Non-spatial Visualisation 8Karel Macků

Abstract

An enormous amount of various data is pro-duced every day. With proper datavisualisation, an information hidden in thedata can be easily and quickly revealed. It isnecessary to create a communication channelthat could quickly and efficiently transfer theinformation from the data to the user. By usingvisual elements like charts, graphs, and maps,data visualisation is an accessible way to seeand understand trends, outliers, and patterns indata. This chapter offers an overview of rele-vant data visualisations divided into thematiccategories and supported by examples.

Keywords

Visualisation · Data · Chart · Information

In the world today, we encounter enormousamounts of data every day. To convert data intouseful information, data must be presented to theuser in a way that allows interpreting, analysingand applying the gained information (Yau 2011).It is necessary to create a communication channelthat could quickly and efficiently transfer theinformation from the data to the user – this canbe done with data visualisation. Tableau Soft-ware, a company offering a software platform

for interactive data presentation, briefly and com-prehensively talks about data visualisation: “Datavisualisation refers to the graphical representationof information and data. By using visual elementslike charts, graphs, and maps, data visualisation isan accessible way to see and understand trends,outliers, and patterns in data” (Tableau Software2018). According to (Friedman 2008, p. 1) the“main goal of data visualisation is to communi-cate information clearly and effectively throughgraphical means. To convey ideas effectively,both aesthetic form and functionality need to gohand in hand, providing insights into a rathersparse and complex data set by communicatingits key-aspects more intuitively”.

Visualisation is an important step in the wholeprocess of data analysis. Legendary statisticianJohn Tukey often mentions visualisation in thecontext of using visualisation to find meaning inthe data (Tukey 1977). Despite his statisticalfocus, he believed that a graphic presentation ofinformation plays an immense role. A propervisualisation based on source data can help tounderstand the data, improve decision makingand provide a more objective preview of theproblem represented by data (Yau 2013). Agraphic can also reveal hidden patterns andrelationships.

Visualisation methods have gone far beyondtraditional data presentation with simple chartsand graphs. Modern trends approach datavisualisation as both a science and an art. Ofcourse, certain standards of correctness (e.g. by

K. Macků (*)Department of Geoinformatics, Palacký UniversityOlomouc, Olomouc, Czech Republice-mail: karel.macku@upol.cz

# The Author(s) 2020V. Pászto et al. (eds.), Spationomy, https://doi.org/10.1007/978-3-030-26626-4_8

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choosing a method according to thecharacteristics of the data) are still kept, butthere is an effort to make the result interestingand catchy to attract the reader’s attention.Sophisticated data visualisation and infographicsmethods offer a variety of exciting charts anddiagrams. The advantage of technologies todayis the possibility of presenting outputs in the formof online interactive web tools, which makes theprocessing of the information, that authorattempts to communicate, even more intuitiveand attractive.

In this chapter, a non-spatial data and itsvisualisation are discussed. Non-spatial dataplays an undeniable role in the field of economicsand business intelligence. For that reason, anoverview of most common and powerfulpossibilities how to visualise it will be presentedon the following pages.

8.1 Software

Nowadays, a variety of software is easily avail-able, knowledge of some of them is a part ofgeneral digital literacy. Almost everyone, whosomehow uses a computer, is able to create anyvisualisation using some of the available soft-ware. Most of the computer users are skilledwith Microsoft Office Excel – software thatdoesn’t need to be presented (or its opensourcealternatives Libre Office/Open Office). Workingin these tools is relatively convenient and straight-forward, as underlying data and graphical toolsare integrated into one user environment, and thewhole process is very intuitive. However, thisapproach does not always offer proper or high-quality graphical outputs and supports the user’stendency to blindly insert data into the providedgraphics templates without deeper thinking. Bythis approach, data loses its ability to interpret thestory that is stored in it (Nussbaumer Knaflic2015). Another point is the technical maturity ofthe output. In the world of modern technologies,where most of the information is distributedonline, it is much more professional to produceoutputs that offer a degree of interactivity andsupport simple distribution in the digital environ-ment. Interactivity allows the viewer to engage

with your data in ways impossible by staticgraphs. With an interactive plot, the viewers canzoom into the areas they care about, highlight thedata points that are relevant to them and hide theinformation that is not (Barter 2017). For thisreason, some tools will be introduced offeringthe possibility of creating interesting graphicaloutputs.

