time and fromwithin a vertical category ‘family’. Over time, the line will progress,possibly switching to a different categorical lane position as the natureof the activity alters. The lines may be of fixed width or proportionallyweighted to represent a quantitative measure. Some activity lines maycease, restart or merge with others to build a multi-faceted narrative.Colour can also be used to present further relevant detail. The mainissue with any connected timeline approach is simply the complexity ofthe content and the number of moving parts crossing over the display.As there are many entry points into reading such a timeline there can beinefficiency in the reading process, but this is usually proportionalsimply to the subject at hand and you may not wish to see these nuancesbeing removed.

EXAMPLE Showing changes in US major college football programmeallegiance to different conferences between 1965 and 2015.

Figure 6.45 Tracing the History of N.C.A.A. Conferences

336

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know what the major categorical ‘lanes’represent and what the range of date values is (min to max). Then try todetermine what each categorical activity line represents. As there are somany derivatives there is no single reading strategy, but generallyglance across the entire chart noting the sequence of the activities; thereis usually a sequential logic attached to their sorting based on the startdate milestone in particular. Follow the narrative from left to right,noting observations about any big, small and medium weighted linesand spotting any moment when they connect with, overlap or detachfrom other activities. Are there any major convergences or divergencesin pattern? Any hubs of dense activity and other sparse moments? Lookfor the length of lines to determine the long, medium and shortdurations of activity. Where available, compare the activities againstannotated references about other key milestone dates that might holdsome significance or influence.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like tick marks and gridlinesin particular can be helpful to increase the accuracy of the reading ofboth the quantitative values and the activity ‘lanes’, which may becoloured to help recognise divisions between categories. Direct

337

labelling is usually seen in these timelines to help maintain associationsacross the display with the categories of characters or activities, perhapsannotating the consequence or cause of lines merging, etc. Thinkcarefully about what is the most useful and meaningful interval for yourtime axis labelling.

COLOUR: Even if colour does not have a direct association with givenactivities, it can be a useful property to highlight certain features of thenarrative, sometimes acting as a container device to group activitiestogether, even if just for a momentary time period.

COMPOSITION: Where possible, try to make the categorical sortingmeaningful, maybe organising values in ascending/descending sizeorder. The vertical (y) or horizontal (x) sequencing of time will dependon the amount of data to show and the space you have to work with.Also, depending on the narrative, the past > present ordering may bereversed.

VARIATIONS & ALTERNATIVES

There are similarities with the organic nature of the ‘alluvial diagram’,which shows ranking and quantitative change over time for a number ofconcurrent categories. When there are fewer inter-activity relationshipsand more discrete categories are involved, then the ‘Gantt chart’ offersan alternative way of showing this analysis.

Charts Activities

Gantt chart

ALSO KNOWN AS Range chart, floating bar chart

REPRESENTATION DESCRIPTION

A Gantt chart displays the start and finish points and durations fordifferent categorical ‘activities’. The display is commonly used in

338

project management to illustrate the breakdown of a schedule of tasksbut can be a useful device to show any data based on milestone datesand durations. The chart is structured around a time-based quantitativex-axis and a categorical y-axis. Each categorical activity is representedby lines positioned according to the start moment and then stretched outto the finish point. There may be several start/finish durations within thesame activity row. Sometimes points are used to accentuate thestart/finish positions and the line may be coloured to indicate a relevantcategorical value (e.g. separating completed vs ongoing).

EXAMPLE Showing the events of birth, death and period serving inoffice for the first 44 US Presidents.

Figure 6.46 A Presidential Gantt Chart

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know with what major categorical values eachGantt bar is associated and what the range of the date values is (min tomax). Follow the narrative, noting the sequence of the categories –there is usually a sequential sorting based on the start date milestone.Glance across the entire chart and perform global comparisons toestablish the high-level ranking of biggest > smallest durations (based

339

on the length of the line) as well as early and late milestones. Identifyany noticeable exceptions and/or outliers. Perform local comparisonsbetween neighbouring bars to identify proportional differences and anyconnected dependencies. Estimate (or read, if labels are present) theabsolute values for specific categories of interest. Where available,compare the activities against annotated references about other keymilestone dates that might hold some significance or influence.

PRESENTATION TIPS

ANNOTATION: Chart apparatus devices like tick marks and gridlines(or row band-shading) in particular can be helpful to increase theaccuracy of the reading of the start point and duration of activities alongthe timeline. If you have axis labels you may not need direct labels forthe values shown with each duration bar – this will be label overload, sogenerally decide between one or the other. Think carefully about whatis the most useful and meaningful interval for your time axis labelling.

COMPOSITION: There is no significant difference in perceptionbetween vertical or horizontal Gantt charts, though horizontal layoutsare more metaphorically consistent with the concept of reading time.Additionally, these layouts tend to make it easier to accommodate andread the category labels. Where possible, try to sequence the categorical‘activities’ in a way that makes for the most logical reading, eitherorganised by the start/finish dates or maybe the durations (depending onwhich has most relevance).

VARIATIONS & ALTERNATIVES

Variations might involve the further addition of different point markers(represented by combinations of symbols and/or colours) along eachactivity row to indicate additional milestone details, using the ‘instancechart’. An emerging trend in technique terms involves preserving theposition of activity lines adjacent to other concurrent activities, ratherthan fixing them to stay within discrete rows. Sometimes there is muchmore fluidity and less ‘discreteness’ in the relationships betweenactivity, so approaches like the ‘connected timeline’ may be morefitting.

Charts Activities

340

Instance chart

ALSO KNOWN AS Milestone map, barcode chart, strip plot

REPRESENTATION DESCRIPTION

An instance chart displays individual moments or instances ofcategorical ‘activities’. There are many variations in approach for thiskind of display but generally you will find a structure based on a time-based quantitative x-axis and a categorical y-axis. For each categoricalactivity, instances of note are represented by different point markersthat indicate along the timeline when something has happened. Thepoint markers may have different combinations of symbols and coloursto represent different types of occurrences, but avoid having too manydifferent combinations so that viewers do not have to learn an entirelynew alphabet of meaning.

EXAMPLE Showing the instances of different Avengers charactersappearing in Marvel’s comic book titles between 1963 and 2015.

Figure 6.47 How the ‘Avengers’ Line-up Has Changed Over the Years

341

HOW TO READ IT & WHAT TO LOOK FOR

Look at the axes so you know with what major categorical values eachrow of instances is associated and what the range of the date values is(min to max). Look up any legend that will explain what (if any)associations exist between the instance markers and theircolour/symbol. Glance down the y-axis noting the sequence of thecategories; there is usually a sequential logic attached to their sortingbased on the start date milestone in particular. Follow the narrative,noting observations about the type and frequency of instances beingplotted. Look across the entire chart to locate the headline patterns ofclustering and identify any noticeable exceptions and/or outliers. Lookacross the patterns within each row individually to learn about eachcategory’s dispersal of instances. Look for empty regions where nomarks appear. How do all these patterns relate to the time framedisplayed? Where available, compare the activities against annotatedreferences about other key milestone dates that might hold somesignificance or influence.

PRESENTATION TIPS

ANNOTATION: The main annotation properties will be used to servethe role of explaining the associations between marks and attributes

342

through clear legends/keys.

COMPOSITION: Where possible, try to sequence the categorical‘activities’ in a way that makes for the most logical reading, eitherorganised by the start/finish dates or maybe the durations (depending onwhich has most relevance).

VARIATIONS & ALTERNATIVES

Some variations may see the size of a geometric shape used instead ofjust a point to indicate also a quantitative measure to go with theinstance. The marking of an instance through a ‘when’ moment couldalso be based on data that talks about positional moments within asequence. If the basic activity is reduced to a start/finish moment thenthe ‘Gantt chart’ will be the best-fit option.

Charts Overlays

Choropleth map

ALSO KNOWN AS Heat map

REPRESENTATION DESCRIPTION

A choropleth map displays quantitative values for distinct, definablespatial regions on a map. Each geographic region is represented by apolygonal area based on its outline shape, with each distinct shape thencollectively arranged to form the entire landscape. (Note that most toolsfor mapping have a predetermined reference between a region name andthe dimensions of the regional polygon.) Each area is colour-coded torepresent a quantitative value based on a scale with colour variationintervals that (typically) go from a light tint for smaller values to a darkshade for larger values. Choropleth maps should only be used when thequantitative measure is directly associated with and continuouslyrelevant across the spatial region on which it will be displayed.

343

Similarly, if your quantitative measure is about or related to theconsequence of more people living in an area, interpretations may bedistorting, so consider transforming your data to per capita or per acre(or other spatial denominator) to standardise the analysis accordingly.

EXAMPLE Mapping the percentage change in the populations ofBerlin’s districts across new and native Berliners since the fall of theBerlin Wall.

Figure 6.48 Native and New Berliners — How the S-Bahn RingDivides the City

HOW TO READ IT & WHAT TO LOOK FOR

Acquaint yourself with the geographic region you are presented withand carefully consider the quantitative measure that is beingrepresented. Establish the colour-scale value associations, usually foundvia a legend. Glance across the entire chart to locate the dark, light andmedium shades (generally darker = larger) and perform globalcomparisons to establish the high-level ranking of biggest values >smallest. Identify any noticeable exceptions and/or outliers. Bewaremaking judgements about the significance of prominent large

344

geographical areas: size is an attribute of the underlying region, not thesignificance of the measure displayed. Gradually zoom in your focus toperform increasingly local comparisons between neighbouring regionalareas to identify any noticeable consistencies or inconsistenciesbetween their values. Estimate (or read, if labels are present) theabsolute values of specific regions of interest.

PRESENTATION TIPS

ANNOTATION: Directly labelling the regional areas withgeographical details and the value they hold is likely to lead to toomuch clutter. You might include only a limited number of regionallabels to provide spatial context and orientation.

COLOUR: Legends explaining the colour scales should ideally beplaced as close to the map display as possible. The border colour andstroke width for each spatial area should be distinguishable to define theshape but not so prominent as to dominate attention – usually a subtlegrey- or white-coloured thin stroke will be fine. As well as variation incolour scales, sometimes pattern or textures may add an extra layer ofdetail to the value status of each region. When including a projectedmapping layer image in the background, ensure it is not overlycompeting for visual prominence by making it light in colour andpossibly semi-transparent. Do not include any unnecessary geographicaldetails that add no value to the spatial orientation or interpretation andclutter the display (e.g. roads, building structures).

COMPOSITION: With Earth being a sphere, there are many differentmapping projections for representing the regions of the world on aplane surface. Be aware that the transformation adjustments made bysome map projections can distort the size of regions of the world,inflating their size relative to other regions.

VARIATIONS & ALTERNATIVES

Some choropleth maps may be used to indicate categorical associationrather than quantitative measurements. Alternative thematic mappingapproaches to representing quantitative values might include the‘proportional symbol map’ and the ‘dot density map’. This is a variationthat involves plotting a representative quantity of dots equally (butrandomly) across and within a defined spatial region. The position ofindividual dots is therefore not to be read as indicative of preciselocations but used to form a measure of quantitative density. This offersa useful alternative to the choropleth map, especially when categoricalseparation of the dots through colour is of value. ‘Dasymetric mapping’

345

is similar in approach to choropleth mapping but breaks the constituentregional areas into much more specific, almost custom-drawn, sub-regions to better represent the realities of the distribution of human andphysical phenomena within a given spatial boundary.

Charts Overlays

Isarithmic map

ALSO KNOWN AS Contour map, isopleth map, isochrone map

REPRESENTATION DESCRIPTION

An isarithmic map displays distinct spatial surfaces on a map that sharethe same quantitative classification. All spatial regions (transcendinggeo-political boundaries) that share a certain quantitative value orinterval are formed by interpolated ‘isolines’ connecting points ofsimilar measurement to form distinct surface areas. Each area is thencolour-coded to represent the relevant quantitative value. The scale ofcolour variation intervals differs between deployments but will typicallyrange from a light tint for smaller values to a dark shade for largervalues. An isarithmic map would be used in preference to a choroplethmap when the patterns of data being displayed transcend the distinctregional polygons. They could be used to show temperature bandings orsmoothed regions of political attitudes.

EXAMPLE Mapping the degree of dialect similarity across the USA.

Figure 6.49 How Y’all, Youse and You Guys Talk

346

HOW TO READ IT & WHAT TO LOOK FOR

Acquaint yourself with the geographic region you are presented withand carefully consider the quantitative measure that is beingrepresented. Establish the colour scale value associations, usually foundvia a legend. Glance across the entire chart to locate the dark, light andmedium shades (generally darker = larger) and perform globalcomparisons to establish the high-level ranking of biggest values >smallest. Identify any noticeable exceptions and/or outliers, includingregions that appear in isolation from their otherwise related values andnotable for their position adjacent to very different shaded regions. Notethat any interpolation used to smooth the joins between data points toform organic surfaces will inevitably reduce the precision of thesurfaces in their relationship to land position. Gradually zoom in yourfocus to perform increasingly local comparisons between neighbouringregional areas to identify any noticeable consistencies or inconsistenciesbetween their values. Estimate the absolute values of specific regions ofinterest.

PRESENTATION TIPS

ANNOTATION: Directly labelling the surface areas to show thequantitative value or range they represent will be too cluttered. Youmight include only a limited number of regional labels to provide

347

spatial context and orientation.

COLOUR: Legends explaining the colour scales should ideally beplaced as close to the map display as possible. If using visible contouror boundary lines there is a clear implication of a location being insideor outside the line, so make these lines as prominent in colour aspossible according to the precision of their representation. If thesmoothing of the surface locations has been applied the representationof these areas should similarly avoid looking definitive. You thereforemight consider subtle colour gradation/overlapping between differentregions to capture appropriately the underlying ‘fuzziness’ of the data.As well as colour scales, sometimes pattern or textures may add anextra layer of detail to the value status of each surface region. Whenincluding a projected mapping layer image in the background, ensure itis not overly competing for visual prominence by making it light incolour and possibly semi-transparent. Do not include any unnecessarygeographical details that add no value to the spatial orientation orinterpretation and clutter the display (e.g. roads, building structures).

COMPOSITION: Be aware that the transformation adjustments madeby some map projections can distort the size of regions of the world,inflating their size relative to other regions.

VARIATIONS & ALTERNATIVES

There are specific applications of isarithmic maps used for showingelevation (‘contour maps’), atmospheric pressure (‘isopleth maps’) ortravel–time distances (‘isochrone maps’). Sometimes you might useisarithmic maps to show a categorical status (perhaps even a binarystate) rather than a quantitative scale.

Charts Overlays

Proportional symbol map

348

ALSO KNOWN AS Graduated symbol map

REPRESENTATION DESCRIPTION

A proportional symbol map displays quantitative values for locations ona map. The values are represented via proportionally sized areas(usually circles), which are positioned with the centre mid-point over agiven location coordinate. Colour is sometimes used to introducefurther categorical distinction.

EXAMPLE Mapping the origin and size of funds raised across the 22major candidates running for US President during the first half of 2015.

Figure 6.50 Here’s Exactly Where the Candidates’ Cash Came From

HOW TO READ IT & WHAT TO LOOK FOR

Acquaint yourself with the geographic region you are presented withand carefully consider the quantitative measure that is beingrepresented. Establish the area size value associations, usually found viaa legend. Glance across the entire chart to locate the large, medium andsmall shapes and perform global comparisons to establish the high-levelranking of biggest values > smallest. Identify any noticeable exceptionsand/or outliers. Gradually zoom in your focus to perform increasinglylocal comparisons between neighbouring regional areas to identify anynoticeable consistencies or inconsistencies between their values.Estimate (or read, if labels are present) the absolute values of specific

349

regions of interest. Also note where there are no markers. If colour isbeing used to further break down the categories of the values shown,identify any grouped patterns that emerge.

PRESENTATION TIPS

INTERACTIVITY: Interaction may be helpful to reveal location andvalue labels through selection or mouseover events.

ANNOTATION: Directly labelling the shapes with geographicaldetails and the value they hold is likely to lead to too much clutter. Youmight therefore include only a limited number of regional labels toprovide spatial context and orientation. Legends explaining the sizescales – and any colour associations – should ideally be placed as closeto the map display as possible. Avoid including unnecessarygeographical details that add no value to the spatial orientation orinterpretation and clutter the display (e.g. roads, building structures).

COLOUR: Sometimes the circular shapes are filled, at other times theyremain unfilled. If colours are being used to distinguish the differentcategories, ensure these are as visibly different as possible. When acircle has a large value its shape will transgress well beyond the originof its geographical location, intruding on and overlapping with otherneighbouring values. The use of outline borders and semi-transparentcolours helps with the task of avoiding occlusion (visually hiding valuesbehind others). When including a projected mapping layer image in thebackground, ensure it is not overly competing for visual prominence bymaking it light in colour and possibly semi-transparent.

VARIATIONS & ALTERNATIVES

Variations may see the typical circle replaced by squares andgeographical space replaced by anatomical regions. Alternatives to theproportional symbol map include the ‘choropleth map’, which colour-codes regions, or the ‘dot map’, which uses a dot to represent aninstance of something. Avoid the temptation to turn the circle symbolsinto pie charts; it is not a good look. If you absolutely positively have toshow a part-to-whole relationship, only show two categories, as per therecommended practice for pies.

Charts Overlays

350

Prism map

ALSO KNOWN AS Isometric map, spike map, datascape

REPRESENTATION DESCRIPTION

A prism map displays quantitative values for locations on a map. Thevalues are represented via proportionally sized lines, appearing as 3Dbars, that typically cover a fixed surface area of space and are justextended in height proportionally to represent the quantitative value forthat location. Being able to judge the dimensions of 3D forms in a 2Dview is very difficult, so they are only ever really used to create a gist ofthe profile of values, enabling recognition of the main peaks inparticular.

EXAMPLE Mapping the population of trees for each 180 square km ofland across the globe.

Figure 6.51 Trillions of trees

351

HOW TO READ IT & WHAT TO LOOK FOR

Acquaint yourself with the geographic region you are presented withand carefully consider the quantitative measure that is beingrepresented. Establish the area size value associations, usually found viaa legend. Glance across the entire chart to locate the large, medium andsmall shapes and perform global comparisons to establish the high-levelranking of biggest values > smallest. Identify any noticeable exceptionsand/or outliers. Gradually zoom in your focus to perform increasinglylocal comparisons between neighbouring regional areas to identify anynoticeable consistencies or inconsistencies between their values.Estimate (or read, if labels are present) the absolute values of specificregions of interest. Also note where there are no bars emerging from thesurface.

PRESENTATION TIPS

352

INTERACTIVITY: Ideally prism maps would be provided withinteractive features that allow panning around the map region to offerdifferent viewing angles to overcome the perceptual difficulties ofjudging the dimensions of 3D forms in a 2D view. Without this, smallervalues will be hidden behind the larger forms, just as smaller buildingsare hidden by skyscrapers in a city.

ANNOTATION: Directly labelling the prism shapes is infeasible – atmost you might include only a limited number of labels to providespatial context and orientation against the largest forms. Legendsexplaining the size scales should ideally be placed as close to the mapdisplay as possible.

COLOUR: Most tools that enable this type of mapping will likely havevisual property settings for a faux light effect, helping the physicalshapes to emerge more prominently through light and shadow. Ensurecolour assist in helping the shape of the forms to be as visible aspossible, maybe with opacity to enable smaller values to be not entirelyhidden behind any larger ones. When including a mapping layer imageon the surface, ensure it is not overly competing for visual prominenceby making it light in colour and possibly semi-transparent. Do notinclude any unnecessary geographical details that add no value to thespatial orientation or interpretation and clutter the display (e.g. roads,building structures).

COMPOSITION: Be aware that the transformation adjustments madeby some map projections can distort the size of regions of the world,inflating their size relative to other regions.

VARIATIONS & ALTERNATIVES

Alternatives to the prism map, especially to avoid 3D form, include the‘proportional symbol map’, which uses proportionally sized geometricshapes, and the ‘choropleth map’, which colour-codes regional shapes.

Charts Overlays

353

Dot map

ALSO KNOWN AS Dot distribution map, pointillist map, locationmap, dot density map

REPRESENTATION DESCRIPTION

A dot map displays the geographic density and distribution ofphenomena on a map. It uses a point marker to indicate a categorical‘observation’ at a geographical coordinate, which might be plottinginstances of people, notable sites or incidences. The point marker isusually a filled, small dot. Colour can be used to distinguish categoricalclassifications. Sometimes a dot represents a one-to-one phenomenon(i.e. a single record at that location) and sometimes a dot will representone-to-many phenomena (i.e. for an aggregated statistic whereby thelocation represents a logical mid-point). As the proliferation of GPSrecording devices increases, the accuracy and prevalence of detailedlocation marked incidences are leading to increased potential for thistype of approach. However, think carefully about the potentialsensitivity of directly plotting a phenomenon or data incidence at agiven location.

EXAMPLE Mapping each resident of the USA based on the location atwhich they were counted during the 2010 Census across differentethnicities.

Figure 6.52 The Racial Dot Map

354

HOW TO READ IT & WHAT TO LOOK FOR

Acquaint yourself with the geographic region you are presented withand carefully consider the phenomenon that is being represented.Establish the unit of this measure (is it a one-to-one relationship or one-to-many?) by referring to a legend. If categorical colours have beendeployed, establish the different classifications and associations. Scanthe chart looking for the existence of noticeable clusters as well as thewidely dispersed (and maybe empty) regions. Some of the mostinteresting observations come from individual outliers that stand outseparately from others. Are there any patterns between the presence ofdots and their geographical location? Are there any patterns across thepoints with similar categorical colour? Gradually zoom in your focus toperform increasingly local assessments between neighbouring regionalareas to identify any noticeable consistencies or inconsistenciesbetween their patterns.

PRESENTATION TIPS

INTERACTIVITY: One method for dealing with plotting highquantities of observations is to provide interactive semantic zoomfeatures, whereby each time a user zooms in by one level of focus, theunit quantity represented by each dot decreases, from a one-to-manytowards a one-to-one relationship.

355

ANNOTATION: Direct labelling is not necessary, just provide alimited number of regional labels to offer spatial context andorientation. Legends explaining the dot unit scale and any colourassociations should ideally be placed as close to the map display aspossible.

COLOUR: If colours are being used to distinguish the differentcategories, ensure these are as visibly different as possible. Whenincluding a mapping layer image in the background, ensure it is notoverly competing for visual prominence by making it light in colourand possibly semi-transparent. Do not include any unnecessarygeographical details that add no value to the spatial orientation orinterpretation and clutter the display (e.g. roads, building structures).

COMPOSITION: Dot maps must always be displayed on a map thatdemonstrates an equal-area projection as the precision of the plottedlocations is paramount. From a readability perspective, try to find abalance between making the size of the dots small enough to preservetheir individuality but not too tiny to be indecipherable.

VARIATIONS & ALTERNATIVES

A ‘dot density map’ is a variation that involves plotting a representativequantity of dots equally (but randomly) across and within a definedspatial region. The position of individual dots is therefore not to be readas indicative of precise locations but used to form a measure ofquantitative density. This offers a useful alternative to the choroplethmap, especially when categorical separation of the dots through colouris of value. Plotting the location of an incidence of a phenomenon cantranscend geographical mapping to any spatial display, such as the seatlayout and availability at a theatre or on a flight, or showing the keypatterns of play across a sports pitch.

Charts Overlays

Flow map

356

ALSO KNOWN AS Connection map, route map, stream map, particleflow map

REPRESENTATION DESCRIPTION

A flow map shows the characteristics of the movement or flow of aphenomenon across spatial regions. It is often formed using line marksto map flow and combinations of attributes to display the characteristicsof this flow. Examples might include the patterns of traffic and travelacross or between given routes, the dynamics of the patterns of weather,or the movement patterns of people or animals. There is no fixedtemplate for a flow map but it generally displays characteristics oforigin and destination (positions on a map), route (using organic orvector paths), direction (arrow or tapered line width), categoricalclassification (colour) and some quantitative measure (line weight ormotion speed).

EXAMPLE Mapping the average number of vehicles using HongKong’s main network of roads during 2011.

Figure 6.53 Arteries of the City

357

HOW TO READ IT & WHAT TO LOOK FOR

Acquaint yourself with the geographic region you are presented withand carefully consider the phenomenon that is being displayed.Establish the association of all visible attributes to understand fully theirclassification and representation, such as the use of quantitative scales(colour, line size or width) or categorical associations (colour). Scan thechart looking for the existence of patterns of movement, maybe throughclustering or common direction, and identify any main hubs anddensities within the network. Find the large and the small, the dense andthe sparse, and draw out any patterns formed by colour classifications.Gradually zoom in your focus to perform increasingly local assessmentsbetween neighbouring regional areas to identify any noticeableconsistencies or inconsistencies between their patterns.

PRESENTATION TIPS

INTERACTIVITY: Animated sequences will be invaluable to conveymotion if the nature of the flow being presented has the relevant physicsof movement.

358

ANNOTATION: Annotation needs will be unique to each approachand the inherent complexity or otherwise of the display. Often thegeneral patterns may offer the sufficient level of readability without theneed for imposing amounts of value labels.

COLOUR: The colour relationship needs careful consideration to getthe right balance between the intricacies of the foreground data layerand the background mapping layer image. Ensure the background is notoverly competing for visual prominence by making it light in colourand possibly semi-transparent. Do not include any unnecessarygeographical details that add no value to the spatial orientation orinterpretation, but do include those features that have a directassociation with the subject matter (such as roads, routes, etc.).

COMPOSITION: Some degree of geographic distortion of routes orconnecting lines may be required practically to display flow data.Choices like interpolation of lines to smooth an activities route or themerging of relatively similar pathways may be entirely legitimate butensure that this is made clear to the reader.

VARIATIONS & ALTERNATIVES

There are naturally many variations in how you might show flow. Itgenerally differs between whether you are showing point A to point B‘connection maps’, more nuanced ‘route maps’ or surface phenomenasuch as ‘particle flow maps’.

Charts Distortions

Area cartogram

ALSO KNOWN AS Contiguous cartogram, density-equalizing map

EXAMPLE Mapping the measures of climate change responsibilitycompared to vulnerability across all countries.

359

REPRESENTATION DESCRIPTION

An area cartogram displays the quantitative values associated withdistinct definable spatial regions on a map. Each geographic region isrepresented by a polygonal area based on its outline shape with thecollective regional shapes forming the entire landscape. (Note that mosttools for mapping have a predetermined reference between a regionname and the dimensions of the regional polygon.) Quantitative valuesare represented by proportionately distorting (inflating or deflating) therelative size of and, to some degree, shape of the respective regionalareas. Traditionally, area cartograms strictly aim to preserve theneighbourhood relationships between different regions. Colour issometimes used to further represent the same quantitative value or toassociate the region with a categorical classification. Area cartogramsrequire the reader to be relatively familiar with the original size andshape of regions in order to be able to establish the degree of relativechange in their proportions. Without this it is almost impossible toassess the degree of distortion and indeed to identify the regionsthemselves.

Figure 6.54 The Carbon Map

HOW TO READ IT & WHAT TO LOOK FOR

Acquaint yourself with the geographic region you are presented withand carefully consider the quantitative measure that is beingrepresented. Establish the quantitative value scales or categoricalclassifications associated with the colour scale, usually found via alegend. Glance across the entire chart to locate the big-, small- andmedium-sized shapes according to their apparent distortion. Identifyany noticeable exceptions and/or outliers. Gradually zoom in your focus

360

to perform increasingly local comparisons between neighbouringregional areas to identify any noticeable consistencies or inconsistenciesbetween their values. Estimate (or read, if labels are present) theabsolute values of specific regions of interest.

PRESENTATION TIPS

INTERACTIVITY: Animated sequences enabled through interactivecontrols can help to better identify instances and degrees of change butusually only over a small set of regions and only if the change isrelatively smooth and sustained. Manual animation will help providemore control over the experience.

ANNOTATION: Directly labelling the regional areas withgeographical details and the value they hold is likely to lead to toomuch clutter. You might include only a limited number of regionallabels to provide spatial context and orientation.

COLOUR: Legends explaining any colour scales should ideally beplaced as close to the map display as possible. The border colour andstroke width for each spatial area should be distinguishable to define theshape but not so prominent as to dominate attention, usually a subtlegrey- or white-coloured thin stroke will be fine.

COMPOSITION: To aid the readability of the size of the distortions, itcan be useful to present a thumbnail view of the undistorted originalgeographical layout to help the readers orient themselves with thechanges.

VARIATIONS & ALTERNATIVES

Unlike contiguous cartograms, non-contiguous cartograms tend topreserve the shape of the individual polygons but modify the size andthe neighbouring connectivity to other adjacent regional polygon areas.The best alternative ways of showing similar data would be to considerusing the ‘choropleth map’ or ‘Dorling cartogram’.

Charts Distortions

361

Dorling cartogram

ALSO KNOWN AS Demers cartogram

REPRESENTATION DESCRIPTION

A Dorling cartogram displays the quantitative values associated withdistinct, definable spatial regions on a map. Each geographic region isrepresented by a circle which is proportionally sized to represent aquantitative value. The placement of each circle generally resembles theregion’s geographic location with general preservation ofneighbourhood relationships between adjacent shapes. Colour is used toassociate the region with a categorical classification.

EXAMPLE Mapping the predicted electoral voting results for eachstate in the 2012 Presidential Election.

Figure 6.55 Election Dashboard

362

HOW TO READ IT & WHAT TO LOOK FOR

Acquaint yourself with the geographic region you are presented withand carefully consider the quantitative measure that is beingrepresented. Establish the quantitative value scales or categoricalclassifications associated with the colour scale, usually found via alegend. Glance across the entire chart to locate the big-, small- andmedium-sized shapes. Identify any noticeable exceptions and/oroutliers. Gradually zoom in your focus to perform increasingly localcomparisons between neighbouring regional areas to identify anynoticeable consistencies or inconsistencies between their values.Estimate (or read, if labels are present) the absolute values of specificregions of interest.

PRESENTATION TIPS

INTERACTIVITY: Interactive features that enable annotation for

363

category and value labelling can be useful to overcome the difficultiesassociated with the geographic distortion.

ANNOTATION: Directly labelling the shapes with geographicaldetails and the value they hold is common, though you might restrictthis to the circles that have sufficient size to hold such annotation.Otherwise you will need to decide how to handle the labelling of smallvalues.

COLOUR: Legends explaining the size scales and colour associationsshould ideally be placed as close to the map display as possible. Ifcolours are being used to distinguish the different categories, ensurethese are as visibly different as possible.

COMPOSITION: Remember that preserving the adjacency withneighbouring regions is important. Dorling cartograms tend not to allowcircles to overlap or occlude, so some accommodation of large valuesmight result in location distortion.

VARIATIONS & ALTERNATIVES

A variation on the approach, called the ‘Demers cartogram’, involvesthe use of squares or rectangles instead of circles, which offers analternative way of connecting adjacent shapes. Other approaches wouldbe through the ‘area cartogram’ and the ‘choropleth map’.

Charts Distortions

Grid map

ALSO KNOWN AS Cartogram, bin map, equal-area cartogram,hexagon bin map

REPRESENTATION DESCRIPTION

A grid map displays the quantitative values associated with distinct

364

definable spatial regions on a map. Each geographic region (or astatistically consistent interval of space, known as a ‘bin’) isrepresented by a fixed-size uniform shape, sometimes termed a ‘tile’.The shapes used tend to be squares or hexagons, though any tessellatingshape would work in theory in order to help arrange all the regionaltiles into a collective shape that roughly fits the real-world geographicaladjacency. Colours are applied to each regional tile either to represent aquantitative value or to associate the region with a categoricalclassification. Note that the mark used for this chart type is a pointrather than an area mark as its size attributes are constant.

EXAMPLE Showing the percentage of household waste recycled ineach council region across London between April 2013 to March 2014.

Figure 6.56 London is Rubbish at Recycling and Many Boroughs areGetting Worse

HOW TO READ IT & WHAT TO LOOK FOR

365

Acquaint yourself with the geographic region you are presented withand carefully consider the quantitative measure that is beingrepresented. Identify the general layout of the constituent tiles todetermine how good a fit they are with their adjacent regions inabsolute and relative geographical terms. Establish the categorical orquantitative classifications associated with the colour scale, usuallyfound via a legend. Glance across the entire chart to locate the big,small and medium shaded tiles (if quantitative) or the main patternsformed by the categorical colouring. Identify any noticeable exceptionsand/or outliers. Gradually zoom in your focus to perform increasinglylocal comparisons between neighbouring regional areas to identify anynoticeable consistencies or inconsistencies between their values.Estimate (or read, if labels are present) the absolute values of specificregions of interest.

PRESENTATION TIPS

INTERACTIVITY: Interactive features that enable annotation forcategory and value labelling can be useful to overcome the difficultiesassociated with the geographic distortion.

ANNOTATION: Directly labelling the shapes with geographicaldetails is usually too hard. Some versions of the ‘grid map’ will includeabbreviated labels, maybe two digits, to indicate the region theyrepresent and to aid orientation. Otherwise it may require interactivityto facilitate such annotations. Legends explaining the colourassociations should ideally be placed as close to the map display aspossible.

COLOUR: If colour is being used to distinguish the differentcategories, ensure they are as visibly different as possible.

COMPOSITION: The main challenge is to find the most appropriateand representative tile–region relationship (what is the right amount andgeographical level for each constituent tile?) and to optimise the best-fitcollective layout that preserves as many of the legitimate neighbouringregions as possible.

VARIATIONS & ALTERNATIVES

‘Hexagon bin maps’ are specific deployments of the grid map that offera layout formed by a high resolution of smaller hexagons to preservelocalised details. Beyond geographical space, the grid map approach isapplicable to any spatial analysis such as in sports.

366

6.3 Influencing Factors and ConsiderationsHaving covered the fundamentals of visual encoding and profiled manychart type options that deploy different encoding combinations you nowneed to consider the general factors that will influence your specificchoices for which chart or charts to use for your data representation.

Choosing which chart type(s) to use is, inevitably, not a single-factordecision. Rather, as ever with data visualisation, it is an imperfect recipemade up of many ingredients. A pragmatic balance has to be foundsomewhere between taking on board the range of influencing factors thatshape selections and not becoming frozen with indecision caused by theburden of having to consider so many different issues.

Firstly, you need to reflect on the relevant factors that emerge from thefirst three ‘preparatory’ stages of the design process and then supplementthis by addressing the guidance offered by the three visualisation designprinciples introduced in Chapter 1. It must be emphasised that there are nodirect answers provided for you here, simply guidance. How you mightresolve the unique challenges posed by your project has to be somethingyou arrive at yourself.

Formulating Your BriefSkills and resources, frequency: What charts can you actually makeand how efficiently can you create them? This is the big question.Having the ability to create a broad repertoire of different chart typesis the vocabulary of this discipline, judging when to use them is theliteracy. What will have a great influence on the ambitions of the typeof charts you might employ is the ‘expressiveness’ of your abilitiesand that of the technology (applications, programs, tools) you haveaccess to. Expressiveness is a term I first heard used in this context byArvind Satyanarayan, a Computer Science PhD candidate at StanfordUniversity. It describes the amount of variety and extent of controlyou are provided with by a given technology in the construction ofyour visualisation solution, so long as you also possess the necessaryskills to exploit such features, of course:

In a data representation context, maximum expressiveness meansyou can create any combination of mark and attribute encoding

367

to display your data – that is, you can create many differentcharts. Programming libraries like D3.js and open source toolslike R offer broad libraries of different chart options andcustomisations. The drawing-by-hand nature of Adobe Illustratorwould similarly enable you to create a wide range of solutions(though unquestionably more manual in effort and lessreplicable).Restricted expressiveness means you have much more limitedscope to adapt different mark and attribute encodings. Indeedyou might be faced with assigning data to the fixed encodingoptions afforded by a modest menu of chart types. A tool likeExcel has a relatively limited range of (useful) chart types in itsmenu. While there are ways of enhancing the options throughplugins and different ‘workaround’ techniques that broaden itsscope, it is a relatively limited tool. It may, however, suffice formost people’s visualisation ambitions. Elsewhere, there aremany web-based visualisation creation tools which are of valuefor those who want quick and simple charting, though theycertainly reduce the range of options and the capability tocustomise their appearance.

‘The capability to cope with the technological dimension is a keyattribute of successful students: coding – more as a logic and a mindsetthan a technical task – is becoming a very important asset for designerswho want to work in Data Visualization. It doesn’t necessarily meanthat you need to be able to code to find a job, but it helps a lot in thedesign process. The profile in the (near) future will be a hybrid one,mixing competences, skills and approaches currently separated intodisciplinary silos.’ Paolo Ciuccarelli, discussing students on hisCommunication Design Master Programme at Politecnico diMilano

As you reflect on the gallery of charts, my advice would be to perform anassessment of the charts you can make using a scoring system as follows:

368

For any of the charts that fail to score 3 points, here are somestrategies to dealing with this:

Tools are continually being enhanced. The applications you usenow that cannot create, for example, a Sankey diagram, maywell offer that in the next release. So wait it out!For those charts that currently score 1 or 0 points, look aroundthe web for examples of workaround approaches that will helpyou achieve them. For example, you might use conditionalformatting in an Excel worksheet to create a rudimentary heatmap. This is not a chart type offered as standard within the toolbut represents an innovative solution through appropriatingexisting features intended to serve other purposes. Any suchsolutions, though, have to be framed by the frequency of yourwork – will this work realistically need to be replicable andrepeatable (for example, every month) and does my solutionmake that achievable?Invest time in developing skills in the other tools to broadenyour repertoire. Tools like R have a large community of userssharing code, tutorials and examples, resources that wouldgreatly help to facilitate your learning.Lower your ambitions. Sometimes the most significant disciplineto demonstrate is acknowledging what you cannot do andaccepting that (at least, for now) you might need to sacrifice theideal choices you would make for more pragmatic ones.

Purpose: Should you even seek to represent you data in chart form? Willit add any value, enabling new insights or greater perceptual efficiencycompared with its non-visualised form? Will portraying your data via anelegantly presented table, offering the viewer the ability to look up andreference values, actually offer a more suitable solution? Do not rule outthe value of a table. Additionally, perhaps you are trying to representsomething in chart form that would actually be better displayed through

369

information-based (rather than data-based) explanations using imagery,textual anecdotes, video and photos? Most of the time the charting of datawill be fit for purpose, but just keep reminding yourself that you do nothave to chart everything – just make sure you are doing it to add value.

‘I was in the middle of this huge project, juggling as fast and as focusedas I could, and I had this idea of a set of charts stuck in my head thatkept resurfacing. And then, as we were heading close to deadline, Irealized I couldn’t do it. I failed. I couldn’t make it work. Because wehad pictures of the children, and that was enough … I had to let it go.’Sarah Slobin, Visual Journalist, discussing a project profiling agroup of families with children who have a fatal disease

Purpose map: In defining the ‘tone’ of the project, your were determiningwhat the optimum perceptibility of your data would be for your audience.Your definitions were based on whether you were aiming to facilitate thereading of the data or more a general feeling of the data? Were youconcerned with enabling precise and accurate perceptions of values or is itmore about the sense-making of the big, medium and small judgments –getting the ‘gist’ of values more than reading back the values? Were thereemotional qualities that you wanted to emphasise perhaps at thecompromise of perceptual efficiency? Maybe there was a balance betweenthe two?

How these tonal definitions apply specifically to data representationrequires our appreciation of some fundamental theory about datavisualisation. In his book Semiology Graphique, published in 1967,Jacques Bertin was the first, most notable author to propose the idea thatdifferent ways of encoding data might offer varying degrees ofeffectiveness in perception. In 1984 William Cleveland and Robert McGillpublished a seminal paper, ‘Graphical Perception: Theory,Experimentation, and Application to the Development of GraphicalMethods’, that offered more empirical evidence of Bertin’s thoughts. Theyproduced a general ranking that explained which attributes used to encodequantitative values would facilitate the highest degree of perceptualaccuracy. In 1986, Jock Mackinlay’s paper, ‘Automating the Design ofGraphical Presentations of Relational Information’, further extended thisto include proposed rankings for encoding categorical nominal andcategorical ordinal data types as well as quantitative ones. The table shownin Figure 6.57, adapted from Mackinlay’s paper, presents the ‘Ranking of

370

Perceptual Tasks’.

In a nutshell, this ancestry of studies reveals that certain attributes used toencode data may make it easier, and others may make it harder, to judgeaccurately the values being portrayed. Let’s illustrate this with a couple ofexamples. Looking at Figure 6.58, ask yourself: if A is 10, how big is B inthe respective bar and circular displays?

In both cases the answer is B = 5, but while the B ‘bar’ being 5 feels aboutright, the idea that the B ‘circle’ is 5 does not feel quite right. That isbecause our ability to perform relative judgements for the length of bars isfar more precise and accurate than the relative judgements for the area ofcircles. This is explained by the fact that when judging the variation in sizeof a line (bar) you are detecting change in a linear dimension, whereas thevariation in size of a geometric area (circle) occurs across a quadraticdimension. If you look at the rankings in Figure 6.57 in the ‘Quantitative’column, you will see the encoding attribute of Length is ranked higher thanthe attribute of Area.

Figure 6.57 The Ranking of Perceptual Tasks

371

Figure 6.58 Comparison of Judging Line Size vs Area Size

Now let’s consider an example (Figure 6.59) that shows the relativeaccuracy of using different dimensions of colour variation to representcategorical nominal values. In the next pair of charts you can see differentattributes being used to represent the categorical groupings of the points inthe respective scatter plots. On the left you can see variation in theattribute of colour hue (blue, orange and green) to separate the categoriesvisually; on the right you will see the attribute of shape (diamond, circleand square) applied to the same category groupings. What you should beexperiencing is a far more immediate, effortless and accurate sense of thegroupings of the coloured category markers compared with the shaped

372

category markers. It is simply easier to spot the associations throughvariation in colour than variation in shape. This explains why colour hue ismuch higher in the proposed rankings for nominal data than shape.

Figure 6.59 Comparison of judging related items using variation in colour(hue) vs variation in shape

So you can see from these simple demonstrations that there are clearlyways of encoding data that will make it easier to read values accuratelyand efficiently. However, as Cleveland and McGill stress in their paper,this ranking should be taken as only one ingredient of guidance: ‘Theordering does not result in a precise prescription for displaying data butrather is a framework within which to work’.

This is important to note because you have to take into account otherfactors. You have to decide whether precise perceiving is actually whatyou need to facilitate for your readers. If you do, then the likes of the barchart – through the variation in length of a bar – will evidently offer a veryprecise approach. As stated in Chapter 3, that is why they are such animportant part of your visual artillery.

However, sometimes getting a ‘gist’ of the data is sufficient. A few pagesago I presented an image of a bubble chart on my website’s home page,showing the popularity of my blog posts over the previous 100-day period.The purpose of this display was purely to give visitors a sense of thegeneral order of magnitude from the most popular to the relative leastpopular posts. I do not need visitors to form a precise understanding ofabsolute values or exact rankings. I just want them to get a sense of theranking hierarchy. I can therefore justify moving down the quantitativeattribute rankings proposed and deploy a series of circles that encode the

373

visitor totals through the size of their area (colour is used to representdifferent article categories). The level of perceptibility (accuracy andefficiency) that I need to facilitate is adequately achieved by the resulting‘frogspawn’-like display. Furthermore, it offers an appealing and varieddisplay that suits the purpose of this front-page navigation device.

In practice, what all this shows is that chart types vary in the relativeefficiency and accuracy of perception offered to a viewer. Moreover, manyof the charts shown in the gallery can therefore only ever facilitate a gist ofthe values of data due to the complexity of their mark and attributecombinations and the amount of data values they might typically contain(e.g. the treemap often has many parts of a whole in a single display). It isup to you to judge what the right threshold is for your purpose.

Working With DataData examination: Inevitably, the physical characteristics of yourdata are especially influential. What types of data you are trying todisplay will have a significant impact on how you are able to showthem. Only certain types of data can fit into certain chart types; onlycertain chart types can accommodate certain types of data. That iswhy it is often most useful practically to think of this task in terms ofchart types and particularly in terms of these as templates, able toaccommodate specific types of data.For example, representing data through a bar chart requires onecategorical variable (e.g. department) and one quantitative variable(e.g. maximum age). If you want to show a further categoricalvariable (let’s say, to break down departments by gender) you aregoing to need to switch ‘template’ and use something like a clusteredbar chart which can accommodate this extra dimension.I explained earlier how the shape of data influenced the viability ofthe flower metaphor used in the ‘Better Life Index’. The range ofcategorical and quantitative values will certainly influence the mostappropriate chart type choice. For example, suppose you want toshow some part-to-whole analysis and you have only three parts(three sub-categories belonging to the major category or whole) thena treemap really does not make a great deal of sense – they are betterat representing many parts to a whole. The unloved pie chart wouldprobably suffice if the percentage values were quite diverse otherwisethe bar chart would be best.

374

Beyond the size and shape of your data you also might be influencedby its inherent meaning. Sometimes, you will have scope in yourencoding choices to incorporate a certain amount of visual immediacyin accordance with your topic. The flowers of the Better Life Indexfeel consistent in metaphor with the idea of better life: the more inbloom the flowers, the more colourful and proud each petal appearsand the better the quality of life in that country. There is a congruencebetween subject matter and visual form. Think about the billionaires’project from earlier in the chapter, with rankings displayed byindustry. Each point marking each billionaire was a small caricatureface. This is not necessary – a small circular mark for each personwould have been fine – but by using a face for the mark it creates amore immediate recognition that the subject matter is about people.Data exploration: One consistently useful pointer towards how youmight visually communicate your data to others is to consider whichtechniques helped you to unearth key insights when you were visuallyexploring the data. What chart types have you already tried out andmaybe found to reveal interesting patterns? Exploratory data analysisis, in many ways, a bridge to visual communication: the charts youuse to inform yourself often represent prototype thinking on how youmight communicate with others. The design execution may end upbeing different once you introduce the influence of audiencecharacteristics into your thinking, naturally, but if a method is alreadyworking, why not utilise the same approach again?

‘Effective graphics conform to the Congruence Principle according towhich the content and format of the graphic should correspond to thecontent and format of the concepts to be conveyed.’ Barbara Tverskyand Julie Bauer Morrison, taken from Animation: Can it Facilitate?

Establishing Your Editorial ThinkingAngle: When articulating the angles of analysis you intend to portrayto your viewers, you are effectively dictating which chart types mightbe most relevant. If you intend to show how quantities have changedover time, for example, there will be certain charts best placed toportray that and many others that will not. By expressing your desirededitorial angles of analysis in language terms, this will be extremelyhelpful in identifying the primary families of charts across the

375

CHRTS taxonomy that will provide the best option.It is vital to treat every representation challenge on its own merits –do not fall into the trap of going through the motions. Just becauseyou have spatial data does not mean that the most useful portrayal ofthat data will be via a map. If the interesting insights are notregionally and spatially significant, then the map may not provide themost relevant window on that data. The composition of a map – theshape, size and positioning of the world’s regions – is so diverse,inconsistent and truly non-uniform that it may hinder your analysisrather than illuminate it. So always make sure you have carefullyconsidered the relevance of your chosen angle through your editorialthinking.

Trustworthy DesignAvoiding deception: In the discussion about tone I explained howvariations in the potential precision of perception may be appropriatefor the purpose and context of your work. Precision in perception isone thing, but precision in design is another. Being truthful andavoiding deception in how you portray data visually are fundamentalobligations.There are many ways in which viewers can be deceived throughincorrect and inappropriate encoding choices. The main issues arounddeception tend to concern encoding the size of quantities. Forbeginners, these mistakes can be entirely innocent and unintended butneed to be eradicated immediately.

Geometric calculations – When using the area of shapes torepresent different quantitative values, the underlying geometryneeds to be calculated accurately. One of the common mistakeswhen using circles, for example, is simply to modify thediameters: if a quantitative value increases from 10 to 20, justdouble the diameter, right? Wrong. That geometric approachwould be a mistake because, as viewers, when perceiving thesize of a circle, it is the area, not the width, of the circle uponwhich we base our estimates of the quantitative value beingrepresented.

Figure 6.60 Illustrating the correct and incorrect circle sizeencoding

376

The illustration in Figure 6.60 shows the incorrect and correctways of encoding two quantitative values through circle size,where the value of A is twice the size of B. The orange circle forB has half the diameter of A, the green circle for B has half thearea of A. The green circle area calculations are the correct wayto encode these two values, whereas the orange circlecalculations disproportionately shrink circle B by halving thediameter rather than halving the area. This makes it appear muchsmaller than its true value.

3D decoration – In the vast majority of circumstances the use of3D charts is at best unnecessary and at worst hugely distorting inthe display of data. I have some empathy for those who mightvolunteer that they have made and/or like the look of 3D charts.In the past I did too. Sometimes we don’t know not to dosomething until we are told. So this is me, here and now, tellingyou.

The presence of 3D in visualisation tends to be motivated by adesire to demonstrate technical competence with the features ofa tool in terms of ‘look how many things I know how to do withthis tool!’ (users of Excel, I am pointing an accusatory finger atyou right now). It is also driven by the appetite of ratherunsophisticated viewers who are still attracted by the apparentnovelty of 3D skeuomorphic form. (Middle and seniormanagement of the corporate world, with your ‘make me a fancychart’ commands, my finger of doom is now pointing in yourdirection.)

Using psuedo-3D effects in your charts when you have only twodimensions of data means you are simply decorating data. Andwhen I say ‘decorating’, I mean this with the same sneer that

377

would greet memories of avocado green bathrooms in 1970sBritain. A 3D visualisation of 2D data is gratuitous and distortsthe viewer’s ability to read values within any degree ofacceptable accuracy. As illustrated in Figure 6.61, in perceivingthe value estimates of the angles and segments in the respectivepie charts, the 3D version makes it much harder to form accuratejudgements. The tilting of the isometric plane amplifies the frontpart of the chart and diminishes the back. It also introduces araised ‘step’ which is purely decorative, thus embellishing thejudgement of the segment sizes.

Figure 6.61 Illustrating the Distortions Created by 3DDecoration

Furthermore, for charts based on three dimensions of data, 3Deffects should only be considered if – and only if – the viewer isprovided with means to move around the chart object to establishdifferent 2D viewing angles and the collective representation ofall the 3D of data makes sense in showing a whole ‘system’.Truncated axis scales – When quantitative values are encodedthrough the height or length components of size (e.g. for barcharts and area charts), truncating the value axis (not starting therange of quantitative values from the true origin of zero) distortsthe size judgements. I will look at this in more detail in thechapter on composition because it is ultimately more about thesize considerations of scales and deployment of chart apparatusthan necessarily just the representation choices.

Accessible Design

378

The bullet chart is a derivative of the bar chart – the older, moresophisticated brother of the idiot gauge chart – but I didn’t think it wasnecessary to profile as a separate chart type.

Encoded overlays: Beyond the immediate combinations of marksand attributes that comprise a given chart type, you may find value inincorporating additional detail to help viewers with the perceivingand interpretation task. Encoded overlays are useful to help explainfurther the context of values and amplify the interpretation of thegood and the bad, the normal and the exceptional. In some ways thesefeatures might be considered forms of annotation, but as theyrepresent data values (and therefore require encoding choices) itmakes sense to locate these options within this chapter. There aremany different types of visual overlays that may be useful to include:

Figure 6.62 Example of a Bullet Chart Using Banding Overlays

Figure 6.63 Excerpt from ‘What’s Really Warming the World?’

Bandings – These are typically shaded areas that provide some

379

sense of contrast between the main data value marks andcontextual judgements of historic or expected values. In a bulletchart (Figure 6.62) there are various shaded bands that mighthelp to indicate whether the bar’s value should be consideredbad, average or good. In the line chart (Figure 6.63) here you cansee the observed rise in global temperatures. To facilitatecomparison with potentially influencing factors, in thebackground there is a contextual overlay showing the change ingreenhouse gases with banding to indicate the 95% confidenceinterval.Markers – Adding points to a display might be useful to showcomparison against a target, forecast, a previous value, or tohighlight actual vs budget. Figure 6.64 shows a chart thatfacilitates comparisons against a maximum value marker.

Figure 6.64 Example of Using Markers Overlays

Figure 6.65 Why Is Her Paycheck Smaller?

380

Reference lines – These are useful in any display that usesposition or size along an axis as an attribute for a quantitativevalue. Line charts or scatter plots (Figure 6.65) are particularlyenhanced by the inclusion of reference lines, helping to directthe eye towards calculated trends, constants or averages and,with scatter plots specifically, the lines of best fit or correlation.

Elegant DesignVisual appeal: This fits again with the thinking about ‘tone’ and mayalso be informed by some of the mental visualisations that might haveformed in the initial stages of the process. Although you should notallow yourself to be consumed by ideas over the influence of the data,sometimes there is scope to squeeze out an extra sense of stylisticassociation between the visual and the content. For example, the‘pizza’ pie chart in Figure 6.66 presents analysis about the politicalcontributions made by companies in the pizza industry. The decisionto use pizza slices as the basis of a pie chart makes a lot of sense. Thegraphic in Figure 6.67 displays the growth in online sales of razors.Like the pizzas, the notion of creating bar charts by scraping awaylengths of shaving foam offers a clever, congruent and charmingsolution.

Figure 6.66 Inside the Powerful Lobby Fighting for Your Right to Eat

381

Pizza

Figure 6.67 Excerpt from ‘Razor Sales Move Online, Away FromGillette’

382

Summary: Data RepresentationVisual Encoding All charts are based on combinations of marks andattributes:

Marks: represent records (or aggregation of records) and can bepoints, lines, areas or forms.Attributes: represent variable values held for each record and caninclude visual properties like position, size, colour, connection.

Chart Types If visual encoding is the fundamental theoreticalunderstanding of data representation, chart types are the practicalapplication. There are five families of chart types (CHRTS mnemonic):

383

Influencing Factors and Considerations

Formulating the brief: skills and resources – what charts can youmake and how efficiently? From the definitions across the ‘purposemap’ what ‘tone’ did you determine this project might demonstrate?Working with data: what is the shape of the data and how might thatimpact on your chart design? Have you already used a chart type toexplore your data that might prove to be the best way to communicateit to others?Establishing your editorial thinking: what is the specific angle of theenquiry that you want to portray visually? Is it relevant andrepresentative of the most interesting analysis of your data?Trustworthy design: avoid deception through mistaken geometriccalculations, 3D decoration, truncated axis scales, corrupt charts.Accessible design: the use of encoded overlays, such as bandings,markers, reference lines, can aid readability and interpretation.Elegant design: consider the scope of certain design flourishes thatmight enhance the visual appeal through the form of your chartswhilst also preserving their function.

Tips and Tactics

Data is your raw material, not your ideas, so do not arrive at this stagedesperate and precious about wanting to use a certain datarepresentation approach.Be led by the preparatory work (stages 1 to 3) but do use the charttype gallery for inspiration if you need to unblock!Be especially careful in how you think about representing instances ofzero, null (no available data) and nothing (no observation).Do not be too proud to acknowledge when you have made a bad callor gone down a dead end.

384

385

7 Interactivity

The advancement of technology has entirely altered the nature of how weconsume information. Whereas only a generation ago most visualisationswould have been created exclusively for printed consumption,developments in device capability, Internet access and bandwidthperformance have created an incredibly rich environment for digitalvisualisation to become the dominant output. The potential now exists forcreative and capable developers to produce powerful interactive andengaging multimedia experiences for cross-platform consumption.

Unquestionably there is still an fundamental role for static (i.e. notinteractive) and print-only work: the scope offered by digital simplyenables you to extend your reach and broaden the possibilities. In the rightcircumstances, incorporating features of interactivity into yourvisualisation work offers many advantages:

It expands the physical limits of what you can show in a given space.It increases the quantity and broadens the variety of angles of analysisto serve different curiosities.It facilitates manipulations of the data displayed to handle variedinterrogations.It increases the overall control and potential customisation of theexperience.It amplifies your creative licence and the scope for exploring differenttechniques for engaging users.

The careful judgements that distinguish this visualisation design processmust be especially discerning when handling this layer of the anatomy.Well-considered interactivity supports, in particular, the principle of‘accessible’ design, ensuring that you are adding value to the experience,not obstructing the facilitation of understanding. Your main concern inconsidering potential interactivity is to ensure the features you deploy areuseful. This is an easy thing to say about any context but just because youcan does not mean to say you should. For some who possess a naturaltechnical flair, there is often too great a temptation to create interactivitywhere it is neither required nor helpful.

386

Having said that, beyond the functional aspects of interactive designthinking, depending on the nature of the project there can be valueattached to the sheer pleasure created by thoughtfully conceivedinteractive features. Even if these contribute only ornamental benefit therecan be merit in creating a sense of fun and playability so long as suchfeatures do not obstruct access to understanding.

There is a lot on your menu when it comes to considering potentialinteraction design features. As before, ahead of your decision makingabout what you should do, you will first consider what you could do. Tohelp organise your thinking, your options are divided into two main groupsof features:

Data adjustments: Affecting what data is displayed.Presentation adjustments: Affecting how the data is displayed.

There is an ever-increasing range of interfaces to enable interactionevents beyond the mouse/touch through gesture interfaces like theKinect device, oculus rift, wands, control pads. These are beyond thescope of this book but it is worth watching out for developments in thefuture, especially with respect to the growing interest in exploring theimmersive potential of virtual reality (VR).

When considering potential interactive features you first need to recognisethe difference between an event, the control and the function. The event isthe input interaction (such as a click), applied to a control (maybe a button)or element on your display, with the function being the resulting operationthat is performed (filter the data).

Where once we were limited to the mouse or the trackpad as the commonperipheral, over the past few years the emergence of touch-screens in theshape of smartphones and tablets has introduced a whole new eventvocabulary. For the purposes of this chapter we focus on the language ofthe mouse or trackpad, but here is a quick translation of the equivalenttouch events. Note that arguably the biggest difference in assigning eventsto interactive data visualisations exists in the inability to register amouseover (or ‘hover’) action with touch-screens.

387

7.1 Features of Interactivity: DataAdjustmentsThis first group of interactive features covers the various ways in whichyou can enable your users to adjust and manipulate your data. Specifically,they influence what data is displayed at a given moment.

I will temporarily switch nomenclature to ‘user’ in this chapter becausea more active role is needed than ‘viewer’.

Framing: There is only so much one can show in a singlevisualisation display and thus giving users the ability to modifycriteria to customise what data is visible at any given point is a strongadvantage. Going back to the discussion on editorial thinking, inChapter 5, this set of adjustments would specifically concern the‘framing’ of what data to isolate, include or exclude from view.

For those of you familiar with databases, think of this group of featuresas similar in scope to modifying the criteria when querying data in adatabase.

388

In ‘Gun Deaths’ (Figure 7.1), you can use the filters in the pop-upcheck-box lists at the bottom to adjust the display of selectedcategorical data parameters. The filtered data is then shown inisolation above the line from all non-selected groups, which areshown below the line. The ‘Remove filters’ link can be used to resetthe display to the original settings.

Figure 7.1 US Gun Deaths

In the bubble map view of the ‘FinViz’ stock market analysis site,you can change the values of the handles along the axes to modify themaximum and minimum axis range, which allows you effectively tozoom in on the records that match this criterion. You can also selectthe dropdown menus to change the variables plotted on each axis.

Notice the subtle transparency of the filter menu (in Figure 7.1) so thatit doesn’t entirely occlude the data displayed beneath.

389

Figure 7.2 FinViz: Standard and Poor’s 500 Index

Navigating: There are dynamic features that enable users to expandor explore greater levels of detail in the displayed data. This includeslateral movement and vertical drill-down capabilities.

You will see that many of these interactive projects include links toshare the project (or view of the project) with others via social media orthrough offering code to embed work into other websites. This helps tomobilise distribution and open up wider access to your work.

Figure 7.3 The Racial Dot Map

390

The dot map in Figure 7.3, showing the 2010 Census data, displayspopulation density across the USA. As a user you can use a scrollablezoom or scaled zoom to zoom in and out of different map view levels.The map can also be navigated laterally to explore different regions atthe same resolution.This act of zooming to increase the magnification of the view isknown as a geometric zoom. This is considered a data adjustmentbecause through zooming you are effectively re-framing the windowof the included and excluded data at each level of view.In the ‘Obesity Around the World’ visualisation (Figure 7.4),selecting a continent connector expands the sub-category display toshow the marks for all constituent countries. Clicking on the sameconnector collapses the countries to revert back to the main continent-level view.The ‘Social Progress Imperative’ project (Figure 7.5) provides anexample of features that enable users to view the tabulated form ofthe data – the highest level of detail – by selecting the ‘Data Table’tab. The data adjustment taking place here is through providingaccess to the data in a non-visual form. Users can also export the data

391

by clicking on the relevant button to conduct further local analysis.

Figure 7.4 Obesity Around the World

Animating: Data with a temporal component often lends itself tobeing portrayed via animated sequences. The data adjustment takingplace here involves the shifting nature of the timeframe in view at anygiven point. Operations used to create these sequences may beautomatic and/or manual in nature.

Figure 7.5 Excerpt from ‘Social Progress Index 2015’

392

This next project (Figure 7.6) plots NFL players’ height and weightover time using an animated heat map. When you land on the webpage the animation automatically triggers. Once completed, you canalso select the play button to recommence the animation as well asmoving the handle along the slider to manually control the sequence.The gradual growth in the physical characteristics of players is clearlyapparent through the resulting effect.Sequencing: In contrast to animated sequences of the samephenomena changing over time, there are other ways in which a morediscrete sequenced experience can suit your needs. This commonlyexists by letting users navigate through predetermined, differentangles of analysis about a subject. As you navigate through thesequence a narrative is constructed. This is a quintessential exampleof storytelling with data exploring the metaphor of the anecdote: ‘thishappened’ and then ‘this happened’…

Figure 7.6 NFL Players: Height & Weight Over Time

393

The project ‘How Americans Die’ (Figure 7.7) offers a journeythrough many different angles of analysis. Clicking on the series of‘pagination’ dots and/or the navigation buttons will take you througha pre-prepared sequence of displays to build a narrative about thissubject.

Figure 7.7 Excerpt from ‘How Americans Die’

394

Sometimes data exists in only two states: a before and after view.Using normal animated sequences would be ineffective – too suddenand too jumpy – so one popular technique, usually involving twoimages, employs the altering of the position of a handle along a sliderto reveal/fade the respective views. This offers a more graduatedsequence between the two states and facilitates comparisons far moreeffectively as exhibited by the project shown in Figure 7.8.A different example of sequencing – and an increasingly populartrend – is the vertical sequence. This article from the Washington Post(Figure 7.9) profiles the beauty of baseball player Bryce Harper’sswing and uses a very slick series of illustrations to break down fourkey stages of his swing action. As you scroll down the page it actslike a lenticular print or flip-book animation. Notice also how welljudged the styles of the illustrations are.

Figure 7.8 Model Projections of Maximum Air Temperatures Nearthe Ocean and Land Surface

395

Figure 7.9 Excerpt from ‘A Swing of Beauty’

396

Contributing: So far the features covered modify the criteria of whatdata is included/excluded, that then help you dive deeper into thedata, and move through sequenced views of that data. The finalcomponent of ‘data adjustment’ concerns contributing data.Sometimes there are projects that require user input, either forcollecting further records to append and save to an original dataset orjust for temporary (i.e. not held beyond the moment of usage)participation. Additionally, there may be scope to invite users tomodify certain data in order to inform calculations or customise adisplay. In each case, the events and controls associated with this kindof interaction are designed to achieve one function: input data.