8.1.1 Tableau Software

The company Tableau Software offers a set oftools of the same name designed for exploratoryanalysis and data visualisation. The product isespecially focused on an effective and highlyaesthetic level of visualisation, which undoubt-edly attracts many customers. The full version ofthe program is paid, but the version Public Tab-leau is freely available. In this version, a user canwork with many formats such as Microsoft Excel,Access, text files, JSON files, databases and alsospatial data (several data formats are supported).After loading the data, the user can easily selectthe attributes they want to visualise and based onthe data type, a set of options is automaticallyoffered to create a visualisation. The main ideaof the Public Tableau is the interactivity andpresentation of the outputs in the online environ-ment so the result can be shared with other usersas attractive interactive data visualisation. Thetools are, of course, multiplatform, they can beused as a desktop, mobile or online version.

8.1.2 HTML, Javascript and CSS

HTML, Javascript and CSS are the basis of everywebpage. With modern technologies representedby HTML 5, advanced data visualisation runningnative in the internet browser can be done. Thissolution is probably suitable only for technically-advanced users/developers, who can handle cod-ing with these technologies. There are severallibraries designed for building interactive/staticweb visualisations, for example, Javascriptlibraries D3.js, Charts.js or FusionCharts. Theselibraries offer dozens of charts; detailed informa-tion can be found on their websites.

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8.1.3 R

It is free and open-source statistical and mathe-matical computing software, primarily focused ondata analysis and modelling. Since R has beendeveloped mainly for statistical analysis, it has asolid background for different types ofcalculations suitable for data analysis. There is alot of packages, which can extend the functional-ity of R software with just a simple code com-mand. Thanks to the packages, R is a very mightytool for data visualisation. Of course, a knowl-edge of code writing is required (as well as withHTML), which makes R for many people inap-plicable. But once this obstacle is overcome, anew world of data handling and visualisation isopened. All graphics can be saved in vectorformats, so it is possible to edit and refine thedesign of the outputs in suitable graphical soft-ware, like Adobe Illustrator or Inkscape. Exceptfor traditional static graphic, also interactiveoutputs can be produced with special R packages.Sometimes, the interactivity is redeemed by com-plexity in the form of one extra line of code!

8.1.4 Datawrapper

Datawrapper is an online tool for making theinteractive charts. It has a very simple interface;a user can upload data from a file or paste thevalue directly into the field. The tool generatesgraphics automatically; a user can choose one of

the 16 types of visualisation. Several refiningsteps can be done, like customising of axis, label-ling or colour setting. This tool is an ideal solutionwhen one needs a quick, simple interactivevisualisation without any programming.

These examples were just a small slice of whatnowaday technologies offers. Everyone is com-fortable with a different level of challenge, con-tent control and output options so everyone canfind their optimal tool for creation of graphicaloutputs. There is an overview of another tools forvisualisation in following table. Of course, thislist is not complete, there are dozens of tools inoffer (Table 8.1).

8.2 Charts Classification

There might be a confusion in terminologyregarding the visualisation of non-spatial data.Usually, words ‘chart’ or ‘graph’ are generallyused to describe any visual output. For manypeople, these two terms mean the same, butthere is a difference. A chart is a superior termfor a group of methods, how to present informa-tion. A graph is a particular graphical tool, whichshows a mathematical relationship between setsof data (Blaettler 2018). With this approach, agraph is a subcategory of a chart. For this reason,the term chart will be rather used in this chapter,to keep the description of different methods moreboard.