397

The first example ‘How well do you know your area?’ (Figure 7.10)by ONS Digital, employs simple game/quiz dynamics to challengeyour knowledge of your local area in the UK. Using the handle tomodify the position along the slider you input a quantitative responseto the questions posed. Based on your response it then providesfeedback revealing the level of accuracy of your estimation.

Figure 7.10 How Well Do You Know Your Area?

In the next project (Figure 7.11), by entering personal details such asyour birth date, country and gender into the respective input boxesyou learn about your place in the world’s population with some rathersobering details about your past, present and future on this planet.

Figure 7.11 Excerpt from ‘Who Old Are You?’

398

Figure 7.12 shows an excerpt from ‘512 Paths to the White House’. Inthis project the toggle buttons are used to switch between threecategorical data states (unselected, Democratic and Republican) tobuild up a simulated election outcome based on the user’s predictionsfor the winners in each of the key swing states. As each winner isselected, only the remaining possible pathways to victory for eithercandidate are shown.

Inevitably data privacy and intended usage are key issues of concern forany project that involves personal details being contributed, so becareful to handle this with integrity and transparency.

Adjusting the position of the handle along the slider in the Better LifeIndex project (Figure 7.13) modifies the quantitative data valuerepresenting the weighting of importance you would attach to eachquality of life topic. In turn, this modifies the vertical positioning ofthe country flowers based on the recalculated average quality of life.

Figure 7.12 512 Paths to the White House

399

Figure 7.13 OECD Better Life Index

7.2 Features of Interactivity: PresentationAdjustmentsIn contrast to the features of ‘data adjustment’, this second group ofinteractive features does not manipulate the data but rather lets youconfigure the presentation of your data in ways that facilitate assistanceand enhance the overall experience.

400

Focusing: Whereas the ‘framing’ features outlined previouslymodified what data would be included and excluded, ‘focus’ featurescontrol what data is visually emphasised and, sometimes, how it isemphasised. Applying such filters helps users select the values theywish to bring to the forefront of their attention. This may be throughmodifying the effect of depth through colour (foreground, mid-ground and background) or a sorting arrangement. The maindifference with the framing features is that no data is eliminated fromthe display but simply relegated in its contrasting prominence orposition.

Figure 7.14 Nobel Laureates

The example in Figure 7.14 provides a snapshot of a project whichdemonstrates the use of a focus filter. It enables users to select a radiobutton from the list of options to emphasise different cohorts of allNobel Laureates (as of 2015). As you can see the selections includefilters for women, shared winners and those who were still living atthe time. The selected Laureates are not coloured differently, rather

401

the unselected values are significantly lightened to create the contrast.

Figure 7.15 Geography of a Recession

The project shown in Figure 7.15 titled ‘Geography of a Recession’allows users to select a link from the list of filters provided on the leftto emphasise different cohorts of counties across the USA. Onceagain, the selected counties are not coloured differently here, theunselected regions are de-emphasised by washing-out their originalshades.

Figure 7.16 How Big Will the UK Population be in 25 Years’ Time?

402

‘Brushing’ data is another technique used to apply focus filters. Inthis next example (Figure 7.16), looking at the UK Census estimatesfor 2011, you use the cursor to select a range of marks from withinthe ‘violin plot’ display in order to view calculated statistics of thosechosen values below the chart.The next example (Figure 7.17), portraying the increase or cuts inWorkers’ Compensation benefits by US state, demonstrates atechnique known as ‘linking’, whereby hovering over a mark in onechart display will then highlight an associated mark in another chartto draw attention to the relationship. In this case, hovering over a statecircle in any of the presented ‘grid maps’ highlights the same state inthe other two maps to draw your eye to their respective statuses. Youmight also see this technique combined with a brushing event tochoose multiple data marks and then highlight all associationsbetween charts, as also demonstrated in the population ‘violin plot’ inFigure 7.16.

403

Figure 7.17 Excerpt from ‘Workers’ Compensation Reforms byState’

Sorting is another way of emphasising the presentation of data. InFigure 7.18, featuring work by the Thomson Reuters graphics team,‘ECB bank test results’, you see a tabular display with sortingfeatures that allow you to reorder columns of data by clicking on thecolumn headers. For categorical data this will sort valuesalphabetically; for quantitative data, by value order. You can alsohand-pick individual records from the table to promote them to thetop of the display to facilitate easier comparisons through closerproximity.

Linking and brushing are particularly popular approaches used forexploratory data analysis where you might have several chart panels andwish to see how a single record shows up within each display.

Annotating: As you saw in the previous chapter on datarepresentation, certain combinations of marks and attributes may onlyprovide viewers with a sense of the order of magnitude of the valuespresented. This might be entirely consistent with the intended tone ofthe project. However, with interactivity, you can at least enableviewers to interact with marks to view more details momentarily.

404

This temporary display is especially useful because most datarepresentations are already so busy that permanently including certainannotated apparatus (like value labels, gridlines, map layers) wouldoverly clutter the display.

Figure 7.18 Excerpt from ‘ECB Bank Test Results’

The example in Figure 7.19, profiles the use of language throughoutthe history of US Presidents’ State of the Union addresses, usingcircle sizes to encode the frequency of different word mentions,giving a gist of the overall quantities and how patterns have formedover time. By hovering over each circle you get access to a tooltipdialogue box which reveals annotations such as the exact word-usequantities and extra contextual commentary.One issue to be aware of when creating pop-up tooltips is to ensurethe place they appear does not risk obstructing the view of importantdata in the chart beneath. This can be especially intricate to handlewhen you have a lot of annotated detail to share. One tactic is to

405

utilise otherwise-empty space on your page display, occupying it withtemporary annotated captions only when triggered by a select orhover event from within a chart.Orientating: A different type of interactive annotation comes in theform of orientation devices, helping you to make better sense of yourlocation within a display – where you are or what values you arelooking at. Some of these functions naturally supplement featureslisted in the previous section about ‘data adjustment’ specifically fornavigation support.

Figure 7.19 Excerpt from ‘History Through the President’s Words’

This snapshot, again from the ‘How Americans Die’ project (Figure7.20), dynamically reveals the values of every mark (both x and yvalues) in this line chart depending on the hover position of thecursor. This effect is reinforced by visual guides extending out to theaxes from the current position.

406

Figure 7.20 Excerpt from ‘How Americans Die’

Figure 7.21 Twitter NYC: A Multilingual Social City

Figure 7.21 displays the language of tweets posted over a period oftime from the New York City area. Given the density and number ofdata points, displaying the details of the mapping layer would be quitecluttered, yet this detail would provide useful assistance for judgingthe location of the data patterns. The effective solution employed letsyou access both views by providing an adjustable slider that allowsyou to modify the transparency of the network of roads to reveal theapparatus of the mapping layer.

Figure 7.22 Killing the Colorado: Explore the Robot River

407

Finally, as mentioned in the previous section, navigating throughdigital visualisation projects increasingly uses a vertical landscape tounfold a story (some term this ‘scrollytelling’). Navigation is oftenseamlessly achieved by using the scroll wheel to move up and downthrough the display. To assist with orientation, especially when youhave a limited field of view of a spatial display, a thumbnail imagemight be used to show your current location within the overalljourney to give a sense of progress. The project featured in Figure7.22 is a great example of the value of this kind of interface,providing a deep exploration of some of the issues impacting on theColorado River.

7.3 Influencing Factors and ConsiderationsYou now have a good sense of the possibilities for incorporatinginteractive features into your work, so let’s turn to consider the factors thatwill have most influence on which of these techniques you might need toor choose to apply.

Formulating Your BriefSkills and resources: Interactivity is unquestionably something that

408

many people aspire to create in their visualisation work, but it issomething greatly influenced by the skills possessed, the technologyyou have access to and what they offer. These will be the factors thatultimately shape your ambitions. Remember, even in commondesktop tools like Excel and Powerpoint, which may appear morelimited on this front, there are ways to incorporate interactive controls(e.g. using VBA in Excel) to offer various adjustment features (e.g.links within Powerpoint slides to create sequences and navigate toother parts of a document).Timescales: It goes without saying that if you have a limitedtimeframe in which to complete your work, even with extensivetechnical skills you are going to be rather pushed in undertaking anyparticularly ambitious interactive solutions. Just because you want todoes not mean that you will be able to.Setting: Does the setting in which the visualisation solution will beconsumed lend itself to the inclusion of an interactive element to theexperience? Will your audience have the time and know-how to takefull advantage of multi-interactive features or is it better to look toprovide a relatively simpler, singular and more immediate staticsolution?Format: What will be the intended output format that this projectneeds to be created for? What device specifications will it need towork across? How adaptable will it need to be?The range and varied characteristics of modern devices presentvisualisers (or perhaps more appropriately, at this stage, developers)with real challenges. Getting a visualisation to work consistently,flexibly and portably across device types, browsers and screendimensions (smartphone, tablet, desktop) can be something of anightmare. Responsive design is concerned with integrating automaticor manually triggered modifications to the arrangement of contentswithin the display and also the type and extent of interactive featuresthat are on offer. Your aim is to preserve as much continuity in thecore experience as possible but also ensure that the same process andoutcome of understanding can be offered to your viewers.While the general trend across web design practice is heading towardsa mobile-first approach, for web-based data visualisationdevelopments there is still a strong focus on maximising thecapabilities of the desktop experience and then maybe compromising,in some way, the richness of the mobile experience.For ProPublica’s work on ‘Losing Ground’ (Figure 7.23), the

409

approach to cross-platform compatibility was based around the rule ofthumb ‘smallify or simplify’. Features that worked on ProPublica’sprimary platform of the desktop would have to be either simplified tofunction practically on the smartphone or simply reduced in size. Youwill see in the pair of contrasting images how the map display is bothshrunk and cropped, and the introductory text is stripped back to onlyinclude the most essential information.

Figure 7.23 Losing Ground

Other format considerations include whether your solution will beprimarily intended for the Web, but will it also need to work in print?The proverb ‘horses for courses’ comes to mind here: solutions needto be created as fit for the format it will be consumed in. The designfeatures that make up an effective interactive project will unlikelytranslate directly as a static, print version. You might need to pursuetwo parallel solutions to suit the respective characteristics of eachoutput format.Another illustration of good practice from the ‘History through thePresidents’ words’ (Figure 7.24) includes a novel ‘Download graphic’function which, when selected, opens up an entirely different staticgraphic designed to suit a printable, pdf format.

Figure 7.24 Excerpt from ‘History Through the President’s Words’

410

Purpose map: Interactivity does not only come into your thinkingwhen you are seeking to create ‘Exploratory’ experiences. You mayalso employ interactive features for creating ‘Explanatory’visualisations, such as portraying analysis across discrete sequenced

411

views or interactively enabling focus filters to emphasise certaincharacteristics of the data. The general position defined on thepurpose map will not singularly define the need for interactivity,rather it will inform the type of interactivity you may seek toincorporate to create the experience you desire.There will also often be scope for an integrated approach wherebyyou might lead with an explanatory experience based around showingheadline insights and then transitions into a more exploratoryexperience through offering a set of functions to let users interrogatedata in more detail.

Working With DataData examination: As profiled with the functions to facilitate drill-down navigation, one of the keen benefits of interactivity is when youhave data that is too big and too broad to show in one view. Torepeat, you can only show so much in a single-screen display. Oftenyou will need to slice up views across and within the varioushierarchies of your data.One particular way the physical properties of the data will informyour interaction design choices is with animation. To justify ananimated display over time, you will need to consider the nature ofthe change that exists in your data. If your data is not changing much,an animated sequence may simply not prove to be of value.Conversely, if values are rapidly changing in all dimensions, ananimated experience will prove chaotic and a form of changeblindness will occur. It may be that the intention is indeed to exhibitthis chaos, but the value of animated sequences is primarily to helpreveal progressive or systematic change rather than random variation.The speed of an animation is also a delicate matter to judge as youseek to avoid the phenomenon of change blindness. Rapid sequenceswill cause the stimulus of change to be missed; a tedious pace willdampen the stimulus of change and key observations may be lost. Theoverall duration will, of course, be informed by the range of values inyour temporal data variable. There is no right or wrong here, it issomething that you will get the best sense of by prototyping andtrialling different speeds.

Establishing Your Editorial Thinking

412

Angle, framing and focus: If you have multiple different angles ofanalysis you wish to portray then these will have to be accommodatedwithin the space allocated. Alternatively, using interactivity, youcould provide access to them via sequenced views or menus enablingtheir selection. The value of incorporating the potential features toachieve this – and the specific range of different options you do wishto facilitate – will be informed by the scope of the decisions youmade in the editorial thinking stages.Thinking again about animations, you must consider whether ananimated sequence will ultimately convey the clearest answer to anangle of interest about how something has changed over time. Thisreally depends on what it is you want to show: the dynamics of a‘system’ that changes over time or a comparison between differentstates over time?The animated project in Figure 7.25 shows the progressive clearing ofsnow across the streets of New York City during the blizzard ofFebruary 2014. The steady and connected fluidity of progress of thesnow-clearing is ideally illustrated through the intervals of changeacross the 24 hours shown.

Figure 7.25 Plow: Streets Cleared of Snow in New York City

413

Sometimes, you might wish to compare one moment directly againstanother. With animated sequences, there is a reliance on memory toconduct this comparison of change. However, our ability to recall isfleeting at best and weakens the further apart (in time) the basis of thecomparison has occurred. Therefore, to facilitate such a comparisonyou ideally need to juxtapose individual frames within the same view.The most common technique used to achieve this is through smallmultiples, where you repeat the same representation for each momentin time of interest and present them collectively in the same view,often through a grid layout. This enables far more incisivecomparisons, as you can see through ‘The Horse in Motion’ work byEadward Muyrbidge, which was used to learn about the gallopingform of a horse by seeing each stage of the motion throughindividually framed moments.

‘Generations of masterpieces portray the legs of galloping horses

414

incorrectly. Before stop-gap photography, the complex interaction ofhorses’ legs simply happened too fast to be accurately apprehended …but in order to see the complex interaction of moving parts, you needthe motion.’ [Paraphrasing] Barbara Tversky and Julie BauerMorrison, taken from Animation: Can it Facilitate?

Figure 7.26 The Horse in Motion

Data RepresentationChart type choice: Some charts are inherently visually complex andideally need interactivity to make them more accessible and readablefor the viewer. The bump chart, chord diagram, and Sankey diagramare just a few of the charts that are far more readable and, byextension, usable if they can offer users the means to filter or focus oncertain selected components of the display through interactivity.

Trustworthy DesignFunctional performance: Faith in the reliability, consistency andgeneral performance of a visualisation is something that impacts onthe perception of a project as ‘trustworthy’. Does it do what itpromises and can I trust the functions that it performs? Projects that

415

involve the collection of user-inputted data will carry extra riskaround trust: how will the data be used and stored? You need toalleviate any such concerns upfront.

‘Confusing widgets, complex dialog boxes, hidden operations,incomprehensible displays, or slow response times … may curtailthorough deliberation and introduce errors.’ Jeff Heer and BenSchneiderman, taken from Interactive Dynamics for Visual Analysis

Accessible DesignUseful: Does it add value? Resort to interactivity only when you haveexhausted the possibility of an appropriate and effective staticsolution. Do not underestimate how effective a well-conceived andexecuted static presentation of data can be. This is not about holding adraconian view about any greater merits offered by static or printwork, but instead recognising that the brilliance of interactivity iswhen it introduces new means of engaging with data that simplycould not be achieved in any other way.Unobtrusive: As with all decisions, an interactive project needs tostrive for the optimum ease of usability: minimise the frictionbetween the act of engaging with interactive features and theunderstanding they facilitate. Do not create unnecessary obstacles thatstifle sparks of curiosity and the scent of intrigue that stirs within theuser. The main watchword here is affordance, making interactivefeatures seamless and either intuitive or at least efficientlyunderstandable.Visual accessibility: To heighten the accessibility levels of yourwork you may offer different presentations of it. For people withvisual impairments you might offer options to magnify the view ofyour data and all accompanying text. For those with colourdeficiencies, as you will learn about shortly, you could offer optionsto apply alternative, colour-blind friendly palettes. A further exampleof this is seen with satellite navigation devices whereby the displayedcolour combinations change to better suit the surrounding lightness ordarkness at a given time of day.

Elegant Design

416

Feature creep: The discipline required to avoid feature creep isindisputable. The gratuitous interactive operation of today is theequivalent of the flashy, overbearing web design trends of the late1990s and early 2000s. People were so quick and so keen to showhow competent and expressive they could be through this (relatively)new technology that they forgot to judge if it added value.If your audience is quite broad you may be (appropriately) inclined tocover more combinations of features than are necessary in the hope ofresponding to as many of the anticipated enquiries as well as possibleand serving the different types of viewer. Judging the degree offlexibility is something of a balancing act within a single project: youdo not want to overwhelm the user with more adjustments than theyneed, nor do you want to narrow the scope of their likelyinterrogations. For a one-off project you have to form your own bestjudgement; for repeatedly used projects you might have scope toaccommodate feedback and iteration.Minimise the clicks: With visualisation you are aiming to make theinvisible (insights) visible. Conversely, to achieve elegance in designyou should be seeking to make visible design features as seamlesslyinconspicuous as possible. As Edward Tufte stated, ‘the best design isinvisible; the viewer should not see your design. They should only seeyour content’.Fun: A final alternative influence is to allow yourself room for atleast a little bit of fun. So long as the choices do not gratuitouslyinterrupt the primary objective of facilitating understanding, oneshould not downplay the heightened pleasure that can be generated byinteractive features that might incorporate an essence of playability.

Summary: InteractivityData adjustments affect what data is displayed and may include thefollowing features:

Framing: isolate, include or exclude data.Navigating: expand or explore greater levels of detail in the displayeddata.Animating: portray temporal data via animated sequences.Sequencing: navigate through discrete sequences of different anglesof analysis.

417

Contributing: customising experiences through user-inputted data.

Presentation adjustments affect how the data is displayed and mayinclude the following features:

Focusing: control what data is visually emphasised.Annotating: interact with marks to bring up more detail.Orientating: make better sense of your location within a display.

Influencing Factors and Considerations

Formulating the brief: skills and resources, timescales, setting, andformat will all influence the scope of interactivity. What experienceare you facilitating and how might interactive options help achievethis?Working with data: what range of data do you wish to include? Largedatasets with diverse values may need interactive features to helpusers filter views and interrogate the contents.Establishing your editorial thinking: choices made about your chosenangle, as well as definitions for framing and focus will all influenceinteractive choices, especially if users must navigate to view multipleangles of analysis or representations portrayed through animatedsequences.Data representation: certain chart choices may require interactivity toenable readability.Trustworthy design: functional performance and reliability willsubstantiate the perception of trust from your users.Accessible design: any interactive feature should prove to be usefuland unobtrusive. Interactivity can also assist with challenges aroundvisual accessibility.Elegant design: beware of feature creep, minimise the clicks, butembrace the pleasure of playability.

Tips and Tactics

Initial sketching of concepts will be worth doing first before investingtoo much time jumping into prototype mode.Project management is critical when considering the impact ofdevelopment of an interactive solution.Backups, contingencies, version control.

418

Do not be precious about – nor overly impressed with – ‘cool’-sounding interaction features that will disproportionately divertprecious resources (time, effort, people).Beware of feature creep: keep focusing on what is important andrelevant. A technical achievement is great for you, but is it great forthe project?Version control and file management will be important here.

419

8 Annotation

Annotation is the third layer of the visualisation design anatomy and isconcerned with the simple need to explain things: what is the right amountand type of help your viewers will need when consuming thevisualisation?

Annotation is unquestionably the most often neglected layer of thevisualisation anatomy. Maybe this is because it involves the least amountof pure design thinking relative to the other matters requiring attention,like interactivity and colour. More likely, it is because effective annotationrequires visualisers truly to understand their intended audience. This canbe a hard frame of mind to adopt, especially when your potential viewersare likely to have a diverse knowledge, range of interests and capability.

In contrast to the greater theoretical and technical concerns around datarepresentation, colour and interactivity, I find thinking about annotationrelatively refreshing. It is not only uncomplicated and based on a hugedose of common sense, but also hugely influential, especially in directlyfacilitating understanding.

Annotation choices often conform to the Goldilocks principle: too muchand the display becomes cluttered, overwhelming, and potentiallyunnecessarily patronising; too little and the viewers may beinappropriately faced with the prospect of having to find their own wayaround a visualisation and form their own understanding about what it isshowing.

Later in this chapter we will look at the factors that will influence yourdecision making but to begin with here is a profile of some of the keyfeatures of annotated design that exist across two main groups:

Project annotations: helping viewers understand what the project isabout and how to use it.Chart annotations: helping viewers perceive the charts and optimisetheir potential interpretations.

420

8.1 Features of Annotation: ProjectAnnotationThis collection of annotation options is related to decisions about howmuch and what type of help you might need to offer your audiences intheir understanding of the background, function and purpose of yourproject.

Headings: The titles and subtitles occupy such prime real estatewithin your project’s layout, yet more often than not visualisers fail toexploit these to best effect. There are no universal practices for what aheading should do or say; this will vary considerably between subjectareas and industries, but should prove fundamentally useful.

Figure 8.1 A Sample of Project Titles

The primary aim of a title (and often subtitle combination) is toinform viewers about the immediate topic or display, giving them afair idea about what they are about to see. You might choose toarticulate the essence of the curiosity that has driven the project byframing it around a question or maybe a key finding you unearthedfollowing the work.Subheadings, section headings and chart titles will tend to be morefunctional in their role, making clear to the viewer the contents orfocus of attention associated with each component of the display.Your judgement surrounds the level of detail and the type of language

421

you use in each case to fit cohesively with the overall tone of thework.Introductions: Essentially working in conjunction with titles,introductions typically exist as short paragraphs that explain, moreexplicitly than a title can, what the project is about. The content ofthis introduction might usefully explain in clear language terms someof the components you considered during the editorial thinkingactivity, such as:

details of the reason for the project (source curiosity);an explanation of the relevance of this analysis;a description of the analysis (angle, framing) that is presented;expression of the main message or finding that the work is aboutto reveal (possibly focus).

Some introductions will extend beyond a basic description of the project toinclude thorough details of where the data comes from and how it has beenprepared and treated in advance of its analysis (including any assumptions,modifications or potential shortcomings). There may also be further linksto ‘read more’ detail or related articles about the subject.

Figure 8.2 Excerpt from ‘The Color of Debt’

Introductions may be presented as fixed text located near the top (orstart) of a project (usually underneath a title) as in Figure 8.2 or,through interactivity, may be hidden from view and brought up in aseparate window or pop-up to provide the details upon request.User guides: As you have seen, some projects can incorporate manydifferent features of interactivity. While they may not necessarily beoverly technical – and therefore not that hard to learn how to use

422

them – the full repertoire of features may be worth walking through,as in Figure 8.3. This is important to consider so that, as a visualiser,you can be sure your users are acquainted with the entire array ofoptions they have to explore, interrogate and control their experience.You should want people to fully utilise all the different features youhave carefully curated and created, so it is in everyone’s interest tothink about including these types of user guides.

Figure 8.3 Excerpt from ‘Kindred Britain’

423

424

Multimedia: There is increasing potential and usage of broadermedia assets in visualisation design work beyond charts, such asvideo and imagery. In visualisation this is perhaps a relativelycontemporary trend (infographics have incorporated such media butvisualisations generally have done so far less) and, in some ways,reflects the ongoing blurring of boundaries between this and otherrelated fields. Incorporating good-quality and sympathetically styledassets like illustrations or photo-imagery can be a valuablecomplementary device alongside your data representation elements.In the ‘Color of Debt’ (Figure 8.4) project, different neighbourhoodsof Chicago that have been hardest hit by debt are profiled usingaccompanying imagery to show more graphic context of thecommunities affected, including a detailed reference map of the areaand an animated panel displaying a sequence of street view images.Imagery, in particular, will be an interesting option to consider whenit adds value to help exhibit the subject matter in tangible form,offering an appealing visual hook to draw people in or simply to aidimmediate recognition of the topic. In Bloomberg’s billionairesproject (Figure 8.5), each billionaire is represented by a pen-and-inkcaricature. This is elegant in choice and also dodges the likely flawsof having to compose the work around individual headshotphotographs that would have been hard to frame and colourconsistently.

Figure 8.4 Excerpt from ‘The Color of Debt’

It was worth Bloomberg investing in the time/cost involved incommissioning these illustrations, given that the project was not aone-off but something that would be an ongoing, updated daily

425

resource.Problems with the integration of such media within a visualisationproject will occur when unsuitable attempts are made to combineimagery within the framework of a chart. Often the lack of cohesioncreates a significant hindrance whereby the data representations areobscured or generally made harder to read, as the inherent form andcolour clashes undermine the functional harmony.Researching, curating, capturing or creating assets of imageryrequires skill and a professional approach, otherwise the resultingeffect will look amateurish. Incorporating these media into a datavisualisation is not about quickly conducting some Google Imagefishing exercise. Determining what imagery you will be able to useinvolves careful considerations around image suitability, quality and,critically, usage rights. Beware the client or colleague who thinksotherwise.

‘Although all our projects are very much data driven, visualisation isonly part of the products and solutions we create. This day and ageprovides us with amazing opportunities to combine video, animation,visualisation, sound and interactivity. Why not make full use of this? …Judging whether to include something or not is all about editing: asking“is it really necessary?”. There is always an aspect of “gut feel” or“instinct” mixed with continuous doubt that drives me in these cases.’Thomas Clever, Co-founder CLEVER°FRANKE, a data drivenexperiences studio

A frequent simple example of incorporated imagery is when you have toinclude logos according to the needs of the organisation for whom yourwork is being created. Remember to consider this early so you at leastknow in advance that you will have to assign some space to accommodatethis component elegantly.

Footnotes: Often the final visible feature of your display, footnotesprovide a convenient place to share important details that furthersubstantiate the explanation of your work. Sometimes thisinformation might be stored within the introduction component(especially if that is interactively hidden/revealed to allow it moreroom to accommodate detail):

Data sources should be provided, ideally in close proximity tothe relevant charts.

426

Credits will list the authors and main contributors of the work,often including the provision of contact details.

Figure 8.5 Excerpt from ‘Bloomberg Billionaires’

Attribution is also important if you wish to recognise theinfluence of other people’s work in shaping your ideas or toacknowledge the benefits of using an open source application orfree typeface, for example.Usage information might explain the circumstances in which thework can be viewed or reused, whether there are anyconfidentialities or copyrights involved.Time/date stamps are often forgotten but they will give anindication to viewers of the moment of production and from thatthey might be able to ascertain the work’s current accuracy andcontextualise their interpretations accordingly.

Figure 8.6 Excerpt from ‘Gender Pay Gap US’

8.2 Features of Annotation: Chart AnnotationThis second group of annotated features concerns the ways you provideviewers with specific assistance for perceiving and interpreting the charts.Think of these as being the features that refer directly to your charts orexist directly within or in immediate proximity to each chart.

Reading guides: These are written or visual instructions that provide

427

viewers with a guide for how to read the chart or graphic and offergreater detailed assistance than a legend (see later). The idea oflearnability in visualisation is an important consideration. It is a two-way commitment requiring will and effort from the viewer andsufficient assistance from the visualiser. This is something to bediscussed in Chapter 11 under ‘Visualisation literacy’.Recognising that their readership may not necessarily understandconnected scatter plots, Bloomberg’s visual data team offer a ‘How toRead this Graphic’ guide immediately as you land on the projectshown in Figure 8.7. This can be closed but a permanent ‘How to’button remains for those who may need to refer to it again. Theconnected scatter plot was the right choice for this angle of analysis,so rather than use a different ‘safer’ representation approach (andtherefore alter what analysis was shown) it is to their credit that theyrespected the capacity of their viewers to be willing to learn how toread this unfamiliar graphical form.

Figure 8.7 Excerpt from ‘Holdouts Find Cheapest Super BowlTickets Late in the Game’

428

Figure 8.8 Excerpt from ‘The Life Cycle of Ideas’

429

The second example shown (Figure 8.8) is from the ‘How to Read’guide taken from the ‘Life Cycle of Ideas’ graphic created byAccurate, a studio renowned for innovative and expressivevisualisation work. Given the relative complexity of the encodingsused in this piece, it is necessary to equip the viewer with as muchguidance as possible to ensure its potential is fully realised.Chart apparatus: Options for chart apparatus relate to the structuralcomponents found in different chart types. Every visualisationdisplayed in this book has different elements of chart apparatus(Figure 8.9), specifically visible axis lines, tick marks or gridlines tohelp viewers orient their judgements of size and position. There is noright or wrong for including or excluding these features, it tends to beinformed by your tonal definitions based on how much precision inthe perceiving of values you wish to facilitate. I will discuss the rangeof different structures underlying each chart type (such as Cartesian,Radial or Spatial) in Chapter 10 on composition, as these have more

430

to do with issues of shape and dimension.

‘Labelling is the black magic of data visualization.’ Gregor Aisch,Graphics Editor, The New York Times

Labels: There are three main labelling devices you will need to thinkabout using within your chart: axis titles, axis labels and value labels:

Axis titles describe what values are being referenced by eachaxis. This might be a single word or a short sentence dependingon what best fits the needs of your viewers. Often the role of anaxis is already explained (or implied) by project annotationselsewhere, such as titles or sub-headings, but do not alwaysassume this will be instantly clear to your viewers.Axis labels provide value references along each axis to helpidentify the categorical value or the date/quantitative valueassociated with that scale position. For categorical axes (as seenin bar charts and heat maps, for example) one of the mainjudgments relates to the orientation of the label: you will need tofind sufficient room to fit the label but also preserve itsreadability. For non-categorical data the main judgement will bewhat scale intervals to use. This has to be a combination of whatis most useful for referencing values by your viewer, what is themost relevant interval based on the nature of the data (e.g.maybe a year-level label is more relevant than marking eachmonth), and also what feels like it achieves the best-lookingvisual rhythm along the chart’s edge. This will be another matterthat is discussed more in the composition chapter.

Figure 8.9 Mizzou’s Racial Gap Is Typical On CollegeCampuses

431

Value labels will appear in proximity to specific mark encodingsinside the chart. Typically, these labels will be used to reveal aquantity, such as showing the percentage sizes of the sectors in apie chart or the height of bars. Judging whether to include suchannotations will refer back to your definition of the appropriatetone: will viewers need to read off exact values or will theirperceived estimates of size and/or relationship be sufficient? Theneed to include categorical labels will be a concern for maps(whether to label locations?) or charts like the scatter plot seen inFigure 8.9, where you may wish to draw focus to a select sampleof the categories plotted across the display.

As you have seen, one way of providing detailed value labels is

432

through interaction, maybe offering a pop-up/tooltip annotationthat is triggered by a hover or click event on different markencodings. Having the option for interactivity here is especiallyuseful as it enables you to reduce clutter from your display thatcan develop as more annotated detail is added.

Redundancy in labelling occurs when you include value labelling ofquantities for all marks whilst also including axis-scale labelling. Youare effectively unnecessarily doubling the assistance being offered andso, ideally, you should choose to include one or the other.

Legend: A legend is an annotated feature within or alongside yourchart that presents one or several keys to help viewers understand thecategorical or quantitative meaning of different attributes.

Figure 8.10 Excerpt from ‘The Infographic History of the World’

For quantitative data the main role for a legend will be if the attributeof area size has been used to encode values, as found on the bubbleplot chart type. The keys displayed there will provide a reference forthe different size scales. Which selection of sizes to show needscareful thought: what is the most useful guide to help your viewersmake their perceptual judgements from a chart? This might not entailshowing only even interval sizes (50, 100, 150 etc.); rather, you mightoffer viewers a indicative spread of sizes to best represent thedistribution of your data values. The example in Figure 8.10 showslogical interval sizes to reflect the range of values in the data and alsohelpfully includes reference to the maximum value size to explainthat no shape will be any larger than this. For categorical data youalso see a key showing the meaning of different colours and shapes

433

and their associated values.

Figure 8.11 Twitter NYC: A Multilingual Social City

A nice approach to getting more out of your legends is shown inFigure 8.11. Here you will see a key explaining the colourassociations combined with a bar chart to display the distribution ofquantities for each language grouping from this analysis of tweetsposted around New York City.Captions: These exist typically as small passages of written analysisthat bring to the surface some of the main insights and conclusionsfrom the work. These might be presented close to related valuesinside the chart or in separate panels to provide commentary outsidethe chart.In ‘Gun Deaths’ (Figure 8.12), there is a nice solution that combinesannotated captions with interactive data adjustments. Below the mainchart there is a ‘What This Data Reveals’ section which some of themain findings from the analysis of the gun death data. The captionsdouble up as clickable shortcuts so that you can quickly apply therelevant framing filters and update the main display to see what thecaptions are referring to.

Figure 8.12 Excerpt from ‘US Gun Deaths’

434

As creative tools become more ubiquitous the possibility forincorporating non-visual data in you work increases. As an alternativeto the written caption there is greater scope to consider using audio asa means of verbally narrating a subject and explaining key messages.Over the past few years one of the standout projects using this featurewas the video profiling ‘Wealth Inequality in America’ (Figure 8.13),as introduced in Chapter 3, where the voiceover provides a verycompelling and cohesive narrative against the backdrop of theanimated visuals that present the data being described.

Figure 8.13 Image taken from ‘Wealth Inequality in America’

435

8.3 TypographyAs you have seen, many features of annotation utilise text. This meansyour choices will be concerned not just with what text to include, but alsowith how it will look. This naturally merits a brief discussion about howtypography will have a significant role in the presentation of your work.

Firstly, some clarity about language. A typeface is a designed collection ofglyphs representing individual letters, numbers and other symbols oflanguage based on a cohesive style. A font is the variation across severalphysical dimensions of the typeface, such as weight, size, condensationand italicisation. A typeface can have one or many different fonts in itsfamily. Type effectively represents the collective appearance formed by thechoice of typeface and the font.

Tahoma and Century Gothic are different typefaces. This font and this fontboth belong to the Georgia typeface family but display variations in size,weight and italicisation.

I discussed earlier the distinction between definitions of datavisualisation and other related fields. I mentioned how the personcreating their design is not necessarily conscious or concerned aboutwhat label is attached to their work, they are simply doing their workregardless. The same could be applied to people’s interchangeable useof and meaning of the terms typeface and font, the clarity of which hasbeen irreparably confused by Microsoft’s desktop tools in particular.

Serif typefaces add an extra little flourish in the form of a small line at theend of the stroke in a letter or symbol. Garamond is an example of a seriffont. Serif typefaces are generally considered to be easier to read for longsequences of text (such as the full body text) and are especially used inprint displays.

Sans-serif typefaces have no extra line extending the stroke for eachcharacter. Verdana is an example of a sans-serif typeface. These typefacesare commonly used for shorter sections of text, such as axis or value labelsor titles, and for screen displays.

In making choices about which type to use, there are echoes with thethinking you are about to face on using colour. As you will see, colour

436

decisions concern legibility and meaning first, decoration last. Withtypeface choices you are not dressing up your text, you are optimising itsreadability and meaning across your display. The desired style of typefaceonly comes into your thinking after legibility and meaning.

In terms of legibility, you need to choose a typeface and font combinationthat will be suitable for the role of each element of text you are using.Viewers need to be able to read the words and numbers on display withoutdifficulty. Quite obvious, really. Some typefaces (and specifically fonts)are more easily read than others. Some work better to make numbers asclearly readable as possible, others work better for words. There are plentyof typefaces that might look cool and contemporary but if they make textindecipherable then that is plain wrong.

Typeface decisions will often be taken out of your hands by the visualidentity guidelines of organisations and publications, as well as bytechnical reasons relating to browser type, software compatibility andavailability.

Just as variation in colour implies meaning, so does variation in typefaceand font. If you make some text capitalised, large and bold-weight this willsuggest it carries greater significance and portrays a higher prominenceacross the object hierarchy than any text presented in lower case, with asmaller size and thinner weight. So you should seek to limit the variationin font where possible.

Text-based annotations should be considered part of the supporting castand the way you consider typeface and font choices should reflect thisrole. Typography in visualisation should be seen but really not heard.Deciding on the most suitable type is something that can ultimately comedown to experience and influence through exposure to other work. Everyindividual has their own relied-upon preferences. In practice, I find there isa good chunk of trial and error as well as viewer testing that goes intoresolving the final selection. Across the spectrum of data visualisationwork being produced there are no significant trends to be informed bylargely because judging the most suitable typography choices will beunique to the circumstances influencing each project.

Typography is just another of the many individual ingredients relevant todata visualisation that exists as a significant subject in its own right. It is

437

somewhat inadequate to allocate barely two pages of this book todiscussing its role in visualisation, but these will at least offer you a bite-sized window into the topic.

‘Never choose Times New Roman or Arial, as those fonts are favoredonly by the apathetic and sloppy. Not by typographers. Not by you.’Matthew Butterick, Typographer, Lawyer and Writer

8.4 Influencing Factors and ConsiderationsHaving become familiar with the principal options for annotating, you nowhave to decide which features to incorporate into your work and how youmight deploy these.

Formulating Your Brief

‘Think of the reader – a specific reader, like a friend who’s curious but anovice to the subject and to data-viz – when designing the graphic. Thathelps. And I rely pretty heavily on that introductory text that runs witheach graphic – about 100 words, usually, that should give the new-to-the-subject reader enough background to understand why this graphic isworth engaging with, and sets them up to understand and contextualizethe takeaway. And annotate the graphic itself. If there’s a particularpoint you want the reader to understand, make it! Explicitly! I often runa few captions typeset right on the viz, with lines that connect them tokey elements in the design.’ Katie Peek, Data Visualisation Designerand Science Journalist, on making complex and/or complicatedsubject matter accessible and interesting to her audience

Audience: Given that most annotations serve the purpose of viewerassistance, your approach will inevitably be influenced by thecharacteristics of your intended audience. Having an appreciation ofand empathy towards the knowledge and capabilities of the differentcohorts of viewers is especially important with this layer of design.How much help will they need to understand the project and also thedata being portrayed? You will need to consider the following:

Subject: how well acquainted will they be with this subjectmatter? Will they understand the terminology, acronyms,

438

abbreviations? Will they recognise the relevance of thisparticular angle of analysis about this subject?Interactive functions: how sophisticated are they likely to be interms of being able to understand and utilise the differentfeatures of interactivity made possible through your design?Perceiving: how well equipped are they to work with thisvisualisation? Is it likely that the chart type(s) will be familiar orunfamiliar; if the latter, will they need support to guide themthrough the process of perceiving?Interpreting: will they have the knowledge required to formlegitimate interpretations of this work? Will they know how tounderstand what is good or bad, what big and small mean, whatis important, or not? Alternatively, will you need to providesome level of assistance to address this potential gap?

Purpose map: The defined intentions for the tone and experience ofyour work will influence the type and extent of annotation featuresrequired.If you are working towards a solution that leans more towards the‘reading’ tone you are placing an emphasis on the perceptibility of thedata values. It therefore makes sense that you should aim to provideas much assistance as possible (especially through extensive chartannotations) to maximise the efficiency and precision of this process.If it is more about a ‘feeling’ tone then you may be able to justify theabsence of the same annotations. Your intent may be to provide moreof a general sense – a ‘gist’ – of the order of magnitude of values.If you are seeking to provide an ‘explanatory’ experience it would belogical to employ as many devices as possible that will help informyour viewers about how to read the charts (assisting with the‘perceiving’ stage of understanding) and also bring some of the keyinsights to the surface, making clear the meaning of the quantities andrelationships displayed (thus assisting with the stage of‘interpreting’). The use of captions and visual overlays will beparticularly helpful in achieving this, as will the potential for audioaccompaniments if you are seeking to push the explanatoryexperience a step further.‘Exploratory’ experiences are less likely to include layers of insightassistance, instead the focus will be more towards project-levelannotation, ensuring that viewers (and particularly here, users) haveas much understanding as possible about how to use the project fortheir exploratory benefit. You might find, however, that devices like

439

‘How to read this graphic’ are still relevant irrespective of thedefinition of your intended experience.Characteristically, ‘exhibitory’ work demonstrates far less annotatedassistance because, by intention, it is more about providing a visualdisplay of the data rather than offering an explanatory presentation orthe means for exploratory interrogation. The assumptions here arethat audiences will have sufficient domain and project knowledge notto require extensive additional assistance. Common chart annotationslike value labels and legends, and project annotations like titles andintroductions, are still likely to be necessary, but these might reflectthe extent required.

Establishing Your Editorial ThinkingFocus: During your editorial thinking you considered focus and itsparticular role in supporting explanatory thinking. Are there specificvalue labels that you wish to display over others? Rather thanlabelling all values, for example, have you determined that onlycertain marks and attributes will merit labelling? As you saw earlierin the example scatter plot about the under-representation of blackstudents in US colleges, only certain points were labelled, not all.These would have been judged to have been the most relevant andinteresting elements to emphasise through annotation.

Trustworthy DesignTransparency: Annotation is one of the most important aids toensure that you secure and sustain trust from your viewers bydemonstrating integrity and openness:

Explain what the project is and is not going to show.Detail where the data came from and what framing criteria wereused during the process of acquisition, and also make what hasbeen ultimately included in the chart(s).Outline any data transformation treatments, assumptions andcalculations. Are there any limitations that viewers need to beaware of?Highlight and contextualise any findings to ensure accuracy ininterpretation.With digital projects in particular, provide access to coding

440

repositories to lay open all routines and programmatic solutions.

Accessible DesignUnderstandable: If you recall, in the section profiling circumstancesyou considered what the characteristics were of the setting orsituation in which your audience might consume your visualisation.Well-judged project and chart annotations are entirely concerned withproviding a sufficient level of assistance to achieve understanding.The key word there is ‘sufficient’ because there is a balance: toomuch assistance makes the annotations included feel overburdening;too little and there is far more room for wrong assumptions andmisconceptions to prosper. A setting that is consistent with the needto deliver immediate insights will need suitable annotations to fulfilthis. There will be no time or patience for long introductions orexplanations in that setting. Conversely, a visualisation about asubject matter that is inherently complex may warrant suchassistance.

Elegant DesignMinimise the clutter: A key concern about annotations is judging themerits of including structural or textual assistance against thepotential disruption and obstruction caused by these to the view of thedata. Any annotation device added to your display has a spatial andvisual consequence that needs to be accommodated. Of course, asmentioned, with the benefit of interactivity it is possible to show andhide layers of detail. Overall, you will have to find the most elegantsolution for presenting your annotations to ensure you do notinadvertently undermine the help you are trying to provide.

Summary: AnnotationProject annotations help viewers understand what the project is about andhow to use it, and may include the following features:

Headings: titles, sub-titles and section headings.Introductions: providing background and aims of the project.

441

User guides: advice or instruction for how to use any interactivefeatures.Multimedia: the potential to enhance your project using appropriateimagery, videos or illustrations.Footnotes: potentially includes data sources, credits, usageinformation, and time/date stamps.

Chart annotations help viewers perceive the charts and optimise theirpotential interpretations and may include the following features:

Chart apparatus: axis lines, gridlines, tick marks.Labels: axis titles, axis labels, value labels.Legend: providing detailed keys for colour or size associations.Reading guides: detailed instructions advising readers how toperceive and interpret the chart.Captions: drawing out key findings and commentaries.

Typography Most of the annotation features you include are based on textand so you will need to consider carefully the legibility of the typeface youchoose and the logic behind the font-size hierarchy you display.

Influencing Factors and Considerations

Formulating the brief: consider the characteristics and needs of theaudience. Certain chart choices and subjects may require moreexplanation. From the ‘purpose map’ what type of tone andexperience are you trying to create and what role might annotationplay?Establishing your editorial thinking: what things do you want toemphasise or direct the eye towards (focus)?Trustworthy design: maximise the information viewers have to ensureall your data work is transparent and clearly explained.Accessible design: what is the right amount and type of annotationsuitable to the setting and complexity of your subject?Elegant design: minimise the clutter.

Tips and Tactics

Attention to detail is imperative: all instructions, project information,captions and value labels need to be accurate. Always spell-check

442

digitally and manually, and ask others to proofread if you are too‘close’ to see.Do not forget to check on permission to use any annotated asset, suchas imagery, photos, videos, quotations, etc.

443

9 Colour

Having established which charts you will use, the potential interactivefunctions that might be required and the annotation features that will beespecially useful, you have effectively determined all the visible elementsthat will be included in your project. The final two layers of designconcern not what elements will be included or excluded, but how they willappear. After this chapter you will look at issues on composition, butbefore that the rather weighty matter of colour.

As one of the most powerful sensory cues, colour is a highly influentialvisual property. It is arguably the design decision that has the mostimmediate impact on the eye of the viewer. All the design features of yourvisualisation display hold some attribute of colour, otherwise they areinvisible:

Every mark and item of apparatus in your charts will be coloured;indeed colour in itself may be an attribute that represents your datavalues.Interactive features do not always have an associated visible property(some are indeed invisible and left as intuitively discoverable).However, those features that involve buttons, menus, navigation tabsand value sliders will always have a colour.Annotation properties such as titles, captions and value labels will allbe coloured.Composition design mainly concerns the arrangement of all the abovefeatures, though you might use colour to help achieve a certain designlayout. As you will see, emptiness is a useful organising device –leaving something blank is a colour choice.

Thankfully, there is a route through all of this potential complexity relyingon just a little bit of science mixed in with lots of common sense. Byreplacing any arbitrary judgements that might have been previously basedon taste, and through increasing the sensitivity of your choices, colourbecomes one of the layers of visualisation design that can be most quicklyand significantly improved.

444

‘Colors are perhaps the visual property that people most often misuse invisualization without being aware of it.’ Robert Kosara, SeniorResearch Scientist at Tableau Software

The key factor in thinking about colour is to ensure you establish meaningfirst and decoration last. That is not to rule out the value of certaindecorative benefits of colour, but to advise that these should be your lastconcern. Besides, in dealing with meaningful applications of colour youwill already have gone a long way towards establishing the ‘decorative’qualities of your project’s aesthetic appearance.

This chapter begins with a look at some of the key components of colourscience, offering a foundation for your understanding about this topic.After that you will learn about the ways and places in which colour couldbe used. Finally, you will consider the main factors that influence colourdecisions.

COLOUR thinking begins from inside the chart(s), working outwardsacross the rest of the visualisation anatomy:

Data legibility.Editorial salience.Functional harmony.

9.1 Overview of Colour TheoryCOLOUR in visualisation is something of a minefield. As with many ofthese design layer chapters, an introduction to colour involves judging theright amount of science and the right amount of practical application. Whatdoes justice to the essence of the subject and gives you the most relevantcontent to work with is a delicate balance.

When you lift the lid on the science behind colour you open up a world ofbrain-ache. When this chapter is finalised I will have spent a great deal oftime agonising over how to explain this subject and what to leave in orleave out because there is so much going on with colour. And it is tricky.Why? Because you almost come face to face with philosophical questionslike ‘what is white?’ and the sort of mathematical formulae that you reallyrather hoped had been left behind at school. You learn how the colours you

445

specify in your designs as X might be perceived by some people as Y andothers as Z. You discover that you are not just selecting colours from aneat linear palette but rather from a multi-dimensioned colour spaceoccupying a cubic, cylindrical or spherical conceptual shape, depending ondifferent definitions.

The basis of this topic is the science of optics – the branch of physicsconcerned with the behaviour and properties of light – as well ascolorimetry – the science and technology used to quantify and describehuman colour perception. Two sciences, lots of maths, loads of variables,endless potential for optical illusions and impairment: that is why colour istricky and why you need to begin this stage of thinking with anappreciation of some colour theory.

The most relevant starting point is to recognise that when dealing withissues of colour in data visualisation you will almost always be creatingwork on some kind of computer. Unless you are creating something byhand using paints or colouring pencils, you will be using software viewedthrough an electronic display.

This is important because a discussion about colour theory needs to beframed around the RGB (Red, Blue, Green) colour model. This is used todefine the combination of light that forms the colours you see on a screen,conceptually laid out in a cubic space based on variations across thesethree attributes.

The output format of your work will vary between screen display and printdisplay. If you are creating something for print you will have to shift yourcolour output settings to CMYK (Cyan, Magenta, Yellow and Black). Thisis the model used to define the proportions of inks that make up a printedcolour. This is known as a subtractive model, which means that combiningall four inks produces black, whereas RGB is additive as the three screencolours combine to produce white.

When you are creating work to be consumed on the Web through screendisplays, you will often program using HEX (Hexadecimal) codes tospecify the mix of red, green and blue light (in the form #RRGGBBusing codes 00 to FF).

While CMYK communicates from your software to a printer, telling it

446

what colours to print as an output, it does not really offer a logical modelto think about the input decisions you will make about colour. Neither, forthat matter, does RGB: it just is not realistic to think in those terms whenconsidering what choices are needed in a visualisation design. There aredifferent levers to adjust and different effects being sought that require analternative model of thinking.

Figure 9.1 HSL Colour Cylinder

Figure 9.2 Colour Hue Spectrum

I share the belief with many in the field that the most accessible colourmodel – in terms of considering the application of colour in datavisualisation – is HSL (Hue, Saturation, Lightness), devised by AlbertMunsell in the 1980s. These three dimensions combine to make up what isknown as a cylindrical-coordinate colour representation of the RGB colourmodel (I did warn you about the cylinders).

Hue is considered the true colour. With hue there are no shades(adding black), tints (adding whites) or tones (adding grey) – a

447

consideration of these attributes follows next. When you aredescribing or labelling colours you are most commonly referring totheir hue: think of the colours of the rainbow ranging through variousmixtures of red, orange, yellow, green, blue, indigo and violet. Hue isconsidered a qualitative colour attribute because it is defined bydifference and not by scale.Saturation defines the purity or colourfulness of a hue. This doeshave a scale from intense pure colour (high saturation) throughincreasing tones (adding grey) to the no-colour state of grey (lowsaturation). In language terms think vivid through to muted.

Figure 9.3 Colour Saturation Spectrum

Lightness defines the contrast of a single hue from dark to light. It isnot a measure of brightness – there are other models that define that –rather a scale of light tints (adding white) through to dark shades(adding black). In language terms I actually think of lightness more asdegrees of darkness, but that is just a personal mindset.

Figure 9.4 Colour Lightness Spectrum

Technically speaking, black, white and grey are not considered colours.

I have deliberately described these dimensions separately because, as youwill see when looking at the applications of colour in visualisation, yourdecisions will often be defined by how you might employ these distinctdimensions of colour to form your visual display. The main choices tend tofall between employing difference in hue and variation in lightness, withthe different levels of saturation often being a by-product of the definitionsmade for the other two dimensions.

Alternative models exist offering variations on a similar theme, such asHSV (Hue, Saturation, Value), HSI (Hue, Saturation, Intensity), HSB(Hue, Saturation, Brightness) and HCL (Hue, Chroma, Luminance).

These are all primarily representations of the RGB model space butinvolve differences in the mathematical translation into/from RGB and

448

offer subtle differences in the meaning of the same terms (local definitionsof hue and saturation vary). The biggest difference relates to theiremphasis as a means of specifying either a colour quality (in an input,created sense) or a colour perception (in how a colour is ultimatelyexperienced).

Pantone is another colour space that you might recognise. It offers aproprietary colour-matching, identifying and communicating service forprint, essentially giving ‘names’ to colours based on the CMYKprocess.

The argument against using the HSL model for defining colour is that,while it is fine for colour setting (i.e. an intuitive way to think about andspecify the colours you want to set in your visualisation work), theresulting colours will not be uniformly perceived the same, from onedevice to the next. This is because there are many variables at play in theprojection of light to display colour and the light conditions present in themoment of perception. That means the same perceptual experience will notbe guaranteed. It is argued that more rigorous models (such as CIELAB)offer an absolute (as opposed to a relative) definition of colour for bothinput and output. My view is that they are just a little bit too hard to easilytranslate into visualisation design thinking. Furthermore, trying to controlfor all the subtleties of variation in consumption conditions is an extraburden you should ideally avoid.

At this stage, it is important to be pragmatic about colour as much aspossible. The vast majority of your colour manipulating and perceptualneeds should be nicely covered by the HSL model. As and when youdevelop a deeper, purist interest in colour you should then seek to learnmore about the nuances in the differences between the definitions of thesemodels and their application.

9.2 Features of Colour: Data LegibilityData legibility concerns the use of the attribute of colour to encode datavalues in charts. The objective here is to make the data being representedby differences in colour as clearly readable and as meaningful as possible.

While you have probably already decided by now the chart or charts you

449

intend to use, you still need to take think carefully – and separately – abouthow you will specifically employ colour. To do this we first need to revisitthe classification of data types and consider how best to use colour forrepresenting each different type.

Nominal (Qualitative)With nominal data colour is used to classify different categorical values.The primary motive for the choice of colour is to create a visibledistinction between each unique categorical association, helping the eye todiscern the different categories as efficiently and accurately as possible.

Creating contrast is the main aim of representing nominal data. What youare not seeking to show or even imply is any sense of an order ofmagnitude. You want to help differentiate one category from the next –and make it easily identifiable – but to do so in a way that preserves thesense of equity among the colours deployed.

Figure 9.5 Excerpt from ‘Executive Pay by the Numbers’

Variation in hue is typically the colour dimension to consider using for

450

differentiating categories. Additionally, you might explore different tones(variations in saturation across the hues). You should not, though, considerusing variations in the lightness dimension. That is because the result isinsufficiently discernible. As you can see demonstrated in Figure 9.5, thelightness variation of a blue tone makes it quite hard to connect the colourscale presented in the key at the top with the colours displayed in thestacked bars underneath. With the shading in the column header and the2011 grey bar also contributing similar tones to the overall aesthetic of thetable our visual processing system has to work much harder to determinethe associations than it should need to do.

Often the categories you will be differentiating with colour will berelatively few in number, maybe two or three, such as in the separationbetween political parties or plotting different values for gender, as seen inFigure 9.6.

Figure 9.6 How Nations Fare in PhDs by Sex

Figure 9.7 How Long Will We Live – And How Well?

451

Beyond these small numbers, you still typically might only need tocontend with assigning colours to around four to six categories, perhaps inanalysis that needs to visually distinguish values for different continents ofthe world, as seen in the scatter plot in Figure 9.7.

As the range of different categories grows, the ability to preserve cleardifferentiation becomes harder. In expanding your required palette, thecolours used become decreasingly unique. The general rule of thumb isthat once you have more than 12 categories it will not be possible to find asufficiently different colour to assign to categories from 13 upwards.Additionally, you are really increasing the demands of learning andrecognition for viewers. This then becomes quite a cognitive burden anddelays the process of understanding.

Figure 9.8 Charting the Beatles: Song Structure

452

Two approaches for dealing with this. Firstly, consider offering interactivefilters to modify what categories are displayed in a visualisation – thuspotentially reducing the impact of so many being available. Secondly,think about transforming your data by excluding or combining categoriesin to a reduced number of aggregate groupings.

Depending on the subject of your data, sometimes you can look tosupplement the use of colour with texture or pattern to create furthervisible distinctions. In Figure 9.8 you can see two patterns being usedoccasionally as additive properties to show the structure of tracks on TheBeatles’ album.

Ordinal (Qualitative)With ordinal data you are still dealing with categories but now they have anatural hierarchy or ordering that can be exploited. The primary motive forusing colour in this case is not only to create a visible distinction betweeneach unique category association but also to imply some sense of an orderof magnitude through the colour variation. The colour dimensions used toachieve this tend to employ variations of either the saturation or thelightness (or a combination of both). You might also introduce differenthues when dealing with diverging (dual-direction) scales rather thansimply converging (single-direction) ones.

453

Figure 9.9 displays a simple example of colour used to display aconverging ordinal variable. This is the teacup that I use in my office. Onthe inside you can see it has a colour guide to help ascertain how muchmilk you might need to add: going through Milky, Classic British,Builder’s Brew, and finally Just Tea (zero milk).

Figure 9.9 Photograph of MyCuppa Mug

A typical example of a diverging ordinal scale might be seen in the stackedbar chart showing the results of a survey question (Figure 9.10). Theanswers are based on the strength of feeling: strongly agree, agree, neutral,disagree, strongly disagree. By colouring the agreement in red (‘hot’sometimes used to represent ‘good’) and the disagreement in blue (‘cold’

454

to mean ‘bad’) means a viewer can quickly perceive the general balance offeelings being expressed.

Figure 9.10 Example of a Stacked Bar Chart Based on Ordinal Data

Another example of ordinal data might be to represent the notion ofrecency. In Figure 9.11 you see a display plotting the 2013 YosemiteNational Park fire. Colour is used to display the recorded day-by-dayprogress of the fire’s spread. The colour scale is based on a recency scalewith darker = recent, lighter = furthest away (think faded memory).

Figure 9.11 The Extent of Fire in the Sierra Nevada Range and YosemiteNational Park, 2013

455

Interval and Ratio (Quantitative)With quantitative data (ratio and interval) your motive, as it is with ordinaldata, is to demonstrate the difference between and of a set of values. In thechoropleth map in Figure 9.12, showing the variation in electricity pricesacross Switzerland, the darker shades of blue indicate the higher values,the lighter tints the lower prices. This approach makes the viewer’sperception of the map’s values immediate – it is quite intuitive torecognise the implication of the general patterns of light and dark shades.

Figure 9.12 What are the Current Electricity Prices in Switzerland[Translated]

456

Typically, using colour to represent quantitative data will involve breakingup your data values into discrete classifications or ‘bins’. This makes thetask of reading value ranges from their associated colour shade or tone alittle easier than when using a continuous gradient scale. While ourcapacity to judge exact variations in colour is relatively low (even with acolour key for reference), we are very capable of detecting local variationsof colour through differences in tint, shade or tone. Assessing the relativecontrast between two colours is generally how we construct a quantitativehierarchy.

Look at the fascinating local patterns that emerge in the next map (Figure9.13), comparing increases in the percentage of people gaining healthinsurance in the USA (during 2013–14). The data is broken down tocounty level detail with a colour scale showing a darker red for the higherpercentage increases.

Some of the most relevant colour practices for data visualisation comefrom the field of cartography (as do many of the most passionate colour

457

purists). Just consider the amount of quantitative and categorical detailshown in a reference map that relies on colour to differentiate types ofland, indicate the depth of water or the altitude of high ground, presentroute features of road and rail networks, etc. The best maps pack anincredible amount of detail into a single display and yet somehow theynever feel disproportionately overwhelming.

Figure 9.13 Excerpt from ‘Obama’s Health Law: Who Was Helped Most’

Aside from the big-picture observations of the darker shades in the westand the noticeably lighter tints to the east and parts of the mid-west, take acloser look at some of the interesting differences at a more local level. Forexample, notice the stark contrast across state lines between the darkregions of southern Kentucky (to the left of the annotated caption) and thelight regions in the neighbouring counties of northern Tennessee. Despitetheir spatial proximity there are clearly strong differences in enrolment onthe programme amongst residents of these regions.

Both of these previous examples use a convergent colour scale, movingthrough discrete variations in colour lightness to represent an increasing

458

scale of quantitative values, from zero or small through to large. Asillustrated with the stacked bar chart example shown earlier, portraying therange of feelings from an ordinal dataset, sometimes you may need toemploy a divergent colour scale. This is when you want to show howvalues are changing in two directions either side of a set breakpoint.

Figure 9.14 Daily Indego Bike Share Station Usage

Figure 9.14 shows a cropped view of a larger graphic comparing therelative peaks and troughs of usage across all bike share stations inPhiladelphia over a 24-hour period. The divergent colour scale uses twohues and variations in lightness to show the increasingly busy andincreasingly slow periods of station activity either side of a breakpoint,represented by a very light grey to indicate the average point. The darkestred means the station is full, the darkest blue means the station is empty.

Regardless of whether you are plotting a converging or diverging scale,judging how you might divide up your colour scales into discrete valuebins needs careful thought. The most effective colour scales help viewersperceive not just the relative order of magnitude – higher or lower – butalso a sense of the absolute magnitude – how different a value might becompared to another value.

There is no universal rule about the number of value bins. Indeed, it is notuncommon to see entirely continuous colour scales. However, a generalrule of thumb I use is that somewhere between between four and ninemeaningful – and readable – value intervals should suffice. There are twokey factors to consider when judging your scales:

Are you plotting observed data or observable data? You might only

459

have collected data for a narrow range of quantities (e.g. 15 to 35) sowill your colour classifications be based on this observed range or onthe potentially observable data range i.e. the values you knowwould/could exist with a wider sample size or on a differentcollection occasion (e.g. 0 to 50)?What are the range and distribution of your data? Does it make senseto create equal intervals in your colour classifications or are theremore meaningful intervals that better reflect the shape of your dataand the nature of your subject? Sometimes, you will have legitimateoutliers that, if included, will stretch your colour scales far beyond themeaningful concentration of most of your data values.

Figure 9.15 Battling Infectious Diseases in the 20th Century: The Impactof Vaccines

You can see this effect in Figure 9.15, showing the incidence of HepatitisA per 100,000 population. There are only three values that exceed 100(you can see them on the top line for Alaska in the late 1970s). Toaccommodate these outliers the colour scale becomes somewhat stretched-out, with a wide range of potential values being represented by a darkyellow to red colour. With 99.9% of the values being under 100 there islittle discernibly in the blue/green shades used for the lower values. Ifoutliers are your focus, it makes sense to include these and colour

460

accordingly to emphasise their exceptional quality. Otherwise if they riskcompromising the discrete detail of the lower values you might look tocreate a broad classification that uses a single colour for any value beyonda threshold of maybe 75, with even value intervals of maybe 15 below thathelp to show the patterns of smaller values.

For diverging scales, the respective quantitative shades either side of abreakpoint need to imply parity in both directions. For example, a shade ofcolour that means +10% one side of the breakpoint should have an equalshade intensity in a different hue on the other side to indicate the sameinterval, i.e. −10%. Additionally, the darkest shades of hues at the extremeends of a diverging scale must still be discernible. Sometimes the darkestshades will be so close to black that you will no longer be able todistinguish the differences in their underlying hues when plotted in a chartor map.

As well as considering the most appropriate discrete bins for your values,for diverging scales one must also pay careful attention to the role of thebreakpoint. This is commonly set to separate values visually above orbelow zero or those either side of a meaningful threshold, such as target,average or median.

One of the most common mistakes in using colour to represent quantitativedata comes with use of the much-derided rainbow scale. Look at Figure9.16, showing the highest temperatures across Australia during the firstcouple of weeks in 2013. Consider the colour key to the right of the mapand ask yourself if this feels like a sufficiently intuitive scale. If the keywas not provided, would you be able to perceive the order of magnituderelationship between the colours on the map? If you saw a purple colournext to a blue colour, which would you expect to mean hotter and whichcolder?