Table 8.1 An example of visualisation tools

Name Output Difficulty Pricing

Plotly Interactive/static Coding required Free with some paid plansHighcharts Cloud Interactive Easy to handle FreeD3.js Interactive Coding required FreeCharts.js Interactive Coding required FreeInforgram Interactive/static Easy to handle Free, extra features paidRAWGraphs Interactive/static Easy to handle FreeDataHero Interactive/static Easy to handle PaidVisually Interactive/static Easy to handle PaidVisme Interactive/static Easy to handle FreeGoogle charts Interactive Easy to handle FreevVizualize.me Static Easy to handle Free

Source: Author

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Different types of charts will be described inthe following chapter. Since there are dozens ofpossibilities of visualisations, only the most inter-esting or most commonly used variants will beintroduced. For better thematic logic, the individ-ual methods were divided into thematic groups.The inspiration for this system was the bookVisualize This (Yau 2011) and the websitewww.datavizproject.com (Ferdio ApS 2017).

8.2.1 Trend Over the Time

Time series are typical data for many phenomena.Things are changing in time, and this change canbe easily captured and presented by suitablegraphics. Talking about time series, users try toexplore the trend in data. Is the value of thephenomena increasing or decreasing? Are thereany repetitive cycles?

Temporal data can be divided into discrete andcontinuous types. The knowledge about this char-acter of data should guide the user in a decision,which kind of graph should be used. For example,a monthly revenue report is an informationreferenced to a one-time step – a month, so thiscan be considered as a discrete phenomenon.Then, a simple bar or point graph can be used.The second type is the continuous data. This iskind of information which can be measured at anytime of day during any day of the year. A typicalexample could be a temperature or another mete-orological phenomenon; regarding the economicdata, we can use stock exchange prices as anexample. The structure of data is same for discreteand continuous phenomena, to distinguish thedifference, the proper way of visualisation shouldbe used. The most primitive solution is to connectdiscreetly plotted data with any line.

8.2.1.1 Bar ChartBar charts are commonly used, which means theuser doesn’t need to ‚learn‘, how to read thegraph. The graphic element is a rectangular barwhose length represents the value. The time axiscaptures time points, which have to be orderedchronologically. Then every bar stands for onediscrete time point. Finally, there are many

additional ways how to tune the bar graph,e.g. bars can be placed horizontally or verticallyor some of the bars can be highlighted by adifferent colour (e.g. time points when the valueis higher than set limit etc.) (Fig. 8.1).

8.2.1.2 Point ChartPoint chart works on same principle as the barchart does, except for used geometrical element –it is a simple point here. This can sometimes bemore suitable since the points do not representsuch graphic content and load as bars. Point chartis also known as a scatterplot when non-temporaldata is used. It is crucial to properly create an axisrepresenting the value of the phenomenon, asthere is no other way to find out the value.

8.2.1.3 Line ChartThe line chart is a type of chart used for continu-ous data. The basis of the chart is the same as thebasis of point chart. The continuity is added byconnection of this points with line segments.Then the chart shows how data changes in thetime (particular value is stored in the point), andthe line segments create a feeling of continuity. Italso better points to the trend between timemarkers (Fig. 8.2).

There is only a minor difference between theline chart and the spline chart. They differ onlywith the way how the points are linked. While theline graph uses straight line segments, the splinechart plots a fitted curve through each point from

Fig. 8.1 Bar chart. (Source: Author)

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the time series. This provides a more smooth andnatural course (Fig. 8.3).

An attractive solution for a description ofchanges between two or several time point is aslope chart. It combines time-approach with mul-tiple observed variables/categories. This helps tosee differences in the development of specifiedcategories and also the rate of change in oneparticular category compared to others(represented by the slope of the connecting line).At the same time, deviations in the general trendcan be perfectly observed (Fig. 8.4).

8.2.1.4 Step ChartLast modification of the line chart is a step chart.This one is formed by stepped lines between thetime points. It is appropriate to use it in the situa-tion, when the data represents a sudden change inirregular time intervals, for example, a price ofany commodity which has been the same for along time, then in one day the price increased(Fig. 8.5).

8.2.1.5 Gantt ChartThis chart visualises via bars duration of severalcategories in a time series. It illustrates the startand end point of occurrence of any activity/phe-nomenon. This chart is typically used as a projectmanagement tool for a graphical representation ofthe sequences of activities over time. Tasks oractivities, which are parts of the whole project,are displayed in the time context (Fig. 8.6).