Figure 9.16 Highest Max Temperatures in Australia

461

While the general implication of blue = ‘colder’ through to red = ‘hotter’ isincluded within sections of this temperature colour scale, it is the presenceof many other hues that obstructs the accessibility and createsinconsistency in logic. For instance, do the colours used to show 24°C(light blue) jumping to 26°C (dark green) make sense as a means forshowing an increasing temperature? How about 18°C (grey) to 20°C (darkblue), or the choice of the mid-brown used for 46°C which interrupts theincreasingly dark red sequence? If you saw on the map a region with thepink tone as used for 16°C would you be confident that you could easilydistinguish this from the lighter pink used to represent 38°C? Unless thereare meaningful thresholds within your quantitative data – justifiablebreakpoints – you should only vary your colour scales through thelightness dimension, not the hue dimension.

One of the interesting recurring challenges faced by visualisers is how torepresent nothing. For example, if a zero quantity or no category is ameaningful state to show, you still need to represent this visuallysomehow, even though it might possess no size, no position and no area.How do you distinguish between no data and a zero value?

Figure 9.17 State of the Polar Bear

462

Typically, using colour is one of the best ways to portray this. Figure 9.17shows one solution to making ‘no data’ a visible value. This map displaysthe population trends of the polar bear. Notice those significant areas ofgrey representing ‘data deficient’. A subtle but quite effective politicalpoint is being made here by including this status indicator. As I mentionedbefore, sometimes the absence of data can be the message itself.

Figure 9.18 Excerpt from ‘Geography of a Recession’

463

When considering colour choices for quantitative classifications, you willneed to think especially carefully about the lowest value grouping: is it tobe representative of zero, an interval starting from zero up to a low value,or an interval starting only from the minimum value and never includingzero? In this choropleth map (Figure 9.18) looking at the unemploymentrate across the counties of the USA, no value is as low as zero. Theremight be value that are close, but nowhere is the unemployment rate at0%. As you can see, the lowest tint used in this colour key is not white,rather a light shade of orange, so as not to imply zero. Whilst not relevantto this example, if you wanted to create a further distinction between thelowest value interval and the ‘null’ or ‘no data’ state you could achievethis by using a pure white/blank.

9.3 Features of Colour: Editorial SalienceHaving considered options for the application of colour in facilitating datalegibility, the next concern is colour used for editorial salience. Whereasdata legibility was concerned with helping to represent data, using colourfor editorial salience is about drawing the viewer’s attention to the

464

significant or meaningful features of your display. Colour offers such apotent visual stimulus and an influential means for drawing out keyaspects of your data and project that you might feel are sufficientlyrelevant to make prominent.

Consider again the idea of photography and the effect of taking aphotograph of a landscape. You will find the foreground objects are darkerand more prominent than the faded view of the background in the distanceas light and colour diminish. Using colour to achieve editorial salienceinvolves creating a similar effect of depth across your visualisation’scontents: if everything is shouting, nothing is heard.

The goal of using colour to facilitate editorial salience is a suitablecontrast. For things to stand out, you are in turn determining which otherthings will not.

The degree of contrast you might seek to create will vary. Often you willbe seeking to draw a significant contrast, maximising the emphasis of avalue or subset of values so the viewer can quickly home in on what youhave elevated for their attention relative to everything else.

For this reason, grey will prove to be one of your strongest allies in datavisualisation. When contrasted with reasonably saturated hues, grey helpsto create depth. Elements coloured in greyscale will sit quietly at the backof the view, helping to provide a deliberately subdued context that enablesthe more emphasised coloured properties to stand proudly in theforeground.

In Figure 9.19, the angle of analysis shows a summary of the mostprevalent men’s names featuring among the CEOs of the S&P 1500companies. As you can see there are more guys named ‘John’ or ‘David’than the percentage of all the women CEOs combined. With the emphasisof the analysis on this startling statement of inequality the bar for ‘Allwomen’ is emphasised in a burgundy colour, contrasting with the grey barsof all the men’s names. Notice also that the respective axis and bar valuelabels are both presented using a bold font, which further accentuates thisemphasis. It is also editorially consistent with the overriding enquiry of thearticle. As discussed in Chapter 3, bringing to the surface key insightsfrom data displays in this way contributes towards facilitating an‘explanatory’ experience.

465

Figure 9.19 Fewer Women Run Big Companies Than Men Named John

Figure 9.20 NYPD, Council Spar Over More Officers

Sometimes, only noticeable contrast – not shouting, just being slightlymore distinguishable – may be appropriate. Compared with the previousbar chart example, Figure 9.20 creates a more subtle distinction betweenthe slightly darker shade of green (and emboldened text) emphasising theNew York figures compared to the other listed departments in a slightlylighter green. As with the CEOs’ example, the object of our attention is the

466

subject of focus in the analysis, in this case regarding a drive for moreNYPD officers. This does not need to be any more contrasting; it is just assufficiently noticeable as the visualiser wishes it to be.

Sometime you will seek to create several levels of visual ‘urgency’ in therelative contrast of your display. The colour choices in Figure 9.21 givesforeground prominence to the yellow coloured markers and values (thedots are also larger) and then mid-ground/secondary prominence to theslightly muted red markers. In perceiving the values of the yellow markers,the viewer is encouraged to concentrate on primarily comparing these withthe red markers. The subtle grey markers are far less visible – closer inshade to the background than the foreground – and deliberately relegatedto a tertiary level so they do not clutter up the display and causeunwarranted attention. They provide further context for the distribution ofthe values but do not need to be any more prominent in their relationshipwith the foreground and mid-ground colours.

Figure 9.21 Excerpt from a Football Player Dashboard

I touched on the use of encoded overlays earlier where coloured areas orbandings can be used to help separate different regions of a display inorder to facilitate faster interpretation of the meaning of values. In thebubble plot in Figure 9.22, you can see the circle markers are colour coded

467

to help viewers quickly ascertain the significance of each location on thechart according to the quadrants in which they fall. Notice how in thebackground the diagonal shading further emphasises the distinctionbetween above the line ‘improvement’ and below the line ‘worsening’, avery effective approach.

Figure 9.22 Elections Performance Index

9.4 Features of Colour: Functional HarmonyAfter achieving data legibility and editorial salience through astute colourchoices, functional harmony is concerned with ensuring that any remainingcolour choices will aid, and not hinder, the functional effectiveness andelegance of the overall visualisation.

‘When something is not harmonious, it’s either boring or chaotic. Atone extreme is a visual experience that is so bland that the viewer is notengaged. The human brain will reject under-stimulating information. Atthe other extreme is a visual experience that is so overdone, so chaotic,that the viewer can’t stand to look at it. The human brain rejects what itcan not organise, what it cannot understand.’ Jill Morton, Colour

468

Expert and Researcher

You must judge the overall balance of and suitability of your collectivecolour choices and not just see these as isolated selections. This is againprimarily a judgement about contrast – what needs to be prominent andwhat needs to be less so. Such an apparent calming quality about a well-judged and cohesive colour palette is demonstrated by Stefanie Posavec’schoices in visualising the structure of Walter Benjamin’s essay ‘Art in theage of mechanical reproduction’ (Figure 9.23). There is effortless harmonyhere between the colour choices extending across the entire anatomy ofdesign: the petals, branches, labels, titles, legend, and background.

A reminder that any and every design feature you incorporate into yourdisplay will have a property of colour otherwise they will be invisible. Inlooking at data legibility and editorial salience you have considered yourcolour choices for representing data. A desire to achieve functionalharmony means considering further colour decisions that will helpestablish visual relationships across and between the rest of yourvisualisation’s anatomy: its interactive features, annotations andcomposition.

Figure 9.23 Art in the Age of Mechanical Reproduction: Walter Benjamin

Interactive features: Visible interactive features will includecontrols such as dropdown menus, navigation buttons, time slidersand parameter selectors. The colour of every control used will need tobe harmonious with the rest of the project but also, critically, must befunctionally clear. How you use colour to help the user discern what

469

is selected and what is not will need to be carefully judged.To illustrate this, Figure 9.24 shows an interactive project thatexamines the connected stories of the casualties and fatalities fromthe Iraqi and Afghan conflicts. Here you can see that there are severalinteractive features, all of which are astutely coloured in a way thatfeels both consistent with the overall tone of the project but alsomakes it functionality evident what each control’s selected status ordefined setting is. This is achieved through very subtle but effectivecombinations of dark and light greys that help create intuitive clarityabout which values the user has selected or highlighted. When abutton has a toggle setting (on/off, something/something else), suchas the ‘Afghanistan’ or ‘Iraq’ tabs at the top, the selected tab ishighlighted in bright grey and the unselected tab in a more subduedgrey. Filters can either frame (include/exclude) or focus(highlight/relegate) the data. The same approach to using brightergreys for the selected parameter values makes it very clear what youhave chosen, but also what you have excluded (while making evidentthe other currently-unselected values from which you can potentiallychoose).

Figure 9.24 Casualties

Annotations: Chart annotations such as gridlines, axis lines andvalue labels all need colouring in a way that will be sympathetic tothe colour choices already made for the data representation and,possibly, editorial contrasting. As mentioned in the last chapter, many

470

annotation devices exist in the form of text and so the relative fontcolour choices will need to be carefully considered. For anyannotation device the key guiding decision is to find the level atwhich these are suitably prominent. Not loud, not hidden, just at theright level. This will generally take a fair amount of trial and error toget right but once again, depending on your context, your firstthought should be to consider the merits offered by different shadesof grey.

You might be starting to suspect I’m a lobbyist for the colour grey.Nobody wants to live in a world of only grey. The point is more about howits presence enables other colours to come alive. The great Bill Shanklyonce said ‘Football is like a piano, you need 8 men to carry it and 3 whocan play the damn thing’. In data visualisation, grey does the heavy liftingso the more vibrant colours can bring the energy and vibrancy to yourdesign.

Figure 9.25 First Fatal Accident in Spain on a High-speed Line[Translated]

Another example of the role of greyscale is demonstrated by Figure 9.25,illustrating key aspects of the tragic rail crash in Spain in 2013. The senseof foreground and background is clearly achieved by the prominence ofthe scarlet-coloured annotations and visual cues offset against the

471

backdrop of an otherwise greyscale palette.

Figure 9.26 Lunge Feeding

472

There are other features of annotation that will have an impact onfunctional harmony through their colouring. Multimedia assets likephotos, embedded videos, images and illustrations need to beconsistent in tone according to their relative role on the display. Ifthey are to dominate the page then unleash the vibrancy of theircolours to achieve this; if they are playing more of a secondary orsupporting role then relegate their constituent colours to allow otherprimary features due prominence.Figure 9.26 includes small illustrations of a whale, showing how itgoes through the stages of lunge feeding. The elegance of the coloursused in these illustrations is entirely harmonious with the look andfeel of the overall piece. They are entirely at one with the rest of thegraphic.Composition: The clarity in layout of a project will often be achievedby the use of background colour to create logical organisation. In the‘Lunge Feeding’ graphic the shading of the blue sea getting darker asit moves down is not attempting to offer a precise representation ofthe sea, but it gives a sense of depth and draws maximum attention tothat panel. It is also naturally congruent with the subject matter.

Figure 9.27 Examples of Common Background Colour Tones

In general, there are no fixed rules on the benefits of any particularcolour for background shading. Your choices will depend mostly onthe circumstances and conditions in which your viewers are

473

consuming the work. Usually, when there is no associated congruencefor a certain background colour, your options will tend to come fromone of the selection of neutral and/or non-colours (Figure 9.27). Thisis because they particularly help to aid accentuation in combinationwith foreground colours.Typically, though, a white background (at least for your chart area)gives viewers the best chance of being able to accurately perceive thedifferent colour attributes used in your data representation and thecontrasting nature of your editorial contrast.White – or more specifically emptiness – is one of your mostimportant options for creating functional meaning for nothingness,something I touched on earlier. The emptiness of uncoloured spacecan be used very effectively to direct the eye’s attention. It organisesthe relationship between space on a page without the need for visibleapparatus, as seen in the left hand column of the lunge feedinggraphic. It can also be used to represent or emphasise values thatmight have the state of ‘null’ or ‘zero’ to maximise contrast.

‘The single most overlooked element in visual design is emptiness.Space must look deliberately used.’ Alex White, Author, TheElements of Graphic Design

9.5 Influencing Factors and ConsiderationsHaving mapped out the ways and places where colour could be used, youwill now need to consider the factors that will influence your decisionsabout how colour should be used.

Formulating Your BriefFormat: This is a simple concern but always worth pointing out: ifyou are producing something for screen display you will need to setyour colour output to RGB; if it is for print you will need CMYK.Additionally, when you are preparing work for print, running offplenty of proofs before finalising a design is imperative. What you arepreparing digitally is a step away from the form of its intendedoutput. What looks like a perfect colour palette on screen may notultimately look the same when printed.

474

Print quality and consistency is also a factor. Graphics editors whocreate work for print newspapers or magazines will often considerusing colours as close in tone as possible to pure CMYK, especially iftheir work is quite intricate in detail. This is because the colour platesused in printing presses will not always be 100% aligned and thusmixtures of colours may be slightly compromised.As black and white printing is still commonplace, you need to beaware of how your work might look if printed without colour. If youare creating a visualisation that might possibly be printed by certainusers in black and white, the only colour property that you canfeasibly utilise will be the lightness dimension. Sometimes, as adesigner or author, you will be unaware of this intent and thecolourful design that you worked carefully towards will end up notbeing remotely readable.

We all refer to black and white printing, but technically printers do notactually print using white ink, it is just less black or no black.

Furthermore, there is an important difference in how colours appear whenpublished in colour and how they appear when published in black andwhite. Hues inherently possess different levels of brightness: the purestblue is darker than the purest yellow. If these were printed in black andwhite, blue would therefore appear a darker, more prominent shade ofgrey. If your printed work will need to be compatible for both colour andblack and white output, before finalising your decisions check that thelegibility and intended meaning of your colour choices are beingmaintained across both forms.

Setting: For digital displays, the conditions in which the work will beconsumed will have some influence over the choice between light anddark backgrounds. The main factor is the relative contrast and thestresses this can place on the eye to adjust against the surroundings. Ifyour work is intended for consumption in a light environment, lighterbackgrounds tend to be more fitting; likewise darker backgroundswill work best for consuming in darker settings. Fortablets/smartphones, the bordering colour of the devices can alsoinfluence the most suitable choice of background tone to mostsympathetically contrast with the surroundings.Colour rules and identities: In some organisations there are style

475

guidelines or branding identities that require the strict use of onlycertain colour options. Similar guidelines may exist if you arecreating work for publication in a journal, magazine, or on certainwebsites. Guidelines like these are well intended, driven by a desireto create conformity and consistency in style and appearance.However, in my experience, the basis of such colour guides rarelyincorporates consideration for the subtleties of data visualisation. Thismeans that the resulting palettes are often a bad fit for idealvisualisation colour needs, providing limited scope for the variationand salience you might seek to portray.Your first task should always be to find out if there is anycompromise – any chance of not having these colour restrictionsimposed. If there is no flexibility, then you will just have to acceptthis and begin acquainting yourself with the colours you do have towork with. Taking a more positive view, achieving consistency in theuse of colour for visualisation within an organisation does have meritsif the defined palettes offer suitably rich variety. Developing arecognisable ‘brand’ and not having to think from scratch about whatcolours to use every time you face a new project is something that canbe very helpful, especially across a team.Purpose map: Does it need to be utilitarian or decorative? Should itbe functional or appealingly seductive? Does it lend itself to beingvivid and varied in colour or more muted and distinguished? Colouris the first thing we notice as viewers when looking at a visualisation,so your choices will play a huge part in setting the visible tone ofvoice. How you define your thinking across the vertical dimension ofyour purpose map will therefore have an influence on your colourthinking.Along the horizontal dimension, the main influencing considerationwill be a desire to offer an ‘explanatory’ experience. As mentioned,some of the tactics for incorporating editorial salience will be ofspecific value if you are seeking to emphasise immediately apparent,curated insights.Ideas and inspiration: In the process of sketching out your ideas andcapturing thoughts about possible sources of influence, maybe therewere already certain colours you had identified as being consistentwith your thinking about this subject? Additionally, you might havealready identified some colours you wish to avoid using.

476

Working With DataData examination: The characteristics of your data will naturallyhave a huge impact, on the decisions you make around data legibility.Firstly, the type of data you are displaying (primarily nominal vs allother types) will require a different colour treatment, as explained.Secondly, the range of categorical colour associations (limits ondiscernible hues) and the range and distribution of quantitative values(numbers of divisions and definition of the intervals across yourclassification scale) will be directly shaped by the work you did in theexamination stage.In Figure 9.28 you can see a census of the prevalence and species oftrees found around the boroughs of New York City. This initial big-picture view creates a beautiful tapestry made up of tree populationsacross the region (notice the big void where JFK Airport is located).

Figure 9.28 Excerpt from ‘NYC Street Trees by Species’

To observe patterns for individual tree types is harder: with 52different tree species there are simply too many classifications to beable to allocate sufficiently unique colours to each. To overcome this,the project features a useful pop-up filter list which then allows you toadjust the data on view to reveal the species you wish to explore.It is often the case when thinking about colour classifications that youmay need to revisit the data transformation actions to find new waysof grouping your data to create better-fit quantitative valueclassifications or to look at ways of grouping your categories. For thelatter, actions such as combining less important categories in an

477

‘other’ bin to reduce the variability or eliminating certain values fromyour analysis may be necessary.

‘If using colour to identify certain data, be careful to not accidentallyapply the same identity to a nearby part of the graphic. Don’t allowcolour to confuse just for the sake of aesthetics. I also like to use colourto highlight. A single colour highlight on a palette of muted colours canbe a strong way to draw attention to key information.’ Simon Scarr,Deputy Head of Graphics, ThomsonReuters

Establishing Your Editorial ThinkingFocus: When considering the perspective of ‘focus’ in the editorialthinking stage, you were defining which, if any, elements of contentwould merit being emphasised. Are there features of your analysisthat you might wish to accentuate? How might colour be used toaccentuate key insights in the foreground and push other (lessimportant) features into the background? What are the characteristicsof your data that you might want to emphasise through changes incolour? For example, are there certain threshold values that will needto be visually amplified if exceeded? Your decisions here will directlyinfluence your thinking about using colour to facilitate editorialsalience.

Data RepresentationChart type choice: Specifically in relation to data legibility,depending on which chart type you selected to portray your data, thismay have attributes requiring decisions about colour. The heat mapand choropleth map are just two examples that use variation in colourto encode quantitative value. Almost every chart has the potential touse colour for categorical differentiation.

Trustworthy DesignData classification: The decisions you make about how to encodedata through colour have a great bearing on the legibility andaccuracy of your design, especially with quantitative data. You will

478

need to ensure the classifications present a true reflection of the shapeand characteristics of your data and do not suppress any significantinterpretations.

‘Start with black and white, and only introduce color when it hasrelevant meaning. In general, use color very sparingly.’ Nigel Holmes,Explanation Graphic Designer

Meaningful: Eliminating arbitrary decisions is not just aboutincreasing the sophistication of your design thinking, it is also anessential part of delivering a trustworthy design. If something looksvisually significant in its data or editorial colouring it will be read assuch, so make sure it is significant, otherwise remove it. Youespecially want to avoid any connotation of significant meaningacross your functional or decorative colour choices. This will beconfusing at best, or will appear deceptive at worst.Do not try to make something look more interesting than itfundamentally is. Colour should not be used to decorate data. Youmight temporarily boost the apparent appeal of your work in the eyeof the viewer but this will be short-lived and artificial.Illusions: The relationship between a foreground colour and abackground one can create distorting illusions that modify theperceived judgement of a colour. You saw an effect of this earlierwith the inverted area chart showing ‘Gun deaths in Florida’,whereby the rising white mountain was seen by some as theforeground data, when in fact it was the background emptinessframed by the red area of data and the axis line. Illusions can affectall dimensions of colour perception. There are simply too many tomention here and they are hard to legislate for entirely; it is reallymore about mentioning that you need to be aware of these as aconsequence of your colour choices.

Accessible DesignConsistency: Consistency in the use of colours helps to avoid visualchaos and confusion and minimises cognitive effort. When youestablish association through colour you need to maintain thatmeaning for as long as possible. Once a viewer has allocated time andeffort to learn what colours represent, that association becomes

479

locked down in the eye and the mind. However, if you then allocatethe same colour(s) to mean something different (within the samegraphic or on a different page/screen view) this creates an additionalcognitive burden. The viewer has almost to disregard the previousassociation and learn the new one. This demands effort thatundermines the accessibility of your design.

Sometimes this can prove difficult, especially if you have a restrictedcolour palette. The main advice here is to try to maximise the ‘space’between occasions of the same colour meaning different things. This spacemay be physical (different pages, interactive views), time (the simpleduration of reading between the associations being changed) or editorial(new subject matter, new angle of analysis). Such space effectively helpsto clean the palate (pun intended). Of course, at the point of any newassignment in your colour usage, clear explanations are mandatory.

Visual accessibility: Approximately 5% of the population have visualimpairments that compromise their ability to discern particular colours andcolour combinations. Deuteranopia is the most common form, oftenknown as red–green colour blindness, and is a particular genetic issueassociated with men. The traffic light scheme of green = ‘good’, red =‘bad’ is a widespread approach for using colour as an indicator. It is aconvenient and common metaphor and the reasons for its use are entirelyunderstandable. However, as demonstrated in the pair of graphics in Figure9.29, looking at some word-usage sentiment analysis, the reds and greensthat most of us would easily discern (from the left graphic) are often not atall distinguishable for those with colour blindness (simulated on the right).

Figure 9.29 Demonstrating the Impact of Red-green Colour Blindness(deuteranopia)

480

Of course, if you have a particularly known, finite and fixed audience thenyou can easily discover if any colour-blindness issues do in fact exist.However, if your audience is much larger and undefined you are going toalienate potentially 1 person in every 14, in which case the use of thedefault red–green colour combination is not acceptable. Be more sensitiveto your viewers by considering other options:

Figure 9.30 Colour-blind Friendly Alternatives to Green and Red

If you are working on an interactive solution, you may considerhaving a toggle option to switch between different colour modes. For

481

print outputs you might normally have reduced flexibility, but incertain circumstances the option of creating dual versions (secondoutput for colour-impaired viewers) may be legitimate.Connotations and congruence: Whether it is in politics, sport,brands or in nature, there are many subjects that already haveestablished colour associations you can possibly look to exploit. Thisassociation may sit directly with the data, such as the normal colourassociations for political party categories, or more through themeaning of the data, such as perhaps through the use of green topresent analysis about ecological topics.In support of accessible design, exploiting pre-existing colourassociations in your work can create more immediacy in subjectrecognition. You might also benefit from the colour learningexperiences your viewers may already have gone through. Thisprovides a shortcut to understanding through familiarity.However, while some colour connotations can be a good thing, insome cases they can be a bad thing and possibly should be avoided.You need to be considerate of and sensitive to any colour usage toensure that you do not employ connotations that may have a negativeimplication and may evoke strong emotions and reactions frompeople.Sometimes a colour is simply incongruent with a subject. You wouldnot use bright, happy colours if you were portraying data about deathor disease. Earlier, in the ‘Vision: Ideas’ section, I described a projectcontext where I knew I wanted to avoid the use of blue colours in aparticular project about psychotherapy treatment in the Arctic,because it would carry an unwelcome clichéd association given thesubject matter. The use of ‘typical’ skin colours to represent ethnicgroups in a visualisation is something that would be immediatelyclumsy (at best) and offensive (at worst).Cultural sensitivities and inconsistencies are also important toconsider. In China, for example, red is a lucky colour and so the useof red in their stock market displays, for example, indicates the risingvalues. A sea of red on the FTSE or Dow Jones implies the opposite.In Western society red is often the signal for a warning or danger.Occasionally established colour associations are out of sync withcontemporary culture or society. For example, when you think aboutcolour and the matter of gender, because it has been so endlesslyutilised down the years, it is almost impossible not to thinkinstinctively about the use of blue (boys) and pink (girls). My

482

personal preference is to avoid this association entirely. I agree withso many commentators out there that the association of pink tosignify the female gender, in particular, is clichéd, outdated and nolonger fit for purpose. It is not too much to expect viewers to learn theassociation of – at most – two new colours for representing gender.

Elegant DesignUnity: As I alluded to in the discussion about using colours foreditorial salience, colour choices are always about contrast. The effectof using one colour is not isolated to just that instance of colour:choosing one colour will automatically create a relationship withanother. There is always a minimum of two colours in anyvisualisation – a foreground and background colour – but generallythere are many more.We notice the impact of colour decisions more when they are donebadly. Inconsistent and poorly integrated colour combinations createjarring and discordant results. If we do not consciously notice colourdecisions this probably means they have been seamlessly blended intothe fabric of the overall communication.Neutral colouring: Even if there is no relevance in the use of colourfor quantitative or categorical classifications, you still have to giveyour chart some colour, otherwise it will be invisible. The decisionyou make will depend again on the relative harmony with othercolour features but should also avoid unnecessarily ‘using up’ auseful colour. Suppose you colour your bars in blue but thenelsewhere across your visualisation project blue would have been auseful colour to show something meaningful; you then haveunnecessarily taken blue out of the reckoning. My default choice is togo with grey to begin with (Figure 9.31) and only use a colour if thereis a suitable and available colour not used elsewhere or if it needs tobe left as a back- or mid-ground artefact to preserve prominenceelsewhere in the display.

Figure 9.31 Excerpt from ‘Pyschotherapy in The Arctic’

483

Justified: Achieving elegant design is about eliminating the arbitrary.In thinking about colour usage I often get quite tough with myself. If Iwant to show any feature on my visualisation display I have to seekpermission from myself to unlock access to the more vibrant coloursby justifying why I should be allowed to use and apply that colour (Iknow what you’re thinking, ‘what a fun existence this guy leads’).Elegance in visualisation design is often about using only the coloursyou need to use and avoiding the temptation to inject unnecessarydecoration. The Wind Map project (Figure 9.32) demonstratesunquestionable elegance and yet uses only a monochromatic palette.There is no colouring of the sea, no topographic detail, noemphasising of any extreme wind speed thresholds being reached.The resulting elegance is quite evident: the map has artistic andfunctional beauty.To emphasise again, I am not advocating a need to pursueminimalism: while you can create incredibly elegant and detailedworks from a limited palette of colours, justifying the use of coloursis not the same as unnecessarily restricting the use of colour.Feels right: The last component of influence is yourself. Sometimesyou will just find colours that feel right and look good when youapply them to your work. There is maybe no underlying sciencebehind such choices, and as such you will simply need to back yourown instinctive judgement as an astute visualiser and know whensomething looks good. Creating the right type of visual appeal,something that is pleasing to the eye and equally fit for purpose in all

484

the functional ways I have outlined, is a hard balance to achieve, butyou will find that weighing up all these different components ofinfluence alongside your own flair for design judgement will give youthe best chance of getting there.

Figure 9.32 Wind Map

Summary: ColourData legibility involves using colours to represent different types of data.The most appropriate colour association or scale decisions will depend onthe data type: nominal (qualitative), ordinal (qualitative), interval and ratio(quantitative).

Editorial salience is about using colour to direct the eye. For whichfeatures and to what degree of emphasis do you want to create contrast?

Functional harmony concerns deciding about every other colour propertyas applied to all interactive features, annotations and aspects of yourcomposition thinking.

Influencing Factors and Considerations

Formulating the brief: format, setting, colour rules and imposedguidelines all have a significant impact. Your definitions about bothtone and and experience, on the purpose map, will lead to specific

485

choices being more suitable than others. What initial ideas did youform? Have any sources of inspiration already implanted ideas insideyour head about which colours you could use?Working with data: what type of data and what range ofvalues/number of classifications have you got?Establishing your editorial thinking: what things do you want toemphasise or direct the eye towards (focus)?Data representation: certain chart type choices will already includecolour as an encoded attribute.Trustworthy design: ensure that your colour choices are faithful to theshape of your data and the integrity of your insights. If somethinglooks meaningful it should be, otherwise it will confuse or deceive.Accessible design: once you’ve committed colour to mean somethingpreserve the consistency of association for as long as possible. Beaware of the sensitivities around visual accessibility andpositive/negative colour connotations.Elegant design: the perception of colours is relative so the unity ofyour choices needs to be upheld. Ensure that you can justify every dotof colour used and, ultimately, rely on your own judgment todetermine when your final palette feels right.

Tips and Tactics

Use the squint test: shrink things down and/or half close your eyes tosee what coloured properties are most prominent and visible – arethese the right ones?Experimentation: trial and error is still often required in colour,despite the common sense and foundation of science attached to it.Developing a personal style guide for colour usage saves you the painof having to think from scratch every time and will help your workbecome more immediately identifiable (which may or may not be animportant factor).Make life easier by ensuring your preferred (or imposed) colourpalettes are loaded up into any tool you are using, even if it is just thetool you are using for analysis rather than for the final presentation ofyour work.If you are creating for print, make sure you do test print runs of thedraft work to see how your colours are looking – do not wait for thefirst print when you (think you) have finished your process.

486

487

10 Composition

Composition concerns making careful decisions about the physicalattributes of, and relationships between, every visual property to ensure theoptimum readability and meaning of the overall, cohesive project.

Composition is the final layer of your design anatomy, but this should notimply that it is the least important part of your design workflow. Far fromit. It is simply that now is the most logical time to think about this, becauseonly at this point will you have established clarity about what content toinclude in your work. As I explained, this final layer of design thinking,along with colour, is no longer about what elements will be included buthow they will appear. Composition is a critical component of any designdiscipline. The care and attention afforded in the precision of yourcomposition thinking will continue until the final dot or pixel has beenconsidered.

Visual assets such as your chart(s), interactive controls and annotations alloccupy space. In this chapter you will be judging what is the best way touse space in terms of the position, size and shape of every visible property.In many respects these individual dimensions of thought are inseparableand so, similar to the discussion about annotation, the division in thinkingis separated between project- and chart-level composition options:

Project composition: defining the layout and hierarchy of the entirevisualisation project.Chart composition: defining the shape, size and layout choices for allcomponents within your charts.

10.1 Features of Composition: ProjectCompositionThis first aspect of composition design concerns how you might lay outand size all the visual content in your project to establish a meaningfulhierarchy and sequence. Content, in this case, means all of your charts,interactive operations and elements of annotation.

488

Where will you put all of this, what size will it be and why? How will thehierarchy (across views) and sequencing (within a view) best fit the spaceyou have to work in? How will you convey the relative importance andprovide a connected narrative where necessary?

I will shortly run through all the key factors that will influence yourdecisions, but it is worth emphasising that so much about compositionthinking is rooted in common sense and involves a process of iterationtowards what feels like an optimum layout. Of course, there are certainestablished conventions, such as the positioning of titles first or at the top(usually left or centrally aligned). Introductions are inevitably useful tooffer early, whereas footnotes detailing data sources and credits might beof least importance, relatively speaking. You might choose to show themain features first, exploiting the initial attention afforded by youraudience, or you may wish to build up to this, starting off with contextualcontent before the big ‘reveal’.

Figure 10.1 City of Anarchy

489

490

The hierarchy of content is not just a function of relative position throughlayout design, it can also be achieved through the relative variation in sizeof the contents. Just as variation in colour implies significance, so too doesvariation in size: a chart that is larger than another chart will imply that theanalysis it is displaying carries greater importance.

The ‘City of anarchy’ infographic demonstrates a clear visual hierarchyacross its design. There is a primary focal point of the main subject‘cutaway’ illustration in the centre with a small thumbnail image above itfor orientation. At the bottom there are small supplementary illustrations toprovide further information. It is clear through their relative placement atthe bottom of the page and their more diminutive stature that they are ofsomewhat incidental import compared with the main detail in the centre.

There are generally two approaches for shaping your ideas about thisproject-level composition activity, depending on your entry-pointperspective: wireframing and storyboarding. I profiled these at the startof this part of the book, but it is worth reinforcing their role now you arefocusing on this section of design thinking.

Wireframing involves sketching the potential layout and size of all themajor contents of your design thinking across a single-page view.This might be the approach you take when working on an infographicor any digital project where all the interactive functions are containedwithin a single-screen view rather than navigating users elsewhere.Any interactive controls included would have a description within thewireframe sketch to explain the functions they would trigger.Figure 10.2 is an early wireframe drawn by Giorgia Lupi whenshaping up her early thoughts about the potential layout of a graphicexploring various characteristics of Nobel prizes and laureatesbetween 1901 and 2012.

Figure 10.2 Wireframe Sketch

491

Storyboarding is something you would undertake with wireframing ifyou have a project that will entail multiple pages or many differentviews and you want to establish a high-level feel for the overallarchitecture of content, its navigation and sequencing. This would bean approach relevant for linear outputs like discrete sequences inreports, presentation slides or video graphics, or for non-linearnavigation around different pages of a multi-faceted interactive. Theindividual page views included as cells in this big-picture hierarchywill each merit more detailed wireframing versions to determine howtheir within-page content will be sized and arranged, and how thenavigation between views would operate.With both wireframing and storyboarding activities all you areworking towards, at this stage, are low-fidelity sketched concepts.Whether this sketching is on paper or using a quick layout tool doesnot matter; it just needs to capture with moderate precision theessence of your early thinking about the spatial consequence ofbringing all your design choices together. Gradually, through furtheriteration, the precision and finality of your solution will emerge.

492

10.2 Features of Composition: ChartCompositionAfter establishing your thoughts about the overall layout, you will nowneed to go deeper in your composition thinking and contemplate thedetailed spatial matters local to each chart, to optimise its legibility andmeaning. There are many different components to consider.

Chart size: Do not be afraid to shrink your charts. The eye can stilldetect at quite small resolution and with great efficiency chartattributes such as variation in size, position, colour, shape and pattern.This supports the potential value of the small-multiples technique, anapproach that tends to be universally loved in data visualisation. As Iexplained earlier, this technique offers an ideal solution for when youare trying to display the same analysis for multiple categories ormultiple points in time. Providing all the information in asimultaneous view means that viewers can efficiently observe overallpatterns as well as perform a more detailed inspection. Figure 10.3provides a single view of a rugby team’s match patterns across thefirst 12 matches of a season. Each line chart panel portrays thecumulative scoring for the competing teams across the 80 minutes ofa match. The 12 match panels are arranged in chronological order,from top left to bottom right, based on the date of the match.

Figure 10.3 Example of the Small Multiples Technique

493

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 categorylabels, so there has to be a trade-off, as always, between the amountof detail you show and the size you show it.Chart scales: When considering your chart-scales try to think abouthow you might use these to tell the viewer something meaningful.This can be achieved through astute choices around the maximumvalue ranges and also in the choice of suitable intervals for labellingand gridline guides.The maximum values that you assign to your chart scales, informedby decisions around editorial framing, can be quite impactful insurfacing key insights. You may recall the chart from earlier thatlooked at the disproportionality of women CEO’s amongst the S&P1500 companies. Figure 10.4 is another graphic on a similar subject,which contextualises the relative progress in the rise of women CEOsamongst the Fortune 500 companies. By setting the maximum y-axisvalue range to reflect the level at which equality would exist, theresulting empty space emphasises the significant gap that stillpersists.

Figure 10.4 Reworking of ‘The Glass Ceiling Persists’

494

Figure 10.5 Fast-food Purchasers Report More Demands on TheirTime

495

Figure 10.5 shows how the lack of careful thought about your scalescan undermine the ease of readability. This chart shows howAmerican adults spend their time on different activities. The analysisis broken down into minutes and so the maximum is set at 1440minutes in a day. For some reason, the y-axis labels and theassociated horizontal gridlines are displayed at intervals of 160minutes. This is an entirely meaningless quantity of time so whydivide the day up into nine intervals? To help viewers perceive thesignificance and size of the different stacked activities it would havebeen far more logical to use 60-minute time intervals as that is howwe tend to think when dividing our daily schedule.Chart orientation: Decisions about the orientation of your chart andits contents can sometimes help squeeze out an extra degree ofreadability and meaning from your display.

Figure 10.6 Illustrating the Effect of Chartorientation Decisions

496

The primary concern about chart orientation is towards the legibilityof labels along the axis. A vertical bar chart, with multiple categoriesalong the x-axis, will present a challenge of making the labels legibleand avoiding them overlapping. Ideally you would want to preservelabel reading in line with the eye, but you might need to adjust theirorientation to either 45° or 90°. My preference for handling this withbar charts is to switch the orientation of the chart and to then have

497

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 inits native setting, Figure 10.6, portraying the split of political partiesin Germany, feels like a missed opportunity to display a political axisof the Left and the Right through using a landscape rather thanportrait layout.As you saw earlier, the graphic about ‘Iraq’s bloody toll’ (Figure1.11) uses an inverted bar chart to create a potent display of data thateffectively conveys the subject matter, but importantly does sowithout introducing any unnecessary obstacles in readability.In the previous section I presented a wireframe sketch of a graphicabout Nobel prize winners. Figure 10.7 shows the final design. Noticehow the original concept of the novel diagonal orientation wasaccomplished in the final composition, exploiting the greater roomthat this dimension of space offers within the page. It feels quiteaudacious to do this in a newspaper setting.

Figure 10.7 Nobels no Degrees

498

Figure 10.8 Kasich Could Be The GOP’s Moderate Backstop

Figure 10.8, from FiveThirtyEight, rotates the scatter plot by 45° andthen overlays a 2 × 2 grid which helps to guide the viewer’sinterpretation by making it easier to observe which values are locatedin each quadrant. It is also used to emphasise the distinction betweenlocation in the top and bottom halves of the chart along the axis ofpopularity, 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 designthinking. They are independent of one another. A bar chart, whichbelongs to the categorical (C) family of charts, could have its datapotentially sorted by location, alphabet, time, category or hierarchy.

499

Chart value sorting: Sorting content within a chart is important forhelping viewers to find and compare quickly the most relevantcontent. One of the best ways to consider the options for value sortingcomes from using the LATCH acronym, devised by Richard SaulWurman, which stands for the five ways of organising displays ofdata: Location, Alphabet, Time, Category or Hierarchy.Location sorting involves sequencing content according to the orderof a spatial dimension. This does not refer to sorting data on a maplocations are fixed, rather it could be sorting data by geographicalspatial relationships (such as presenting data for all the stops along asubway route) or a non-geographical spatial relationship (like asequence based on the position of major parts of the body from headto toe). You should order by location only when you believe it offersthe most logical sequence for the readability of the display or if thereis likely to be interest or significance in the comparison ofneighbouring values. An example of location sorting is displayed in‘On Broadway’ (Figure 10.9) on the following page, an interactiveinstallation that stitches together a sequenced compilation of data andmedia related to 30 metre intervals of life along the 13 miles (21 km)of Broadway that stretches across the length of Manhattan. Thiscontinuous narrative offers compelling views of the fluctuatingcharacteristics as you transport yourself down the spine of the city.

Figure 10.9 On Broadway

Alphabetical sorting is a cataloguing approach that facilitates efficientlookup and reference. Only on rare occasions, when you are

500

especially keen to offer convenient ordering for looking upcategorical values, will you find that alphabetical sorting alone offersthe best sequence. In Figure 10.10, investigating different measures ofwaiting times in emergency rooms across the United States, the barcharts are presented based on the alphabetical sorting of each state.This is the default setting but users can also choose to reorder thetable hierarchically based on the increasing/decreasing values acrossthe four columns.

Data representation techniques that display overlapping connections,like Sankey diagrams, slope graphs and chord diagrams, also introducethe need to contemplate value sorting in the z-dimension: that is, whichof these connections will be above and which will be below, and why.

Alphabetical sorting might be seen as a suitably diplomatic optionshould you not wish to imply any ranking significance that would bedisplayed when sorting by any other dimension. Additionally, there isa lot of sense in employing alphabetical ordering for values listed indropdown menus as this offers the most immediate way for viewersto quickly find the options they are interested in selecting.

Figure 10.10 ER Wait Watcher: Which Emergency Room Will SeeYou the Fastest?

501

Time-based sorting is used when the data has a relevant chronologicalsequence and you wish to display and compare how changes haveprogressed over time. In Figure 10.11, you can see a snapshot of agraphic that portrays the rain patterns in Hong Kong since 1990. Eachrow of data represents a full year of 365/366 daily readings runningfrom left to right. The subject matter and likely interest in theseasonality of patterns make chronological ordering a common-sensechoice.

Figure 10.11 Rain Patterns

Categorical sorting can be usefully applied to a sequence ofcategories that have a logical hierarchy implied by their values orunique to the subject matter. For example, if you were presentinganalysis about football players you might organise a chart based onthe general order of their typical positions in a team (goalkeeper >defenders > midfielders > forwards) or use seniority levels as a wayto present analysis about staff numbers. Alternatively, if you haveordinal data you can logically sort the values according to theirinherent hierarchy. In Figure 10.12, that you saw earlier in the profileof ordinal colours, the columns are sequenced left to right in orderfrom ‘major deterioration’ to ‘major improvement’, to help reveal thebalance of treatment outcomes from a sample of psychotherapyclients.

Figure 10.12 Excerpt from ‘Pyschotherapy in The Arctic’

502

Hierarchical sorting organises data by increasing or decreasingquantities so a viewer can efficiently perceive the size, distributionand underlying ranking of values. In Figure 10.13, showing thehighest typical salaries for women in the US, based on analysis ofdata from the US Bureau of Labour Statistics, the sorting arrangementpresents the values by descending quantity to reveal the highestrankings values.

Figure 10.13 Excerpt from ‘Gender Pay Gap US’

In Figure 10.12 the bubbles in each column do not need to be colouredas their position already provides a visual association with the‘deterioration’ through to ‘improvement’ ordinal categories. Theattribute of colour, specifically, can therefore be considered redundantencoding. However, you might still choose to include this redundancy if

503

you believed it aided the immediacy of association and distinction. Inthis case, the chart was part of a larger graphic that employed the samecolour associations across several different charts and therefore it madesense to preserve this association.

10.3 Influencing Factors and ConsiderationsYou are now familiar with the array of various aspects of compositionthinking. At this point you will need to weigh up your decisions on howyou might employ these in your own work. Here are some of the specificfactors to bear in mind.

Formulating Your BriefFormat: Naturally, as composition is about spatial arrangement, thenature and dimensions of the canvas you have to work with will havea fundamental bearing on the decisions you make. There are twoconcerns here: what will be the shape and size of the primary formatand how transferable will your solution be across the differentplatforms on which it might be used or consumed?Another factor surrounding format concerns the mobility of viewingthe work. If the form of your output enables viewers to easily move adisplay or move around a display in a circular plane (such as lookingat a printout or work on a tablet) this means that issues such as labelorientation can be largely cast aside. If your output is going to beconsumed in a relatively fixed setting (desktop/laptop or via apresentation) the flexibility of viewing positions will be restricted.

Working With DataData examination: Not surprisingly, the shape and size of your datawill directly influence your chart composition decisions. Whendiscussing physical properties in Chapter 4, I described the influenceof quantitative values with legitimate outliers distorting ideal scalechoices. One solution for dealing with this is to use a non-linearlogarithmic (often just known as a ‘log’) scale. Essentially, eachmajor interval along a log scale increases the value at that markedposition by a factor of 10 (or by one order of magnitude) rather than

504

by equal increments. In Figure 10.14, looking at ratings for thousandsof different board games, the x-axis is presented on a log scale inorder to accommodate the wide range of values for the ‘Number ofratings’ 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 layoutwould have meant squashing values below 1000 into such a tightlypacked space that you would hardly see the patterns. Alternatively, awide rectangular chart would have been necessary but impracticalgiven the limitations of the space this chart would occupy.I have great sympathy for the challenges faced by designers likeZimbabwe-based Graham van de Ruit, when working on typesetting abook titled Millions, Billions, Trillions: Letters from Zimbabwe,2005−2009 in 2014. The book was all text, apart from one or twotables. One of the tables of data supplied to Graham showedZimbabwe’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 ispresented in tabular form there is a strong visual impact created byallowing the sheer spatial consequence of the exceptional mid-2008numbers to cause the awkward widening of the final column. I thinkthis makes the point much more effectively than a chart might, in thiscase.

Figure 10.14 The Worst Board Games Ever Invented

505

Figure 10.15 From Millions, Billions, Trillions: Letters from Zimbabwe,2005−2009

506

‘I thought that a graph might be more effective, but I quickly realisedthat the scale would be a big challenge… The whole point of graphingwould have been to show the huge leap in 2008, something that I feltthe log scale would detract from and was impractical with the spaceconstraints. I also felt that a log scale might not be intuitive to the targetaudience.’ Graham van de Ruit, Editorial and Information Designer

Establishing Your Editorial ThinkingAngles: The greater the number of different angles of analysis youwish to cover in your work, the greater the challenge will be toseamlessly accommodate the resulting chart displays in one view. Themore content you include increases the need to contemplatereductions in the size of charts or a non-simultaneous arrangement,perhaps through multi-page sequences with interactive navigation.In defining your editorial perspectives, you will have likelyestablished some sense of hierarchy that might inform which anglesshould be more prominent (regarding layout position and size) andwhich less so. There might also be some inherent narrative binding

507

each slice of analysis that lends itself to being presented in adeliberate sequence.

Data RepresentationChart type choice: Different charts have different spatialconsequences. A treemap generally occupies far more space than apie chart simply because there are many more ‘parts’ being shown. Apolar chart is circular in shape, whereas a waffle chart is squared.With each chart you include you will have a uniquely shaped piecethat will form part of the overall jigsaw puzzle. Inevitably there willbe some shuffling of content to find the right size and placementbalance.The table in Figure 10.16 summarises the main chart structures andthe typical shapes they occupy. This list is based only on the chartsincluded in the Chapter 6 gallery but still offers a reasonablecompilation of the main structures. These are ordered in descendingfrequency as per the distribution of the different structures of charts inthe gallery.

Figure 10.16 List of chart structures

508

Trustworthy DesignChart-scale optimisation: Decisions about chart scales concern themaximum, minimum and interval choices that ensure integritythrough the representation as well as optimise readability.Firstly, let’s look at decisions around minimum values used on thequantitative value axis, known as the origin, and the reasons why it isnot OK for you to truncate the axis in methods like the bar chart. Anydata representation where the attribute of size is used to encode aquantitative value needs to show the full, true size, nothing more andnothing less. The origin needs to be zero. When you truncate a barchart’s quantitative value axis you distort the perceived length orheight of the bar. Visualisers are often tempted to crop axis scaleswhen values are large and the differences between categories aresmall. However, as you can see in Figure 10.17, the consequence isthat it creates the impression of highly noticeable relative differencebetween values when the absolute values do not support this.

509

Figure 10.17 Illustrating the Effect of Truncated Bar Axis Scales

The single instance in which it is remotely reasonable to truncate anaxis would be if you had a main graphic which effectively offered athumbnail view of the whole chart for orientation positionedalongside a separate associated chart (similar to that on the right).This separate chart might have a truncated axis that would provide amagnified view of the main chart, showing just the tips of the bar, tohelp viewers see the differences close up.In contrast to the bar chart, a line chart does not necessarily needalways 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 ascale (attribute). It therefore does not encode quantitative valuesthrough size, like the bar chart does, so the truncation of a value axiswill not unduly impact on perceiving the relative values against thescale and the general trajectory. For some data contexts the notion ofa zero quantity might be impossible to achieve. In Figure 10.18,showing 100m sprint record times, no human is ever going to be ableto run 100m in anywhere near zero seconds. Times have improved, ofcourse, but there is a physical limit to what can be achieved. To showthis analysis with the y-axis starting from zero would be unnecessaryand even more so if you plotted similar analysis for longer distanceraces.However, if you were to plot the 100m results and the 400m resultson the same chart, you would need to start from zero to enableorientation of the scale of comparable values. This sense ofcomparable scale is missing from the next chart, whereby includingthe full quantitative value range down to zero would be necessary toperceive 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

510

looking at part-to-whole analysis, the y-axis should really bedisplayed from an origin of zero. The maximum doesn’t need to goup to 100%, the highest observed value is fine in this case, but itcould be interesting to set the maximum range to 100% in order tocreate a similar sense of the gap to be bridged before 100% ofrespondents are in agreement.

Figure 10.18 Excerpt from ‘Doping under the Microscope’

Figure 10.19 Record-high 60% of Americans Support Same-sexMarriage

Aspect ratios: The aspect ratio of a line chart, as derived from theheight and width dimensions of the chart area, can have a largeimpact on the perceived trends presented. If the chart is too narrow,

511

the steepness of connections will be embellished and look moresignificant; if the chart is stretched out too wide, the steepness ofslopes will be much more dampened and key trends may besomewhat disguised. There is no absolutely right or wrong approachhere but clearly there is a need for sensitivity to avoid the possibilityof unintended deception. A general rule of thumb is to seek a chartarea that enables the average slope to be presented at 45°, though thisis not something that can be easily and practically applied, especiallyas there are many other variables at play, such as the range ofquantitative and time values and the scales being used. My advice isjust to make a pragmatic judgement by eye to find the ratio that youthink is faithful to the significance of the trends in your data.Mapping projections: One of the most contentious matters in thevisual representation of data relates to thematic mapping andspecifically to the choice of map projection used. The Earth is not flat(hopefully no contention there, otherwise this discussion is ratheracademic), yet the dominant form through which maps are presentedportrays the Earth as being just that. Features such as size, shape anddistance can be measured accurately on Earth but when projected on aflat surface a compromise has to occur. Only some of these qualitiescan be preserved and represented accurately.

I qualify this with ‘dominant’ because, increasingly, advances intechnology (such as WebGL) mean we can now interact with sphericalportrayals of the Earth within a 2D space.

There are lots of exceptionally complicated calculations attached tothe variety of spatial projections. The main things you need to knowabout 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, thegreater the distortion;there is no single right answer – it is often about choosing theleast-worst case.

Thematic mapping (as opposed to mapping spatially for navigation orreference purposes) is generally best portrayed using mapping projectionsbased 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

512

typically plotting – are correctly represented by proportion of regionalarea. For choosing the best specific projection, in the absence of perfect,damage limitation is often the key: that is, which choice will distort thespatial truth the least given the level of mapping required. There are somany variables at play, however, based on the scope of view (world,continent, or country/sub-region), the potential distance from the equatorof 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, adiscussion about mapping projections requires a dedicated text but let meat least offer a brief outline of five different projections to begin youracquaintance:

Many tools that offer rudimentary mapping options will tend to onlycome with a default (non-adjustable) projection, often the Mercator (orWeb Mercator). The more advanced geospatial analysis tools will offerpre-loaded or add-in options to broaden and customise the range ofprojections. Hopefully, in time, an increasing range of the morepragmatic desktop tools will enhance projection customisations.

Figure 10.20 A Selection of Commonly Deployed Mapping Projections

513

Accessible DesignGood design is unobtrusive: One of the main obstructions tofacilitating understanding through a visualisation design is whenviewers are required to rely on their memory to perform comparisonsbetween non-simultaneous views.When the composition layout requires viewers to flick between pagesor interactively generated views, they have to try store one view intheir mind and then mentally compare that against the live view that

514

has arrived on the screen. This is too hard and too likely to fail giventhe relatively weak performance of the brain’s working memory.Content that warrants direct comparison should be enabled throughproximity to and alignment with related items. I mentioned in thesection on animation that if you want to compare different states overtime, rather than see the connected system of change, you will need tohave access to the ‘moment’ views simultaneously and without areliance on memory.

‘Using our eyes to switch between different views that are visiblesimultaneously has much lower cognitive load than consulting ourmemory to compare a current view with what was seen before.’Tamara Munzner taken from Visualization Analysis and Design

Elegant Design

‘I’m obsessed with alignments. Sloppy label placement on final filescauses my confidence in the designer to flag. What other details haven’tbeen given full attention? Has the data been handled sloppily as well?… On the flip side, clean, layered and logically built final files are athing of beauty and my confidence in the designer, and their attention todetail, soars.’ Jen Christiansen, Graphics Editor at ScientificAmerican

Unity: As I discussed with colour, composition decisions are alwaysrelative: an object’s place and its space occupied within a displayimmediately create a relationship with everything else in the display.Unity in composition provides a similar sense of harmony andbalance between all objects on show as was sought with colour. Theflow of content should feel logical and meaningful.The enduring idea that elegance in design is most appreciated when itis absent is just as relevant with composition. Look around and openyour eyes to composition that works and does not work, andrecognise the solutions that felt effortless as you read them and thosethat felt punctured and confusing. This is again quite an elusiveconcept and one that only comes with a mixture of common-sensejudgement, experience and exposure to inspiration from elsewhere.Thoroughness: Precision positioning is the demonstration of

515

thoroughness and care that is so important in the pursuit of elegance.You should aim to achieve pixel-perfect accuracy in the position andsize of every single property.Think of the importance of absolute positioning in the context ofdetailed architectural plans that outline the position of every finedetail down to power sockets, door handles and the arc of a window’sopening manoeuvre. A data visualiser has to commit to ultimateprecision and consistency because any shortcomings will beimmediately noticeable and will fundamentally impact on thefunction of the work. If you do not feel a warm glow from everyemphatic snap-to-grid resize operation or upon seeing the results of amass alignment of page objects, you are not doing it right. (Honestly,I am loads of fun to be around.

Summary: CompositionProject composition defines the layout and hierarchy of the entirevisualisation 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 elementsizes?Absolute positioning: where specifically should certain elements beplaced?

Chart composition defines the shape, size and layout choices for allcomponents within your charts and may include the following features:

Chart size: don’t be afraid to shrink charts, so long as any labels arestill readable, and especially embrace the power of small multiple.Chart scales: what are the most meaningful range of values given thenature of the data?Chart orientation: which way is best?Chart value sorting: consider the most meaningful sortingarrangement for your data and editorial focus, based on the LATCHacronym.

Influencing Factors and Considerations

Formulating the brief: what space have you got to work within?

516

Working with data: what is the shape and size of your data and howmight 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 forthese angles that might influence a sequence or hierarchy betweenthem?Data representation: any chart has a spatial consequence – differentcharts have different structures that will create different dimensionsthat 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 youwant to facilitate comparisons between different chart displays theseideally need to be presented within a simultaneous view.Elegant design: unity of arrangement is another of the finger-tip sensejudgments but will be something achieved by careful thinking aboutthe relationships between all components of your work.

Tips and Tactics

You will find that as you reach the latter stages of your designprocess, the task of nudging things by fractions of a pixel andrealigning features will dominate your attention. As energy andattention start to diminish you will need to maintain a commitment tothoroughness and a pride in precision right through to the end!Empty space is like punctuation in visual language: use it to break upcontent when it needs that momentary pause, just as how a comma orfull stop is needed in a sentence. Do not be afraid to use empty spacemore extensively across larger regions as a device to create impact.Like the notes not played in jazz, effective visualisation design canalso be about the relationship between something and nothing.

517

518

Part D Developing Your Capabilities

519

11 Visualisation Literacy

This final chapter explores some of the important ingredients and tacticsthat will help you continue to develop and refine your data visualisationliteracy. By definition, literacy is the ability to read and write. Applied todata visualisation, this means possessing the literacy both to createvisualisations (write) and consume them (read).

Data visualisation literacy is increasingly an essential capability regardlessof the domain in which we work and the nature of our technical skills. Justas computer literacy is now a capability that is expected of everyone, onecan imagine a time in the not-too-distant future when having datavisualisation capabilities will be viewed as a similarly ‘assumed’ attributeacross many different roles.

In exploring the components of visualisation literacy across this chapterwe will look at two sides of the same coin: the competencies that make upthe all-round talents of a visualiser but, first, the tactics and considerationsrequired to be an effective and efficient viewer of data visualisation.

11.1 Viewing: Learning to SeeLearning how to understand a data visualisation, as a viewer, is not a topicthat has been much discussed in the field until recently. For many the ideathat there are possible tactics and efficient ways to approach this activity israrely likely to have crossed their mind. We just look at charts and readthem, don’t we? What else is there to consider?

Many of the ideas for this section emerged from the Seeing Datavisualisation literacy research project (seeingdata.org) on which Icollaborated.

The fact is we are all viewers. Even if you never create a visualisationagain you will always be a viewer and you will be widely exposed todifferent visual forms of data and information across your daily life. Youcannot escape them. Therefore, it seems logical that optimisingvisualisation literacy as a consumer is a competency worth developing,

520

Let’s put this into some sort of context. As children we develop the abilityto read numbers and words. These are only understandable because we aretaught how to recognise the association between numeric digits and theirrepresentation as numbers and the connection between alphabeticalcharacters with letters and words. From there we begin to understandsentences and eventually, as we build up a broader vocabulary, we acquirethe literacy of language. This is all a big effort. We are not born knowing alanguage but we are born with the capacity to learn one.

Beyond written language, something as simple and singular as, forexample, the Wi-Fi symbol is now a universally recognised form of visuallanguage but one that only exists in contemporary culture. For millions ofpeople today, this symbol is a signal of relief and tangible celebration –‘Thank God, Wi-Fi is available here!’ The context of the use of thissymbol would have meant nothing to people in the 1990s: it is a symbol ofits time and we have learnt to recognise its use and understand its meaning.

Across all aspects of our lives, there are things that once seemedcomplicated and inaccessible but are now embedded within us asautomatic competencies: driving a car, using a keyboard, cooking a meal. Ioften think back to growing up as a kid in the 1980s and my first(functioning) computer, the mighty Commodore 64 (C64). One of the mostfamous games in the UK from this period was Daley Thompson’sDecathlon. Of particular nostalgic fame was the brutally simple operationof maniacally waggling the single joystick arm left and right to control therunning events (if memory serves me correctly, the single button came intouse when there were hurdles to jump over).

Consider the universally and immediately understandable controlconfiguration of that game with the frankly ludicrous number of optionsand combinations that exist on the modern football games, such as theFIFA series on contemporary consoles like the Xbox or PS4. The controlcombinations required to master the array of attacking moves alone requirean entire page of instruction and remarkable levels of finger dexterity. Yetyoung kids today are almost immediate masters of this game. I shouldknow – I have been beaten by some awfully young opponents. It hurts. Butthey have simply utilised their capacity to learn through reading andrepeated practice.

As discussed in Chapter 1 when looking at the principle of ‘accessible’design, many data visualisations will be intended – and designed – for

521

relatively quick consumption. These might be simple to understand andoffer immediately clear messages for viewers to easily comprehend. Theyare the equivalent of the C64 joystick controls. However, there will beoccasions when you as a viewer are required to invest a bit more time andeffort to work through a visualisation that might be based on subject matteror analysis of a more complex nature, perhaps involving many angles ofanalysis or numerous rich features of interactivity. This is the equivalentprospect of mastering the Xbox controls. Without having the confidence orcapability to extract as much understanding from the viewing experienceas possible and doing so as efficiently as possible, you are potentiallymissing 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 whentrying to grasp what’s going on in, say, arc diagrams, circular hierarchygraphs, hyperbolic tree charts, or any of the seemingly outlandishvisualisations … I haven’t thought much about this flip side, except thatI do find I now view other people’s visualisations with a more criticaleye.’ Marcia Gray, Graphic Designer

As viewers, we therefore need to acknowledge that there might be a needto learn and a reward from learning. We should not expect every type ofvisualisation to signpost every pearl of insight that is relevant to us. Wemight have to work for it. And we have to work for it because we are notborn with the ability or the right to understand everything that is presentedto us. Few of us will have ever been taught how to go about effectivelyconsuming charts and graphics. We might be given some guidance on howto read charts and histograms, maybe even a scatter plot, if we study mathsor the sciences at school. Otherwise, we get by.

But ‘getting by’ is not really good enough, is it? Even if, through exposureand repetition, we hope gradually to become more familiar with the mostcommon approaches to visualising data, this does not sufficiently equip uswith the breadth and range of literacy that will be required.

I mentioned earlier the concept, proposed by Daniel Kahneman, of System1 and System 2 thinking. The distinctions of these modes of thoughtmanifest themselves again here. Remember how System 1 was intuitiveand rapid whereas System 2 was slow, deliberate and almost consciouslyundertaken? For example, you are acutely aware of thinking when trying

522

to run a mathematical calculation through your mind. That is System 2 atwork. In part, due to the almost hyperactive and instinctive characteristicsof System 1, when there is a need for System 2 thinking to kick intoaction, we might try to avoid whatever that activity entails. We get lazyand resort to shortcut solutions or decisions based on intuition. System 1almost persuades System 2 to sit back and let it look after things. Anythingto avoid having to expend effort thinking deeply and rationally.

The demands of learning anything new or hard can trigger that kind ofresponse. It is understandable that somebody facing a complex orunfamiliar visualisation that needs learning might demonstrate antipathytowards the effort required to learn.

Of course, there are other factors involved in learning, such as having thetime, receiving assistance or tuition, and recognising the incentive. Theseare all enablers and therefore their absence can create obstacles to learning.Without assistance from the visualiser, viewers are left to fend forthemselves. The role of this book has primarily been to try to raise thestandard of the design choices that visualisers make when creatingvisualisations. Visualisers do not want to obstruct viewers from being ableto read, interpret and comprehend. If work is riddled with design errorsand misjudgements then viewers are naturally going to be disadvantaged.

However, even with a technically perfect design, as I explained in thedefinition section of the first chapter, we as visualisers can only do somuch to control this experience. There are things we can do to make ourwork as accessible as possible, but there is also a partial expectation of theviewer to be willing to make some effort (so long as it is ‘proportional’) toget the most out of the experience. The key point, however, is that thiseffort 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. Theyneed learning. Your confidence in being able to read different types ofcharts is something that will develop through practice and exposure. It willbe slow and deliberate at first, probably a little consciously painful, butthen, over time, as the familiarity increases and the experiential benefitskick in, perceiving these different types of representations will becomequite effortless and automatic. System 2 thinking will then transform into areliably quick form of System 1 thinking.

523

Over the next few pages I will present a breakdown of the components ofeffectively working with a visualisation from the perspective of being aviewer. This demonstration will provide you with a strategy forapproaching any visualisation with the best chance of understanding howto read it and ensure you gain the benefit of understanding from being ableto read it.

To start with I will outline the instinctive thoughts and judgements youwill need to make before you begin working with a visualisation. I willthen separate the different features of a visualisation, first by consideringthe common components that sit outside the chart and then some pointersfor how to go about perceiving what is presented inside the chart. This partwill also connect with the content included in the chart type gallery foundin Chapter 6 describing how to read each unique chart type. Finally, I willtouch on the attributes that will lead you, in the longer term, to becoming amore sophisticated viewer.

It is important to note that not all data visualisation and infographicdesigns will have all the design features and apparatus items that I describeover the next few sections. There may be good reasons for this in eachcase, depending on the context. However, if you find there are significantgaps in the work you are consuming, or features of assistance have beendeployed without real care or quality, that would point to flawed design. Inthese cases the viewer is not really being given all the assistance required:the visualiser has failed to facilitate understanding.

Figure 11.1 The Pursuit of Faster

524

To illustrate this process I will refer to a case-study project titled ‘ThePursuit of Faster: Visualising the Evolution of Olympic Speed’. As the titlesuggests, the focus of this work was to explore how results have changed(improved or declined) over the years of the Olympics for those eventswhere speed (as measured by a finishing time) was the determinant ofsuccess.

Before You BeginHere are some of the instinctive, immediate thoughts that will cross yourmind as soon as you come face to face with a data visualisation. Onceagain, these are consistent with the impulsive nature of the System 1thoughts mentioned earlier.