Fig. 8.2 Line chart. (Source: Author) Fig. 8.3 Spline chart. (Source: Author)

Fig. 8.4 Slope chart. (Source: Author)

Fig. 8.5 Step chart. (Source: Author)

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8.2.2 Proportions

Proportion data is grouped by categories/types.Each category represents a possibility, which ispart of the certain unit. This distribution ofproportions is the most important informationfor comparing groups between themselves. Withproportional visualisation, questions like “Are allof the categories equally represented? Is there anycategory which dominates?” can be answered.

For this type visualisation, a data needs to havea form of proportions that add up to 1 or 100%.Every part could be stored relatively (as a propor-tion) and absolutely – total values allow to com-pare not only proportional part but also total size/amount in different categories.

8.2.2.1 Pie ChartA pie chart is one of the most often used charts andis typical for an explanation of proportions. Thecircle which is representing the whole is dividedinto sectors. The arc length of each segment(or interior central angle, or area) is illustratingthe proportion of individual categories. Allcategories together must form a unit/100%.

8.2.2.2 Doughnut Chart (Fig. 8.7)The doughnut chart is just a modification of a piechart, only the blank centre is added. That allowspresenting of multiple information at the sametime since the inner blank space could be filledwith additional related data.

According to some of the resources(Nussbaumer Knaflic 2015), the pie or doughnutchart is an inappropriate way how to visualise

proportional data. This is caused by the greaterdifficulty of perceiving angles or area thandistances (which are the key information regard-ing, e.g. bar charts), it is a common property ofhuman eye perception. In a situation when two ormore categories are represented by an approxi-mately same value, it’s difficult to decide whichone is greater. This issue can be solved by addinglabels. Still, several authors recommend usingdifferent proportional methods, like a stacked/simple bar charts.

8.2.2.3 Stacked Bar ChartInstead of pie/doughnut charts, simple bar chartordered from highest value to least can be used.All bars have the same baseline; the endpoint iseasier to compare. Even small differences can bedistinguished. The length of bars is recalculatedin that way that their sum equals to whole/100%.

A stacked bar chart is a perfect solution forvisualising proportion and comparing severalclasses at the same time. Because of their geomet-rical representation, they are even more space-saving than pie charts. Stacked bar chart containsmultiple values on top of each other, which showsthe division of the whole into categories. Concur-rently, individual bars represent the different levelof categories or even time points. For example –the stacked bar chart can represent sale strategies:every bar signifies a particular strategy (A-E inthe Fig. 8.8), different colour shades represent atype of product, and on the y-axis, total sales aredisplayed.

8.2.2.4 Tree Map/Area ChartThis type of charts uses a structure of rectanglesand their area to express the proportion of thewhole part. Size of every rectangle represent themetrics. The outer rectangle represents parentcategories, and rectangles within the parent aresubcategories (Yau 2011). Therefore, primaryrequirement is that data has to have a tree-basedstructure (Fig. 8.9).

There is a similar alternative, which doesn’trequire tree structure in the input dataset. Simplesquare area chart, also called a waffle chart, uses aregular grid of small cells. If the value of the cellis set, then the proportion is expressed by a num-ber of cells (Fig. 8.10).

Fig. 8.6 Gantt chart. (Source: Author)

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Regarding the tree map or area charts, there isthe same issue with the perception oftwo-dimensional object as was discussed in piechart paragraph. In this case, if the area map has acell-based regular structure, the perception ofinformation can be done correctly by simplecounting of cells. Nussbaumer Knaflic (2015,p. 59) describes another situation when areacharts are quite helpful: “when visualisation ofnumbers of vastly different magnitude is needed.The second dimension you get using a square forthis (which has both height and width, comparedto a bar that has only height or width) allows thisto be done in a more compact way than possiblewith a single dimension”.

8.2.3 Relations and Correlation

There are many ways how to quantify relationsbetween several variables/group. A statisticalapproach provides mathematical tools, such as

correlation or regression (if a conditions regard-ing the characteristics of variables are fulfilled).Sometimes it is much easier just to plot the data toreveal the hidden relations. A correlation simplydescribes, how two variables change together.Sometimes it is forgotten that correlation doesn’tequal causation. Basic correlation of twovariables expressed with chart can quicklydescribe the behaviour of the data, a rate of rela-tion can be estimated, maybe a clustering ten-dency can be discovered.