Setting: Think about whether the setting you are in is conducive to

525

consuming a visualisation at that moment in time. Are you under anypressure of time? Are you on a bumpy train trying to read this on yoursmartphone?Visual appeal? In this early period of engaging with the work youwill be making a number of rapid judgements to determine whetheryou are ‘on board’. One of the ingredients of this is to considerwhether the look and feel (the ‘form’) of the visualisation attract youand motivate you to want to spend time with it.Relevance? In addition to the visual appeal, the second powerfulinstinct is to judge whether the subject matter interests you. Youmight have decided you are on board with your instinctive reaction tothe visuals but the key hurdle is whether it is even interesting orrelevant to you. Ask yourself if this visualisation is going to deliversome form of useful understanding that confirms, enlightens or thrillsyou about the topic.If you respond positively to both those considerations you will likelybe intent on continuing to work with the visualisation. Even if you arejust positive about one of these factors (form or subject) you willmost probably persevere despite the indifference towards the other. Ifyour thoughts are leaning towards a lack of interest in both therelevance of this work and its visual appeal then, depending oncircumstances, your tolerance may not be high enough to continueand it will be better to abandon the task there and then.Initial scan? It is inevitable that your eyes will be instinctively drawnto certain prominent features. This might be the title or even the chartitself. You may be drawn to a strikingly large bar or a sudden upwardrise on a line chart. You might see a headline caption that capturesyour attention or maybe some striking photo imagery. It is hard tofight our natural instincts, so don’t. Allow yourself a brief glance atthe things you feel compelled to look at – these are likely the samethings the visualiser is probably hoping you are drawn to. Quicklyscanning the whole piece, just for an initial period of time, gives youa sense of orientation about what is in store.In ‘The Pursuit of Faster’ project you might find yourself only drawnto this if you have a passing interest in the Olympics and/or thehistory of athletic achievement. On the surface, the visuals might lookquite analytical in nature, which might turn some people off. Theinitial scan probably focuses on elements like the Olympic rings andthe upward direction of the lines in the chart which might offer adegree of intrigue, as might the apparent range of interactive controls.

526

Outside the ChartBefore getting into the nuts and bolts of understanding the chart displays,you will first need to seek assistance from the project at large tounderstand in more detail what you are about to take on and how youmight need to go about working with it.

The Proposition

Considering the proposition offered by the visualisation is aboutdetermining how big a task of consuming and possibly interacting youhave ahead of you. What is its shape, size and nature?

Format: Is it presented in a print, physical or digital format and whatdoes this make you feel about your potential appetite and the level ofyour engagement? Is it static or interactive and what does this presentin terms of task?

If it is a static graphic, how large and varied is the content – is ita dense display with lots of charts and text, or quite a small andcompact one? Does the sequence of content appear logical?If it is interactive, how much potential interactivity does thereappear to be – are there many buttons, menus, options, etc.?Where do the interactive events take you? Are there multipletabs, pages or layers beneath this initial page? Have a clickaround.

Shape and size: Do you think you will probably to have to put in alot of work just to scan the surface insights? Is there a clear hierarchyor sequence derived through the size and position of elements on thepage? Does it feel like there is too much or too little content-wise? Ifthe project layout exceeds the dimensions of your screen display, howmuch more scrolling or how many different pages will you have tolook through to see the whole?

This initial thinking helps you establish how much work and effort you aregoing to be faced with to explore the visualisation thoroughly. In ‘ThePursuit of Faster’ project, it does not feel like there is too much contentand all the possible analysis seems to be located within the boundaries ofthe immediate screen area. However, with a number of different selectabletabs, interactive options and collapsible content areas lurking beneath thesurface, it could be more involving than it first appears.

527

What’s this Project About?

Although you have already determined the potential relevance of thissubject matter (or otherwise) you will now look to gain a little moreinsight into what the visualisation is specifically about.

Title: You will have probably already glanced at the title but nowhave another look at it to see if you can learn more about the subjectmatter, the specific angle of enquiry or perhaps a headline finding. Inthe sample project (Figures 11.2 and 11.3), the presence of theOlympic rings logo on the right provides an immediate visual cueabout the subject matter, as you might have observed in the initialscan. The title, ‘The Pursuit of Faster’, is quite ambiguous, but as thesupporting subtitle reveals, ‘Visualising the evolution of Olympicspeed’ helps to explain what the visualisation is about.

Figure 11.2 Excerpt from ‘The Pursuit of Faster’

Source: If it is a web-based visualisation the URL is worthconsidering. You might already know where you are on the Web, butif not you can derive plenty from the site on which this project isbeing hosted. An initial sense about trust in the data, the author andthe possible credibility of insights can be drawn from this single bit ofinformation. This particular project is hosted on my website,visualisingdata.com, and so may not carry the same immediaterecognition that an established Olympics or sport-related site mightcommand. There is nothing provided in the main view of thevisualisation that informs the viewer who created the project.Normally this might have been detailed towards the bottom of thedisplay or underneath the title, but in this case viewers have to clickon a ‘Read more…’ link to find this out. If there are no detailsprovided about the author/visualiser, as a viewer, this anonymitymight have any affect on your trust in the work’s motives and quality.Introduction: While some visualisation projects will be relativelyself-explanatory, depending on the familiarity of the audience withthe subject matter, others will need to provide a little extra guidance.

528

The inclusion of introductory text will often help ‘set the scene’,providing some further background about the project. If, as theviewer, the introduction fails to equip you with all the informationyou feel you need about the visualisation, then the visualiser hasneglected to include all the assistance that might be necessary.

In ‘The Pursuit of Faster’ project, the introductory text provides sufficientinitial information about the background of the project based on a curiosityabout what improvements in speed have been seen throughout the historyof the Olympics. As mentioned, there is a ‘Read more…’ link to find moreinformation that was perhaps too much to include in the main openingparagraph. This includes a comprehensive ‘How to use it’ guide providinga detailed account about the content and role of each section of the project,including advice on how to read the chart and utilise the interactivefeatures.

Figure 11.3 Excerpt from ‘The Pursuit of Faster’

What Data?

Any visualisation of data should include clear information to explain theorigin of the data and what has been done with it in preparation for itsvisual portrayal.

529

Data source: Typically, details of the data source will be located inthe introduction, as a footnote beneath a chart or at the bottom of apage. It is important to demonstrate transparency and give credit tothe origin of your data. If none is provided, that lowers trust.Data handling: It is also important to explain how the data wasgathered and what, if any, criteria were applied to include or excludecertain aspects of the subject matter. These might also mentioncertain assumptions, calculations or transformations that have beenundertaken 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 onlyincludes medal winners from summer Olympic events that have a time-based measure.

What Interactive Functions Exist?

As you have seen in Chapter 8, interactive visualisations (typically hostedon the Web or in an app) aim to provide users with a range of features tointerrogate and customise the presentation of the data.

Sometimes, interactive features are enabled but not visible on the surfaceof a project. This might be because visualisers feel that users will beexperienced enough to expect certain interactive capabilities withouthaving to make these overly conspicuous by labelling or signposting theirpresence. For example, rather than show all the value labels on a bar chartyou might be able to move the mouse over a bar of choice and a pop-upwill 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 withthe mouse or through touch in order to gain a sense of all the differentvisible and possibly invisible ways you can interact with the visualisation.

In ‘The Pursuit of Faster’ project (Figure 11.4), you will see multiple tabsat the top, one for each of the four sports being analysed. Clicking on eachone opens up a new set of sub-tabs beneath for each specific event withinthe chosen sport.

Figure 11.4 Excerpt from ‘The Pursuit of Faster’

530

Choosing an event will present the results in the main chart area (Figure11.5). Once a chart has loaded up, you can then filter for male/female andalso for each of the medals using the buttons immediately below the chart.Within the chart, hovering above a marker on the chart will reveal thespecific time value for that result. Clicking on the marker will show thefull race results and offer further analysis comparing those results with theall-time results for context.

Figure 11.5 Excerpt from ‘The Pursuit of Faster’

Finally, the collapsible menus below the chart show further detailedanalysis and comparisons within and between each sporting event (Figure11.6). The location of this implies that it is of lower relative importancethan the chart or maybe is a more detailed view of the data.

Figure 11.6 Excerpt from ‘The Pursuit of Faster’

531

Inside the ChartNow you have acquainted yourself with the key features of a visualisationoutside the chart, the next stage is to start the process of derivingunderstanding from the chart.

The process of consuming a chart varies considerably between differentchart types: the approach to drawing observations from a chart showingtrends over time is very different from how you might explore a map-based visualisation. The charts I profiled in Chapter 6 were eachaccompanied by detailed information on the type of observations youshould be looking to extract in each case.

In Chapter 1 you learnt how there were three elements involved in theachievement of understanding a chart: perceiving, interpreting andcomprehending. Let’s work through these steps by looking at the analysisshown for the 100m Finals.

Perceiving: The first task in perceiving a chart is to establish yourunderstanding about the role of every aspect of the display. Here wehave a line chart (Figure 11.7) which shows how quantitative values

532

for categories have changed over time. This chart is structured arounda horizontal x-axis showing equal intervals from the earliestOlympics (1896) on the left through to the most recent (2012) on theright, although the latest values in the data only seem to reach 2008.Depending on your interest in this topic, the absence of data for themore recent Olympics may undermine your sense of its completenessand representativeness.

Figure 11.7 Excerpt from ‘The Pursuit of Faster’

The vertical y-axis is different from what you might normally see fortwo reasons. Firstly, it moves downwards below the x-axis (ratherthan upwards, as is more common), and secondly, there is nolabelling, either of the variable plotted or of scale values.I can see that the encoding is formed by points (marking the raceresults) and connecting lines showing the change over time. Throughthe use of colour there are plotted lines for the gold, silver and bronzemedal winning times for each Olympics. There are two sets of medallines 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 nameand time in a medal-coloured box. I compare tooltip info for the linesat the top and those below and discover the lower lines are thewomen’s results and the upper lines are the men’s results.From the tooltip info I can determine that the quicker times (the goldmedal line) are at the top so this suggests that the y-axis scale isinverted with quicker (smaller) times at the top and slower (larger)times at the bottom. This also reveals that there is no origin of zero inthe vertical axis; rather the quickest time is anchored just below the

533

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 explainthat the focus was on patterns of relative change over time, notnecessarily absolute result times. As every different event has adifferent distance and duration behind the final timed results, a commonscale for all results needed to be established, which is why this decisionwas taken to standardise all results and plot them across the verticalchart space provided.

Inside the chart I now try clicking on the markers and this brings updetails about the event (for that gender), including the three medalwinners, their times and small flags for the countries they represented.I can also read an interesting statistic that explains if the time for themedallist I selected had been achieved throughout the event’s history,it would have secured gold, silver or bronze medals on x number ofoccasions.I now know enough about the chart’s structure and encodings to beable to start the process of perceiving the patterns to make someobservations about what the data is showing me:

I can see that there is a general rise across all Olympics for theevent 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 theclosest that the respective times have been – her result therewould have been good enough for a men’s bronze in 1956.There are no real patterns between medal times; they are neitheralways more packed closely together, nor always spread out – itchanges on each occasion.I notice the gaps where there were no events, during the Firstand Second World Wars, and also the presence of an obscure1906 event, the only Olympic Games that did not follow thefour-year interval.

Interpreting: As someone who follows a lot of sport and, like mostpeople, is particularly familiar with the 100m event, I feel there is a lot ofinformation I can get out of this display at both a general level, looking atthe relative patterns of change, and a local level, checking up on individualmedallists and their absolute values. Thinking about what these patterns

534

mean, on looking at the times from the first Olympic Games in 1896 untilthe 1960s there was a lot of improvement and yet, since the 1960s, there isgenerally a much flatter shape – with only a gradual improvement in thetimes for both genders. This tells me that maybe the threshold for thecapacity of athletes to run faster is getting closer. Even with all thecontributions of sports science over the past few generations, the increasein speed is only ever marginal. That was until Usain Bolt blew the worldaway in 2008 and, likewise for women, Shelly-Ann Fraser improved thewomen’s results for the first time in 20 years.

Comprehending: What does this all mean to me? Well it isinteresting and informative and, while I have no direct investment inthis information in terms of needing to make decisions or it triggeringany sense of emotion in me, in outcome terms I feel I have learntmore about a topic through this chart than I would have done justlooking at the data. My understanding of the history of the Olympic100m final has been expanded and, in turn, I have a betterappreciation of the advancements in speed across and between bothgenders.

Becoming a More Sophisticated ConsumerEffective visualisation requires the visualiser and viewer to operate inharmony, otherwise the possibility of facilitating understanding iscompromised. Beyond the mechanics of perceiving a visualisation, thereare softer ‘attitudinal’ differences you can make to give yourself evenmore of a chance of gaining understanding. This is about modifying yourmindset to be more critically appreciative of the challenges faced by thevisualiser responsible for producing the work as well as its intendedpurpose. It is about showing empathy in your critical evaluation which willmarkedly help you become an increasingly sophisticated consumer.

Appreciation of context: When consuming a visualisation try toimagine some of the circumstances and constraints that might haveinfluenced the visualiser’s decisions:

You might not find the subject matter interesting, but otherpeople might. You have the right not to read or interact with avisualisation that has no relevance to you. If it should haverelevance, then that’s when there may be some problems!If you are struggling to understand a visualisation it could be that

535

the project was aimed more at specialists, people with specificdomain knowledge. Your struggles are possibly not a reflectionof an ineffective visualisation or any deficit in your expectedknowledge – it just was not intended for you.If the size of the text is frustratingly tiny on your screen, maybeit was intended primarily for printing as a poster and would havebeen the right size if consumed in its native format?When criticising a work, spare a thought for what could havebeen done differently. How would you imagine an alternativeway to represent the data? What other design solutions wouldyou have tried? Sometimes what is created is a reflection ofcrippling constraints and might more closely resemble the least-worst solution than the best.

Overview first, details if provided: Sometimes a visualiser only aims tooffer a sense of the big picture – the big values, the medium and the smallones. Just because we cannot instantly read precise values from a chart it isimportant to avoid getting frustrated. Our default state as viewers is oftento want every detail available. Sometimes, we just need to accept the ideathat a gist of the hierarchy of values is of more worth than the precisedecimal point precision of specific values. It may be that it was notfeasible 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 weare just a mouseover or click away from bringing up the details we desire.

False consciousness: Do you really like the things you like?Sometimes we can be too quick to offer a ‘wow’ or a ‘how cool isthat?’ summary judgement before even consuming the visualisationproperly. It is quite natural to be charmed by a superficial surfaceappeal (occasionally, dare I say it, following the crowd?). Askyourself if it is the subject, the design or the data you like? Could anyportrayal of that compelling data have arrived at an equallycompelling presentation of that content?Curiosities answered, curiosities not answered: Just because thecuriosity you had about a subject is not answerable does not make thevisualisation a bad one. Statements like ‘This is great but I wishthey’d shown it by year …’ are valid because they express your owncuriosity, to which you are entirely entitled. However, a visualiser canonly serve up responses to a limited number of different angles ofanalysis in one project. The things you wanted to know about, which

536

might be missing, may simply have not been possible to include orwere deemed less interesting than the information provided. If youare thinking ‘this would have been better on a map’, maybe there wasno access to spatial data? Or maybe the geographical details were toovague or inaccurate to generate sufficient confidence to use them?

11.2 Creating: The Capabilities of theVisualiserNow that you are reaching the end of this journey, it will be quite evidentthat data visualisation design is truly multidisciplinary. It is the variety thatfuels the richness of the subject and makes it a particularly compellingchallenge. To prepare you for your ongoing development, the second partof this final chapter aims to help you reflect on the repertoire of skills,knowledge and mindsets required to achieve excellence in datavisualisation design.

The Seven Hats of Data VisualisationInspired by Edward de Bono’s Six Thinking Hats, the ‘Seven hats of datavisualisation’ is a breakdown of the different capabilities that make up themulti-talented visualiser. The attributes listed under each of these hats canbe viewed as a wish-list of personal or team capabilities, depending on thecontext of your data visualisation work.

Project Manager

The coordinator – oversees the projectInitiates and leads on formulating the briefIdentifies and establishes definitions of key circumstancesOrganises the resources according to the ambition of a projectManages progress of the workflow and keeps it cohesiveHas a ‘thick skin’, patience and empathyGets things done: checks, tests, finishes tasksPays strong attention to detail

Communicator

537

The broker – manages the people relationshipsHelps to gather and understand requirementsManages expectations and presents possibilitiesHelps to define the perspective of the audienceIs a good listener with a willingness to learn from domain expertsIs a confident communicator with laypeople and non-specialistsPossesses strong copy-editing abilitiesLaunches and promotes the final solution

Scientist

The thinker – provides scientific rigourBrings a strong research mindset to the processUnderstands the science of visual perceptionUnderstands visualisation, statistical and data ethicsUnderstands the influence of human factorsVerifies and validates the integrity of all data and design decisionsDemonstrates a system’s thinking approach to problem solvingUndertakes reflective evaluation and critique

Data Analyst

The wrangler – handles all data workHas strong data and statistical literacyHas the technical skills to acquire data from multiple sourcesExamines the physical properties of the dataUndertakes initial descriptive analysisTransforms and prepares the data for its purposeUndertakes exploratory data analysisHas database and data modelling experience

Journalist

The reporter – pursues the scent of an enquiryDefines the trigger curiosity and purpose of the projectHas an instinct to research, learn and discoverDriven by a desire to help others understandPossesses or is able to acquire salient domain knowledgeUnderstands the essence of the subject’s dataHas empathy for the interests and needs of an audience

538

Defines the editorial angle, framing and focus

Designer

The conceiver – provides creative directionEstablishes the initial creative pathway through the purpose mapForms the initial mental visualisation: ideas and inspirationHas strong creative, graphic and illustration skillsUnderstands the principles of user interface designIs fluent with the full array of possible design optionsUnifies the decision-making across the design anatomyHas a relentless creative drive to keep innovating

Technologist

The developer – constructs the solutionPossesses a repertoire of software and programming capabilitiesHas an appetite to acquire new technical solutionsPossesses strong mathematical knowledgeCan automate otherwise manually intensive processesHas the discipline to avoid feature creepWorks on the prototyping and development of the solutionUndertakes pre- and post-launch testing, evaluation and support

Assessing and Developing Your CapabilitiesData visualisation is not necessarily a hard subject to master, but there areplenty of technical and complicated matters to handle. A trained or naturaltalent in areas like graphic design, computer science, journalism and dataanalysis is advantageous, but very few people have all these hats. Thosethat do cannot be exceptional at everything listed, but may be sufficientlycompetent at most things and then brilliant at some. Developing masteryacross the full collection of attributes is probably unachievable, but itoffers a framework for guiding an assessment of your current abilities anda 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).

539

Compromise is required with the things you do not like – there are alwaysgoing to be unattractive tasks, so just bite the bullet and get on with them.Otherwise, you must seek either to address your skills gap throughlearning and/or intensive practice, finding support from elsewhere throughcollaboration, or to simply limit your ambitions based on what you can do.

Regardless of their background or previous experience, everyone hassomething to contribute to data visualisation. Talent is important, ofcourse, but better thinking is, in my view, the essential foundation to focuson first. Mastering the demands of a systems’ thinking approach to datavisualisation – being aware of the options and the mechanics behindmaking choices – arguably has a greater influence on effective work.Thereafter, the journey from good to great, as with anything, involves hardwork, plenty of learning, lots of guidance and, most importantly, relentlesspractice.

‘Invariably, people who are new to visualisation want to know where tobegin, and, frankly, it’s understandably overwhelming. There is somuch 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 Idon’t think it matters where you start. In fact, it’s best to start whereveryou are now. Start from your own experience, and move forward. Onereason I love this field is that everyone comes from a differentbackground – I get to meet architects, designers, artists, coders,statisticians, journalists, data scientists … Data vis is an inherentlyinterdisciplinary practice: that’s an opportunity to learn something abouteverything! The people who are most successful in this field are curiousand motivated. Don’t worry if you feel you don’t have skills yet; juststart from where you are, share your work, and engage with others.’Scott Murray, Designer

The Value of the TeamThe idea of team work is important. There are advantages to pursuing datavisualisation solutions collaboratively, bringing together different abilitiesand perspectives to a shared challenge. In workplaces across industries andsectors, as the field matures and becomes more embedded, I would expectto see a greater shift towards recognising the need for interdisciplinaryteams to fulfil data visualisation projects collectively.

540

The best functioning visualisation team will offer a collective blend ofskills 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 intechnologists or a concentration of ‘ideas’ people whose work neverprogresses past the sketchbook. You need the right blend in any team.

We have seen quite a lot of great examples of visualisation and infographicwork from newspaper and media organisations. In the larger organisationsthat have the fortune of (relatively) large graphics departments, teamworking is an essential ingredient behind much of the success they havehad. Producing relentlessly high-quality, innovative and multiple projectsin parallel, within the demands of the news environment, is no mean feat.Such organisations might have the most people and also some of the bestpeople, but their output is still representative of their punching above theirweight, no matter how considerable that base.

Developing Through EvaluatingThere are two components in evaluating the outcome of a visualisationsolution that will help refine your capabilities: what was the outcome ofthe work and how do you reflect on your performance?

Outcome: Measuring effectiveness in data visualisation remains anelusive task – in many ways it is the field’s ‘Everest’ – largelybecause it must be defined according to local, contextual measures ofsuccess. This is why establishing an early view of the intended‘purpose’, and then refining it if circumstances change, was necessaryto guide your thinking throughout this workflow.Sometimes effectiveness is tangible, but most times it is entirelyintangible. If the purpose of the work is to further the debate about asubject, to establish one’s reputation or voice of authority, then thoseare hard things to pin down in terms of a yes/no outcome. One optionmay be to flip the measure of effectiveness on its head and seek outevidence of tangible ineffectiveness. For example, there may besignificant reputation-based impacts should decisions be made oninaccurate, misleading or inaccessible visual information.There are, of course, some relatively free quantitative measures thatare available for digital projects, including web-based measures suchas visitor counts and social media metrics (likes, retweets, mentions).

541

These, at least, provide a surface indicator of success in terms of theproject’s apparent appeal and spread. Ideally, however, you shouldaspire also to collect more reliable qualitative and value-addedfeedback, even if this can, at times, be rather expensive to secure.Some options include:

capturing anecdotal evidence from comments submitted on asite, opinions attributed to tweets or other social mediadescriptors, feedback shared in emails or in person;informal feedback through polls or short surveys;formal case studies which might offer more structured interviewsand observations about documented effects;experiments with controlled tasks/conditions and trackedperformance measures.

Your performance: A personal reflection or assessment of yourcontribution to a project is important for your own development. The bestway to learn is by considering the things you enjoyed and/or did well (anddoing more of those things) and identifying the things you did not enjoy/dowell (and doing less of those things or doing them better). So look backover your project experience and consider the following:

Were you satisfied with your solution? If yes, why; if no, why andwhat would you do differently?In a different context, what other design solutions might you haveconsidered?Were there any skill or knowledge shortcomings that restricted yourprocess and/or solution?Are there aspects of this project that you might seek to recycle orreproduce in other projects? For instance, ideas that did not make thefinal cut but could be given new life in other challenges?How well did you utilise your time? Were there any activities onwhich you feel you spent too much time?

Developing effectiveness and efficiency in your data visualisation workwill take time and will require your ongoing efforts to learn, apply, reflectand repeat again. I am still learning new things every day. It is a journeythat 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 executeexactly the same way second time around. I could conceivably pick any

542

of them – and probably the thing they could all benefit most from?More inter-disciplinary expertise.’ Alan Smith OBE, DataVisualisation Editor, Financial Times

However, to try offer a suitable conclusion to this book, at least, I willleave 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 someonehad told this to me. All of us who do creative work, we get into itbecause we have good taste… [but] there is this gap and for the firstcouple of years that you’re making stuff, what you’re making is justnot 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. Andyour taste is why your work disappoints you. A lot of people neverget past this phase, they quit. Most people I know who do interesting,creative work went through years of this. We know our work doesn’thave 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 gottaknow it’s normal and the most important thing you can do is do a lotof work. Put yourself on a deadline so that every week you will finishone story. It is only by going through a volume of work that you willclose that gap, and your work will be as good as your ambitions. AndI 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 gottafight 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 consuminga visualisation? In a rush? Travelling?Visual appeal: are you sufficiently attracted to the appearance of thework?Relevance: do you have an interest or a need to engage with this

543

topic?Initial scan: quickly orientate yourself around the page or screen, andallow yourself a brief moment to be drawn to certain features.

Outside the Chart

The proposition: what task awaits? What format, function, shape andsize of visualisation have you got to work with?What’s the project about?: look at the titles, source, and read throughany introductory explanations.What data?: look for information about where the data has originatedfrom and what might have been done to it.What interactive functions exist?: if it is a digital solution browsequickly and acquaint yourself with the range of interactive devices.

Inside the Chart Refer to the Chart Type Gallery in Chapter 6 to learnabout 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 visualiserhave been faced with that are hidden from you as a viewer?Overview first, details if provided: accept that sometimes a projectonly aims to (or maybe only can) provide a big-picture gist of thedata, rather than precise details.False consciousness: don’t be too quick to determine that you like avisualisation. Challenge yourself, do you really like it? Do you reallygain understanding from it?Curiosities answered, curiosities not answered: just because it doesnot answer your curiosity, it might answer those of plenty of others.

Creating: The Capabilities of the Visualiser

The Seven Hats of Data Visualisation Design

544

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 thework you have created and assessing your own performance duringits production.

Tips and Tactics

The life and energy of data visualisation are online: keep on top ofblogs, the websites of major practitioners and agencies creating greatwork. On social media (especially Twitter, Reddit) you will find avery active and open community that is willing to share and help.Practise, practise, practise: experience is the key – identify personalprojects to explore different techniques and challenges.Learn about yourself: take notes, reflect, self-critique, recognise yourlimits.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 creativeand communication fields: writing, video games, graphic design,architecture, cartoonists.

545

References

These references relate to content mentioned in the body text and/orattributed quotes that do not come from individual interviews with theauthor. Extensive further reading lists to support each chapter’s content areprovided in the companion digital resources.Bertin, Jacques (2011) Semiology of Graphics: Diagrams, Networks,

Maps. Redlands, CA: ESRI Press.

Beveridge, Harriet and Hunt-Davis, Ben (2011) Will it Make the Boat GoFaster? Olympic-winning Strategies for Everyday Success. Leicester:Matador.

Booker, Christopher (2004) The Seven Basic Plots: Why We Tell Stories.New York: Continuum.

Buckle, Catherine (2014) Millions, billions, trillions: Letters fromZimbabwe, 2005−2009. http://www.cathybuckle.com/Millions-Billions-Trillions.php

Butterick, Matthew (2013) Practical Typography.http://practicaltypography.com

Buxton, Bill (2007) Sketching User Experiences. San Francisco, CA:Elsevier.

Cairo, Alberto (2012) The Functional Art. San Francisco, CA: Peachpit.

Chimero, Frank (2012) The Shape of Design.http://shapeofdesignbook.com/

Cleveland, William S. and McGill, Robert M. (1984) ‘Graphical

546

Perception: Theory, Experimentation, and Application to theDevelopment of Graphical Methods’. Journal of the AmericanStatistical Association, vol. 79, no. 387, pp. 531–54.

Coats, Emma (2011) Originally via Twitter, collated viahttp://www.aerogrammestudio.com/2013/03/07/pixars-22-rules-of-storytelling/

Cox, Amanda (2013) Harvard Business Review.https://hbr.org/2013/03/power-of-visualizations-aha-moment/

Crawford, Kate (2013) Harvard Business Review.http://blogs.hbr.org/cs/2013/04/the_hidden_biases_in_big_data.html

de Bono,Edward (1985) Six thinking hats. New York: Little, Brown.

Dilnott, Andrew (2013) Presented at a conference about the Office forNational Statistics.https://twitter.com/GuardianData/status/313965008478425089

Fernandez, Manny (2013) New York Times.http://www.nytimes.com/2013/06/30/us/from-americas-busiest-death-chamber-a-catalog-of-last-rants-pleas-and-apologies.html?_r=0

Glass, Ira (2009) Open Culture.http://www.openculture.com/2009/10/ira_glass_on_the_art_of_story_telling.html

Gore, Al (2006) Presentation from An Inconvenient Truth, directed byDavis Guggenheim.

Heer, Jeffrey and Schneiderman, Ben (2012) ‘Interactive Dynamics forVisual Analysis’. ACM Queue, vol. 10, no. 2, p. 30.

547

Ive, Jonny, Kemp, Klaus and Lovell, Sophie (2011) Dieter Rams: As LittleDesign As Possible. London: Phaidon Press.

Jordan, Chris (2006) TEDtalk.http://www.ted.com/talks/chris_jordan_pictures_some_shocking_stats.html

Kahneman, Daniel (2011) Thinking Fast and Slow. New York: Farrar,Straus & Giroux.

Kosara, Robert (2013) eagereyes: How The Rainbow Color Map Misleads.http://eagereyes.org/basics/rainbow-color-map

Lupi, Giorgia (2014) Green Futures Magazine.https://www.forumforthefuture.org/greenfutures/articles/why-i-draw-giorgia-lupi-art-visual-understanding

Mackinlay, Jock (1986) ‘Automating the Design of GraphicalPresentations of Relational Information’. ACM Transactions onGraphics (TOG), vol. 5, no. 2, pp. 110–41.

Meyer, Robinson (2014) ‘The New York Times’ Most Popular Story of2013 Was Not an Article’. The Atlantic.http://www.theatlantic.com/technology/archive/2014/01/-em-the-new-york-times-em-most-popular-story-of-2013-was-not-an-article/283167/

Morton, Jill (2015) Color Matters. http://www.colormatters.com/color-and-design/basic-color-theory

Munzner, Tamara (2014) Visualization Analysis and Design. Boca Raton,FL: CRC Press.

Reichenstein, Oliver (2013) Information Architects. https://ia.net/know-how/learning-to-see

548

Rosling, Hans (2006) TEDtalk.https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen

Rumsfeld, Donald (2002) US DoD News Briefing.https://en.wikipedia.org/wiki/There_are_known_knowns

Satyanarayan, Arvind and Heer, Jeffrey (2014) ‘Lyra: An InteractiveVisualization Design Environment’. Computer Graphics Forum(Proceedings of EuroVis, 2014).

Shneiderman, Ben (1996) ‘The Eyes Have It: A Task by Data TypeTaxonomy for Information Visualizations’. Proceedings of the IEEESymposium on Visual Languages. Washington, DC: IEEE ComputerSociety Press, pp. 336–43.

Slobin, Sarah (2014) Source. https://source.opennews.org/en-US/learning/what-if-data-visualization-actually-people

Stefaner, Moritz (2014) Well-Formed Data. http://well-formed-data.net/archives/1027/worlds-not-stories

Tukey, John W. (1980) ‘We Need Both Exploratory and Confirmatory’.The American Statistician, vol. 34, no. 1, pp. 23–5.

Tversky, Barbara and Bauer Morrison, Julie (2002) ‘Animation: Can itfacilitate?’. International Journal of Human-Computer Studies – Specialissue: Interactive graphical communication, vol. 57, no. 4, pp. 247–62.

Vitruvius Pollio, Marcus (15 BC) ‘De architectura’.

White, Alex (2002) The Elements of Graphic Design. New York: AllworthPress.

549

Wolfers, Justin (2014) TheUpshot.http://www.nytimes.com/2014/04/23/upshot/what-good-marathons-and-bad-investments-have-in-common.html

Wooton, Sir Henry (1624) The Elements of Architecture. London:Longmans, Green.

Wurman, Richard Saul (1997) Information Architects. New York:Graphis.

Yau, Nathan (2013) Data Points. Chichester: Wiley.

550

Index

Titles of charts are printed in italics

absent data 115, 275accessibility 30, 37–42, 218–219

and angles of analysis 133of annotation 259–260audience influence on 38–41and colour 265, 275, 286–288in composition 307–309of data 108, 115of interactive design 223, 243, 244and purpose 83testing 147and visual impairment 244visualiser’s influence on 41–42see also audiences

Accurat viii, 252aesthetic qualities see eleganceaims/uses of this book 3, 5, 6–7Aisch, Gregor 29, 252Aisch, Gregor and Kevin Quealy (The New York Times) viii, 138–140Al-Jamea, Sohail, Wilson Andrews, Bonnie Berkowitz and ToddLindeman (Washington Post) xi, 231Albers Equal-area Conic projection 308analysis vs communication 8–9Andrews, Wilson, Amanda Cox et al vii, 84angle/slope as attribute 153angles of analysis 116, 132–134, 136, 216

and composition 303number of 133–134relevance 132–133

animation 241, 242speed 241

annotation 137, 247–261absence of 37, 42audiences 258

551

captions 255–256chart apparatus 252, 253clutter 260focus 259footnotes 251headings, subheadings and titles 248imagery 249, 250introductions 248–249labels 47, 252–254legends 254–255multimedia 249–251, 250

attribution 250credits 250data sources 250integration 250–251time/date stamps 250usage 250

project annotations 260reading guides 251–252tone and experience 258–259transparency 259typography 256–257understanding 259–260user guides 249voiceover 255–256

area marks 153Art in the Age of Mechanical Reproduction: Walter Benjamin 279Arteries of the City 206Asia Loses Its Sweet Tooth for Chocolate 45attention to detail 58–59attributes and marks 21, 151–152audiences 1, 70

and annotation 258and anticipated intrigue 67and data handling 36definition 49feelings and emotion 85interests 40, 70, 85moods and attitudes 41needs of 38, 39, 40, 66, 70, 133

552

personal tastes 41and relevance 133size of 75stakeholders 66of this book 3

prerequisites 4–5time needed to view and understand 40understanding/knowledge 39–41, 70see also accessibility

axes 51line charts 305truncated 37, 304–305

bandings charts 218bar charts 31, 51, 161, 215Battling Infectious Diseases in the 20th Century: The Impact ofVaccines 273–274Beer Brands of SAB InBev 181Benjamin Walter 278, 279Berkowitz, Bonnie, Emily Chow and Todd Lindeman (WashingtonPost)Berliner Morgenpost xiii, 268Bertin, Jacques 212bias 36, 37Big Data 50black and white printing 282–283Bloch, Matthew, Lee Byron, Shan Carter and Amanda Cox (NewYork Times) x, 197Bloomberg Billionaires 155, 215, 250, 251Bloomsberg Visual Data vii, ix, x, 101, 166, 194, 203, 218, 251Bocoup and the World Economic Forum ix, 168Boice, Jay, Aaron Bycoffe, Andrei Scheinkman and Simon Jackmanx, 208Bolt, Usain 325Booker, Christopher 1Bostock, Mike and Jason Davies x, 182Bostock, Mike and Shan Carter and Kevin Quealy (New York Times)x, 198Bostock, Mike, Shan Carter (New York Times) xi, 233brushing 234, 235

553

Buckle, Catherine, and Graham van de Ruit xiv, 303Buckminster Fuller, Richard 43Bui, Quoctrung viii, 136bullet charts 218Bump, Phillip (Washington Post) ix, 180Burn-Murdoch, John 85Butterick, Matthew 257Buxton, Bill 68Buying Power: The Families Funding the 2016 Presidential Election84

Cairo, Alberto 29, 64Cameos, Jorge 29Carbon Map 207Carli, Luis vii, 79cartesian charts 304Casualties 279–280categorical attributes 154categorical charts 158Census Bump: Rank of the Most Populous Cities at Each Census,1790-1890 192central tendency 113challenges 10Chang, Kai ix, 185Chart Structures 304chart types 21, 42, 50–51, 126, 137, 157–160

acronym CHRTS 157, 158, 216, 220choosing 210–220, 243

accessibility 218–219angles of analysis 216assessing chart types 211data examination 215data exploration 215elegance 219–220perception 214purpose 211–212ranking of tasks 212, 213skills and tools 211tone 212trustworthiness 216–218

554

examplesarea cartogram 207area chart 195back-to-back bar chart 178bar chart 161box-and-whisker plot 171bubble chart 167bubble plot 184bump chart 192, 212chord diagram 189, 299choropleth map 201, 271clustered bar chart 162connected dot plot 164connected scatter plot 194connected timeline 198dashboards 159dendrogram 181Dorling cartogram 208dot map 205dot plot 161, 162, 163dual families 159flow map 206Gantt chart 199grid map 209heat map 186histogram 173horizon chart 196instance chart 200isarithmic map 202line chart 191, 305matrix chart 187node–link diagram 188parallel coordinates 185pictogram 165pie chart 37, 157, 158, 175, 303polar chart 169, 304prism map 204proportional shape chart 166proportional symbol map 203radar chart 168

555

Sankey diagram 190, 211, 299scatter plot 137, 183slope graph 193, 299stacked bar chart 177, 270stream graph 197sunburst chart 182treemaps 45, 88–89, 179, 303univariate scatter plot 172Venn diagram 180waffle chart 176word clouds 158, 174

storytelling 159–160text visualisation 159

Charting the Beatles: Song Structure 269Chen, Lina and Anita Rundles viii, 155Cheshire, James,Ed Manley, John Barratt and Oliver O’Brien xi, 238Chimero, Frank 43Christiansen, Jan 309circle size encoding 216–217City of Anarchy 294Ciuccarelli, Paolo 113Clark, Duncan and Robin Houston (Kiln) ix, 182, 207Cleveland, William and McGill, Robert 212, 214Clever, Thomas 44, 250clutter 260Coal, Gas, Nuclear, Hydro? How Your State Generates Power 193Coats, Emma 59Color of Debt 249, 250colour 77, 91, 263–291

accessibilitycolour associations 287–288colour incongruence 288consistency 286cultural sensitivities 288visual 286–287

black and white printing 282–283categorical colours 267–269, 268chart-type choice 285choices 33–34, 42, 137CIELAB 266

556

CMYK (Cyan, Magenta, Yellow and Black) 265, 282contrast 276–278, 280data encoding 285data examination 284diverging/converging scales 269, 270–272

rainbow scale 274–275editorial salience 276–278elegance

justification for colour use 289neutral colouring 288–289unity 288

format 282–283functional harmony 278–282

annotations 280–281composition 281–282interactivity 279multimedia 280–281

greyscale 280HSL (Hue, Saturation, Lightness) 265, 266, 267hue 213, 265ideas and inspiration 283–284illusions 286inappropriate usage 37interval and ratio 270–276lightness 266, 267meaningfulness 285nominal data 267–269ordinal data 269–270purpose map 283quantitative data 270–276RBG (Red, Blue, Green) colour model 264, 265, 266, 282saturation 153, 265, 266setting 283style guidelines of organisations 283and texture 269theory 264–267white background 283

colour blindness 286–287Colour-blind Friendly Alternatives to Red and Green 287columnar charts 304

557

communication 58, 149comparison of judging line size vs area size 213Comparison of Judging Related Items using Variation in Colour(hue) vs Variation in Shape 214completion 148complex subjects 39, 41complexity of data visualisation 2complicated subjects 39composition 138, 293–311

angles of analysis 303aspect ratios 306–307chart composition 295–301

acronym LATCH 298–299chart orientation 297–298chart scales 296–297chart size 296value sorting 298–301

chart type choice 303–304chart-scale optimisation 304–306data examination 302–303elegance

thoroughness 309unity 309

format 302mapping projections 307project composition 293–295

hierarchy of content 294hierarchy and size 294

unobtrusive design 307, 308–309comprehending 22, 23, 26–27, 74, 79conceiving ideas 144, 145consumption

definition 49frequency 71settings 72

context 36, 64–75audiences 70circumstances 68–69constraints and limitations 74consumption 71–72

558

curiosity 64–68format 73pressures 70–71purpose 74–75quantities/workload 72resources 73–74rules 71stakeholders 69

correlations 52Corum, Jonathan (New York Times) and Nicholas D. Pyenson xiii,281Countries with the Most Land Neighbours 83Cox, Amanda 83Crawford, Kate 36creation: definition 49Crime Rates by State 184critical friends 147critical thinking 7–8, 10Critics Scores for Major Movie Francises 172Crude Oil Prices 195Culp, S. xiv, 297curiosity 64–68, 131

anticipated intrigue 67audience intrigue 66personal intrigue 65potential intrigue 67–68and purpose 75stakeholder intrigue 66

Current Electricity Prices in Switzerland 271

Daily Indego Bike Share Station Usage 272Daily Mail 33–34data 97–129

absence 275acquisition 36, 106–110, 128

quantity needed 106–107resolution 107sources 50, 107–110

API (Application Programme Interface) 110foraging 108

559

issued by client 109pdf files 108raw (primary) data 108system report or export 109third-party services 109–110web 108–109

when available 110data literacy 97–98examination 110–117, 128

absence of data 115animation 241and choice of chart types 215completeness 115–116data operations 112identification of type 111influence of examination 116–117inspection and scanning 112for interactivity 241meaning of data 113–114quality 111size 111statistical methods 112–113underlying phenomena of data 114

exploratory data analysis (EDA) 121–128analyst instinct 124–125chart types 126choosing chart types 215datasets 127efficiency 125finding nothing 127interrogating the data 125knowns and unknowns 122–124, 123machine learning 127need 128reasoning 125research 126–127statistical methods 127

filtering 134fundamental role in visualisation 20and goals 121, 124

560

‘open data’ 108range 117raw (primary) 49–50, 99, 108representativeness 115samples 115source 322statistics 98, 105–106

in data examination 112–113inference techniques 105univariate and multivariate techniques 105

tabulated datasets 99cross-tabulated datasets 99, 100normalised datasets 99

transformation 118–121, 128, 141backups 118cleaning 118–119consolidation 121conversions 119–121

example 119quantitative 120textual data 119–120

creating 120–121junk 119

transparency in handling 36types 20, 100–105, 111

acronym TNOIR 100, 128discrete and continuous 104–105qualitative

nominal 101–102ordinal 102–103textual 100–101, 119–120

quantitativeinterval 103ratio 103–104

temporal 104data art 48data journalism 48data privacy 233data representation 19, 21, 151–221

chart types 157–160, 161–209

561

choosing chart types 210–220deception 36, 36–37, 216–218definition 19unfamiliar 40visual encoding 151–157, 152–156

data science 48data visualisation: definition 19–20

and data vis 47datapresentation

forms of deception 37datasets: definition 50Deal, Michael xiii, 269deception 36, 36–37, 216–218decisions/choices 1, 8, 10, 29

heuristic judgements 57significance 53and truth 32see also chart types; design

decoration 44–46deductive reasoning 125D’Efilippo, Valentina and James Ball xii, 254D’Efilippo, Valentina and Nicolas Pigelet viii, 146D’Efilippo, Valentina 149depth 41design 28

Dieter Ram’s general principles 37, 41, 42environmentally friendly 31guiding principles 29–30, 146–147innovative design 30‘invisibility’ of 244–245long lasting 31rules 71see also chart types

deuteranopia 286–287diagrams 51digital resources 9Dilnott, Andrew 32Dimensional Changes in Wood 79–80distortions created by 3D decoration 217–218Doping under the Microscope 306

562

duration of task 70dynamic of need 39

Ebb and Flow of Movies: Box Office Receipts 1986-2008 197ECB Bank Test Results 236Economic Research Service (USDA) xiv, 297editorial thinking 36, 131–142, 139, 140

angle/s 132–134, 136, 138–139, 142, 216annotation 137, 141colour 137, 139, 141and design choices 137–141, 140–141example: Fall and Rise of U.S. Inequality, in Two Graphs135–138, 136example: Why Peyton Manning’s Record Will Be Hard to Beat138–141, 139, 140focus 135, 137, 139, 142framing 134, 136–137, 139, 142influences 135–141interactivity 137, 140–141representation 137, 140

Election Dashboard 208Elections Performance Index 278elegance 30, 42–46, 147

in composition 309decoration 44–46definition 42–43eliminating the arbitrary 43–44in interactive design 244–245style 44–46thoroughness 44visual appeal 219–220

Elliot, Kennedy 35, 55Elliot, Kennedy, Ted Mellink and Richard Johnson (WashingtonPost) xi, 237emptiness/nothingness, representations of 275, 282enclosure charts 304encoded overlays 78, 218, 277environmentally friendly design 31ER Wait Watcher: Which Emergency Room Will You See the Fastest?299, 300

563

errors 58–59Excel 112, 239Executive Pay By Numbers 267exhibitory visualisation 25, 76, 77, 81–82, 259experimental 361explanatory visualisation 25, 76, 77–79, 241, 258exploratory visualisation 76, 77, 79–80, 241, 259expressiveness 210–211

facilitating understanding 21, 28, 38see also accessibility

Fairfled, Hannah 67Fairfield, Hannah and Graham Roberts (New York Times) xi, 219Fall and Rise of U.S. Inequality, in 2 Graphs 191Fast-food Purchasers Report More Demands on Their Time 297Fedewa, Peter A. vii, 35feedback 147–148Few, Stephen 29Fewer Women Run Big Companies Than Men Named John 276, 296figure-ground perception 34filtering 134financial restraints 70Financial Times 85Finviz 225, 226First Fatal Accident in Spain on a High-Speed Line 280fit 117Five Hundred and Twelve Paths to the White House 233FiveThirtyEight vii, 78, 298flat design 31Florida: Murders by Firearms 34–35, 114flow maps 160focus 135, 137, 139, 142fonts 256, 257Football Player Dashboard 277footnotes 251For These 55 Marijuana Companies, Every Day is 4/20 166form marks 153formats 41, 42, 51, 88

restrictions 69formulating briefs 36, 63–95

564

context 36, 64–75curiosity 64–68definitions 63, 64establishing vision 75–94

framing 134, 136–137, 139, 142, 225–226Fraser, Shelly-Ann 325Frequency of Words Used in Chapter 1 of This Book 174fun 84, 85functional restrictions 71functionality 43functions 51

Gagnon, Francis xiv, 297Gallup xiv, 306gateway layers 89Gemignani, Zach 39Gender Pay Gap US? 164, 251, 301Geography of a Recession 234, 275–276geometric calculations 216–217geometric zoom 226Glass Ceiling Persists 296, 297Glass, Ira 332Global Competitveness Report 2014-2015 168Global Flow of People 189Goddemeyer, Daniel, Moritz Stefaner, Dominikus Baur and LevManovich xiv, 299Goldsberry, Kirk 114Gore, Al 82grahics 51Grape Expectations 91Graphic Language: The Curse of the CEO 101graphs 51Gray, Marcia 98, 316greyscale 280Groeger, Lena 41, 148Groeger, Lena, Mike Tigas and Sisi Wei (ProPublica) xiv, 300Groskopf, Christopher, Alyson Hurt and Avie Schneider x, 193guiding principles

accessibility 37–42elegance 42–46

565

trustworthiness 30, 32–37Gun Deaths in Florida 34, 286Gun Deaths in Florida redesign 35

Harper, Bryce 231Hemingway, Ernest 160Here’s Exactly Where the Candidates’ Cash Came From 203heuristic techniques 57HEX codes 265hierarchical charts 158Highest Max Temperatures in Australia (1st to 14th January 2013)274historical context 8History Though the President’s Words 237, 240Hobbs, Amanda viii, 163Hobbs, Amanda 40, 58Holdouts Find Cheapest Super Bowl Tickets Late in the Game 194,252Holmes, Nigel 93, 285home owners

Falling Number of Young Homeowners 33–34Housing and Home Ownership in the UK 33

Horse in Motion 243How Americans Die 230, 237How the ‘Avengers’ Line-up Has Changed Over the Years 186, 200How Big Will the UK Population be in 25 Years’ Time? 234How the Insane Amount of Rain in Texas Could Turn Rhode IslandInto a Lake 156How Long Will We Live – And How Well? 183, 268How Nations Fare in PhDs by Sex 163, 268How Old Are You? 233How Well Do You Know Your Area? 232How Y’all, Youse and You Guys Talk 80, 202Hubley, Jill xiii, 284Hunt-Davis, Ben 30Hurt, Alyson 70, 125, 127

ideasconceiving 145keywords 91–92

566

limitations 93mental visualisation 90–91other people’s 94research and inspiration 93Sketch by Giorgia Lupi 92sketching ideas 92, 145sources of imagery 93

If Vienna Would be an Apartment 45Image From the Home Page of visualisingdata.com 156Images from Wikipedia Commons 308Impact of colour blindness 286inductive reasoning 125inference techniques 105info-posters 47Infographic History of the World 254infographics 46, 47–48information design 48information visualisation 47Ingold, David, Keith Collins and Jeff Green vii, 101Ingraham, Christopher (Washington Post) viii, 156innovative design 30Inside the Powerful Lobby Fighting for Your Right to Eat Pizza 220Interactive Fixture Molecules 187interactivity 21, 42, 51, 137, 223–246

advantages 223data adjustments

animating 228–229, 241, 242, 243contributing data 232–233framing 225–226, 242navigating 226–228sequencing 230–231

event, control and function 224influencing factors

angle 241chart-type choice 243data examination 241ease of usability 244feature creep 244format 239–240fun 245

567

purpose map 241setting 239skills and resources 238–239timescales 239trustworthiness 243visual accessibility 244

presentation adjustmentsannotating 235–236focusing 234–235orientating 236–238

usefulness 223, 244interests of audiences 40interpreting 22–23, 24–26, 74, 79, 126

factors 25and previous knowledge 25–26, 27

Iraq’s Bloody Toll 34, 35, 298

Jaws (film) 74Jenkins, Nicholas and Scott Murray xii, 249Jones, Ben x, 199Jordan, Chris 87

Kahneman, Daniel 90–91, 317Kane, Wayne 25, 26Kasich Could Be The GOP’s Moderate Backstop 298Katz, Josh (New York Times) vii, 80Keegan, Jon (Wall Street Journal) ix, 186Killing the Colorado: Explore the Robot River 238Kindred Britain 249Kirk, Andy 289, 301, 318–324Kirk, Andy and Andy Witherley xiv, 318–324Klein, Matthew C. and Bloomberg Visual Data xi, 230knowns and unknowns 122–124, 123Kosara, Robert 263

labels 47, 252–254axis labels 252, 253axis titles 252categorical labels 253–254value labels 253–254

568

Lambert Azimuthal Equal-area 308Lambrechts, Maarten (Mediafin) ix, 181launching 144, 148–149layouts 37, 71legends 51levels of data see data, typesLife Cycle of Ideas 152line charts 140, 157

aspect ratios 37, 306–307truncated axes 305

line marks 153linking data 234, 235Lionel Messi: Games and Goals for FC Barcelona 23, 24, 25, 27, 28,155, 156listening 58Literacy Proficiency 177London is Rubbish at Recycling and Many Boroughs are GettingWorse 209long lasting design 31Losing Ground 89, 90, 239Lunge Feeding 281Lupi, Giorgia viii, 46, 92, 295Lustgarten, Abrahm, Al Shaw, Jeff Larson, Amanda Zamora, LaurenKirchner and John Grimwade xii, 238

McCandles, David 29McCandles, David, Miriam Quick and Philippa Thomas xii, 251McCandles, David and Tom Evans xi, 233McKinlay, J.D. x, 213Mackinley, Jock 212McLean, Kate 113, 147managing progress 56maps/mapping 37, 51, 157, 216

projections 307, 308thematic 307zooms 226see also under chart types

markers overlays 219market influences 71Marshall, Bob, The Lens, Brian Jacobs and Al Shaw (ProPublica) vii,

569

89Martin, Andrew and Bloomberg Visual Data xi, 220Martino, Mauro, Clio Andris, David Lee, Marcus J. Hamilton,Christian E. Gunning and John Armistead Selde ix, 188meaning 74Meirelles, Isabel 38memorability 31Mercator projection 307, 308Messi, Lionel 23, 24, 25, 27, 28Mider, Zach, Christopher Cannon, and Adam Pearce (BloombergVisual Data) x, 203Minard, Charles Joseph 8Mizzou’s Racial Gap Is Typical On College Campuses 77–78, 253mock-ups see prototypesModel Projections of Maximum Air Temperatures Near the Oceanand Land Surface on the June Solstice in 2014 and 2099 231Mollweide projection 308Morton, Jill 278mouse/trackpad events 224Movies Starring Michael Caine 173multiple assets 88multivariate analysis 127Munsell, Albert 265Munzner, Tamara 29Murray, Scott 69, 330Muybridge, Eadweard xii, 243MyCuppa Mug 269

narrative visualisation 78National Public Radio (NPR) 135, 136Native and New Berliners – How the S-Bahn Ring Divides the City201needs of audiences 38, 39, 40, 66, 70, 133Nelson, John 114, 126network diagrams 159, 160NFL Players: Height and Weight Over Time 229Nightingale, Florence 8Nobel Laureates 234Nobels no Degrees 298non-linear logarithmic scales 302

570

note-taking 57–58Nutrient Contents 185NYC Street Trees by Species 284, 285NYPD, Council Spar Over More Officers 277

Obama, Barack 114Obama’s Health Law: Who Was Helped Most 271–272Obesity Around the World 226, 228objectives of this book 9–11objectivity 32OECD Better Life Index 89–90, 116, 117, 215, 233Olson, Randy xiii, 272On Broadway 299On NSA, 30 Percent Either Want No Limits on Surveillance or Say‘Shut It Down’ 178ONS Digital Content team vii, 33‘open data’ 108organisation and contents of this book 11–15Ortiz, Santiago 90outliers 51, 117

Peek, Katie 58, 258pen and paper 57Per Capita US Cheese Consumption 20perceiving 22, 23–24, 26, 74, 79Percentage Change in Price for Select Food Items, Since 1990 196perfection, pursuit of 53–54personal tastes 41pie charts 37, 157, 158, 175, 215plagiarism 93planning 56Playfair, William 8plots 51Plow 242point marks 153Political Polarization in the American Public 170Pong, Jane 73, 93Pong, Jane (South China Morning Post) xiv, 300Posavec, Stefanie 43, 93, 145, 278PowerPoint 239

571

pragmatic approach 7–8, 54precision 216prerequisites for data visualisation 4–5presentation of work in progress 70Presidential Gantt Chart 199pressures 70–71print 73production cycle 56, 144–149

conceiving ideas 144, 145launching 144, 148–149prototypes 144, 146refining and completion 144, 148testing 144, 146–148wireframing and storyboarding 144, 145–146

project: definition 49Proportion of Total Browser Usage for Internet Explorer andChrome 176ProPublica 239, 240prototypes 144, 146Psychotherapy in the Arctic 289, 301publicising 149purpose 74–75

and curiosity 75purpose map 76–90

experience 76–82in practice 86–90tone 82–86, 87, 88, 116, 212

Pursuit of Faster 318–326

Qiu, Yue and Michael Grabell (ProPublica) xi, 235quantitative attributes 153–154quantity 153

Racial Dot Map 205, 227radial charts 304Rain Patterns 300Rams, Dieter 29, 33, 37, 41, 42range charts 170Rapp, Bill 72Razor Sales Move Online, Away From Gillette 220

572

reading guides 251–252reasoning 125Record-high 60 Percent of Americans Support Same-sex Marriage306redundant encoding 301Rees, Kim 112, 113reference lines 219refining 148Reichenstein, Oliver 44, 55relational attributes 154relational charts 158Relative Value of the Daily Performance of Stocks Across the 500Index 179relevance of content 41research and inspiration 93, 126–127resources 73–74responsive design 239Rise of Partisanship and Super-cooperators in the US House ofRepresentatives 188Rosling, Hans 82Roston, Eric and Blacki Migliozzi (Bloomberg Visual Data) x, 218Rumsfeld, Donald 122

Saint-Exupéry, Antoine de 148sampling 115Sander, Nikola, Guy J. Abel and Ramon Baur ix, 189Satanarayan, Arvind 210scales 51scales of measurement see data, typesScarr, S., C. Chan, and F. Foo (Reuters Graphics) viii, 91Scarr, Simon vii, 34 134, 284Schneiderman, Ben 88Scientific American 85scientific visualisation 48scope of data visualisation 3–4Seeing Data Visualisation project 39, 315series of values 50settings 42, 72Shankley, Bill 280Shaw, Al, Annie Waldman, and Paul Kiel (ProPublica) xii, 249

573

shelf life 149Silva, Rodrigo, Antonio Alonso, Mariano Zafra, Yolanda Clementeand Thomas Ondarra (El Pais) xiii, 280Simmon, Robert xiii, 270simplicity 39, 40simplification 39, 41size 153, 304–305sketching 92, 145, 295skeuomorphism 31skills 73Slobin, Sarah vii, 20, 30, 43, 132small multiples technique 296Smith, Alan 109, 332Snow, John 8Social Progress Imperative 228sources of data 322spark bars 161spatial charts 158, 304speaking 58Spielberg, Steven 74Spotlight on Profitability 81–82, 117stakeholders 1, 107, 147

as audience 66Stalemate 297State of the Polar Bear 275statistics 98, 105–106

in data examination 112–113and data exploration 127inference techniques 105multivariate analysis 127univariate and multivariate techniques 105

Stefaner, Moritz 117, 132Stefaner, Moritz, Dominikus Baurs and Raureif GambH vii, 89Stevens, Joshua xi, 231Stevens, Stanley 100storyboarding 144, 145–146, 146, 295storytelling 1, 51, 78, 159–160style 44–46style guidelines 71subject neutrality of data visualisation 4

574

subject-matter appeal 39subject-matter knowledge 25–26, 27, 39subjectivity 32, 36Summary of Eligible Voters in the UK General Election 2015 175support for the work 149Swing of Beauty 231Sydney Olympics 2000 30System 1 and System 2 thinking 90–91, 317Szücs, Krisztina vii, 81

tables 20–21tabulation 50teamwork 330–331technical skills needed 5–6technological tools 6, 73temporal charts 158Ten Actors with the Most Oscar Nominations but No Wins (2015)161, 162testing 144, 146–148

‘Texas Department of Criminal Justice’ Website 86–87meaning and completeness of data 114, 115

text visualisation 159theoretical context 7This Chart Shows How Much More Ivy League Grads Make ThanYou 171Thorp, Jer 115three D data representation 37, 217time slider control 137time to consume a visualisation 40timescales 56, 70tone 76, 259Total Sightings of Winglets and Spungles 26, 27tower graphics 47Tracing the History of N.C.A.A. Conferences 198treemaps 45, 88–89, 179, 215Tribou, Alex and Adam Pearce (Bloomberg Visual Data) ix, 166Tribou, Alex, David Ingold and Jeremy Diamond (Bloomberg VisualData) xii, 251Trillions of Trees 204Tröger, Julius, André Pätzold, David Wendler and Moritz Klack x,

575

201trustworthiness 30, 32–37, 98, 113

annotation 259and colour 285–286in composition 304–307and deception 216–218, 217of interactive designs 243key matters 35–37testing 147

truth 32, 33, 131Tufte, Edward 29, 85, 244–245Tukey, John 124Tulp, Jan Willem x, 204Tulp, Jan Willem 73, 121Twitter NYC: A Multilingual Social City 238, 255typefaces 256–257

UK Election Results by Political Party, 2010 vs 2015 190UK Office for National Statistics 33, 34Ulmanu, Monica, Laura Noonan and Vincent Flasseur (ReutersGraphics) xi, 236UN Global Pulse Survey 270understanding 19, 21

and complexity 42facilitating 21, 28, 38and needs of audiences 38of new symbols/technology 316–317process 74and purpose 74stages of 22–28

unfamiliar representation 40univariate and multivariate techniques 105updating 149US Gun Deaths 225, 255US Presidents by Ethnicity (1789 to 2015) 114user guides 249

Vallandingham, Jim x, 192value sorting

LATCH 298–301

576

location 298–299alphabetical 299, 300time-based 300categorical 300–301hierarchical 301

values 111, 112–113frequency counts 112frequency distribution 112range 117spread/dispersion 113

Van de Ruit, Graham: Millions, Billions, Trillions 302–303variables 50, 111, 112–113Veltman, Noah xi, 229Viégas, Fernanda and Martin Wattenberg xiii, 289viewers

definition 49effort needed by 317learning new symbols/technology 316–317number of 75see also audiences

vision: definition 76visual analytics 48visual appeal 219–220visual encoding 151–157

attributes 152, 153–154form 156, 157marks 21, 151–152, 153, 155, 156see also chart types

visual mood 88visualisation

experience 76–82exhibitory 76, 77, 81–82, 88explanatory 76, 77–79, 88exploratory 76, 77, 79–80, 88narrative 78

harnessing ideas 90–92tone 82–86, 87–88

feeling tone 83–86reading tone 82–83

visualisation literacy 315–334

577

first thoughtsinitial scan 319relevance 319setting 319visual appeal 319

key featuresdata 322format 320interactive functions 322–323introductory text 321–322shape and size 320source 321studying the project 320subject matter 320

taskscomprehending 326interpreting 325observations 325perceiving 324–325

understanding symbols 315–316viewer–visualiser harmony 326

appreciation of context 326false consciousness 326–327

curiosities 327overview 326

visualiserscapabilities 327–329

communicator 328data analyst 328designer 329journalist 328–329project manager 327scientist 328technologist 329

definition 49developing capabilities 329–330

evaluatingoutcome 331performance 331–332

teamwork 330–331

578

Vitruvius Pollio, Marcus 46voiceover 255–256Voronoi treemap 158

Washington Post 231waterfall charts 158Wayne Kane: Games and Points for Toronto Rangers 25–26Wealth Inequality in America 78, 256web design 239Weber, Matthew (Reuters Graphics) xi, 234Weldon Cooper Center for Public Service, Rector and Visitors of theUniversity of Virginia (Dustin A. Cable, creator) x, 205What Good Marathons and Bad Investments Have in Common 124What’s Really Warming the World? 218Where You Can Both Smoke Weed and Get a Same-sex Marriage 180Which Fossil Fuel Companies are Most Responsible for ClimateChange? 182White, Alex 282Who Wins the Stanley Cup Playoff Beards 165Why Is Her Paycheck Smaller 219Wind Map 289Winkel-Tripel projection 308Wireframe Sketch 295wireframing 144, 145–146, 294–295Wolfers, Justin viii, 124Wooton, Sir Henry 46workflow

processadaptability 55experimentation 54–55four stages 54importance of process 53–54mindset activities: thinking, doing, making 56ongoing tasks 55, 88pragmatism 54purpose 54

process in practiceattention to detail 58–59communication 58heuristics 57

579

honesty with yourself 59making it work 59management 56note-taking 57–58pen and paper 57reflective learning 59research 58thinking 56–57

workload 72Worst Board Games Ever Invented 303Wurman, Richard Saul 298

Yau, Nathan ix, 184Years Since First Movie table 152YouTube user ‘Politizane’ vii, 78

zero quantity 305zooms 226

580

Table of Contents

Half Title 2Publisher Note 3Title Page 4Copyright Page 5Contents 7Illustration List 8Acknowledgements 18About the Author 19Introduction 21Part A Foundations 411 Defining Data Visualisation 422 Visualisation Workflow 94Part B The Hidden Thinking 1053 Formulating Your Brief 1064 Working With Data 1535 Establishing Your Editorial Thinking 202Part C Developing Your Design Solution 2196 Data Representation 2277 Interactivity 3868 Annotation 4209 Colour 44410 Composition 488Part D Developing Your Capabilities 51911 Visualisation Literacy 520References 546Index 551

581