Fig. 8.7 Pie and dougnnutchart. (Source: Author)

Fig. 8.8 Stacked bar chart. (Source: Author)

Fig. 8.9 Tree map. (Source: Author)

Fig. 8.10 Area chart. (Source: Author)

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8.2.3.1 ScatterplotA scatterplot is one of the fundamental chartsused for plotting of relations and dependencies.The data is displayed as a set of points placed in aCartesian coordinate system. Therefore, the chartis limited for displaying of relations between onlytwo variables. The placement of points in thechart helps to easily estimate the correlationbetween variables – if they are positivelycorrelated, points are formed in the line-shapedgroup, rising with the value of the representedphenomenon. If the correlation is negative, thisline group has a decreasing trend. With no signif-icant correlation, points are not grouped in theline-shaped and spread in the field randomly.Another categorical information can be addedinto the chart in the form of point colour ordifferent shapes. Then, it is possible to observedifferences between particular types; they mighttend to create a cluster which indicates their simi-larity, or controversy, points might beoverlapping, then there is no clear pattern in thecategorical groups (Fig. 8.11).

8.2.3.2 Bubble PlotIt is possible to add a third variable into thescatterplot and compare more information at thesame time. The size of the bubbles expresses thethird variable – the measure here is an area, notradius nor diameter. The only area can accuratelyrepresent differences related to original number:if the displayed value is doubled than another, thearea of a graphical element must also be doublesize. If another measure is used (e.g. diameter),the ratio between value and the area of a graphicelement wouldn’t be the same. Of course, thechart can be modified regarding shapes, squaresor triangles could also be used. Attention must bepaid to the position of a particular graphics ele-ment – the bigger element must not overlap thesmaller one, so it would be not visible. This ruleshould be implemented in software (Fig. 8.12).

8.2.3.3 Scatterplot MatrixIn case that exploration of more than threevariables is required, a scatterplot matrix is asolution. In this case, every possible combination

of pairs is plotted by a single scatterplot; subse-quently they are all organised into a matrix. Thisvisualisation could be a first step in an exploratorydata analysis when the analyst has no clue what isthe data about and what is its behaviour. With anincreasing number of variables, interpretation ofthat kind of graphic presentation is more compli-cated, and the information is still kept on theelementary level of variable pairs (Fig. 8.13).

8.2.4 Differences and Comparison

Comparing a single variable is not a demandingtask, the value of every record is displayed by oneof the previous-mentioned methods and analysed.Bar charts or simple point charts may well serve

Fig. 8.11 Scatterplot. (Source: Author)

Fig. 8.12 Bubble plot. (Source: Author)

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to this task. Considering two or three variables,several charts for this type of visualisation havebeen introduced in the previous sections. Regard-ing the data with more variables, known as multi-dimensional data, different graphical methodshave to be used.

8.2.4.1 HeatmapA heatmap is a simple way how to look at all dataat once. Information is displayed in a matrix ofregular graphical elements, mostly rectangles orsquares. The value for every record’s attribute isindicated by colour intensity. The size of the heatmap is defined by the number of rows times thenumber of attributes, heat map has the same num-ber of elements as the input table does. This typeof visualisation is not sophisticated for thereading of accurate values in a particular recordbut provides a great overall view on the completedataset. Some characteristic patterns in data can

be revealed, e.g. tendency to clustering(Fig. 8.14).

8.2.4.2 Paralel CoordinatesParallel coordinates is another common chart forvisualisation of multidimensional data. The num-ber of attributes defines a number of used verticalaxes – every single of them represents one vari-able. It follows that different axes have a different

Fig. 8.13 Scatterplot matrix. (Source: Author)

Fig. 8.14 Heatmap. (Source: Author)

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scale. To avoid labelling and adding more graph-ical ballast into the chart, data can be scaled; thenall axes have the same scale. Parallel coordinatesare suitable for revealing similarities betweenrecords. For that reason, labels are not alwaysnecessary, it is enough if the plot can definegroups of records with a similar pattern or trendon the observed variables (Fig. 8.15).

8.2.5 Statistical Charts

8.2.5.1 HistogramA histogram is a specific statistical chart whichdescribes the frequency of occurrence of values.The geometric element is a bar again, the heightof the bar represent a frequency, i.e. the numberof occurrences in the category, which belongs tothe bar. The category here doesn’t stand for adifferent type, it rather defines the range ofvalues, in which are data are binded. It followsthat both the horizontal and vertical axes arecontinuous (Fig. 8.16).

8.2.5.2 Distribution PlotAlthough the horizontal axis of the histogram iscontinuous, the distribution is still divided intointervals. If the interval size is not set properly, alot of information about inter-interval variation islost. On the other hand, plotting of every singlerecord would make the chart messy and confus-ing. A compromise between this approaches is adistribution plot, which is able to capture the

smaller variation within the distribution and alsosmoothen the detailed original data. The verticalaxis represents the probability of occurrence ofvalue from the sample population. The area underthe curve has to be equal to one (or 100%)(Fig. 8.17).

8.2.5.3 BoxplotA boxplot is an important graphical tool fordescriptive statistics. In one picture, it candescribe several numeric information – median,first and third quartiles, and minimum and maxi-mum (sometimes minimum and maximum isreplaced by the value calculated: mean + � 1.5� interquartile range of lower/upper quartile).Outliers (values out of this range) are plotted aspoints. The spacings between parts of boxplotsdescribe how the raw data is dispersed or

Fig. 8.15 Parallelcoordinates. (Source:Author)

Fig. 8.16 Histogram. (Source: Author)

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condensed. With boxplots, several groups of datacan be quickly compared (Fig. 8.18).

8.3 A Good Design

In the beginning, the data analyst has to know thedata in detail. Once the analyst understands whatkind of information is hidden in the data, what isthe data type and character, he can decide whichtype of chart is the best solution for propervisualisation. Then another step of chart design-ing follows. The raw default output from thesoftware is not wrong, but usually, it is also notthe most attractive result. With an additionalimprovement of the graphics, information whichthe author tries to deliver with the chart might beeasier to perceive.

It must be always considered, who is the audi-ence, the reader of the created chart, for whichpurpose is the chart created. By design, the authorcan manipulate with the way how the chart isread. If it wants to focus on a significant trend,the axis and labels can be de-emphasised withgrey colour, and the primary trend line ishighlighted. Then, the trend is the informationwhich draws the attention at first. On the otherhand, a chart designed with the purpose ofreading exact values must have readable andaccurate labels of all axes.

Generally, some recommendation can bemade. Mostly modest colours should be used.Some of the colour schemes can evoke emotionsor feel (e.g. red colours indicate activity thatshould be addressed; neutral pastel coloursmeans that all features in the chart are equaletc.). Proper labelling should be done – throughuser might know the context in which is the chartplaced, he doesn’t know the meaning of everysingle element of the chart. Therefore, a title ofthe chart, axes names and value labels and legendwith explanations of colours should be a part ofthe visualisation. Geometrical aspects are alsoimportant. Sometimes it’s more suitable just torotate the chart, what makes it much easier to read(e.g. bar chart with long category names – rota-tion to horizontal is more natural, because itfollows the way how we read the common text).A different spatial arrangement of geometricalfeatures can solve the issues with blank space orcan fit better into a whole graphic design (text orposter). Transforming the geometrical elementsinto pseudo-3D and displaying data that wayshould be avoided (the only exception is plottinga three-dimensional data with a 3D plot). Unfor-tunately, for example, visualisation of the piechart in 3D is quite popular. As discussedabove, the pie chart is not always a good choicefor visualisation of proportional data; the combi-nation with 3D makes it much more difficult toread or compare with others pie charts becausethe third dimension is problematic for perception.A perspective in 3D visualisation can also bemisused for promotion – a segment of pie chartplaced in the foreground looks larger than a seg-ment of similar size in the background.

Fig. 8.17 Distribution plot. (Source: Author)

Fig. 8.18 Boxplot. (Source: Author)

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